´socially active space´ a proposal for ravine´s self organised urban settlements, valparaíso chile by Patricia Ojeda del Rio

´Socially Active Space ´

a proposal for Ravine´s Self-Organised Urban Settlements, Valparaíso - Chile

AA Architectural Association School of Architecture - Emergent Technologies and Design 2015 - 2017 Patricia Ojeda del Rio [M Arch]

´Socially Active Space ´

a proposal for Ravine´s Self-Organised Urban Settlements, Valparaíso - Chile

Emergent Technologies and Design Architectural Association School of Architecture

Patricia Ojeda del Rio

´Socially Active Space´

a proposal for Ravine´s Self-Organised Urban Settlements, Valparaíso - Chile

Master of Architecture, Emergent Technologies and Design Architectural Association School of Architecture Course Director Studio Master Studio Tutor Studio Tutor

Michael Weinstock Evan Greenberg Elif Erdine Manja Van de Worp

MArch Candidate Patricia Ojeda del Rio

Architectural Association School of Architecture Graduate School Programme Programme Term Student Name Submition Tittle Course Director Course Title Submition date Declaration Signature

Emergent Technologies and Design 4 (2016-2017) Patricia Ojeda del Rio ´Socially Active Space´ a proposal for Ravine´s Self-Organised Urban Settlements, Valparaíso-Chile Michael Weinstock Emergent Technologies and Design Master of Architecture

‘I certify that this piece of work is entirely my own and that any quotation or paraphrase for the published or unpublished work of others is duly acknowledged.’

Acknowledgement This work would not have been possible without the tangible and intangible convergence of events that materialised in it. A great desire to pursue new fields in architecture drove me to follow that inspiration some years ago. From that time, a chain of small events was giving me light to keep that dream as my day to day path to the Architectural Association. Emergence Technologies and Design Program Director Michael Weinstock friendly received me in his office where throughout our conversation I breathed how powerful Emergence Phenomenon could be and influence my future professional vision and contribution. In the first place, I would like to thanks the Architectural Association School of Architecture for having me awarded with a Bursary for funding part of the Scholarship. Particular gratitude to EmTech Collaborator Mohammed Makki whose intellectual and human generosity gave me confidence many times, necessary to move forward in the thesis process; EmTech Program friends and colleges from whom I learned endless professional skills and above all human values and especially to Diego Valdivia, Sharon Ann Philip whose time and patience had an invaluable significance in my apprenticeship time. Also, my dear friends Pablo Moreira and Ignacia Moreno who believed and helped me from the very beginning with this project. Finally, to my beloved family whose constant support and demonstrations of love and trust throughout the whole process gave me courage for standing up when my inner strength weakened. Thanks to you mamá, papá, Juan Andrés and Andrea. I especially dedicate this thesis to my grandson Pedro who was born this very powerful year. I would like to end with an enlightening thought:

“Everything is what it is because it got that way.” [Sir D'Arcy Wentworth Thompson]

Abstract A persistent tension between private and public space has been observed in informal settlements in most urbanised areas in the world. This condition is a serious concern for Latin American urban landscapes, with 21% of the population living in informal settlements, where the unbalanced relationship between individual and collective needs gradually increases along with the growth of settlements. The problem arises when the only form of public space is one that has been reduced to circulation routes. The present research hypothesises that it is possible to develop an urban model for informal urban settlements in ValparaĂso - Chile, based on the promotion of social integration through transforming natural barriers, such as steep terrains and ravines, as opportunities for including a Socially Active Space system into the urban fabric. Key words public and private space / informal settlements /social integration / natural barriers / socially active space system

Acknowledgements 09 Abstract 11

01 Introduction

1.1

1.3

1.4

1.5

1.6

1.7

1.8

1.9

1.2

O N

14 Latin AmericaUrban Growth and Self-organised urban settlements16 Self-Organisation as a Community Answer 16 Breaking Point in the Densification Process 16 Ignored Public Space 17 The valuable Role of Social Active Spaces 17 Networks and Social Active Spaces Together Create a System 17 Valparaíso City Port - Chile 18 Valparaíso Informal Settlements Distribution and Numbers 20 Design ambition 21

02 Domain

2.1 Overview

2.2

Informal Urban Settlements emergent process in three Latin American cities 2.3 Self-organised urban settlements 2.4 Learning from bacteria about social network 2.5 Case Study Grotao Neighbourhood, Paraisópolis Favela Sao Paulo, Brazil 2.5.1 Grotao Case introduction 2.5.2 Topographical Analysis 2.5.3 Network Urban Analysis 2.5.4 Urban Space Variety 2.5.4.1 Four Open Space Type Analysis 2.5.4.2 Open Spaces and Neighbourhood Entrances Distance Summary 2.5.5 Isovists Analysis 2.5.5.1 Isovists Inner Patch General Observation 2.5.5.2 Four Open Spaces Isovist Analysis 2.5.6 Urban Project a Result of Bottom-Up + Top-Down Approach 2.5.7 Case Study Conclusion 2.6 Domain Conclusions

T E N

03 Methods

S 12

Proposed design methodology 3.2 Environmental Analysis - Solar Radiation Analysis 3.3 Topographical Analysis 3.3.1 Aspect Analysis 3.3.2 Slope Angle Analysis 3.3.3 Surface Area Analysis 3.4 Urban Network Analysis [UNA] 3.5 Isovist Analysis - Visual fields 3.6 Social Network Analysis 3.7 Genetic Algorithm 3.7.1 Generative process 3.8 Network Generation 3.1

14 24 25 26 28 30 32 34 36 37 38 40 42 42 44 46 47 49

50 52 54 55 55 56 57 58 59 60 62 64 66

04 The Site,

´Quebrada Jaime´ [Jaime´s Ravine]

The site, main Valparaíso basins, identification of ravine understudy Informal settlements land appropriation overview 4.3 Topographical condition 4.3.1 Informal Settlements location and land property distribution 4.4 Hydrological condition 4.5 Solar radiation analysis, Valparaíso annual and daily radiation 4.5.1 Sun path over slope degrees of the patch analysed 4.6 Valparaíso urban patterns analysis, three main strips 4.6.1 Natural land and urban grid overlay pattern processing 4.6.2 General North to South city section and street configurations 4.7 Informal Settlements growing urban patterns 4.7.1 Initial patterns of land occupation [lines] 4.7.2 Informal settlements configurations, three local types [branches] 4.7.3 Informal settlements housing occupation process and pattern 4.7.3.1 Global analysis 4.7.3.2 Local analysis 4.7.4 Network branch type, hierarchical configuration 4.8. Valparaíso existing urban typologies 4.8.1 Built environment, ravine´s housing types 4.8.1.1 Flat house 4.8.1.2 Stilt house 4.8.1.3 Flat and Stilt house slope occupation analysis 4.8.2 Open Space 4.8.2.1 Network element types stairways and 'ascensores' 4.8.2.2 Social active space types 4.8.2.2.1 ´Plazas´ 4.8.2.2.2 ´Miradores´ and ´urban openings´ 4.8.2.2.3 Amphitheatres ´spontaneous amphitheatre´ 4.9 Valparaíso´s ravine social aspects and dynamics 4.8.1 Social Network data summary and Conclusions 5.0 Site conclusion 4.1

4.2

68 70 71 72 74 75 76 77 78 79 80 82 82 82 83 83 84 85 86 87 88 89 90 92 92 96 96 100 104 106 116 117

05 Design Development & Strategy

Urban patch analysis 5.2 Urban Network Analysis, Centrality Indices, 4 clusters 5.2.1 Analysis cluster 1 5.2.2 Analysis cluster 2 5.2.3 Analysis cluster 3 5.2.4 Analysis cluster 4 5.2.5 Cluster´s conclusion 5.3 Topography and solar radiation analysis and filtration strategy 5.3.1 Filtration process 5.3.1.1 Slope gradients on the terrain 5.3.1.2 Slope gradients patch analysis 5.3.1.3 Solar radiation patch analysis 5.3.1.4 Overlapping slope gradient and solar radiation 5.3.1.5 Synthesis map and diagrams and conclusion 5.3.1.6 Conclusive diagrams 5.4 Creation of Ravine Polygon 5.5 Polygon Elevation analysis 5.6 Polygon Aspect Analysis 5.7 Polygon Slope Analysis 5.8 Overlay strategy for elevation, aspect and slope 5.9 Elevation, Aspect and Slope program Strategy 5.10 Housing Logic 5.10.1 Housing Type Definition

5.1

Fittest individual dwellings Definition 5.11 Network Generation Logic 5.11.1 Route inclination differenciation defined by slope degree 5.11.2 Walking Distance and Slope Degree 5.11.3 Stairways and Funiculars 5.12 Socially Active Spaces Logic 5.12.1 ´Plazas´ emergence process 5.12.2 ´Miradores´ emergence process 5.12.2.1 M1 Fittest Miradores 5.12.2.2 M1 Solar Radiation Miradores 5.12.2.3 M4 Fittest Miradores 5.12.2.4 M4 Solar Radiation Miradores 5.12.2.5 M5 Fittest ´Miradores´ 5.12.2.6 M5 Solar Radiation ´Miradores´ 5.12.3 ´Amphitheatre´ emergence process 5.10.2

5.10.3 Cluster´s

118 120 121 122 124 126 128 130 132 132 133 134 134 134 136 138 140 142 143 144 145 145 148 148 150 152 154 155 156 157 158 158 160 162 162 163 163 164 165 166

06 Design Proposal 6.1 Design

evaluation 6.2.1 Urban Network Analysis [UNA], test patch. 6.2.2 Urban Network Analysis [UNA], cluster 1. 6.2.3 Urban Network Analysis [UNA], cluster 2. 6.2.4 Urban Network Analysis [UNA], cluster 3. 6.2.5 Isovist Analysis, cluster 1. 6.2.6 Isovist Analysis, cluster 2. 6.2.7 Isovist Analysis, cluster 3.

07 Conclusion

Conclutions 7.2 Appendix 7.2.1 Excel Spreadsheet, Social Network Analysis Matrices 7.2.2 Housing type Octopus experiments 7.2.3 Cluster´s type Octopus experiments 7.3 References 7.3.1 Books Reference List 7.3.2 Videos Reference List 7.3.3 Publications Reference List 7.3.4 List of Figures 7.3.4.1 01 Introduction List of Figures. 7.3.4.2 02 Domain List of Figures. 7.3.4.3 03 Methods List of Figures. 7.3.4.4 04 Site List of Figures. 7.3.4.5 05 Design Development & Strategy List of Figures. 7.3.4.6 06 Design Proposal List of Figures. 7.3.4.7 Appendix List of Figures. 7.3.4.8 Back and Cover Pages, List of Figures.

7.1

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186 188 190 192 208 208 210 212 212 212 213 213 213 215 216 219 223 223 223

01 INTRODUCTION Fig. 01 Google Earth [2012] 'Natural opened areas, used as soccer field' source: Google Earth, imagery Date: 02.2012 Image © 2018 Google 33°03´25.41" S 71°36´30.90" W Elev. 11 m eye alt. 3 m.

1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9

Urban Growth and Self-Organised Urban Settlements in Latin America Self-Organisation as a Community Answer Breaking Point in the Densification Process Ignored Public Space The Valuable Role of Social Active Spaces Networks and Social Active Spaces Together Create a System Valparaíso City Port - Chile Valparaíso City Informal Settlements Distribution and Numbers Design Ambition

Introduction

Latin America Urban Growth and Self-organised urban settlements Since the Industrial Revolution, rural to urban migration and population growth, mainly in Europe, has become a challenging scenario for urban organisation having grown in that period 57%. However, the largest migration to urban areas in Latin America occurred from the fifties at the 20th C. In 1950, 40 percent of the region's population was urban, but by 1990 it was up to 70 percent. Nowadays 50% of the world population is living in urban centres and it is estimated that by 2050 that percentage will reach 75% [UN 2012] increasing cities density. Urban centres were not prepared for the sudden high housing demand consequently, the self-organised urban settlements we see today have their origins in that historical scenario. In Latin America and the Caribbean region [LAC], 23.5 %—or 113 million people—were living in slums in 2012. Based on the rate of slum growth, the United Nations estimates that over 160 million households in the LAC region will be living in slums in 2020. [UN-Habitat, 2012b]. Furthermore, it is estimated that by 2050, 90% of the population will be in towns and cities, increasing housing demand to the highest level ever seen. 1.1

Fig. 02 OjedadelRio, P. 2017 'Urban Social Dancing & Happiness' [photograph] Valparaíso´s own private collection. Spontaneous Social gathering in an open space in Valparaiso. The dancing Group 'Swim Valpo' and a music band meet at that point to appeal Social interaction. Location: Lautaro Rosas Street in ´Cerro Alegre´ - Valparaíso.

1.2

Self-Organisation as a community answer

Shortage of formal housing offer for lower income groups has forced them to occupy vacant plots of land to self-build a temporary housing solution progressively enlarged and improved over time. Although there is a considerable diversity in terms of population density between these types of settlements in the region, most share key characteristics. First, the urbanization process has been the result of a high degree of social organization without the intervention

Introduction

of external regulations and stakeholders. The second aspect to highlight is that the land occupation and the self-build sheltering occur simultaneously. Third, most of them are located on the city outskirts [Klaufus 2017], being considerably more complex when geographical conditions such as steep hills add an extra challenge to the planning and provision of housing . 1.3

Breaking Point in the Densification Process

Despite the absence of support from professionals and authorities, informal settlements are an example of how communities’ self-organisation has been an intuitive path to answer fundamental demands such as the one of housing. However, at certain point of settlements growth management of open space requires external involvement and investment, that could be understood as an opportunity to explore solutions resulting from the interchange between communities' practical knowledge of their urban logic and professional and technical aspects from formal organisations. As J. Minnery has pointed out, pivotal to improving the social, economic and environmental needs of those living in informal settlements are the policies and strategies of city governments. [Minnery et. al, 2013]. Particularly from a collaborative philosophy that approaches the urban environments improvement from the recognition of cultural values tangibly manifested in their urban organisation.

1.4

Ignored public space

One of the most important concerns of dwellers during the process of self-built ´the own house´, is the delimitation of the boundaries of the appropriated land as it will directly affect future possibilities of expanding the dwelling unit. Such concern makes each family seeks individually for the basic need of placing a house for live in. Paradoxically, although the urban space belongs to everybody, most of the time everybody is actually, nobody. Therefore, the tension of the two scales of development increases through the proliferation process of settlements. It has been noted that an implicit arrangement of how units grow amongst neighbours does exist though appears not to be the main conflict catalyst within clusters. The problem starts when public space, considered spare space, is restricted and diminished by individual needs of dwelling expansions. Over time higher densities and overcrowded public areas show how a lack of Social Active Spaces negatively affects communities’ quality of life. 1.5

The valuable role of Social Active Space

Social inequality is manifested in cities, amongst other aspects, through urban segregation and a lack of Social Active Spaces for citizens to interact in. Within cities, green areas are not always equitably distributed. Access is often highly stratified based on income, ethno-racial characteristics, age, gender, [dis] ability, and other axes of difference. [Byrne, Wolch & Zhang; McConachie & Shakleton, 2010]. Access to green areas is therefore increasingly recognised as an environmental justice issue. [Wolch J., Byrne J., Newell J. 2014]. Consequently, the promotion of social encounter through urban open spaces

should be understood as a fundamental concern in Latin American cities, with 21% of its total population living in informal settlements where population density and land availability are critical factors that strongly impact the urban land distribution. Based on this data and the importance of the general access to open spaces this thesis aims at proposing a new urban model for informal urban settlements based on the concept of Social Active Space as leading spatial configurations of the urban order.

Networks and Social Active Spaces, together create a system The success of open spaces depends among other factors on the built environment that shapes them and as well as build the connections of those spaces to other areas of the city. Human interactions are its principle and foundation. Therefore, if open spaces are part of an active network they may strengthen their existence as part of a an urban system and not as isolated urban elements. In recent decades, there has been interest in creating interconnected open space systems for recreation or nature conservation. In the late 1980s´, ´greenways´ became a widely used term to designate citywide or rural-scale open space system [Little, 1990]. Greenways attempt to link together existing open spaces into a public accessible network joined by trails, paths and greenbelts. [Carr 1992]. In light of this, it can be argued that setting the basis for the emergence of a system of Socially Active Spaces into informal settlements could help to improve their urban quality and the lifestyle that they produce.

1.6

Introduction

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This city is the second largest populated urban area in Chile, with a population of 263.499 [INE 2016] spread over 47.33 Km2.Its geography is composed by a flat area surrounded by a sequence of hills and ravines shaped as a natural amphitheater. Its urban fabric is a sample of the historical occupation process of the city, beginning in the flat area and continuing to the hills up to 120m above sea level. At that point, between 1876 and 1927, a road was built to connect most of its hills East to West. ´Alemania´ Avenue was understood by the population as the urban boundary between the formal and the informal city. Following the world-wide tendency of high informal urban settlements presence in cities, the city has shown in the last century a significant growth of them. Self-organised communities extended main roads of the planned city oriented north to south to the empty land to start the appropriation of ground. Thus, the predominant occupation pattern is long lines of dwellings along those roads, manifesting two conditions. First, the absence of defined clusters in most of them, and consequently no social active spaces. Secondly, the lack of connections between different settlements with ravines in between. E252000

Fig.05 Valparaiso District highlighting [red] Valparaiso City Port. Source: Valparaíso Municipality.

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Fig. 04 Valparaiso´s Regional map highlighting [grey] Valparaiso District.

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Fig. 03 South America Map highlighting Chile [orange] and Valparaiso City Port [white dot].

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Introduction

Fig. 05

Fig. 06 Â´TechoÂ´ [2013], Valpariso informal settlements spreading in the upper part of the mountains. The picture shows the extension of the city through informal urban settlements to the upper hills source: http://www.laotravoz. cl/columna-lov-tras-lareproduccion-socialde-los-campamentos/

Fig. 06

Introduction

1.8 Fig. 07 Valparaíso´s Informal Urban Settlements distribution, size of dots is related with the number of families by settlement [see the Legend] Source: Document ‘Catastro 2011: Mapa Social de Campamentos’ resultados generales Housing and Urbanism Ministry.

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Legend Alemania Road, boundary between formal and informal areas of the city

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source House and Urbanism Chilean Ministry publication ‘Catastro 2011: Mapa Social de Campamentos’

Valparaiso's Region has the largest concentration of Informal Settlement in the country both in number of settlements and in the population that reaches 7.531 even larger than the Metropolitan area.

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Fig. 09 Chilean Region´s number of households living in Informal settlements bar graph. Source: 'Catastro 2011: Mapa Social de Campamentos' [ Informal Settlements Cadastre] published by 'Ministerio de Vivienda y Urbanismo' [Chilean House and Urbanism Ministry]

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Fig. 08 Chilean Region´s number of informal settlements bar graph. Source: 'Catastro 2011: Mapa Social de Campamentos' [Informal Settlements Cadastre] published by 'Ministerio de Vivienda y Urbanismo' [Chilean House and Urbanism Ministry]

Fig. 09

In a world, now dominated by communications and in a world where most people will be living in cities by the end of this century, it is high time we changed our focus from locations to interactions, from thinking of cities simply as idealized morphologies to thinking of them as patterns of communication, interaction, trade, and exchange; in short, to thinking of them as networks. To build a science of cities that meets the challenge of seeing locations as patterns of interactions [Batty, 2013]

Fig. 10

1.9

Fig. 11

Design Ambition

Spontaneous dwelling growth dubbed 'informal settlements' due to cities' increasing population has reduced through time urban space to roads and paths. That lack of space for social interaction has created a citizenship of individuals commuting from home to work and vice versa losing their sense of being social beings. Hence, the focus of the current thesis research is designing a built environment composed of a housing system of units that vary in size which can be aggregated around open spaces. The model will include spatial provision for social interchange and community activities recognising that social active urban spaces are the core of communities’ perpetuation through the promotion of clusters' identity. Criteria for developing the model are based on the analysis made of existent urban conditions in four representative clusters of the area. These areas are considerably separated but can be interconnected using the main principles of Valparaiso’s path network composed by pedestrian paths, stairways and funiculars that will be redesigned to provide an active connective tissue between the four social spaces. The principles of the housing aggregation logic will be extracted from the rationale of pattern growth in local dwellings. As well as the principles for Social Active Spaces development will be driven by local spatial and cultural properties of 'plazas' and 'miradores'. In conclusion, through the integration of these two design ambitions will emerge a primary social spatial network. A final aim is to connect both planned and unplanned areas of the city to promote the use of those Social Active Spaces by the population no matter which part of the city they come from.

Introduction

Fig. 10 Rigid urban network connection between Valparaíso informal urban settlements. Fig. 11 Flexible urban network, conceptual proposal for connecting Valparaíso informal urban settlements.

02 DOMAIN 2.1 Overview Fig. 12 2.2 Informal Urban Settlements emergent process in three Latin American cities La Vega aerial 2.3 Self-organised Urban Settlements photograph, ´Unidades de 2.4 Learning from Bacteria about Social Network Planificación Física 2.5 Case Study Grotao Neighbourhood, Paraisópolis Favela - Sao Paulo, Brazil 10 La Vega´. [Physical Planning Units 2.5.1 Grotao Case introduction N° 10 La Vega]. 2.5.2 Topographic Analysis Source: Image © Enlace 2.5.3 Urban Network Analysis Arquitectura 2.5.4 Urban Space Variety https:// www.plataformaarqui 2.5.4.1 Four Open Space Type Analysis tectura.cl/ 2.5.4.2 Open spaces and Neighbourhood Entrances Distance Summary cl/789996/48-anos-deasentamientos-infor 2.5.5 Isovists Analysis males 2.5.5.1 Urban Patch Isovists en-caracas/ 5772a744e58ece 2.5.5.2 Four Open Spaces Isovist Analysis cfd8000017 2.5.6 Urban Project a result of Bottom-up + Top-down Approach 48-anos-de-asentamientos 2.5.7 Case Study Conclusion -informales-en-cara 2.6 Domain Conclusions cas-foto

Domain

2.1

Overview

Fig. 11

The chapter begins with a discussion on the concept of self-organisation, and its predominant presence in informal urban settlements. It attempts to establish the essential aspects of self-organisation and what opportunities and deficiencies this process creates in informal urban settlements. Such settlements are an example of how human self-organisation can produce a large piece of the city without external intervention. Notwithstanding, the imperative need for shelter drives the low-income population to a sort of improvisation that produces in time urban complexities that escape possibilities of being improved without external contribution. In the process of formation of informal urban settlements, can be observed short, medium and long-term actions for answering different scale needs. Short term needs are related with firstly, the appropriation of the land and secondly, housing self-build action, mainly at the beginning a single room that will be upgraded over time. At this point, it is relevant to mention the concept of aggregation, because unit’s aggregation will be sooner than later a factor of conflict between private and public space when the urban settlement reaches a certain density. When the piece of land is selected by communities' pioneers, the whole land can be understood as a ‘public space’. The relationship between built and unbuilt areas with a starting point of zero m2 of dwellings versus hundred percent of ´public land´ changes in time dramatically. The urban and natural context are factors that will influence permanently informal settlements growth patterns. After the informal settlement has a general shape, long term actions are required as a result of collective needs for Social Active Spaces [SAS]. It is crucial to recognise that those are urban spaces where the community builds its social identity and value within the city. Although informal settlements share essential aspects such as spontaneous dwelling growth and free land occupation they also show particularities that are attached to their social, cultural and geographical location. The case of the

Domain

Chilean coastal city of Valparaíso shows particularities in the land occupation, growing by rows of dwellings that have been the result of an individual aggregation system that differs from most common informal settlement formations where the land is occupied at once by large number of people. Another particularity is that, its population density is far below informal settlements in Sao Paulo, Brazil and Medellin, Colombia considered the densest cities in Latin America. Through a comparative analysis of networks, open spaces organisation and urban configuration the present study will state parameters for informing the development of the new urban model for Valparaiso self-organised urban areas. Since the present work is developed under the umbrella of the paradigm of emergence the research draws upon into how self-organisation processes act in bacteria colonies. The concept of self-organisation is analysed observing bacteria colonies behaviour, as Ben-Jacob in 2004 pointed out that bacteria colonies, under unpredictable hostile environmental conditions, utilise a wide range of strategies for adaptable collective responses. His research has also identified that these cooperative modes of behavior are manifested through remarkable different patterns formed during self-organisation in the colony. Furthermore, the experiments have revealed that there is striking evidence of an ongoing cooperation that enables the bacteria to achieve a proper balance of individuality and sociality as they battle for survival, while utilising patternformation mechanisms. [Ben-Jacob, 2004] Looking at these biological social patterns the aim is to compare them to communities' urban organisation and finding concepts that can be applied in the generation of the new urban model. Finally, the chapter will finish with a series of parameters and relations between them. In terms of proportions and how they affect one another. Furthermore, pointing out the connection between them and the urban and social context of the research project.

2.2

Informal Urban Settlements emergent process in three Latin American cities Patterns of formations and coincidently lack of Social Active Spaces

Rural to Urban Migration + Population Growth URBAN GROWTH Urban Land composition

[planned areas]

unplanned areas SELF-ORGANISATION community answer

collective land occupation patterns

circular Medellin

radial Valparaiso

INFORMAL URBAN SETTLEMENTS Latin America t

spots Sao Paulo

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t i m

unit aggregation process

BREAKING POINT DENSIFICATION PROCESS

Fig. 13 Social Active Space absence in Informal Urban Settlements. The diagram shows the sequential process of Informal Urban Settlements emergence in Latin American cities. The process is driven by two main factors, urban growth and inadequacy of governmental institutions for housing solutions on time for the imperative demand of low income population. Improvised bottomup solutions have emerged with diverse urban manifestations. The breaking point of such a complex process is manifested when the percentage of the built unbalanced the percentage of the urban void necessary for the emergence of Social Active Spaces, essencial for social interaction.

IGNORED PUBLIC SPACE INDIVIDUAL VERSUS COLLECTIVE GROWTH TENSION

Fig. 13

Domain

2.3

Self-organised urban settlements

Fig. 14 vectordragonfly[2015], ´Petare´ Neighbourhood, Caracas - Venezuela. [size: 1200x674] source: http://www.dronestagr. am/favelas-of-petareneighborhood-caracasvenezuela-5/ Fig. 15 Soccer is absurdly popular in Brazil. The country hosted the 2014 World Cup, which was ironically the occation for radical slum clearence in Brazil. Also, note that the fantasy depicted in the mural is of another favela where kids are playing soccer, but with a slightly higher quality ball. size: 3495x2310 Source: http:// all-that-is-interesting. com/brazil-favelas#5 Fig. 16 Comuna 13, Medellín Colombia. size: 1556x1000 Source: https://spark. adobe.com/page/ ogHrs/ *System: a system is a purposeful collection of interralated components that work together to achieve some objective. *Complex systems exhibit four characteristics: Self-Organisation Non-linearity Order/Chaos Dynamic Emergence *Nonlinear is a system in which the change of the output is not proportional to the change of the input.

As Portugali pointed out in his article, it was Peter Allen who first developed a complexity theory of cities [Allen and Sanglier, 1981] and by doing so he opened the domain of CTC – complexity theories of cities. A small but active community of researcher’s studies in the domain of CTC have demonstrated that cities as open and complex systems* exhibiting all the properties of natural complex systems: they are open, complex, bottom-up and are often chaotic. But, once we accept that all living systems are open systems, and exchange matter and energy with their surroundings, then we see that this breaks the seeming infallibility of science and its capacity to predict what must happen. On complex systems Peter Allen, reflects that ´in a way complexity is telling us that we can never know for sure that a new ‘case’ will behave as expected on the basis of previous examples´ [Allen, 2016]. Being Cities understood as complex systems, they are nonlinear* and sensitive to initial conditions, so that small changes in such conditions may produce turbulent behaviour at the global scale. Additionally, as self-organised systems, cities are chaotic in several respects. Like open and complex systems in general, the evolution of self-organising cities exhibits a very distinct and routinised path: a long period of ‘steady state’ followed by a short period of strong fluctuation of chaos from which the system re-emerges to a new level of ‘steady state and structural stability and so on [Portugali, 1999]. We are forced to see that hidden internal details and seemingly irrelevant collective effects can change entirely how a system develops. Above all we see that history, and detail are all important in complex, evolving systems. We can never really say that one system or city is ‘like’ another since their histories cannot be identical. This means that we cannot build up a set of ‘exemplars’ of like systems, whose observed behavior can safely and certainly be supposed for the next ‘case’. Everything we do or suppose is an experiment [Allen,2016]. In that respect, Grotao favela [Sao Paulo] case study has been analysed in this chapter as an

Domain

urban object to recognise urban patterns in that very dense patch, its spatial and network parameters, numerical ranges and hierarchical relations. These parameters will be incorporated into a series of experiments in the design process of a new urban model for informal urban settlements located in Valparaíso´s ravines. An alternative bottom-up approach starts from the recognition that a complex system is a very large number of small and simple components, each of which is semi-autonomous but interacts with its neighbours. The behaviour of the global system emerges from the interactions and local behaviour of the individual components or agents. [Weinstock and Stathopoulos, 2006]. In other words, without interaction between components there is no possible emergence of any kind. Thus, interaction become a central matter of the present study. What conditions are needed to ease interaction between neighbours? Distances, environmental conditions, built and unbuilt space relations, are factors influencing interaction. Another interesting aspect to be developed throughout this research has to do with what makes interactions have a major influence in the whole urban system. Meaning how through the influence of interactions a better adaptation to environmental and social changes can emerge. Cities are not surprisingly more complex than the images shown here, that show a very high dwelling density filling the whole taken land. Very few are open spaces deliberated left for social interaction and cultural expression. Generally, those spaces are soccer fields that while a type of open space are not the only expression of them leaving the rest of the population without any urban place to visit. Usually, open spaces are roads and in fortunate cases some empty areas, such as the image at the centre, which due to housing arrangement has emerged in the urban system and become used eventually for social interaction.

´Only if you have a genuine ‘community’, where people, for the sake of others, are willing to sacrifice some of their own potential gains to increase the collective good, can such modelling really pay off.´ Allen P.

Domain

2.4

Fig. 17 Ben-Jacob [ Vortex Green 2. physicist leader in Self-organisation Theory and pattern formation in open system. https://imgur.com/ gallery/idkEd Fig. 18 Ben-Jacob [2006], Self-engeneering capabilities of bacteria. Published 22 February 2006.DOI: 10.1098/ rsif.2005.0089 The image shows how under natural growth conditions, bacteria can utilize intricate communication capabilities to cooperatively form complex colonies with elevated adaptabilityâ&#x20AC;&#x201D; the colonial pattern is collectively engineered according to the encountered environmental conditions. Source: http://rsif. royalsociety publishing.org/content /3/6/197

Learning from bacteria about social network

Fig. 17

As Ben-Jacob has pointed out, in bacteria colonies behaviour there are important lessons for improving social organisations. 'Bacteria, being the first form of life on earth, had to devise ways to synthesize the complex organic molecules required for life' [Ben-Jacob, 2011]. Behaviour such as social collaborative intelligence, distribution of tasks and cell differentiation, distributed information processing, learning from experience, and planning for the future because they know things can change, are the strategies applied to adapt to changes in the environment. They demonstrate different expertise in different sectors. Some bacteria are experts in facing new conditions. Furthermore, they utilise collective decision making and rapid collective adaptability. All of these collective behaviours give them the power of social networks, cooperation and self organisation. In summary, bacteria read the conditions, they communicate and they act. When bacteria read an environmental change that puts them in risk, the first social behaviour observed is their level of communication for informing each other of changes in environmental conditions, secondly as they have the information needed they face those changes developing different branching patterns which call for cooperation and self-organisation. When you have a complex system where the elements are not all the same and they can change their character the network as a whole is much more adaptable. They can easily change the overall organisation [Ben-Jacob, 2011]. In terms of social behaviour, when

Domain

Fig. 18

there is a homogenous society it becomes very rigid and it is prone to some systemic collapses, so that distribution of tasks and cell differentiation are key strategies to maintain the life of the community. All of these concepts were discussed by Eshel Ben-Jacob in a Google Tech Talk in September 2011 intended to transmit the fertile field of bacterial social intelligence into human social organisation [Ben-Jacob, 2011]. Kishon Lab at Harvard Medical School and TechnionÂ´s Scientists have designed a simple way to observe how bacteria move as they become impervious to drugs. The experiment aims to visualise mutation and selection in a bacterial population. It can be seen that at each point where the concentration in antibiotic changes, there is a lag and then growth at distinct points begins, where something has happened that allowed the colonization of the higher concentration of antibiotic to happen. One of the most interesting results from this study was the need for the bacteria to be exposed to intermediate concentrations of antibiotic in order to evolve. The authors found that bacteria were unable to adapt directly from zero to the highest concentration of either drug. Also, interestingly, mutations leading to higher resistance usually led to strains with decreased growth rates, demonstrating that there is generally a cost associated with being able to deal with higher concentrations of antibiotics.

Fig. 11 10000

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The diagram above shows the branching pattern developed as long as the bacteria move along the petri dish. When concentrations of antibiotics increase a small group of bacteria adapted and survived. Resistance occurred through the successive accumulation of genetic changes. In the span of 10 days, bacteria produced mutant strains capable of surviving a dose of the antibiotic trimethoprim 1,000 times higher than the one that killed their progenitors. The fittest, most resistant mutants were not always the fastest. They sometimes stayed behind weaker strains that braved the frontlines of higher antibiotic doses. ´What we saw suggests that evolution is not always led by the most resistant mutants, ´ Baym said. ´Sometimes it favours the first to get there. The strongest mutants are, in fact, often moving behind more vulnerable strains. Who gets there first may be predicated on proximity rather than mutation strength. This experiment has been incorporated to the research to first illustrate in a simple way how self-organisation is a powerful behaviour of environmental adaptation in biological communities and second to inform the design process of the project. Concepts such as collective action, individual expertise development, development of new skills for survival, collective cost for facing environmental changes, cooperation, period for adaptation, are translated into an urban language such as urban system connectedness, spatial interaction and flexibility, open spaces as part of an overall system.

The main legacy from bacteria colony behaviour to an urban language for developing Socially Active Spaces is that environmental conditions and changes on those conditions are opportunities to incorporate variation and flexibility in the urban order which are both the main drivers of emergent processes. As mentioned by Portugali, first, a system that is open and thus part of the environment can attain spatio-temporal structure and maintain it in far from conditions of equilibrium; not in spite of, but as a consequence of, a sufficient flow of energy and matter. Second, that this flow of energy and matter through its boundaries allows the system also to ‘create’ or ‘invent’ novel structures and new novel modes of behaviour. Self-organised systems are thus said to be creative. Third, self-organised systems are complex in two respects: in the first place, their parts are so numerous that there is no technical way to establish causal relations among them; in second place, parts and components are interconnected in a non-linear fashion by a complex network of feedback and feedforward loops [Portugali, 2011]. Finally, in self-organised system a principle of non-causality can operate. In certain situations, external forces acting on the system do not determine its behaviour but trigger an internal and independent process by which the system spontaneously self-organises itself. [Portugali, 2011].

Domain

Fig. 19 Freel [2016], Visualising the evolution of bacteria resistance on a ´Mega Plate´ Petri Dish [Kishony Lab]. The Scientist from the Kishon Lab at Harvard Medical School and Technion have design a simple way to observe how bacteria move as they become impervious to drugs. The experiments increasingly higher doses of antibiotics an adapt to surviveand thrive-in them. Source: http://www. molecularecologist. com/2016/09/ visualizing-theevolution-ofbacterial-resistance/ https://hms. harvard.edu/videos/ bugs-screen

Case Study Grotao Grotao Neighbourhood, Paraisópolis Favela - Sao Paulo, Brazil 2.5.1 Grotao Case introduction 2.5.2 Topographic Analysis 2.5.3 Network Urban Analysis 2.5.4 Urban Space Variety 2.5.4.1 Four Open Space Type Analysis 2.5.4.2 Distance summary between open spaces and entrances to the urban patch 2.5.5 Isovists analysis 2.5.5.1 Isovists inner patch general observation 2.5.5.2 Four Open Spaces Isovists analysis 2.5.6 Urban project for enhancing the urban patch, a result of Bottom-up + Top-down approach 2.5.7 Case Study Conclusion 2.5

Fig. 20 Stockler [2014], work titled ´Terrão de Cima´ southern and western fringes of Sao Paulo, Brazil. Source: http://rebrn. com/re/a-soccer-fieldin-brazil-508535/

Domain

2.5.1

Grotao Case introduction

Fig.21 South America map highlighting Brazil and Sao Paulo.

Fig. 22 Sao Paulo map highlighting Paraisopolis Favela and Grotao Neighbourhood. Fig. 23 Paraisopolis urban perimeter highlighting Grotao Neighborghood.

Grotao 0.12 km2

Fig. 21

Fig. 24 Factors affecting Social Active Space in highly dense populated areas.

Fig. 22

Sao Paulo is one of the most densely populated cities in Latin America and hosts a significant number of informal settlements locally called 'favelas'. ParaisĂłpolis favela is located in southwest SĂŁo Paulo, adjacent to Morumbi, one of the wealthiest neighborhoods in the city. Despite the challenging topography and recurrent dangerous mudslides in periods of heavy rainfall, approximately 80,000 people are currently living in the territory in a highly dense informal settlement covering almost one square kilometer. The big favela is divided into neighbourhoods and Grotao, at the south-east has particularities that make it an interesting case study for the current research. With an area of 0.12 Km2 and a population of around eight thousand people, the need of open space for social encounters becomes crucial since highly dense populated areas and reduced housing space can be compensated through collective areas acting as generators of social interaction for improving quality life standards. What makes Grotao special is the configuration of the street network and the variety of open spaces within the definition of its urban boundaries. Additionally, another factor that makes that urban condition more complex is the steep topography that dramatises the relationship between land availability and housing aggregation process.

Domain

Fig. 23

Population Density

Slope Terrains

population > open urban space

slope > land availability

Factors affecting Social Active Spaces in highly dense populated areas Social Encounter

Bottom-up solutions small scale housing solutions

Top-down interventions economic resources for large scale interventions

Fig. 24

T Fig.25 Gustavo Dudamel free Concert, celebrating 442 years of Caracas’ foundation. The event took place in ‘La Vega’ Neighbourhood Caracas - Venezuela, a very popular ‘barrio’. The area has a population of 150.000 inhabitants and 12.64 km2. This image was included in the Urban Think Tank Grotao project´s document as a fact of the collective need for Social Active Spaces that allow and promote cultural expresions through Social interactions. source: http://parroquialavega.blogspot. co.uk/2010_12_01_ archive.html http://www.noticias24. com/actualidad/ noticia/71443/dudamely-la-orquesta-sinfonicaestremecieron-a-la-vega/

Domain

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Fig. 26 Grotao topographic site plan, identifying topographic profiles A, B, C, D, E, and floodingÂ´s higher levels area.

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Grotao land occupation pattern logic has followed the principle of filling the land from the top to the bottom levels. Due to its higher population density and consequently the unstoppable housing aggregation unsuitable land areas were also built upon. Graphically, in the top view and in sections A, B, C, D, E of the patch can be observed the steep slope that gives no route for rainwater to flow out of the terrain. Common rain patterns in Sao Paulo result in aftermaths such as floods at the bottom of that urban land which affects the whole built environment. Due to lack of drainage the poorer and more vulnerable houses are flooded and become uninhabitable until the rainwater is absorbed by the soil. The worst area is highlighted in fig. 26. Since slope gradient directly affects land occupation patterns, these could be altered leaving more challenging steep slopes for social infrastructure that implies higher levels of investment.

Fig. 28 ‘Paraisopolis Urban Area suffers severe flooding due to heavy rainfall and lack of adequate runoff systems’. Image source: Google Earth, imagery Date: 19.04.2015 Image © 2017 DigitalGlobe 23°37´14.89" S 46°43´33.21" W Elev. 0 m eye alt. 515 m.

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Domain

2.5.3

Network Urban Analysis

Fig. 30 Grotao Urban Network Breakdown.

Legend CarsÂ´ roads Pedestrian paths to get the Social Infrastructure Inner pedestrian paths Cull-de-sac Grotao roadsÂ´perimeter External roads Entrances to Grotao urban patch

social infrastructure

Fig. 31

terraced gren area

Fig. 32 Fig. 31 Stairway Grotao - Paraisopolis Favela. Image source: Wagner Rebehy Urban ThinkTank Partner. Fig. 32 Pedestrian ways Grotao - Paraisopolis Favela. Image source: Wagner Rebehy Urban ThinkTank Partner. Fig. 33 Cull-de-sac streets Grotao - Paraisopolis Favela. Image source: Wagner Rebehy Urban ThinkTank Partner .

Fig. 30 Fig. 33

Domain

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Grotao Network Analysis Total Urban Patch Built area (foot print) Urban space

98.698 m2 56.788 m2 (58 %) 41.910 m2 (42 %)

527

Urban space breakdown open spaces 5.375 m2 social infrastructure 5.482 m2 green terraced area 7.946 m2 local open spaces 571 m2 Street Network 22.536 m2 (Total street length 5.517 m with average width 4m)

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Fig. 34

Fig. 34 Grotao Urban Patch identifying urban spaces variety. The plan was elaborated from information received from Urban-Think-Tank architect Wagner Rebehy.

The open space within the urban patch has a variety of open spaces interconnected by the roads network. Fifteen open spaces were identified varying in shape, size and located in different contour levels on the topography. As a consequence of those characteristics, they affect and are affected by the urban space in different manners. In the following pages four types of open spaces will be selected with the goal of acquiring quantitative and qualitative information that can be useful to understand ingenious logics and patterns improved by punctual formal interventions. A particularity in the variety of open spaces are the â&#x20AC;&#x2DC;cul the sacâ&#x20AC;&#x2122; that directly affect the connectivity of those areas with others having just one possible access to houses located around them. This space condition has to be considered with their advantages and disadvantages. The former gives the surrounding dwellings a private open space shared only by locals, however the latter reduces urban connectivity limiting the access to one road. Considering the existence of a commercial street and public transport in the west-side neighbourhood boundary, the major connection between the inner patch and the rest of the favela and the formal city is through nine east-west oriented roads.

Domain

2.5.4.1

Four Open Space Type Analysis

The dense arrangement of the buildings within the patch creates a variety of irregular blocks Amongst the open spaces resulting from the building arrangement it has been typified the most characteristics. Fig. 35-a Centralised Open Space diagram.

CENTRALISED OPEN SPACE 16

EXPOSED OPEN SPACE 15

Fig. 35-b Open Space [1] distances to nearest open spaces and entrances to the patch.

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Fig. 36-a Exposed Open Space diagram.

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e16 / 142m

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Centralised open space, result of a more densified built area. Parameters extracted: open space size: 536m2 [0.54% of the total patch area] number of branches reaching the open space: 4. [2 pedestrian paths and 1 vehicular road passing through the space]. streetâ&#x20AC;&#x2122;s width: pedestrian paths 3m / vehicular road ranges from 4 to 6m. distances from the open pace [1] to the nearest central spaces: 98m [1-2], 134m [1-5], 96m [1-3], 106m [1-0] / mean: 109m distances from the open space [1] to the entrances of the patch: 96m [entrance 1], 118m [e 2], 149m [e 13], 143m [e 14], 102m [e 15], 76m [e 16] / mean: 114m

Domain

Fig. 36-b

Exposed open space, result of a lower densified built area Parameters extracted: open space size: 328 m2 [0.33% of the total patch area] number of branches reaching the central space: 8 streetâ&#x20AC;&#x2122;s width: pedestrian paths between 1 and 3m distances from the open space [2] to the nearest central spaces: 47m [2-0], 98m [2-1], 86m [2-4] / mean: 77m distances from the open space [2] to the entrances of the patch: 119 [entrance 12], 116m [e 13], 110m [e 14], 142m [e 16], / mean: 122m

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CENTRALISED WITH EXTENTIONS OPEN SPACE

Fig. 37-b Open Space [9] distances to nearest open spaces and entrances to the patch.

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Fig. 38-a Centralised with extentions Open Space diagram.

Fig. 38-b Open Space [13] distances to nearest open spaces and entrances to the patch.

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Connective open space, sequence of small open spaces. Parameters extracted: open space size: 1,133 m2 [1.15 % of the total patch area] number of branches reaching the central space: 4. [3 pedestrian paths and 1 vehicular road passing through the space]. street’s width: pedestrian paths range 1 to 4m vehicular roads 5m distances from the open space [9] to the nearest central spaces: 114m [9-7], 62m [9-8], 52m [9-10], 220m [9-0] / mean: 112m distances from the open space [9] to the entrances to the urban patch: 199m [entrance 5], 114m [e 6], 274m [e 10], 262m [e 11] / mean: 212m

e10 / 161m

OS-14 / 107m Fig. 38-b

Centralised with extensions open space, widely open to the network around it. Parameters extracted: open space size: 1,117 m2 [1.13 % of the total patch area] number of branches reaching the open space: 6 pedestrian paths. street’s width: pedestrian paths, range 2 to 4m. distances from open space [13] to the nearest central spaces: 54m [13-11], 53m [13-12], 107m [13-14], 349m [13-0] / mean: 141m. distances from the open space [13] to the entrances to the urban patch: 99m [entrance 7], 101m [e 9], 161m [e 10], 325m [e 11] / mean: 172m.

Domain

2.5.4.2

Distance summary between open spaces and entrances to the Summary and comparison

Open Space 1

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Fig. 36-d

OS-0

In order to understand the rationale of the open spaces analysed, the distances from each open space [defining its centre point] to their nearest open spaces were first calculated. Second distances between the former and their nearest entrances to the patch were also measured. Due to the importance of social infrastructure associated to the Central Open Space, for all open spaces the distance between their centre point and the larger open space [OS-0] centre point was calculated too. This was done in order to see how far they are from social infrastructure ´Fábrica da música´ located at the lower level of the terrain. Understanding that the boundaries of the patch are well defined by a continuous façade, openings on the west façade are controlled entrances to the inner neighbourhood. Those entrances are vital for connecting the neighbourhood mainly with the commercial street, but also transportation to travel outside Paraisópolis favela to the rest of the city. Alternative road possibilities to connect those entrances and open spaces is an advantage in terms of the flexibility that is required in a highly dense urban area. Passing through open spaces to reach to the central one is also a condition that decreases the perception of the objective distance.

Fig. 35-d Open Space [01] synthesis diagram. Fig. 36-d Open Space [02] synthesis diagram. Fig. 37-d Open Space [09] synthesis diagram. Fig. 38-d Open Space [13] synthesis diagram. Legend

OS-0 Central open

Fig. 37-d

space ´Fabrica de Musica´ open spaces analysed nearest open spaces to [OS] analysed entrances to the neighbourhood paths between nodes

Fig. 38-d

Domain

2.5.5

Isovists analysis

2.5.5.1

Urban Patch Isovists

Fig. 39 Grotao Urban Patch, Convex Isovist map Sao Paulo, Brazil latitude 23°37´4.51"S longitude 46°13´32.78"W

11 10

11a

13a

Fig. 39

Domain

Despite the importance of considering individual open spaces as urban individualities, it is also important to think of them as nodes within a wider open space system in the form of a network of inter-connected spaces. Individual open spaces should be integrated into such an overall system and depending on where they are located in relation to each other, and to the system as a whole, they can play different roles and can be expected to fulfil different functions. The concept of hierarchies of open spaces is connected to the idea of catchment areas: depending on the size of an open space and the facilities it provides; different group of people are willing to travel different distances to visit it. The connectivity of urban spaces is important for a number of reasons. Structural and symbolic functions relate to both the wider functions which open spaces can play at the city and local scale, as well as their more intangible but not less important functions. These include articulating, dividing and linking areas of the urban fabric; improving the legibility of the city or neighbourhood; establishing a sense of place; acting as a carrier of identity, meanings and values among others. [Stiles, 2016] Grotao´s urban patch has developed a variety of open spaces in terms of size, shape and isovists. Fifteen open spaces were mapped within the patch that are the result of both, a bottom-up urban network developed in time, - resulting from housing aggregation - and top-down city council-led design for enhancing social life through the incorporation of a hierarchical sequence of open spaces implementing social infrastructure. The largest open space within the patch identified in the top plan as [0] has been converted from a constant flooded housing area into a social infrastructure centre dubbed ´Fábrica de Música´ [music factory]. The project also considered terracing the land for shaping the spatial void into a sense of amphitheatre that visually and functionally connects the top and the bottom of the ground relief, creating multiple visual perspectives that enriches the visual fields of that central open space. The present study is an analysis of the isovists of some open spaces along the patch. Particularly, Grotao has an urban cadastral pattern made-up of many irregular sized street blocks, having then a fine urban grain, conditions that improves, what Hillier calls ´visual permeability´ [Hillier, 1996] offering pedestrian movements a great choice of routes. Considering that every trip in an urban system has three elements: an origin, a destination, and the series of spaces that are passed through on the way from one to the other, location in the grid therefore has a crucial effect, it either increases or diminishes the degree to which movement by-product is available as potential contact. This applies not only to individual lines, but to the group of lines that make up local areas [Hillier,1996]. Taking this into account, the intention of analysing open space isovists is to focus the spatial inquiry on the ´series of spaces that are passed through on the way from one to the other´ as a key urban aspect of integration and connectedness. Technically the analysis has defined the centre point of each open space as destination points and entrances to the patch as origin points to observe how are they connected and extract indicators of the relation between open spaces and between them and the outside of the patch.

Fig. 40 Rocco R., [2011] Jardim Angela favella. [image - size: 853x1280] Available at: https://www. domusweb.it/en/ architecture/2009/01/16/ urban-age.html.

Fig. 40

Fig. 41

As Hillier [1996] points out that ‘spatial configuration influences patterns of movement in space, and movement is by far the dominant form of space use´ [Hillier, 1996], it raises the question what makes individuals move? It could be argued that movement is a reaction to a spatial attraction that in the urban space is directly related to the ´visual permeability´ from one space to another. This quantification of the spatial connection between open spaces called ‘strategic value’ of the isovist determine open spaces levels of success. In other words, visual permeability of the space measured through its isovists, reveals the degree of ´connectedness´ of open spaces that manifest the integration value into the urban tissue. Hence, the level of integration of open spaces into the overall urban patch depends on their visual fields result of their building configuration. As the main topic of the thesis is urban social interaction, the spatial characteristics for the pedestrian use of public spaces is particularly significant. Furthermore, Hillier [2007] has described the essence of open space as being an urban place to sit and watch others pass by with optimal development on strategic visual fields, scaled up in proportion to the scale of the space [Hillier, 1996].

The urban patch analysed was not designed all at once, conversely it was built up over time as a consequence of dwelling aggregation. Therefore, its open spaces natural ´connectedness´ has been the result of a collective ingenious intuition. In this respect, the particular analysis of four open spaces has calculated the relation between the size of the actual open space [open space size-m2] and the isovist size, obtaining for each open space a number that indicates the increase of the visual field. It was found that there are some beneficial relations between two open spaces such as a small open space neighbouring a bigger one, allowing a significant visual field expansion of the former. Additionally, more or less integrating areas depends on how the internal structure of the area is married into the larger-scale structure of the grid, and this will mean also areas with more by-product and areas with less. [Hillier, 1996]. Although, some open spaces of the patch have the condition of being identified as individual urban spaces, because of their position on the urban grid they overlap with neighbour’s open spaces. This effectively creates a hybrid open area made-up by a sequence of three open spaces [see fig. 39]. This particular condition creates a sense of being contained by an endless open space.

Domain

Fig. 41 Rocco R., [2011] Favela Paraisópolis sidestreet Sao Paulo [image - size: 685x1024] Available at: https://www.flickr.com/ photos/ robertorocco/ 6256501245

2.5.5.2

Four Open Spaces Isovist analysis

Isovists 1 and 1a

Isovist 2 Fig. 42

Legend

Fig. 43

built environment open space centre

lines startig at open space centre ending at built area surrounding the open space

open space area

isovist area

longest and shortest isovist lines

intersection area between two open space isovists

Fig. 42 Isovist Open Space 1 and 1a. Fig. 43 Isovist Open Space 0 and 2.

isovist m2 open space m2

= increment ratio

open space name 1 1a open space m2 536m2 528m2 isovists m2 709m2 753m2 perimeter 196m 127m shortest line 7m 7m longest line 42m 41m intersection region m2 307m2

OS-1 m2 = 709 = 1.32 m2 I-1 m2 = 536 OS-1 m2 = 753 = 1.43 m2 I-1 m2 = 528

These two neighbour open spaces are similar in size and shape, as well as in their relation between open space size and isovist size. Their isovists overlap each other in a very similar proportion so that the overlapping area enlarges the perceptions of the space of both spaces. As Hiller describes in his book ´the area of overlap will itself form a smaller convex element from which both overlapping convex spaces will be fully visible, that is, will be convex, although these spaces are not convex to each other. ´ [Hillier, 2007] The relation between ´isovist area´ and ´open space area´ respectively are: [OS-1]: 709/536 = 1.32; [OS-1a]: 753/528 = 1.43. Hence, the relation between these two open spaces is more balanced in terms of their reciprocal visual fields in that one does not dominate the other and vice versa.

Domain

open space name 0 2 open space m2 6,382m2 328m2 isovists m2 7,065m2 1,534m2 perimeter 497m 108m shortest line 30m 5m longest line 93m 1,111m intersection region m2 776m2

OS-0 m2 = 7,065 I-0 m2 = 6,382

= 1.11 m2

OS-2 m2 = 1,534 I-2 m2 = 328

= 4.68 m2

This open space sequence is made by two open spaces, the central and bigger one of the patch and a second one - a type of appendage of the former. The isovists of [OP-2] have mainly two directions, one extending all across the central space and the other to the perimeter of the east side of the patch. These long ´spatial arms´ result in an increase of the isovist area 4.676 times from the initial open space m2. The ratio between ísovist area´ and ópen space area´ respectively are: [OS-0]: 7,065/6,382 = 1.107; [OS-2]: 1,534/328 = 4.676 Although [OS-2] is a small open space, through its isovist connection with central open space ´0´ it enriches its own urban space quality through the visual participation of the central space activities. To the east side, there is a more diffused area without buildings and without the minimum spatial constraints to be understood as an open space. That gives [OS-2] an opportunity to expand in that direction. Open space ´0´ is the central open space of the patch analysed. It has the lowest number of m2 incremental between ópen space area´ and ísovist area´ a ratio of 1.107 giving it a more intimate character, being an interesting urban feature when a more controlled space is desirable. However, that intimate condition appears contradicted by the scale of the space itself, being the largest Social Active Space of the patch.

Legend

built area around open space open spaces boundaries

isovist centre points

isovist shortest and longest lines

isovist lines

intersection area between isovists

Isovist 9, 9a, 9b

Isovist 13 and 13a Fig. 44

Fig. 45

13a 9b 9

open space name 9 9a open space m2 623m2 595m2 isovists m2 714m2 830m2 perimeter 159m 189m shortest line 7m 5m longest line 39m 90m intersection region m2 4m2 35m2

9b 259m2 327m2 94m 4m 31m 46m2

OS-9 m2 = 714 I-9 m2 = 623

= 1.15 m2

OS-9a m2 = 830 = 1.40 m2 I-9a m2 = 595 OS-9b m2 = 327 = 1.26 m2 I-9b m2 = 259

This open space sequence made by three open spaces has the particularity that [OS-9a] is connected with the main external street of the urban patch meaning that this isovist functions as an entrance to the neighbourhood. Therefore, is hierarchically more important than [OS-9b] and [OS-9]. The relation between open space m2 and isovist m2 respectively are: [OS-9]: 714/623 = 1.146; [OS-9a]: 830/595 = 1.394; [OS-9b]: 327/259 = 1.262. These relations were calculated to see how much convex space grows through isovists, how much they expand the physical constraints through their openings. As they have bigger numbers, the impact outside of its own boundaries is bigger. However, that has to be analysed in terms of the range that numbers are useful to the purpose of promoting social interaction.

open space name 13 13a open space m2 1,017m2 119m2 isovists m2 1,175m2 368m2 perimeter 198m 48m shortest line 8m 4m longest line 54m 53m intersection region m2 107m2

OS-13 m2 = 1,175 = 1.16 m2 I-13 m2 = 1,017 OS-9a m2 = 368 = 3.09 m2 I-9a m2 = 119

Fig. 44 Isovist Open Space 9, 9a and 9b. Fig. 45 Isovist Open Space 13 and 13a.

The relation between these two open spaces is similar to [OS-0] and [OS2] open spaces. A big open space connected with a tiny one. The isovists of [OP-13a] have mainly three directions, one extending all across [OS-13] and the other two as shown in the diagram. These long ´spatial arms´ result in an increase of the isovist area to 3.09 times from the open space m2. The relation between ísovist area´ and ópen space area´ respectively are: [OS-13]: 1,175/1,017 = 1.155; [OS-13a]: 368/119 = 3.09. Although [OS-13a] is a small open space, it has spatial extensions that allows it to increase its spatial boundaries to reach 3.09 times its physical open space area. Similarly, to Central Open space [OS-0], [OS-13] has a very low number of m2 incremental between ópen space area´ and ísovist area´ 1.155. From this shared characteristic of these two big open spaces it is thought that the relation between ópen space area´ and ísovist area´ tend to decrease when the m2 increase. At the same time there is a mutual space improvement due to these connections between different neighbours open spaces.

Domain

2.5.6

Urban project for enhancing the urban patch, a result of Bottom-up + Top-down approach 1

Fig. 46 Grotao Urban Patch plan highlighting houses in danger due to steep slope, floods, landslides. The proposal considers the removal of those dwellings asumming that these residents sacrifice is upon a Collective Wellfare liberating space for Social Infrastructure. 3000 m2 were demolished, and 6000 m2 were built: ´Centro da Musica´ building, and the southeastern side terracing for developing Urban Agriculture.

527

Fig. 47 Ducci [2015], Grotao social provision future location. [image, size: 2000x1312] Available at: http://www.archdaily. com.br/br/760744/ grotinho-de-paraisopolis-construindo-espacos-de-convivio-boldarini-arquitetos-associados. The picture shows the stage of the intervention when houses at the bottom of the terrain were already removed.

212

Fig. 47

Conclusions Fig. 47

Although Grotao’s original informal settlement grew until the limit of any possibility of building more houses, the public space [roads and open spaces] developed interesting variations resulting from a non-planned system of growth. In other words, the urban space was reduced to narrow roads and stairways where intersections of the network by spatial openings have been significantly valued. Consequently, an external approach from the local council in conjunction with Urban-Think Tank, and in conversation with the community agreed that the implementation of social infrastructure would be an opportunity to enhance the urban conditions. The project´s main driver has been the relevance of Socially Active Spaces in informal settlements. Analysing the area, the professional team realised that there were geographical factors such as steep hills and flooding caused by soil erosion that had to be included in the project. Unfortunately, although the area was densely occupied by dwellings, for the implementation of the project it was decided to clear the land by demolishing dwellings located mostly at the bottom part of the terrain.

removed dwellings

Fig. 46

Domain

Case Study Conclusion 1

212

Fig. 48 Grotao Urban Patch plan highlighting the area prepared for receiving the Social Infrastructure [1] and the terraced land for developing Urban Agriculture [2]. Fig. 49 Lafarge Holcim Foundation [2011], Urban Think Tank architectural and urban proposal for Grotao - Paraisópolis Favela. [image-size: 2800x2100] Source: https://www. lafargeholcimfoundation.org/ awards/3rd-cycle/ latin-america/ Winners.

780

527

2.5.7

Fig. 49

2 Fig. 49

The social cost was 3720 m2 of dwelling demolition versus the construction of ‘Fábrica do Música’ that ringfences 6382 m2 for the collective welfare of the community. What had been topographically disadvantageous for housing location was taken as an opportunity to locate social infrastructure for promoting social integration. The proposal would transform an inaccessible void within the dense fabric into a community hub. Two goals were accomplished by the project. Firstly, placing the social building where it was inappropriate to build houses due to permanent flooding. Secondly, the volumetric impact of the new infrastructure into the urban environment has been reduced due the location at the lower part of the terrain, terracing the land in the surrounding areas of the building gradually manage the height difference between the top and the bottom levels of the terrain. A final comment is that, an evaluation of the social impact of the project, will be related to the degree of social connections that can be generated by ´Fabrica da Música´within Grotao neighbourhood and between this urban patch and the wider Paraisópolis favela.

1 - terraced land next to the Social Infrastructure 2 - terraced land for urban agriculture

Fig. 48

Domain

2.8

Domain Conclusions

The urban population of Latin America has increased approximately 40% over the last 40 years, and UN-Habitat has projected that by 2050, 90% of the population will be in towns and cities. Formal responses to this phenomenon have failed and it seems that in terms of urban organization new orders of land occupation have to be explored. Observing what has been happening over time in Latin American cities, a common factor is that new urban settlers arrive and self-organise to meet shelter requirements - their primary need. A central point made in the discussion of the present chapter has been selforganisation as a paradigm of bottom-up organisation that can be observed in different living collective forms. This chapter aimed to see self-organisation at two complete different scales. In the biological scale, bacteria colony behaviour in response to environmental changes was analysed to abstract principles of their battle for survival. At the urban and social scale of self-organisation, a very dense informal urban settlement located in Sao Paulo was analysed. An essential aspect of bacteria to adapt to environmental changes is their social cohesion that derives into a series of strategies to maintain that condition that is ultimately the survival guarantee of colonies. Strategies such as distribution of tasks, cell differentiation, distributed information processing, learning from experience and planning for the future are key aspects for the evolution of the colony. At the social and urban scale, informal urban settlements are an example of how self-organisation [spontaneous order as it is called in social sciences] works in urban organisation. As shown in the Grotao case study, collective ingenuous intuition of land occupation and practical knowledge of self-construction are the basis of that piece of city that is a good sample of the basic principles shared by settlements built from the same logic. The emergence of a network and convex spaces through the process of colonisation is what makes informal settlements an adaptive urban configuration that is able to change when conditions change. From open space analysis through its network and a variety of convex spaces is recognised an approximation to the ground that brings to the analysis the capacity of solving simultaneously the need for shelter and the occupation of a specific piece of land as recognised in bacteria colonies as cell differentiation and different expertise in different sectors.

However, that initial collective intelligence in the favela was broken when the proportion of empty land was reduced to limits generating inner conflicts that called for the creation of external alliances for cooperation systems. When those forces interact [people from formal institutions and local community leaders] following bottom-up principles emerging solutions adapt to the existent environment. Since Grotao new social infrastructure is under construction there is still lessons to learn in terms of how this project will affect the social and urban orders. It is expected that the new social provision and punctual urban interventions will promote novel social interactions, being an interesting further stage of analysis. Overall, it has been concluded that the organisation of the territory is crucial when settlers take a land for developing new urban patches. Although an adaptive system is able to constantly incorporate environmental changes into the system itself, initial decisions of land occupation have important effects of urban configuration and the type of social interaction that will arise from it. The goal of this research is to initiate a reflection of a new model of human settlements by using basic principles of self-organisation. A set of experiments will be run to find a range of possible solutions for central deficiencies found in the case study analysed where urban space for social interaction has partially been an absent element until external forces have been involved. Therefore, the central aspect of the research project will be the definition of principles of land occupation and unitÂ´s aggregation for guaranteeing a system of Social Active Space as a core element of this new urban model. This, through the recognition that self-organised urban settlements can reconfigure themselves to changing demands, therefore it has to be based on a flexible urban configuration that allows adaptation over time. The research question arises - can a system of emerging Socially Active Space adapt to the pressures of population growth that consequently will materialise in the built environment while maintaining the spatial conditions of those places for social interaction? Is it possible to design a model that through the nature of its own configuration keeps two contradicting forces, on the one hand the built environment and on the other hand, the open urban space in a dynamic equilibrium?

Domain

Methods

03 METHODS Proposed design methodology Environmental Analysis - Solar Radiation Analysis 3.3 Topographical Analysis - morphometric parameters 3.3.1 Aspect Analysis 3.3.2 Slope Angle Analysis 3.3.3 Surface Area Analysis 3.4 Urban Network Analysis [UNA] 3.5 Isovist Analysis - Visual fields 3.6 Social Network Analysis 3.7 Genetic Algorithm 3.7.1 Generative process 3.8 Network Generation 3.1 3.2

Methods

structures in terms of nodes (indiv the network) and the �es, edges, or connect them. These networks are o which nodes are represented as poi Social network analysis has emerged as 3.1

Proposed Design Methodology R

Environmental Analysis Solar Radiation Analysis Grasshopper+Ladybug

variables extracted from

Fig. 50 Research Methodology diagram.

case study informal urban settlement

Ucinet + Net draw

Ucinet 6.85 So�ware Pla�orm

Grotao Sao Paulo - Brazil to extract and analyse metrics, mathematical relations and design logics [territory+unit growth+network+open space]

through

Land Analysis Surface Analysis Slope Aspect Elevation Urban Analysis

site analysis Jaime´s Ravine Valparaíso - Chile - South America

1 8 26

59 7

6 7

215

121

140

248

8 21

Urban Network Analysis [UNA]

P N R O

Fig. 50

P D I

Isovists

Social Analysis Social Network Analysis UCI Ucinet 6 for Windows--Version 6.636

D The rise of the science of complexity, which has changed the direction of systems theory from top-down to bottom-up, is one that treats such systems as open, based more on the product of evolutionary process than of a grand design [Portugalli, 2000]. In line with the understanding of cities as forever being bottom-up process, the methodological framework of the present research project will be based on concepts of emergence and evolutionary ideas of natural selection for multi objective optimisation through Genetic Algorithms [GA] that produces iterations that are evaluated through the definition of evolutionary goals. These concepts have been broadly developed in parametric models in Rhino 3D and Processing. Generative algorithms and associative modelling were implemented throughout the design process. A first stage considers the examination of a highly dense informal settlement located in Paraisópolis-Sao Paulo that also shares with the project site a steep topographical condition. This informal settlement has been completely developed through communities’ self-organisation. Interestingly, it has been the object of punctual top-down interventions including an external parameter of analysis. Additionally, four informal settlements located in Valparaíso´s ravines were analysed as small case studies too in Design Development & Strategy chapter.

Methods

CLl QD

Case studies will be the subject of the application of a set of analytical computational tools. From that empirical inquiry, several parameters are extracted and analysed to reveal the metrics and mathematical relation within elements of the network systems and geometries to inform the main drivers of the urban project. These comparative data sets offer quantifiable insights into the characteristics and organisational principles of multiple urban contexts from which to set and evaluate the desired properties for the new urban system. The urban context of the case study comprises three fields of inquiry: first, geographical analysis through slope, aspect, elevation and solar radiation, second, urban analysis through Urban Network Analysis and Isovists, and third, Social Network Analysis through Ucinet and Net Draw. Comparison for finding convergences and deviations [discrepancies] requires reflection and careful consideration. The identification of causal patterns is also an important element taking into consideration that these relations will affect the whole urban system. Also, the limitations and constraints of each computational tool is relevant to identify.

O main thesis drivers

built environment

parameters

Social Active Spaces interaction

generative process Genetic Algorithm [create initial population

relation mass - void density

built form cluster

variation] basic set of primitives

[aggregation system]

building types private public

evaluation

generation

Social Active Spaces generation

Final Model Fitness Asignment

within and between clusters

public space

network connection

network generation

agent-based path finding

reproduction

intersecting new and existing network

At a second stage a generative process through GA will start making experiments at the three urban levels of the project, the built form developing a building catalogue [housing units, clustering and infrastructure], Socially Active Spaces catalogue including ´plazas´ and ´miradores´, and finally a new network complementing the existing. It will be a parallel process of development for the three-urban levels of the project followed by an integration of the three levels into one proposal. Location of open spaces is driven by two different factors: proposed built environment density within the urban fabric, and low density in empty land between clusters. Preliminary results, from experiments at different stages of the design process, will be evaluated with the same range of analytical tools that were used to analyse the case studies. A comparative study at every stage and synthesis of the relevant aspects will be carried out to pass to the next phase. Following Grasshopper principles, the visual programming language developed by David Rutten at Robert McNeel & Associates, which runs the Rhinoceros 3D CAD application, the recursive comparison at every stage of design helps to critically assess the results and refine the definition of goals and the methodology to pursue them in the following stages. This evolutionary process provides an understanding of the emergent aspects of

selection

the design process. In terms of the post design evaluation there will be an objective evaluation through computational tools included in the Design Methodology which allows numerical comparisons between different solutions, subsequently an interpretive evaluation of the reults will include a reflection of possibilities and constraints of the achievements.

Evaluation 'Unlike the scientific method, evaluating designs against preset goals does not involve taking the design elsewhere and testing it against an independent set of goals. The process of design does not involve any such independence. The design is tested against the very same goals that are used in the process of synthesis that generates the design in the first place. There is an inevitable and intrinsic circularity, and probably there is no true test of a design, since consensus is very often not the purpose for which the design was produced. In fact, in this context, consensus will be important, but this is never a requirement for good design'. [Batty, 2013]

Methods

3.2

Environmental Analysis Solar Radiation Analysis

Ladybug Grasshopper Plug-ins

Solar irradiance calculated for Valparaíso test patch. kWh/m2 [kiloWatts per meter squared per hour] 1907.90<=

Fig. 51 Radiation Analysis SANTIAGO _CHL 1 JAN 1:00 - 31 DEC 24:00 Source: https://energyplus. net/weather-location/ south_america_wmo_ region_3/CHL//CHL_ Santiago.855740_ IWEC.

1821.70 solar irradiance site average 1656.78 kWh/m2

1735.49 1649.29 1563.08 1476.88 1390.67 1304.47

Fig. 52 Sun path diagram for Latitude 33°3´ and Longitude 71°36´ showing the Sun angle degree in Winter Solstice [33°] [21 June] Sommer Solstice [85°] [21 December].

6.22% of the site

1218.26 1132.06 <=1045.85

Fig. 51

Summer Solstice 21 December

Winter Solstice 21 June

33° 85° Fig. 52

One of the most important parameters of passive energy efficiency is insolation, or the energy received on a given surface during a given time measured in Watt per square meter [Wh/m2]. Chile is one of the countries that receives the largest solar radiation in the world [Inter-American Development Bank , 2011]. Variations in solar radiation levels are related to two factors, latitude due to the country going from 17°30´ to 56°30´ and seasons of the year. For instance, in the north of the country in the Atacama Desert [24°30´S] it is estimated a solar radiation between 7 and 7.5 Wh/m2, Valparaíso located at 33°3´S has a seasonal variation from 6.5 Wh/m2 in summer and 2.5 Wh/m2 in winter. This environmental condition has important effects on the urban life at its different scales. Additionally,

Methods

solar radiation impact over the ground varies in relation to the slope degree and the aspect of the surface. Therefore, due to the steep topography on Valparaíso´s ravines solar radiation will be an important parameter of the project. The solar radiation analysis will be evaluated through Ladybug tools, that imports standard Energy Plus Weather files [.EPW] into Grasshopper and Dynamo. It provides a variety of 2D and 3D interactive climate graphics that support the decision-making process during the early stages of design. Ladybug also supports the evaluation of initial design options through solar radiation studies, view analyses, sunlight-hours modelling, and more. Integration with visual programming environments allows instantaneous feedback on design

3.3

Topographical Analysis 3.3.1 Aspect Analysis

44°

35° 64° 45° 85° 83°

plane normal vector

112° 99° 68° 99°

103°

where: N = Geographic North = Aspect angle 0 = Slope angle p

0 XY plane

33°

34° 38° 16° 19° 31° 59°

37° 47° 14° 1° 53°

Fig. 53-d

60° 56° 10° 11° 30°

Fig. 53-a

NW 337.5° 315°

49° 56° 25° 19° 34° 48° 46° 39° 57° 15° 18° 37° 43° 7° 46° 32° 29° 32° 28° 27° 21° 7° 60° 43° 38° 33° 26° 60° 68° 63° 52° 43° 25°

63°

5° 17°

2° 11° 13° 58° 27° 14° 22° 69° 74°

111° 108° 88° 77° 69° 32°

4°

5° 58° 78° 49°

8° 23° 28° 24°

96° 92° 88° 78° 69° 60° 16° 9° 16° 22° 33° 95° 90° 93° 104° 104° 90° 71° 25°

2° 20° 42°

89° 99° 114° 122° 115° 109° 88° 59° 7°

1° 30°

88° 118° 135° 135° 135° 133°128° 109° 58° 15°

9°

148° 145° 146° 138° 134° 131°125° 115° 70° 10° 21° 153° 137° 131° 122° 112° 94° 78° 32° 2° 119° 112° 108° 106° 102° 75° 30° 112° 116° 129° 137° 137° 128° 118° 133° 134° 131° 121° 95° 67° 122° 136° 125° 117° 109° 107° 109° 104° 104° 106° 109° 113° 108° 104° 82° 96° 105° 114° 119° 121° 144° 107°109° 113° 118° 131° 148°144° 142°

Aspect orientation weighted

123°134° 137° 150° 148° 151°144° 125° 124° 137°144° 155° 155° 152° 135° 128° 134° 116° 135° 150° 151° 144° 132°128° 128° 136° 123° 120° 137° 129° 119° 121°121° 124° 112° 105° 126° 122° 123° 120°118° 107° 104° 132° 126° 118° 123° 127°

Fig. 53-c

NE & W

E & SW

[337.5° to 22.5] [292.5° to 337.5] [22.5° to 67.5] [67.5° to 112.5] [112.5° to 157.5] [157.5° to 202.5]

107° 178° 139° 126° 120° 94° 71° 25° 19° 11° 24° 35° 42° 14°

S/ 0 = 0.29 % cells SE / 1= 26.86 % cells SW / 2= 22.29 % cells NE & W / 3= 32.29 % cells NW / 4= 0.00 % cells N / 5= 18.29 % cells

135° SE Fig. 53-f

4° 95°

53° 31° 18° 19° 16° 13° 31° 50° 33° 20° 10° 60° 69°

4° 31° 20°

157.5° 180°

3° 46° 45° 30° 48° 58° 49° 98°

71° 96° 105° 96° 70° 57° 15°

112.5°

225° SW 202.5°

Fig. 53-e

20° 14° 18° 12° 15° 41° 51° 32° 31° 43° 28° 86°

54° 58° 39° 35°

90° E

247.5°

54° 46° 30° 54° 61° 52° 38°

NE 45° 67.5°

W 270°

33° 42° 67° 68° 60° 40°

28° 18° 23° 15° 15° 61° 49° 28° 40° 52°

22.5°

292.5°

6°

48° 58° 68° 65° 58° 31° 33°

18° 13° 14°

0°

91°

Fig. 53-b

[247.5° to 292.5][202.5° to 247.5]

Fig. 53-g

Aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbours. It can be thought of as the slope direction. The values of each cell in the output raster indicate the compass direction that the surface faces at that location. It is measured clockwise in degrees from 0 [due north] to 360 [again due north], coming full circle. Flat areas having no downslope direction are given a value of -1. Conceptually, the aspect tool fits a plane to the z-values of a 3 x 3 cell neighborhood around the processing or centre cell. The direction the plane faces is the aspect for the processing cell. A moving 3 x 3 window visits each cell in the input raster, and for each cell in the centre of the window, an aspect value is calculated using an algorithm that incorporates the values of the cell's eight neighbours. The cells are identified as letters a to i, with e representing the cell for which the aspect is being calculated. An important function of aspect is that it calculates the solar illumination for each location in a region having a very significant influence on its local climate [microclimate]. For example, in southern regions the sun's rays are in the east facing surfaces during the morning with warm rays and are shaded during the afternoon and west facing surfaces receive the hottest time of day in the afternoon, determining solar radiation fluctuations during the day and during the season of the year [see fig. 83-a, 83-b in page 76]. Consequently, aspect is a variable that strongly affects built and non-built urban areas in slope terrains due to the number of hours receiving a type of solar impact. Remarkably, the research project is located in a ravine where the axis has a slope direction south to north [from top of the hill to the sea level respectively], therefore the two faces of the ravine are the east and the west side.

Methods

Fig. 53-a Aspect analysis on the northeastern side of the polygon. Fig. 53-b Aspect degree calculation for each cell of the northeastern side of the polygon. Fig. 53-c Northeastern side of the polygon aspect cell orientation summary table. Fig. 53-d Aspect directions source: Burrough, P. A., and McDonell, R. A., 1998. Principles of Geographical Information Systems [Oxford University Press, New York], p.190 Fig. 53-e Aspect algorithm source: Burrough, P. A. and McDonell, R.A., 1998. Principles of Geographical Information Systems [Oxford University Press, New York], p. 190. Fig. 53-f Aspect compass orientation. Fig. 53-g Legend of the weight given to Aspect degree ranges.

3.3.2

Slope Angle Analysis

Fig. 54-a Slope angle analysis of a selected patch in Valparaíso´s ravines. Latitude: 33°3´34"W Longitude: 71°36´60"S

1°- 6° 7°- 12° 13°- 18°

Pacific Sea

19°- 20° 21°- 26° 27°- 30° 31°- 35° 36°- 54°

Fig. 54-b Slope angle calculation.

Fig. 54-a

As the terrain in Valparaíso´s informal settlements has a heterogeneous steep topography, the slope angle analysis is a deterministic factor to be analysed. A horizontal grid of 21 x 21 meters has been drawn and projected on the irregular surface. The result is 1,350 cells where the slope angle is calculated for each one. Taking their centre point as a reference point the slope angle at that point will be taken as the average slope of each cell. The equation calculates the angle based on the normal and tangent vectors at the centre points of the particular cell. A wide range of slope degree are found, inclinations from 1° to 54° are spreaded in the whole test area. In page 133 figure 169 Slope degree bar graph illustrates the percentages of cells in each slope degree range.

Methods

Y m 0

tan (0) = m -1 0 = tan (m)

Fig. 54-b

3.3.3

Surface Area Analysis

horizontal plane ...minimal surface area tilted plane ...increased surface area

An imaginary square grid is projected onto a steep terrain. The diagram shows that in horizontal plane there is a minimal surface area while in tilted plane there is an increased surface area. Surface area is a morphological parameter that relates slope angle with land area available. The diagram below shows the proportion of increased surface land when slope increase. From a slope of 0° with and increment of 0%, the percentage of increment increases up to 70.06 % for a slope degree of 54°, in other words the higher the slope angle degrees the bigger the surface areas.

elevation

Fig. 55-a Surface area increases proportionally when inclination increases. Use surface area for realistic calculations Source: http://www. innovativegis.com/ basis/mapanalysis/ topic11/topic11.htm

surface area = planimetric area / cosine [slope angle]

[tilted plane m2-horizontal plane m2] * 100 = increased area [ia %] horizontal plane m2

Fig. 55-a

ai% = 0.68%

ai% = 2.49%

ai% = 5.21%

ai% = 5.67%

ai% = 6.35%

ai% = 12.25%

ai% = 15.42%

ai% = 23.58%

ai% = 70.06%

54° 36° 30° 27° 20°

Fig. 55-b Surface area increment. There is an increament in the percentage of surface when the slope degree increse. The proportions are: 0° = 0.00 ia % 7° = 0.68 ia % 13° = 2.49 ia % 18° = 5.21 ia % 19° = 5.67 ia % 20° = 6.35 ia % 27° = 12.25 ia % 30° = 15.42 ia % 36° = 23.58 ia % 54° = 70.06 ia %

19° 18°

21m 21m

13° 7° 0°

Fig. 55-b

Methods

3.4

Urban Network Analysis

Fig. 56 Clossest Facility measure - Valparaíso informal settlements located above ´Alemania´ Avenue. Fig. 57 Betweenness measure - Valparaíso informal settlements located above Alemania Avenue. betweenness high 347

low 1

Fig. 56

Urban Network Analysis [UNA] is a new Toolbox for ArcGis developed by City Form Lab offering powerful methods for assessing distances, accessibilities and encounters between people or places along spatial networks. The new UNA Rhino toolbox makes urban network analysis available in Rhino 5, adding a number of new features and functionalities. Having UNA metrics in Rhino, not only allows one to analyse how a specific spatial network performs, but to also incorporate the analysis into a fast and iterative design process, where networks can be designed, evaluated and redesigned in seamless cycles to rapidly improve the outcome. [City Form Lab, 2015] This toolbox can be used to evaluate five types of graph analysis on spatial networks: Reach, Gravity, Betweenness, Closeness, and Straightness. Redundancy Tools additionally calculate the Redundancy Index, Redundant Paths, and the Wayfinding Index. Network centrality measures area mathematical methods of quantifying the importance of each node in a graph. The reach measure [Sevtsuk, 2015] of a node in a graph describes the number of other nodes that are reachable at a shortest path distance within a specified network radius. Within the scope of the present research, this tool is used to identify and locate public facilities for ease of use. The Betweenness Centrality estimates the number of times a node lies on the shortest paths between pairs of other

Methods

Fig. 57

reachable nodes that are within a specified network radius. [Freeman 1977]. Also, Betweenness measure may be used to estimate the potential of passersby at different locations of the network. Closeness of an origin is defined as the average distance required from that origin to all the specified destinations that fall within the search radius along the shortest paths [Sabidussi 1966]. Service area tool selects or copies destination points and path segments that fall within a given network radius from origins. In the present research the tool will be used for selecting all Social Open Spaces from the urban patch [destinations] that fall within 400 m radius from the dwellings. Despite the multiple advantages of the Toolbox, some limitations were found related mostly with the kind of topography the informal urban settlements are located in. As has been previously mentioned, those settlements occupy very steep slope terrains, so that slope degree add an extra difficulty to reaching any origin to destination point. The computational program has been developed for application in theory on flat urban surfaces, thus the Toolbox will be an approximation to obtain some indicators of the urban model proposed but it cannot be expected to obtain a high level of accuracy. Therefore, due to steep terrains, network elements such as stairways can produce distortion in terms of being wrongly considered as flat connections by the program, again will be aspects to discuss in the final conclusions.

3.5

Isovist - Visual Field

Grasshopper isovist component plane[1] count[2] radius[3] obstacles[4]

P N R O

P D I

points[5] distances[6] index[7]

[1] height of the plane [2] number of radial lines [3] maximum length of the radial lines [4] physical urban boundaries [5] end points of each radial line, from those the isovist polygon is made of. [6] length fo each radial line [7] list of obstacle indices for each hit. fig. 58

Fig. 58 Grasshopper isovist component description. Fig. 58-a Construction process of three neighbours´ open space isovits. 1.Open space boundaries.

fig. 58-a

fig. 58-b 2.Centre point definition.

The notion of ‘isovist’ was first presented in the field of landscape geography by Tandy [1967], but the concept was introduced in architectural studies by the urbanist Benedikt M. [1979]. Isovist is defined as the field of view, available from a specific point of view. Usually, in the scientific literature, the isovist represents a horizontal slice through this field of view taken at eye height and parallel to the ground plane. Batty [2013] has defined an isovist as follow, ´isovist defines a field of vision from which various geometrical properties, such as area and perimeter, can be calculated. Isovists can be defined for every vantage point constituting an environment, and the spatial union of any particular geometrical property defines a particular isovist field´. Essentially, his analysis share with Benedikt's [1979], an emphasis on measures of the Euclidean geometry of the space which, in the case of Batty strongly presents as it is a prerequisite to any subsequent analysis of spatial relations. The first dimax = max three measures define standard radial distances: the maximum or{dfarthest ij } , j distance which can be seen from the vantage point i, di; the minimum distance dimin = min {dij } , di, and the average distance di computed as: j dimax = max {dij } , j

dimax = max {dij } , j

min

dimin = min {dij } , j

j = cniZi

dij

di = min {dij } , Σ dij j j di space = cniZi relies mainly in how that space is Considering the success of an open Σ dij connected j c Zi integrated to the network the calculation of an isovist can di = and manifest the nidegree of such integration. In light of this, an essential tool for assessing quantitatively the spatial impact of the Socially Active Space system in Valparaíso´s ravines into the urban tissue will be isovists and overlapping isovists.

One of the aims of the project is the achievement of an open space system accomplished by the interconnection of the open spaces within the system. Following this idea, is brought Benedikt’s reflection agreeing with Giedion that the architecture of the Modern movement is characterised by its free placement of visual space definers and the resultant ever changing visual experience. ´Boundaries become fluid, space is conceived as flowing – a countless succession of relationships´ [Moholy, 1928]. That experience of a continuous passing-by seduced by the succession of visual fields can be achieved by the kinds of isovists and isovist fields they generate. Benedikt questions whether isovists are less ´real´ than the environments they are unique to and in answer proposes ´to design environments not by the initial specification of real surfaces but by specification of the desire [potential] experience-in-space in the first place; that is, by designing fields directly´ [Benedikt, 1979]. Since the proposal for a Socially Active Space system will be developed in Valparaíso’s ravines, the steep topography will pose an extra challenge due to the fact that in less built-up areas the boundaries of isovists became less certain. While, actual open spaces can be surrounded by buildings along their perimeter, due to slope degrees some of their boundaries are not visible from every possible vantage point. Leaving the visual field free of obstacles, therefore, the degree of transparency of borders will affect results when applying isovist analysis. This condition can be an opportunity to develop Benedikt’s suggestion to design fields directly defining boundaries that allow controlled vistas as visual connections between open spaces and between them and the natural environment.

Methods

3. Radial lines [250].

4. Isovist polygon made by connecting end points of radial lines [250].

5. overlapped isovists´ areas

Fig. 58-b Three overlapped isovist´s polygons.

3.6

Social Network Analysis

Fig. 59

Fig. 59 Grandjean, 2014, Graph representing the metadata of thousands of archive documents, documenting the social network of hundreds of League of Nations personals. Source: Grandjean, Martin [2014] ´La connaissance est un réseau´ Les Cahiers du Numérique 10[3]: 37-54. DOI:10.3166/ LCN.10.3.37-54

Social network analysis [SNA] is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes [individual actors, people, or things within the network] and the ties, edges, or links [relationships or interactions] that connect them. These networks are often visualized through Sociograms in which nodes are represented as points and ties are represented as lines. Actor attributes are measures associated with the nodes and the full set of actor attributes is the network composition. The pattern of all the ties between actors is the network structure [Wasserman and Faust, 1994]. Two perspectives dominate SNA: the socio-centred and ego-centred perspective. The former analyses overall network structure. It looks for patterns of ties that indicate cohesive social groups, central actors that may be paramount to the integration of the social

Methods

network, and asymmetries that may reflect social prestige or social stratification. Due to the nature of Valparaíso's Informal Settlements social configuration, the present research has applied an ego-centred perspective that focuses on the composition of local network structure, although each individual network has network information as a socio-centred Social Network. The actor at the centre of this perspective is called the ´Ego´ while all the actors he or she is connected to are referred to as ´alters´ [Wellman 1997]. Another way to conceptualize networks mathematically is using matrices. [Borgatti, Everett, Johnson, 2013] The row and column headings for an adjacency matrix are identical, listing the names of the actors involved in the network. In the simplest case, the cells of the matrix are coded with a ´1´ if a tie exist between the actors or ´0´ if no tie exist.

Ucinet + Net draw Ucinet + Net draw Ucinet 6.85 So�ware Pla�orm Ucinet 6.85 So�ware Pla�orm

code code

nodes nodes

Ties or links Ties or links code code

UCI UCI

D CLl QD D CLl QD

Social Network Analysis

Social network analysis (SNA) is the process of inves�ga�ng social structures through the use of networks and graph theory.[1] It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the �es, edges, or links (rela�onships or interac�ons) that connect them. These networks are o�en visualized through sociograms in which nodes are represented as points and �es are represented as lines. Social network analysis has emerged as a key technique in modern sociology. w

nodes Network size: 8

name

Centrality indices Centrality indices

Fig. xxx Social Network Basic elements nodes are connected by ties or linkages

name code

name

code

code code

Ucinet + Net draw Ucinet 6.85 So�ware Pla�orm

code

Matrix

nodes

Netdraw

Ties or links

Fig. 60

code

Nodes - Alters - Actors

UCI DL Editor - Import Text Data via Spreadsheet Interface File Edit View

alter 1 alter 2 alter 3 alter 4 alter 5 alter 6 alter 7 alter 8

CLl QD

alter 1 alter 2 alter 3 alter 4 alter 5 alter 6 alter 7 alter 8 0 0 1 0 1 1 0 0 1 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0

Centrality indices

Fig. 60 Ucinet 6 for Window, Version 6.636 Software environment.

Network size: 8

Alter name or code Alter name or code Alter name or code

alter 1

alter 5

alter 4

+ alter 2 alter 6 alter 3

alter 7

nodes

Fig. 61 Ucinet 6, DL Editor, Import Text Data via Spreadsheet Interface ´1´ indicates relationship between two alters and ´0´ indicates no relation. The diagonal with zeros [red ] corresponds to the alters´ itself relation. Fig. 62 Social Network Basic elements, nodes are connected by ties or linkages .

Ties or links 57

alter 8 Fig. 61

Ties may also be ‘‘valued.’ Values indicate a characteristic of the relationship that the research has quantified, for example, in this research ties were differentiated by the frequency of the interactions between the égo´ and its álters´. Numerous analytical measures of social networks have been developed to help evaluate network structure and flow. These measures can be applied at the node level and at the network level, and some measures can be applied to both. Some are intuitive and easy to calculate, such as ´degree´, which is measured as the number of actors directly connected to any given actor. Others, are more complicated and computationally intensive, such as ´betweenness´ centrality, which measures the centrality of an actor in the network based on how much others depend on that actor for connectivity and ćloseness´ on average, how close a node is to all other nodes. These values

Fig. 62

can be used to characterize the strength and topology of an entire network or the importance or influence of a single node [Freeman, 1978]. Ucinet 6.85 Software for Windows is one of the main computing tools for the analysis of social networks. The complete software package contains three basic programs: Ucinet, Spreadsheet and Net Draw; the first calculates the indicators of social networks’ analysis, the second allows to capture relational data in the form of adjacent or attribute matrixes and the third whose purpose is the graphic visualization of social networks. The software was developed by Lin Freeman, Martin Everett and Steve Borgatti. [Borgatti, Everett, Freeman, 2002] Although Net Draw versatile graphic program accomplishes quick Social Network visualisation the present work has developed social networks representation in Illustrator CC 2017 for better graphic communication.

Methods

code code

3.7

Genetic Algorithm

multi-criteria optimisation

networks open spaces

Start

Research problem definition & selection of the imput parameters

Set GA parameters

built environment

Generate initial random population Evaluate Fitness of each chromosome in the population

Best Chromosome

New popuplation

Are optimisation / termination criteria met? No Parents selection for next generation

End Crossover of Parents Chromosome

GA operators

Yes

Mutation of chromosome Fig. 63

Fig. 63 Genetic Algorithm development diagram. *Evolutionary solvers, defined to be an algorithm or program that can solve instances of a given problem. The emergence of several CAD-based evolutionary solvers have increased the utilisation of evolutionary computation as a design strategy. [Makki, 2015]

Nowadays, the architectural design process can materialise increasingly complex tasks to solve with multiple parameters and targets to achieve. The complexity of both contemporary natural and social environments requires high standards of computational analytical tools, and the definition of multiple objectives call for optimal solutions generated simultaneously in accordance with faster evolution of societies. Cities are composite organisms made of an intricate web that connects the built environment through the open space which form a continuous flow shaped by the built by contraction and expansion. The approach for making an urban proposal will be a back and forth of decomposing and composing the urban patch, the former to critically analyse local logics and from them exploring new sets of organisations for composing new proposals. Following evolutionary multiobjective optimisation [EMO] through computation processes the research project will initiate a set of experiments to achieve defined goals for the urban patch selected. These experiments in parallel will develop networks, open spaces and built environment and later investigate their behaviour in the realisation of their own goals. Selected parameters serve as the variable ´genes´ and some important measurable outputs are defined as the ´objectives´. The GA is used to simulate the survival of the most optimal solution, not necessarily the fittest, over consecutive generations. Each iteration presents a point in search that presents

Methods

a possible solution. A fitness score is assigned to each solution representing the abilities of an individual to compete in each criterion. For multi-objective optimization problems, no single solution exists that simultaneously optimizes each objective. In that case, the objective functions are said to be conflicting, and there exists a [possibly infinite] number of Pareto optimal solutions. Taking that into consideration, a second stage of experimentation via multiple parameters and conflicting objectives will make couples between these three urban levels [networks, open space and built environment] such as networks with open spaces, network with built area, and open spaces with built area. Through the observation and analysis of coupling behaviours variables features are refined to inform experiments considering the overall system. The goal may be to find a representative set of Pareto optimal solutions, and quantify the trade-offs in satisfying the different objectives established at the starting point of the experiments. However, De Jong [2006] argues that if an evolutionary system is viewed as a ´complex, adaptive system that changes its make-up and its responses over time as it interacts with a dynamically changing landscape, then an evolutionary algorithm is represented as a feedback control mechanism responsible for maintaining some sort of system stasis in the face of change´ [De Jong, 2006].

Network generation Socially Active Space generation

Agent based path finding

COMPUTATIONAL

TOOLS

Unit-growth generation

Multi criteria [or multi objective] optimisation is used to combine different evaluation parameters into the design of a system. When two contradicting parameters are evaluated, as they are in the present thesis the need for housing [individual need] and the need for open space [collective need], individual m2 versus collective m2, a range of solutions are produced. Thus, solutions are called Pareto optimal, and occur when no solution can be improved with regards to a specific criterion without compromising the performance in the other criteria. Genetic algorithm [GA] applies concepts of emergence and evolutionary ideas of natural selection, producing iterations that are evaluated through the definition of evolutionary goals. Optimised solutions for a problem are generated using genetic principles such as inheritance, cross-over and elitism crossbreeding, selection and mutation to increase the success of the process. The challenging stage for the designer is precisely to correctly identify the predominant variables [Genes] and the hierarchical relation amongst them to produce competent solutions [Phenotypes]. ´The use of evolutionary solvers* in design has introduced the potential of dealing with multiple conflicting objectives under a single design model´ [Makki, Navarro, Farzaneh 2015]. Back, Hammel and Shwefel [1997] have argued that ´the most significant advantage of using evolutionary search lies in the gain of flexibility and adaptability to the task at hand, and while the optimal solution for a single objective problem

Evolurionary Octopus 3D

is clearly defined, multiple objective problems require ´robust and powerful search mechanisms´ [Zitzler, 1999] of evolutionary algorithms to find the fittest solution candidates that take into consideration all of the assigned objectives. The progression of different evolutionary strategies over the past few decades has revolved around the efficiency of an algorithm to apply to the set two basic principles in order to achieve the two most fundamental objectives of multiobjective optimisation [Zitzler, 1999]: First, the application of the most efficient assessment and selection methods to achieve the optimal set of trade-off solutions - the Pareto optimal set. Second, maintaining a diverse population throughout the simulation run in order to diminish the probability of premature convergence as well as maintain a dispersed Pareto optimal set. The benefits of using this evolutionary method is to display to the designer a range of optimal solutions that can be objectively evaluated through the performance of the different variables that a specific design might have, [Mitchel 1996]. The evolutionary Octopus 3D, set of optimal solutions that take into account all of the objective criteria without the need to employ a trade-oﬀ strategy to arrive at a given solution set. As such, the algorithm associated with Octopus3D is the Strength Pareto Evolutionary Algorithm2 [SPEA-2].

Methods

3.7.1

Generative process

21.0

Fig. 65 Cell subdivision into four plots for future housing development.

21.0

Fig. 64 Initial stage, a regular grid [21x21] projected on the terrain.

housing court yard

Fig. 66 Sub-cells motion definition.

fig. 64

fig. 65

fig. 66

The generative process starts with the definition of a regular grid [21x21] projected onthe terrain. The centre point of each cell is the reference point for cellÂ´s interventions . As each dwelling rotates following the nearest contour line of the terrain, the initial ortogonal organisation of the cell adapts to the topography conditions. Primarily each cell is subdivided into four parcels and a centre empty area for developing dwellingÂ´s courtyards. The definition of a body plan will set rules of movement and performances for the emergence of the built environment. The 2D and 3D subdivisions attempt to delineate the spatial boundaries within the virtual bounding box. The definition of movement is set in relation to the cell position within the grid. For housing experiments, goals are set for the attaining environmental better performances such as maximise solar radiation in the northern faĂ§ade; increasing interaction between neighbours and increasing interactions between clusters in Social Active Spaces.

Methods

1 1

Fig. 67 CellÂ´s range of movements.

Fig. 68 Cluster four cells after cellÂ´s rotation. Fig. 69 Generative process concluded for three storey floor house.

initial state [1x8=8] overlapped area 1 0.5 1

0.5 0.5 0.5 1 fig. 67

1 1

1 hypothetical state after cell motion [1x8]+[0.5x4]= 10 an increment of 25% in skin surface area

fig. 68

fig. 69

Methods

3.8

Network Generation

Agent based path finding

Fig. 70-a Definition of points and connections between them.

Steep topographies create a big challenge to successful urbanisation. Due to slope heterogeneity the construction of houses and infrastructure constitute specific answers to specific locations on the land. However, the slope strongly constrains the network that is required to provide the solution to the problem of how to connect that built environment along the whole territory. Considering that pedestrian possibilities are divided into categories such as disabled people, children and elderly people, adults, bicycle riders, criteria for designing a flexible network have to be defined by groups limitations, generating walkable paths within slope degree ratio of 5%, 8%, 12% and 27% respectively. [see fig. 204 in pag. 158] A definition introduced in Grasshopper calculates the distance of possible routes between two points and consequently identifies the shortest paths with different slope gradients defined by the possibilities of each group described. The criteria for the application of this tool will be to identify centre points of Social Open Spaces so as to connect them through a flexible network made of pedestrian paths with the inclination mentioned above. When slopes are over 20Â° pedestrian paths are not possible therefore lines with a slope degree between 20Â° and 54Â° will be designed for stairways and funiculars. By introducing agent movement in the above path finding definition various alternative paths can be achieved. Besides, it is critical to set suitable fitness criteria to select optimal paths among all the generated paths. An important criterion for the selection of optimal paths is the relation between slope and length of the path and distances between destination points.

Methods

Fig. 70-b Agent based path finding.

Methods

04 THE SITE, ´Quebrada Jaime´ [Jaime´s Ravine]

Fig. 71 OjedadelRio, P. 2017 ´René Lagos ´ Road, 225m level in ´Monjas´hill. Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions: [4032 x 3024] size 3.3 MB This picture was taken June 3rd 2017

The Site, main Valparaíso basins, identification of ravine understudy Informal Settlements Land appropriation Overview 4.3 Topographical Condition 4.3.1 Informal Settlements Location and Land Property Distribution 4.4 Hydrological Condition 4.5 Solar Radiation Analysis, Valparaíso Annual and daily Radiation 4.5.1 Sun Path Over Slope Degrees Of The Patch Analysed 4.6 Valparaíso Urban Patterns Analysis, tThree Main Strips 4.6.1 Natural Land and Urban Grid Overlay Pattern Processing 4.6.2 General North to South City Section and Street Configurations 4.7 Informal Settlements Growing Urban Patterns 4.7.1 Initial Patterns of Land Occupation [lines] 4.7.2 Informal Settlements Configurations, Three Local Types [branches] 4.7.3 Informal Settlements Housing Occupation Process and Pattern 4.7.3.1 Global Analysis 4.7.3.2 Local Analysis 4.7.4 Network Branch Type, Hierarchical Configuration 4.8. Valparaíso Existing Urban Typologies 4.8.1 Built Environment, Ravine´s Housing Types 4.8.1.1 Flat House 4.8.1.2 Stilt House 4.8.1.3 Flat and Stilt House Slope Occupation Analysis 4.8.2 Open Space 4.8.2.1 Network Element Types Stairways and Funiculars 'Ascensores' 4.8.2.2 Socially Active Space Types 4.8.2.2.1 ´Plazas´ 4.8.2.2.2 ´Miradores´ and Urban Openings´ 4.8.2.2.3 Amphitheatres and ´spontaneous amphitheatre´ 4.9 Valparaíso´s Ravine Social Aspects and Dynamics 4.8.1 Social Network Data Summary and Conclusions 5.0 Site Conclusion

4.1

4.2

The Site

4.1

The Site,

main Valparaíso basins, identification of ravine under study

Fig. 72 Official map of the city with main basins. Base map: Municipality of Valparaiso, redraw highlighting Jaime´s ravine, research project site.

N 1

main basins watercourse sewage system

Pacific Ocean Sea

4 5

Fig. 73 Jaime´s ravine nolli map.

3 6 7 1 Carampangue 2 Cajilla 3 Tomas Ramos 4 Urriola 5 Melgarejo 6 Cumming - Amte Montt 7 Ferrari 8 Jaime (Francia) 9 Uruguay 10 Las Zorras

9 10

Valparaiso Self-Organised Settlements Urban Conditions

Urban boundary ´Alemania´ Avenue

rural-urban migration appropriation of the land self-built dwellings outskirts of the city the ideal of homeownership Fig.72

Fig. 74 OjedadelRio, P. 2017 ´Carlos Pezoa Veliz´ Road, 300 m level in ´Miraflores´hill. Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions: [9350 x 3720] size 9.2 MB This picture was taken November 4th 2017

Fig.74

The Site

Fig.73

4.2

Informal Settlements Land Appropriation Overview

Latin America constitutes the most urbanised region worldwide. The majority of the poor live in dwellings that have been gradually self-built, often without complying with formal procedures and legal frameworks. Nonofficial housing solutions are labelled ‘informal housing’. Informal houses are generally situated in unplanned peripheral areas, within or outside the city’s jurisdiction. From the fifties, the primacy of urban living became a general feature of Latin America. The majority of the new urban residents found a place to live in informal settlements. The ideal of the homeownership over rental housing is deeply embedded in Latin American culture where a significant part of the ´informal housing ‘homeowners have informal tenure [Turner, 1967]. These informal settlements patterns are present in Chile where the region of Valparaíso has the highest concentration of informal settlement in the country. However, land appropriation patterns - ‘la toma de terreno’ – practiced in Valparaíso differs from that occurring in the Chilean capital, Santiago. For the latter is a collective action in contrast with individual actions by single families or small groups in Valparaiso’s case. Despite this process of land appropriation via small actions, these groups have given way to the so called ‘Conjuntos Residenciales Familiares’ [CRF] [Family Residential Groups] that over time acquires a collective character by addition [Pino, 2012]. Bottom-up urban processes arouse different philosophical approximations. On the one hand, a Turnerian approach was inspired by the bottom-up planning philosophy, promoting governmental assistance for self-provision on the basis of considerations such as freedom of choice and empowerment of a deprived population. This idea is based on the almost impossible idea of providing formal housing for growing population from governments. On the other hand, critical Marxist scholars like Rod Burgess and Emilio Pradilla stated that the Turnerian approach gave authorities an excuse to withdraw from the responsibility to provide decent lowcost housing. Turner´s central ideas of an appropriate encounter between governmental institutions and self-organised communities, is interesting when seen in the light of the recent concept of urban acupuncture coined by Barcelonan architect and urbanist, Manuel de Sola Morales and developed further by Finnish architect and Casagrande Marco and Brazilian architect Lerner Jamie. Urban acupuncture is a theory that applies the metaphor of Chinese acupuncture to urban design using small-scale interventions to transform the larger urban context. Sites are selected through analysis of aggregate social, economic and ecological factors, and are developed through a dialogue between designers and the community. Just as the practice of acupuncture is aimed at relieving stress in the human body, the goal of urban acupuncture is to relieve stress in the built environment. [´Urban Acupuncture´, 2013]

Fig. 75 Jaime´s ravine aerial view. Source: Municipality of Valparaíso.

Fig.75

The Site

4.3

Topographical Condition 1

4 N 6341000

Pacific Ocean Fig. 76 Valparaíso topography, highlighting the site location. Contour lines source: Municipality of Valparaíso.

Pacific Ocean

Fig. 77 Valparaíso topography, site location detail. Contour lines source: Valparaíso Municipality.

The Site

Fig. 77

N 6339000 F

E 2560000

For the present project’s research Valparaíso specific case represents one of a very common type of Latin American city that meets two conditions. First, a flat area [seaside or valley] surrounded by a steep topography and second, an unplanned urban growth created by settlers who occupy those hilly areas. Generally, it can be said that the common sense of the settlers guides them to select the terrains with less slope difficulties. Consequently, the summits of the hills are the first occupied areas, leaving the ravines for further occupations. As a consequence, ravines become urban barriers between populated hills. More than thirty hills shape this natural amphitheatre so that the land has plenty of ravines. For the research purpose, a ravine where the separation of clusters were clearly defined by natural barriers such as steep terrains was selected. This is the case of ´Quebrada Jaime´ [Jaime´s Ravine] that divides ´Cerro Monjas´ and ´Cerro La Cruz´ [Monjas Hill and La Cruz Hill]. The map shows the delimitation of the site where natural and urban conditions will be analysed.

N 6340000 A

E 2550000

Fig. 76

A A Sections A, B, C, D, E and F were drawn for being analysed in conjunction with sections 1, 2, 3 and 4. Longitudinal sections [1, 2, 3 and 4] follow the relief from the Sea level [0] to the hills [up to 300 m]; transverse sections [A, B, C, D, E and F] oriented East to West evidence the ground heterogeneity. Sections D, E, F, pass through informal settlements locations over Alemania Avenue [AA] that can be seen in sections 1, 2, 3 and 4 highlighted in red. Informal urban settlements land ownership distribution pie graph show that 39.19 % of the land is public land meaning that the owner is the State through different public institutions. A Similar percentage 39.90% belongs to the private sector.

´QJ´

1000 1000

AR Alemania Road

´QJ´ ´Quebrada Jaime´ [Jaime´s Ravine]

108 108

´QJ´ 1000 1000

1 AR

133

´QJ´

133 1000 1000

Section C 1

D D

Fig.78 Valparaíso topography cross sections: A, B, C, D, E, F.

100

Section B

C C

100

Section A

B B

´QJ´ 187 187

1000 1000

Section D 1

E E

´QJ´

219 219

1000

Section E

1000

4 275 275

F F

200 200

´QJ´

249

100

249

100

1000

Section F

1000

0 0

The Site

4.3.1

Informal Settlements location and land property distribution

Fig. 79 Valparaíso Camp´s Social Map publication. Land property distribution [%] pie chart [´Mapa Social de Campamentos´] Source: Housing and Urbanism Ministry Executive Secretary. [Secretaría Ejecutiva de Campamentos Ministerio de Vivienda y Urbanismo.] Publication date: 2013

Fig. 80 Topographic northsouth sections highlighting in red Informal Urban Settlement location. AA: Alemania Avenue ISL: Informal Settlement Location

39.90 % Private land 21.92 % Serviu - Urbanisation and Housing Services 39.19 % public land

3.23 % Ministry of National Assets 2.63 % Railway State Company 15.96 % mixt [private and public] 4.95 % without information

Fig. 79

300 275 250 225 200 175 150260 125 100 75 50 25 0

250 225 200 175 150 125251 100 75 50 25 0

275 250 225 200 175 150275 125 100 75 50 25 0

275 250 225 200 175 150265 125 100 75 50 25 0

11.41 % Council

Section 1

Informal Settlement Location -LSl -

2464

Section 2

LSL

2330

Section 3

LSL

2445

Section 4 - "Quebrada Jaime" (Jaime´s Ravine)

The Site

E LSL

D AA

2505

0 Fig. 80

Hydrological condition

Fig. 81 Valparaíso Hydrological system Source: Ministry of Public Works, Valparaíso Headquarter, information received in May 2017.

Fig. 81

120 100 80

Higher rainfall levels in Valparaiso are concentrated between May and August, reaching an annual precipitation rate 372.5 mm. At the present time the total amount of rainfall water is lost after running superficially through the ravines until Alemania Avenue [125 m level] where it is piped and conducted to the sea.

60 40 20 june

may

april

mar

feb

jan

dec

nov

oct

sept

aug

0 jul

4.4

Fig. 82 Valparaíso Annual Precipitation rate, 372.5mm per year Source: www.climatemps.com

Fig. 82

The Site

Solar Radiation Analysis, Valparaíso Annual And Daily Radiation 8

Fig. 83-b Valparaíso Radiation Daily cycle variation. Radia�on [kWh/m2/day] [Latitude: -33.0482 W, Longitude: -71.6035 S] Source: Energy Ministry, http:// www.minenergia.cl/ exploradorsolar/

Radiation [kWh/m 2/day

Fig. 83-a Valparaíso Radiation Annual variation [Latitude: -33.0482 W, Longitude: -71.6035 S] Source: Energy Ministry, http:// www.minenergia.cl/ exploradorsolar/

Direct in tilted plane Horizontal direct Difused in tilted plane Horizontal difused

Reﬂected on the ground on tilted plane

January

February

March

April

May

June

July

August

September October November December Fig. 83-a

Legend

Direct in tilted plane Horizontal direct

Fig. 83-b

800

Difused in tilted plane Horizontal difused Reﬂected on the ground on tilted plane

Radiation [W/m ]

600 2

400

200

The Site

:0 0

As sunlight passes through the atmosphere, some of it is absorbed, scattered, and reflected by air molecules, water vapor, clouds, dust, pollutants, forest fires and volcanoes. This is called diffuse solar radiation [represented in the graphs in dark blue for diffusion in tilted plane, and light blue for horizontal diffusion]. The solar radiation that reaches the Earth's surface without being diffused is called direct beam solar radiation [represented in the graphs in red for direct in tilted plane and light red for horizontal direct]. The sum of the diffuse and direct solar radiation is called global solar radiation. Atmospheric conditions can reduce direct beam radiation by 10% on clear, dry days and by 100% during thick, cloudy days. Radiation data for solar electric (photovoltaic) systems are often represented as kilowatt-hours per square meter [kWh/m2]. Direct estimates of solar energy may also be expressed as watts per square meter

:0 0

0 00

cember

4.5

(W/m2). As shown in the graphs above Valparaíso solar radiation varies during the year [Fig. XXX] from a range between 6 and 7 [kWh/m2/day] to less than 4 [kWh/m2/day] in winter time from May to July. An important factor in terms of radiation is its different effect on tilted surfaces compared with horizontal surfaces. The former receives higher levels while the latter lower. This is due to the angle of inclination between the sun rays and the surface that receives the rays which entails the Sun rays’ intensity. For instance, in a rugged topography slope degree angles directly in conjunction with the Sun rays angle affect solar radiation per m2. The next experiment analyses the Sun angle variation from Winter Solstice [33°] to Summer Solstice [85°] in surfaces from 0° of inclination to 54° which is the steepest surfaces in the site selected.

Sun Path Over Slope Degrees Of The Patch Analysed

4.5.1

[80°]

[85°] [SS]

90° [85°]92° [SS] [80°]87°

[70°]

[70°] 83°

[70°] 77°

[60°]

[50°] 63°

[50°] 57° [40°] 47°

[40°] [33°] [WS]

Fig. 84-a Sun path from Winter solstice [WS] - 21 June to Summer solstice [SS] - 21 December in 33°3´ South Latitude, for surfaces with a slope degree from 0° to 54°.

[60°] 73°

[60°] 67°

[50°]

[80°]93° [85°] 98° [SS] 90°

[40°] 53°

[33°] 40° [WS]

[33°] 46° [WS]

Fig. 84-b The table summarises Sun Path over the Slope degree of the patch under study, highlighting the period of the year when each slope degree receives Sun rays at 90°.

13°

7° 0°

[80°]99° 90° [70°] 89°

[SS] [85°] 104° [80°]100°

[SS] [85°] 105°

[80°]107° [70°] 97° 90° [60°] 87°

[70°] 90°

[60°] 79°

[60°]80°

[50°] 69°

[50°] 70°

[50°] 77°

[40°] 60°

[40°] 59° [33°] 52° [WS]

[40°] 67°

[33°]53° [WS]

19°

[SS] [85°] 112°

20°

27°

[33°] 60° [WS]

[SS] [85°] 139° [80°]134°

[80°]110°

[SS] [85°] 115°

[80°]116° [70°] 106°

[70°] 100° [50°] 80°

[40°] 76°

[40°] 70°

Fig. 84-a

54°

[40°] 94° 90° [33°] 87° [WS]

Fig. 84-b

Sun path degrees

Slope degrees

[50°] 104°

36°

[33°] 69° [WS]

30°

[33°] 63° [WS]

0° 7° 13° 19° 20° 27° 30° 36° 54°

[60°] 114°

[60°] 96° 90° [50°] 86°

[60°] 90°

[70°] 124°

[SS] [85°] 121°

June 21 [WS]

33°

36°

33° 40° 46° 52° 53° 60° 63° 69° 87° 90°

July 21

40° 40° 47° 53° 59° 60° 67° 70° 76° 94°

August 21

50°

54°

50° 57° 63° 69° 70° 77° 80° 86° 90° 104°

60°

Sept 21 63°

Oct 21

70°

71° 77°

60° 70° 67° 77° 73° 83° 90° 79° 89° 90° 80° 90° 87° 90° 97° 90° 100° 96° 106° 114° 124°

Nov 21 Dec 21 [SS]

80°

80° 87° 93° 99° 100° 107° 110° 116° 134°

83° 90°

85°

85° 92° 98° 104° 105° 112° 115° 121° 139°

In Latitude 33°3´ and Longitude 71°36´ the Sun path angles vary from 33° in Winter Solstice to 85° in Summer Solstice. Those angles are defined over and hypotetically horizontal surface of zero slope degree. Therefore, to those angles it has to been added the correspondant slope angle of each of the surfaces analysed. The diagrams show angles between the Sun path rays and the tilted surfaces. There was highlighted 90°angle for each tilted surface which correspond to different seasonal periods of the Sun path from June to December. For a slope of 0° there is no possible 90° of Sun inclination, 7° slope receives 90° of Sun rays in Novembre, 13° slope in October, 19° slope in early October, 20° slope in Septembre, 27°slope in August, 30° slope in August, 36° slope in August and 54° slope in June. These calculations are taking the slopes as facing the north perpendicularly hence with 0° azimuth angle.

The Site

4.6

Valparaíso Urban Patterns Analysis, Three Main Strips

Fig. 85-a Valparaíso aerial photograph illustrate Valparaíso urban morphology. Image source: Google Earth, imagery Date: 13.08.2017 Image © 2017 CNES / Airbus 33°03´20.71" S 71°38´01.25" W Elev. 0 m eye alt. 4.45 Km.

Third strip Fig. 85-b Valparaíso three urban strips Strip 1: Between the sea and Ćolón´ Avenue. [0-25 m sea level] Strip 2: Between Ćolón Avenue and Álemania Avenue´. [25-125 sea level] Strip 3: Between Álemania´ Avenue and the top of the hills. [125-300 sea level]

+350

S Second strip

+125 ´Alemania´ Avenue

Valparaiso has three main urban strips defined by the influence of its topographical relief into the urban network. Those strips are parallel to the Pacific Ocean. Buildings and networks were constructed following that natural order, starting with the occupation of the flat area next to the ocean and progressively filling the land to the hills. Hence the age of the construction is directly related to the distance from the sea. The oldest urban area of the city and first fringe occupies the flat ground, from 0-25m above sea level .The second fringe develops mainly from 25m to 100m above sea level At those levels [25 and 100m] the urban network has important roads connecting the city East to West, such as ´Colón´ Avenue coinciding with 25m, and Alemania Avenue coinciding with 100mConsequently, defining urban boundaries between

The Site

P L A I N

Fig. 85-a

E A

First strip

+25 ´Colón´ Avenue

Fig. 85-b

strips. Alemania Avenue connecting most of the hills of the bay defines the urban boundary between the formal and the informal city. The third strip has developed both informally and gradually from 100m to 350m above sea level. The urban pattern morphology is analysed in this chapter to find spatial and network parameters that will inform the definition of Social Active Spaces in informal urban settlements located in the third strip. The occupation pattern follows a linearity along the hills´ axies. The first occupation stage covers the area between the level 125m to 275m, and the second occupation stage the area from 275m to 400 m. The third occupation stage occupies from that level up to ´Camino La Pólvora´ Highway that connects Valparaíso with other cities.

4.6.1

Natural Land And Urban Grid Overlay Pattern s o u t h

P L A I N

SEA

Fig. 86 Overlapping natural and urban layers diagram, four stages. Sequence of diagrams showing the urban evolution of Valparaíso Territory.

n o r t h

flat land

steep land

Stage 1, original and natural state of the land, composed by a Sea shore, facing the Ocean Pacific Sea. First strip: thin flat portion of ground with little inclination up to 25m. From there rising to the steep hills arriving in 400 m length from level cero to 350 m over the sea level.

natural layers original state

Stage 1 representation

1930

imposing urban grid

1875

Stage 2, first Urbanisations attempt were done around the end of the XIX Century. Initially without an Urban Plan some Institutional building were built near the Seashore and that started to draw a square grid on the flat area. The extension of that square grid deforms since it moves to the transition land between flat and steep terrains. At the south end of that ´organic grid´ it was built a road ´Alemania Avenue´ for connecting all the hills. That road marked at that time the end of the City.

initial urban condition

Stage 2 representation

1950 community land self-organisation

Stage 3, from Alemania Av. that determined at that time the South Urban Boundary, gradually started an informal process of land appropriation. This is represented by the thinner lines that started at Alemania Av. It is noticeable that in the previous stage it is possible to see an integrated grid north to south and east to west directions. However, in the third stage connections east - west are missing. Leaving isolated settlements in each hill.

current urban condition

Stage 3 representation

2017 NEW MODEL

future expantions

inclusion of Social Active Spaces for Social Integration

Stage 4, shows in red the area core of the present project research that pursues the transformation of that residual land Ravines - into a Social Active Space System to integrate isolated settlements into the urban system.

Stage 4 representation

Fig. 86

The Site

General North to South City Section 350+350m m

325 m +325m

300 m +300m

275 m +275m

250 m +250m

225 m +225m

200 m +200m

175 m +175m

150 m +150m

125 m +125m

´Alemania´ Avenue

Fig. 87 Valparaíso City longitudinal section. Relation between the topography and urban development and the location of informal settlements highlighted in green. ´Alemania´ Avenue and ´Colón´ Avenue are urban delimitations between the three urban strips.

2,500 m Figures 88-a; 88-b and 88-c show street views from the three different urban strips of Valparaíso urban configuration.

2392.36 m

Second Strip

Third Strip

Fig. 88-a Precariousness of some streets from ÁlemaniaÁvenue. Ćerro Toro´hill. ´Mobil Arquitectos´ architectural practice source: http:// mobilarquitectos.cl/ urbano/cerro-toro size: [1300x731] Fig. 88-b OjedadelRio, P. 2017 Ćalle Urriola´ Road, typical street from the second strip between 25m and 100m sea level in Álegre´hill. Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions [6794 x 3830] size 6.3 MB This picture was taken June 4th 2017 Fig. 88-c ´Trolley buses´ belongs to the first strip - flat area.This is one of the means of transportation of that part of the city. [photograph] Fong, C. source: http:// fongandcarolyn. blogspot.cl/2014/04/ enjoying-santiagovalparaiso-partying. html size: [1600x1066]

100 m +100m

Fig. 88-a

The Site

75 m +75m

50 m +50m

25 m +25m

00 m

+0m

´Colón´ Avenue

N Fig. 87

First Strip

Fig. 88-b

Fig. 88-c

The Site

4.7

Informal Settlements growing urban patterns

4.7.1 Initial Fig. 89 Informal settlements initial patterns of land occupation. Main roads of the formal city cross Alemania Avenue, urban boundary that divides the city into two, the formal and the informal. The pioners of land appropriation followed those traces to maintain connection with the previous urbanised area.

patterns of land occupation [lines]

4.7.2 Informal

settlements configurations, three local types [branches]

formal city

100

Alemania Avenue urban boundary

327.6540

125

Fig. 90 Three types of Informal Settlement configurations highlighting in red main roads of each type. [1,2,3]

150

175 200

225 250 250 300

informal urban settlements

325

350

N Fig. 89

Analysing the urban fabric of ValparaisoÂ´s formal city different types of roads can be observed. Most of them main roads running North [sea] to South [hills], secondary roads running East-West following the contour levels of the topography, and finally pedestrian roads defined by locals connecting in different directions the main network. The image above shows a portion of the city where the main roads North-South oriented terminates at the end of the city at 100m above sea level defined by the main road East-West oriented Alemania Avenue. That road demarcates the end of the formal city at 100m above sea level and from where informal settlers started to colonise the hills in the mid-1920s. It would be expected to find alternative approaches to define the urban tissue at this urban strip [Fig. XXX]. After all the pioneers were defining their own piece of the city without imposed rules from the established urban order. However, the Spanish city grid is embedded in the collective unconscious of the Latin American citizens hence the existing main roads, from the formal city have been kept as the main axis for the new urban layout. The traces of those roads of the formal city follow the summit of the hills that in this particular area is flatter than the rest, thus being suitable for housing settlement. Secondary roads oriented East-West also mimic the existing ones

The Site

Fig. 90

in the formal city. These ones follow the topography contours connecting shorter distances. This provides a rough description of how informal settlements started defining their urban fabric, similar to the existing city creating a network that performs satisfactorily in each cluster. 4.7.3.1 Global

analysis For explanatory purposes, in the next page the graphics show that the land appropriation process has two directions. The first movement is the colonisation North to South, following the main lines from the formal city as mentioned before. Consequently, group dwellings are set in lines along the centre axis of the flat area at the summit of the hills. A densification process occurs when these lines extend North to South and a second motion East-West, materialising a second line behind the first and then the third until the flat area is completely taken. New dwellings then start occupying the terrains with higher slope degree approximating the ravines. That parallel process of moving to the North and moving East-West for colonising the land is repeated iteratively however expressing diverse morphological urban forms due to slope terrain variations.

4.7.3 Informal

settlements housing occupation process and pattern, three stages global and local analysis global analysis

4.7.3.1

First occupation stage motion North to South

Second occupation stage

motion North to South + motion East to West and viceversa

Third occupation stage

motion north to south + motion East to West and viceversa

Fig. 91 Two directions for land appropriation: a. first movement direction: North to South, b. second movement direction: East-West.

N W

E S

Fig. 91

The Site

4.7.3.2

Local Analysis [dwelling occupation pattern]

First Occupation line Second Occupation line

hill´s summit

bottom of the Ravine

hill´s summit

Fig. 92 Land occupation pattern section, showing the process of populating the land from the summit of the hills on initial stages, followed by second and third lines of dwellings and as many lines as possible until reaching the ravines.

bottom of the Ravine

Third Occupation line

Fig. 92

Fig. 93 Informal settlement´s land occupation process. Legend Dirt road Paved Road Pedestrian Path Connection 1st dwellings row roads Connection 2nd dwellings row roads First dwelling rows Second, third or more dwelling rows

[1]

[2]

[3] Fig. 93

For analysing the occupation patterns of the territory, three samples of the branch type 3 were selected [Fig.90 page 82] because it is less developed and can easily be decomposed. Houses follow an aligned pattern that consider road paths as the main axis for occupying the ground. The diagrams above show three stages of the land appropriation. In stage [1], the process of occupation is made by the arrival of single units along existent paths. This diagram shows an array of points representing dwellings along the road where the connection between them is through a central line, the road. In this initial period of occupation houses keep a certain distance between them. The connection between private and public space is direct and the only type of public space is the road. In the stage [2], an additional second row of dwellings is made. It can be seen that the first occupation line starts to be denser due to the fact new dwellings [points] occupy interstitial space left by the initial settled units. That second line of dwellings reaches the road through the narrow space between houses in the first line. In the case of those dwellings [red] the relation with roads is indirect. In stage [3], a random allocation of units between the previous two

The Site

lines incorporates new challenges for keeping connected, with internal paths appearing. The first occupation line can still clearly be read but the second is blurred with the addition of a more random occupation system. At this point in time, main roads have evolved from dirt roads to paved roads and from those is developed an irregular network of secondary roads and pedestrian paths. Public space is reduced to a network of roads conceived just as a means of communication from one point to another. There is an absence of open spaces for social interaction and there is no clear definition of clusters. During the densification process a network starts to emerge. The next page shows the branch type 1 [Fig.90 page 82] where main roads are identifiable at the external borders and also pedestrian secondary paths [EastWest]. Therefore, the network in bottom-up urban processes is an emerging element - where there are two points [houses] a line [road] appears.

Network branch type, hierarchical configuration c i t y

Fig. 94 Informal settlement´s network branch type.

f o r m a l

Fig. 95-a OjedadelRio, P. 2017 'Aquiles Ramirez Road' [n°446] Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions: [4032 x 3024] size 2.5M This picture was taken June 4th 2017, This road constitutes the West main axis of of the two the network branch drawn at the diagram beside. It has an indlination of 16.70° = 30%

first occupation

´Alemania´ Avenue

Fig. 95-a

s e t t l e m e n t s

i n f o r m a l

second occupation

c Fig. 95-b OjedadelRio, P. 2017 'Miguel Angel Road' [n°514] Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions: [4032 x 3024] size 2.5M This picture was taken June 4th 2017, This road constitutes the East main axis of of the two the network branch drawn beside. It has an indlination of 16.70° = 30%

Fig. 95-b

Fig. 95-c Samuel, [11.02.2006] 'Aquiles Ramirez 11th passageway' San Juan de Dios hill. [photograph] Image source: blogspot http:// valparaisoenfotos. blogspot.cl/2006/02/ size: [640x 480] This passageway constitutes an example of the type of secondary pedestrian paths are developed by locals for connecting main roads.

third occupation

4.7.4

Fig. 94 Fig. 95-c

The Site

4.8

Valparaíso existing urban typologies

Valparaíso existent Typologies Built environment Dwellings flat

The Site

Open Space

Infrastructure stilt

Social Active Spaces

Network Elements staiways

funiculars

‘plazas’

‘miradores’

‘amphitheater’

4.8.1

Built environment, ravine´s housing types Fig. 96 OjedadelRio, P. 2017 'Ravine Stilt housing' [photograph] Valparaíso´s own private collection. At contour level between 200m and 225 m, the photography was taken from ´Cerro Monjas´ to ´Cerro La Cruz´.

Fig. 96

Informal settlements in Valparaiso’s city show two preeminent housing types closely related with the piece of ground they are built on. Considering the way the house will be posed over the terrain there are two possible technical solutions to apply: ´cut and fill´ and ´stilt´. Topographically the former can be applied on terrains up to 13° and the latter between 13° up to 27°. ´Cut and fill´ methods are usually made on terrains next to the summit altering natural slope degrees to create leveled areas for housing placement. This building technique has been observed just in small number of houses for two reasons: first, surfaces with a slope degree less 13° represent 18.07% of the patch selected for slope analysis [Fig.168-a page 133]. Second, although this method can be applied onto steeper slope degrees the process would require more sophisticated technology adding extra support such as retaining walls which the local population cannot afford. The ‘cut and fill’ dwellings are composed of one or two-storey structures within a simple parallelogram volume with a flat frontal facade. Some elements such as balconies or terraces are also present in their volumetric composition. In areas with slope degree from 13° up to 30° which represent 63.86% of the total land analysed, dwellings are built on stilts. As mentioned before, the main principles for positioning dwellings onto the terrain are based on environmental and topographical reasons such as orientation of the sun and slope gradient, and urban conditions such as connection between the entrance of the house and the street. The structure rests on a stilt frame in which the height of each stilt is in proportion to the slope gradient so that the distance from the street

level defines how long each one will be. For that reason, distance between house centre point and tangent road define sub-categories; the houses next to the road and the those behind the first row of dwellings have two options for connecting the main road: building a bridge between the entrance of the house and the road or building a stairway to step up or down from house to access the road. The stilt structure supports the house as a table, four legs and a flat surface that receive a ´box´ that is the house itself. There are multiple versions of the stilt type. The following are examples of how the same principles [genes] can be expressed differently [phenotypes]. Fig. 102-a illustrates a very basic expression, however it follows stilt house principles containing all the constitutive elements: the structural framework, the ´boxes´ above with the balcony, and placement at a different level from the street connected via a stairway it counts as a stairway for connection purposes. Another more elaborate version Fig. 102-b is a hybrid solution of one line of concrete stilts with a horizontal steel frame to pose the ´box´ house. Part of the volume rests directly on the terrain, this ingenious proposal creates the possibility of building a house where the sharp relief of the terrain [>30°] would not otherwise permit a construction. A last example Fig. 102-c is a more advanced version of the stilt type, it considers the construction of a concrete stilt and beam structure for receiving the ´box´ above as the other examples. This is also an inventive solution for a plot on which it would not be possible to build if not through a columniation of the terrain. Due to a corner location the entrance of the house was determined in the highest level of the terrain to decrease the level

The Site

4.8.1.1

Fig. xxx Bar graph source: https:// weatherspark. com/ averages/33540/ Valparaiso-Chile

Wind Direc�ons Over the en�re year

Flat house

Valparaíso existent Typologies Public Space

Built environment Wind Direc�ons Over the en�re year

Fig. xxx Bar graph source: https:// weatherspark. com/ averages/33540/ Valparaiso-Chile

Dwellings

The wind is Infrastructure most o�en out of the south west (14% of the �me) and west Network (13% Elements of the �me). The wind is least o�en out of the north east (2% of the �me), north (3% of the �me), south east (3% of the �me, east (3% of the �me, and funiculars staiways south (4% of the �me).

Social Active Spaces ‘plazas’

‘miradores’

‘amphitheater’

The frac�on of �me spent with the wind blowing from the various direc�ons

Built environment over the en�re year. Values do not sum to 100% because the wind direc�on is

building undeﬁned when the wind speed location is zero. Fig. 97 Cut and fill technical flat Housing morphologies vary mainly in rela� on to their loca� on on the terrain. procedure redrew from original The wind is most o�en out of the south west (14% of the �me) and image in publication gradual slope dewest (13% l gra of the �me). The wind is least o�en out of the north east ´Steep Slopes Guide / ura(2% of the �me), t a Model Regulations´ - steep slope north (3% of the �me), south east (3% of the �me, eastn (3% of the �me, and Lehigh Valley Planning south (4% of the �me). gentle slope Commission, November cut 2008. The frac� on land of �me spent with the wind blowing from the various direc�ons gradient refers to the steepness of the (slope) Source: http:// theslope en�reofyear. Values do not sum to 100% because the wind direc�on is fill www.lvpc.org/pdf/ contour lines represent theover steep the mountain volume of cut undeﬁned when the wind speed is zero. SteepSlopes.pdf the slope or angle of a mountain Fig. 98-a Flat House1 0° slope gradient 33°04´05.53"S 71°36´36.76"W countour level +300 image source: Google Earth Fig. 98-b Flat House2 5° slope gradient 33°03´39.76"S 71°36´43.95"W countour level +225 image source: Google Earth

fill on on the terrain. Housing morphologies vary mainly in rela�onvolume to theirofloca� when there is less slope or the slope is more gentle the contour lines are further apart gradual slope how steep steep slopethe terrain is.. ﬂat plateau gentle slopeand valleys steep sides U shapedrefers valleyto the steepness of the land (slope) gradient C shapedlines valley contour represent the steep slope of the mountain the slope or angle of a mountain a hillside streamthere basinisstructura� when less slopeon or methodology the slope is more gentle the contour lines are topographical further apart proﬁles

Fig. 97

Fig. 98-a

how steep the terrain is.. flat plateau and valleys steep sides U shaped valley C shaped valley Fig. 100 Flat house 1 number material number height [m] number material material meters Summary table a hillside 1 2 3 w s c 0 1 2 3 4 5 6 7 8 2 3 4 5 6 1 2 3 4 w s c t gs fc vs 1 2 3 4 5 6 stilt material storey floors stream basin structura�on methodology w: wood s�lts topographical profiles s: steel box c: concrete box structure material box structure material box cladding material w: wood distance entrance/street s: steel slope degree c: concrete balconies box cladding material Fig. 99-a Flat house1 elevation Fig. 99-b Flat house2 elevation

t: timber gs: galvanised sheet fc: fiber cement vs: vinyl siding

Flat house 2

storey ﬂoors s�lts box box structure material box cladding material distance entrance/street slope degree balconies Fig. 100

The Site

number material

number

height [m]

number

material

meters

1 2 3 w s c 0 1 2 3 4 5 6 7 8 2 3 4 5 6 1 2 3 4 w s c t gs fc vs 1 2 3 4 5 6

Fig. 99-a

degrees

number

<7° <13°<20°<26° <31°

1 2 3

degrees

number

<7° <13°<20°<26° <31°

1 2 3

Fig. 98-b

Fig. 99-b

The table shows data about houses located in flat terrains. Representative examples of those houses were selected in order to extract architectural parameters to be incorporated into the generative design later. Storey floors vary mostly between 1 and 2, exceptionally 3. Houses located in slope terrains smaller than 7° apply cut and fill technique for settle the house in a flat terrain, so that very few houses in those slope terrains consider stilt structural solution. Wood, steel and concrete 179 are the materials with which the population build, the criterion for selecting them is related with the family budget. Similarly, with cladding material that vary amongst timber, galvanised sheet steel, fibre cement and vinyl siding. In this case the selection depends on the budget and aesthetic aspect too. The distance between the street and the house entrance is determinant179on the method used to solve that connection. This can be direct, or through stairways or bridges. Lastly, balconies are part of house type in the area, they allow occupants to have a place to see the surrounding environment and also, they shade the floor underneath.

4.8.1.2

Fig. xxx Bar graph source: https:// weatherspark. com/ averages/33540/ Valparaiso-Chile

Stilt house

Wind Direc�ons Over the en�re year

building location

Fig. xxx Bar graph source: https:// weatherspark. com/ averages/33540/ Valparaiso-Chile

stilt

Wind Direc�ons Over the en�re year The wind is most o�en out of the south west (14% of the �me) and west (13% of the �me). The wind is least o�en out of the north east (2% of the �me), de north (3% of the �me), south east (3% of the �me, east (3% of the �me, and l gra ura t south (4% of the �me). a n

Fig. 101

The frac�on of �me spent with the wind blowing from the various direc�ons over the en�re year. Values do not sum to 100% because the wind direc�on is undeﬁned when the wind speed is zero. Wind Direc�ons Over the en�re year Housing morphologies vary mainly in rela�on to their loca�on on the terrain. Fig. xxx Bar graph source:

gradual slopehttps:// steep slope gentle slope

weatherspark. com/ averages/33540/ Valparaiso-Chile

Fig. 101 Stilt house technical information from LOW-VOLUME ROADS ENGINEERING Best Management Practices Field Guide By Gordon Keller & James Sherar USDA Forest Service/USAID

The wind is most o�en out of the south west (14% of the �me) and west (13% of the �me). The wind is least o�en out of the north east (2% of the �me), north (3% of the �me), south east (3% of the �me, east (3% of the �me, and south (4% of the �me).

The frac�on of �me spent with the wind blowing from the various direc�ons gradient refers to the steepness of the land (slope) over the en�re year. Values do not sum to 100% because the wind direc�on is contour lines represent the steep slope of the mountain undeﬁned when the wind speed is zero. the slope or angle of a mountain Housing morphologies vary mainly in rela�on to their loca�on on the terrain. when there is less slope or the slope is more gentle the contour lines are The wind is most o�en out of the south west (14% of the �me) and west (13% gradual slope further apart of the �me). The wind is least o�en out of the north east (2% of the �me), Fig. 102-a 2 Fig. 102-b 3 steep slopethe terrain is.. north (3% of the �me), south east (3% of the �me, east (3% of the �me, and how steep gentle slopeand valleys south (4% of the �me). ﬂat plateau

Fig. 102-c

2.71

steep sides The frac� on land of �me spent with the wind blowing from the various direc�ons gradient of the (slope) U shapedrefers valleyto the steepness theslope en�reofyear. Values do not sum to 100% because the wind direc�on is contour represent theover steep the mountain C shapedlines valley undeﬁned when the wind speed is zero. the slope or angle of a mountain a hillsidemorphologies vary mainly in rela�on to their loca�on on the terrain. Housing when less slopeon or methodology the slope is more gentle the contour lines are streamthere basinisstructura� further apart topographical gradual slope proﬁles

The distance between the street and the house entrance is determined by the way used to create that connection - direct, though stairways or bridges. Lastly, balconies are part of house type in the area, they allow occupants to have a place to see the surrounding environment and also, they shade the floor underneath. Therefore, the main difference between flat and stilt houses are their structural system to anchor the house on the ground.

Stilt house 3

storey ﬂoors s�lts box box structure material box cladding material distance entrance/street slope degree balconies

number material

number

height [m]

number

7.62

8.60

material

Fig. 102-b Concrete stilt house2 30° slope gradient 33°03’46.36’’S 71°37’05.04’’W contour level +245 image source: Google Earth. 4.76

4.12

how steep steep slopethe terrain is.. at plateau gentle slopeand valleys Fig. 103-a fl Fig. 103-b steep sides U shapedrefers valleyto the steepness of the land (slope) gradient C shapedlines valley represent the steep slope of the mountain The same table is applied to stilt house examplescontour number material number height [m] number material material meters Stilt house 1 of a mountain or angle selected. Stilt houses are connected to the groundthe slope a hillside 1 2 3 w s c 0 1 2 3 4 5 6 7 8 2 3 4 5 6 1 2 3 4 w s c t gs fc vs 1 2 3 4 5 6 through their stilts which vary in material dimensionswhen storey flisoors stream basin structura� there less slopeon or methodology the slope is more gentle the contour lines are s�lts and distances. As these houses are self-built, distancesfurther topographical apart profiles between structural elements have not been found to box box structure material steep the terrain is.. be regular. Storey floors vary mostly between 1 andhow box cladding material flat plateau and valleys entrance/street 2, exceptionally 3. They are located in slope degreessteepdistance sidesdegree slope between 7° and 30° approximately. Usually the houseU shaped balconies valley leans on the terrain along one of the long edges.C shaped valley number material number height [m] number material material meters Stilt house 2 Therefore, there is an economy of building stilts on thata hillside 1 2 3 w s c 0 1 2 3 4 5 6 7 8 2 3 4 5 6 1 2 3 4 w s c t gs fc vs 1 2 3 4 5 6 edge, consequently only one line of stilts is considered.stream storey floors basin structura�on methodology s�lts profiles Similarly, to houses located on flat terrains variationstopographical box in material range through wood, steel and concrete for box structure material the structure as well as cladding material vary amongst box cladding material distance entrance/street timber, galvanised sheet steel, fibre cement and vinyl slope degree balconies siding. 2.80

Fig. 102-a Wooden stilt house1 15° slope gradient 33°04’02.15’’S 71°36’39.72’’W contour level Image source: Google Earth.

meters

1 2 3 w s c 0 1 2 3 4 5 6 7 8 2 3 4 5 6 1 2 3 4 w s c t gs fc vs 1 2 3 4 5 6

Fig. 103-c

degrees

number

<7° <13°<20°<26° <31°

1 2 3

Fig. 103-a Stilt house 1 elevation

degrees

number

<7° <13°<20°<26° <31°

1 2 3

Fig. 103-b Stilt house 2 elevation Fig. 103-c Stilt house 3 elevation Fig. 104 Stilt house 1, 2 and 3 summary table.

degrees

number

<7° <13°<20°<26° <31°

1 2 3

Fig. 104

The Site

4.8.1.3

Flat and Stilt house slope occupation analysis

Fig. 105-a Overlapping housing and slope gradients. 48.39% of housing in slope gradient 1° to 6.99°.

Slope gradient 1°- 6° 62 cells = 100% 30 cells are occupied 48.39 % 32 empty cells 51.61 %

Fig. 105-b 48.99% of housing in slope gradient 7° to 12.99°. Fig. 105-c 58.51% of housing in slope gradient 13° to 18.99°.

e - 51.61/ o-48.39

Fig. 105-a

Fig. 105-d 50.98% of housing in slope gradient 19° to 19.99°.

e - 51.61/ o-48.39

e - 51.01/ o-48.99

Fig. 105-f 45.70% of housing in slope gradient 27° to 30.99°.

e - 49.02/ o-50.98

e - 51.61/ o-48.39

e - 49.02/ o-50.98

e - 42.76/ o-57.24

e - 51.01/ o-48.99

e - 65.90/ o-34.10

e - 54.30/ o-45.70

Fig. 105-g 34.10% of housing in slope gradient 31° to 35.99°.

e - 51.01/ o-48.99

e -Fig. 51.61/ o-48.39 105-b

e - 49.02/ o-50.98

e - 41.49/ o-58.51

e - 65.90/ o-34.10

e - 66.66/ o-33.33

e - 42.76/ o-57.24

e - 51.01/ o-48.99

e - 65.90/ o-34.10

e - 42.76/ o-57.24

Slope gradient 31° - 35° 132 cells = 100% 45 cells are occupied 34.10 % e - 49.02/ o-50.98 e - 42.76/ o-57.24 87 empty cells 65.90 %

e - 66.66/ o-33.33

e - 54.30/ o-45.70

e - 41.49/ o-58.51

Fig. 105-c

Fig. 105-g

e - 65.90/ o-34.10

e - 49.02/ o-50.98

e - 42.76/ o-57.24

e - 65.90/ o-34.10

e - 51.01/ o-48.99

e - 41.49/ o-58.51

e - 65.90/ o-34.10

e - 66.66/ o-33.33

Slope gradient 19°- 20° 51 cells = 100% 26 cells are occupied e - 51.01/ o-48.99 50.98 % e - 51.61/ o-48.39 25 empty cells 49.02 %

Fig. 105-d

e - 41.49/ o-58.51

e - 49.02/ o-50.98

Slope gradient 36°- 54° 36 cells = 100% 12 cells are occupied 33.33 % e - 42.76/ o-57.24 24 empty cells 66.66 %

e - 54.30/ o-45.70

e - 49.02/ o-50.98

e - 42.76/ o-57.24

e - 66.66/ o-33.33

The Site

e - 65.90/ o-34.10

e - 41.49/ o-58.51

e - 54.30/ o-45.70

Fig. 105-f

e - 51.61/ o-48.39 e - 51.61/ o-48.39

e - 51.01/ o-48.99

e - 66.66/ o-33.33

Slope gradient 13° - 18° 270 cells = 100% 158 cells are occupied 58.51 % 112 empty cells 41.49 % e - 49.02/ o-50.98

e - 54.30/ o-45.70

Slope gradient 27°- 30° 151 cells = 100% 69 cells are occupied e - 41.49/ o-58.51 45.70 % 82 empty cells 54.30 %

e - 51.61/ o-48.39

Fig. 105-h 33.33% of housing in slope gradient 36° to 54°.

e - 42.76/ o-57.24

Fig. 105-e

Slope gradient 7°- 12° 198 cells = 100% 97 cells are occupied 48.99 % 101 empty cells 51.01 %

Fig. 105-e 57.24% of housing in slope gradient 20° to 26.99°.

e - 41.49/ o-58.51

Slope gradient 21°-26° 304 cells = 100% 174 cells are occupied e - 51.01/ e - 41.49/ o-58.51 57.24o-48.99 % 130 empty cells 42.76 %

e - 66.66/ o-33.33

e - 54.30/ o-45.70

e - 65.90/ o-34.10

Fig. 105-h

e - 66.66/ o-33.33

e - 54.30/ o-45.70

e - 54.30/ o-4

Fig. 105-i Housing land occupation key map highlighting the area analysed. Fig. 105-j Housing land occupation summary diagram Fig. 105-k Housing land occupation summary table.

Fig. 105-i

Fig. 105-j

slope gradient

1° to 6°

7° to 12°

13° to 18°

19° to 20°

21° to 26°

empty cells percentages

51.61 %

51.01%

41.49 %

49.02 %

42.76 %

occupied cells percentages

48.39 %

48.99 %

58.51 %

50.98 %

57.24 %

27° to 30°

31° to 35°

36° to 54°

54.30 %

65.90 %

66.66 %

45.70 %

34.10 %

33.33 %

Fig. 105-k

The relation between housing land occupation and slope degree is analysed running an experiment breaking down the urban patch into eight ranges of slope degree, then overlapping housing occupation [black dots] with the cells that represent the specific slope degree under analysis. Figures 105-a to 105-h show the proportion for each slope of occupied cells and empty cells. Slopes between 13° and 18.99 °, and between 21° and 26.99° are the most populated with 58.51% and 57.24% respectively. Not surprisingly the steepest slopes between 31° and 35.99°, and between 36° and 54° are the least occupied by housing with 34.10% and 33.33% respectively. Although one might think that slopes between 1° and 6.99 °, and between 7° and 12.99° could be the most occupied by housing due to the ease of building on them, they have an occupation rate of 48.39% and 48.99% respectively. Overall buildings occupy the land in percentages from 45.70% to 58.51% in slope degrees from 1° to 30.99° and the percentage of occupation decreases to 33% and 34% when slopes increase degrees from 31° to 54 °.

To some extent, those figures express that there is a certain random distribution of housing over the land that is corroborated by the distribution of housing occupying on average 50% of the land available in each slope gradient. Considering that fact, the study is moved to include other factors that influence the logic of housing distribution over the land. Later in this chapter the social aspects and dynamics of Valparaíso´s ravines will be analysed [page 106] which provides evidence to complement the understanding of housing occupation including local social networks as a crucial element when settlers select the piece of land for self-building their house. Being near the family and acquaintances is more relevant than facing the difficulties the slope poses at the moment of building the dwelling.

The Site

4.8.2

Public Space 4.8.2.1

Network element types stairways and funiculars 'ascensores' Valparaíso Typologies Private Space

Public Space Network Elements

Dwellings flat

Social Active Spaces ‘plazas’

stilt

‘miradores’

funiculars ´ascensores´

staiways

70°

.00

58°

100m Fig. 106 Stairways and funiculars angle variations.

75m

50m

25m

Fig. 106

The Site

° 27.50

30.0 0

44.

45.

27°

00°

.00

44°

destination point b a

c2 c1 origin point

100

125

c3 75

Legend F unicular S tairways L ocal Street route G lobal Street route origin point destination point convergent point c1, c2, c3

Fig. 107-a

Stairways and funiculars are fundamental elements of Valparaíso´s network. As the city develops on heterogeneous steep land, transportation between two points [as defined in Urban Network Analysis origin and destination points] becomes a challenging goal. This is especially so when to slope and distance independent variables are included time and effort dependent variables to move through the city. Therefore, stairways and funiculars become crucial urban network elements to attain a flexible network that allows pedestrian population to navigate the city. However, this hybrid network, composed by streets, stairways and funiculars working together, only serves the city up to 75m above sea level leaving the hills from up to 300 m just with steep streets acting as an overall system supported by local stairways as a very good example of a bottom-up emerging system. The analysis of Valparaíso´s network is constrained by analytical tools that do not consider degree inclination of network lines. Although, stairways and funiculars appear as straight lines in plans they have a three-dimensional factor that affects any calculation made on the basis of flat terrain. Consequently, human effort to move along that inclined line [street] when moving from the starting point to the end point is completely different when take alternative routes. Specifically, a flat line of four hundred meters takes on average 4.8 walking min., the same four hundred meters with an inclination of 13° takes on average 10.90 walking min. In the case of stairways, they solve the connection of two points in a very short distance in xy plane and a high distance in z direction. Moreover, funicular systems are able to connect two points with an inclination

route a route b route c1 route c2 route c3 route d

total distance[m] timemin. street[m] stairway[m] funicular[m] 218 06 56 0 162 117 06 351 0 234 08 341 180 61 0 76 362 08 286 0 08 340 229 111 0 0 2,097 0 2,097 30

Fig. 107-b

between 27° up to 70°. Hence, they cover slope degrees that are not possible by stairways. Figure XXX shows a diagram that illustrates a typical Valparaíso hybrid network composed by streets, stairways and funicular. The exercise is to define an origin point [top of ´Mariposa´ funicular] and a destination point [bottom of ´Mariposa ‘funicular], and analyse different possibilities to move from the former to the latter. This funicular is 177m in length and rises from 25m up to 75m above sea level with a 40° slope. Eight possible routes were found. The variables analysed are: the total distance travelled, time to travel that distance and the composition of the route. Unfortunately, effort variable could only be possible making an in-situ experiment beyond the present research possibilities. The shortest route is á´ a combination of funicular and street walking. The longest route is ´d´, the purpose of taking that route is to have a route only composed by a walkable path with different inclinations but without other kind of network elements. This to make manifest how effective in terms of time and distance is the incorporation of stairways and funiculars in a hilly network. Route composed by walkable path and stairways such as ´b´, ć1´, ć2´, ć3´ have subtle variations in the variable measured. Also, there is an extra variable which is that those routes are surrounded by dwellings, thus depending of their location pedestrians will take their specific shortest path, for instance if a person lives in the middle of the blue route they would take a shortest path from that point to the destination point defined by the experiment.

The Site

Fig. 107-a Alternative routes from origin to destination point at ´Mariposa´ funicular top view diagram Fig. 107-b Alternative routes comparative table.

Valparaíso traditional funicular ´Ascensor Barón´

Fig. 108 OjedadelRio, P. 2017 'Valparaíso traditional funicular, Ascensor Barón' [photograph] Valparaíso´s own private collection.

The Site

Fig. 109 OjedadelRio, P. 2017 'Valparaíso traditional stairway, ´Ascensor Cordillera Stairway' [photograph] Valparaíso´s own private collection. Stairway next to ‘Ascensor Cordillera’ Funicular. This stairway has 150 steps and 1.5 meters width and a horizontal projection of 69 m. length reaching 30 m.height information source ‘Paisajes Verticales en Valparaíso, movilidad, pertenencia y comprensión del orden urbano’ Donoso M.

Valparaíso traditional stairways The Site

4.8.2.2

Social active space types

4.8.2.2.1

Valparaíso existing Typologies

´Plazas´

Open Space

Built environment Dwellings

Network elements

Infrastructure

funiculars

staiways

stilt

flat

Social SocialActive ActiveSpace Spaces

‘plazas’

‘miradores’

‘amphitheater’

Fig. 110 Valparaíso places for urban social encounter mapped in the city plan. Criterion for the definition of categories was difined by Size in m2. 7 categories were defined and ranked. The line in red represents Alemania Avenue demonstrating that open spaces for social interaction over that contour line [125m] are very few. Also, those spaces destination mostly is soccer fields, leaving all other social activities with any possible urban place to be performed. Just 3.58% of the urban land corresponds to urban open spaces, for a population of 248,070 [2015] Legend Alemania Avenue boundary between formal and informal city

Pacific Ocean

Green areas and open spaces Fig. 111 Valparaíso original flooding areas at the first occupation of the territory. The diagram shows four original basins of Valparaíso bay. Later, those flooding areas were transformed into its main ´plazas´.

Fig. 112 Valparaíso green areas comparative table. Elaborated froom Valparaíso plan.

1-a

Fig. 110

m2 ratio quantities percentages sum each category percentages

The Site

2-a

Fig. 111

[162m2 to 448m2] 9 24.32 %

[556m2 to 979m2] 9 24.32 %

[1.036 to 1.961] 9 24.32 %

[2.284 to 4.864] 4 10.81 %

[5.067 to 9.340] 3 8.10 %

[10.301m2 to 12.308 m2] 2 5.40 %

[27.865m2] 1 2.70 %

TOTAL 37 100%

20.703m2 19.24%

22.609m2 21.02%

27.865m2 25.90%

107.577m2 100%

2.631 m2 2.45%

6.514m2 6.06%

13.079m2 12.16%

14.176m2 13.17%

Fig. 112

3-a

planned urban square

spontaneous urban place Fig. 113 ´Plaza Echaurren´ is the oldest square of the city. Campos M., 2016 Image source : http:// img.soy-chile.cl/ Fotos/2016/05/26/ file_20160526182758. jpg

4-a

´What are visible and therefore obviously spatial about societies are the encounters and interactions of people. These are the spatio-temporal realisations of the more complex and abstract artefact that we call society. Moreover, encounters and interactions are space-time embodiment of solidarities [Hillier, 1984]. The urban space as such is the prime spatial entity for social interaction and within it open spaces in essence are urban places where intangible aspects of society materialise and renew. Therefore, we could argue that most of the relation of society and space can be understood studying cities´ open spaces. As mentioned by Hillier the first condition of open spaces is that spatial integration creates the possibility of a trans-spatial integration. Thus, a non-existent urban spatiality for a spontaneous encounter and interaction between citizens is a scenario in which higher level of connection could not emerge. The spatial analysis of open spaces can shed light on how sociocultural characteristics can arise in a physical organisation. As Hillier asked, ´how could encounter systems acquire differential properties, such that they would have different manifestations in space? ´ [Hillier, 1984] This question leads the present research to pay attention to the demographic and cultural aspect of the ravine’s population for whom a Socially Active Space System will be developed. Valparaíso has a powerful cultural identity and this condition is fully present in its traditional open spaces. Valparaiso has different types of urban open space for social encounters; the historic area has been the result of a gradual development of the traditional Spanish grid design. At the very beginning before rainwater was piped it accumulated in some parts of the flat area of the city. Thus, from a gradual taming of the natural environment emerged the most significant urban public spaces of the city [see Fig. 111] [Alvarez, 2001]. With the process of urbanization those places called ´plazas´ were being consolidated with the construction of a solid perimeter of significant buildings of the city and hence were immersed in the urban fabric. These ´plazas´ are open and do not have any restriction for being visited by anyone who wants to spend leisure time there. That non-discriminatory access generates a reach social diversity that strengthens local identity and sense of belonging. Figures 115, 116, 117, 118, illustrate the social aspect of ´plazas´ where an heterogeneous population uses the space harmonically. There is place for everyone on a typical Sunday afternoon.

Another type, located in the flat area also call ´plan´ is the one that has become Fig. 114 a recognisable open space for social interaction after being used continuously and spontaneously by the population due to its spatial characteristics. This is the case of figure 114 where a play is performed in the Summer urban festival ´Invación callejera´, thus transforming that everyday urban space into a theatrical stage surrounded by approximately seventy people. This is the kind of flexibility that open urban spaces require to be alive in urban life. Since the city started to grow from the flat area continuing to the hills, the open urban space was mainly shaped by the built environment as a consequence of how buildings were finding spaces to settle in accordance to the topography. Due to the geographical nature of the bay as a natural amphitheater, a type of elongated portion of land as terraced open space called ´miradores´ or ´paseos´ [promenades] appears. Considering that those urban balconies, with privileged views, draw citizens interest for seeing the bay and the Pacific Ocean, they are frequently visited, promoting social interaction. Those spaces have also given rise to a versatile use of the space such as artistic performances and itinerant craft fairs. Moving to figures in terms of green areas, the worrying fact is that the sum of the square meter of ´plazas´ in Valparaíso reaches 5.37 hectares and the number for ‘miradores’ is 0.71 Ha. Just about 3.58% of the urban land corresponds to urban open spaces, for a population of 248,070 [2015]. Those figures about the total green space in Valparaiso have shown that there is between 2 and 3 m2 of green space per person which is far below 9 m2/person suggested by the Global Health Organization. Worryingly as the city reaches higher altitude levels the urban open space decreases dramatically. In informal urban settlements, over 125m, the type of open space described above is practically nonexistent. As the fundamental subject of the present thesis is the creation and re-creation of a Socially Active Space system in Valparaíso´s ravines, it is recognised that as pointed out by Hillier random and durably reproduced encounters between individuals rely solely on the existence of urban space for the former and the result of spatial proximity for the latter [Hillier,1984]. Both are subjects of interest in the present work due to the fact random encounters can also evolve through time into constant encounters propitiated by the spatial quality of the open space itself.

The Site

Fig. 114 Summer Urban Festival, ‘Invación callejera 2013’ took place in different places of the city and ´Plaza Aníbal Pinto´ was one of them. Anonymous, 2013, Image source: http:// festivalinvasion callejerablogspotco. uk/2012/12/lista-laprogramacion-deinvasion.html

The Site

Fig. 115 OjedadelRio, P. 2017 'children playing at ´Plaza Victoria´, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 4th 2017, a typical afternoon Sunday where citizens meet at Valparaíso´s Plazas. Fig. 116 Sepúlveda C., 2010 ´El club de la brisca´ This picture was taken in Plaza O´Higgins a well known plaza where retired men meet every day to play cards and sharing with friends and hobbies partners.

Fig. 116

Fig. 117 OjedadelRio, P. 2017 'Readers at Plaza Victoria, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture shows the different activities happening at the same time and place in harmony; children playing around while elder people read. Fig. 118 OjedadelRio, P. 2017 'Jugglers at ´Plaza Victoria´, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 4th 2017, a typical afternoon Sunday where citizens meet at Valparaíso´s Plazas and do different social activities, that in the case of the image beside are a young group of jugglers that entertain people passing by.

Fig. 117

Fig. 119 ´Plaza Victoria´ plan

Fig. 118

The Site

Fig. 115

4.8.2.2.2

´Miradores´ and ´urban openings´

Valparaíso existing Typologies Open Space Social Active Spaces

‘plazas’

‘miradores’

‘amphitheater’

Fig. 120 Valparaíso plan four ´miradores´ locations: 1. ´21 Mayo Mirador ´ 2. ´Gervasoni Mirador ´ 3. Átkinson Mirador´ 4. Éspíritu Santo Mirador´. All of these ´miradores´ are examples of urban balconies located at contour level 50m sea level, illustrated by the segmented line. Álemania Avenue´ is highlighted in red.

Fig. 120 Fig. 121 ´Miradores´ and openings data summary table. The table is a summary of all the ´miradores´and openings of the city, ranking them by sizes defining seven

100

Fig. 121 m2 ratio length quantities percentages sum each category percentages

The Site

[13 m to 63 m] 8 12.70 %

[63 m to 126 m] 22 34.91 %

259 m 2.56 %

2.113 m 20.92 %

[126 m to 168 m ] 13 20.64 % 1.833 m 18.15 %

[168 m to 210 m] 5 7.93 %

[210 m to 252] 4 6.35 %

[252 m to 504 m] 9 14.29 %

[504 m to 735m] 2 3.18 %

TOTAL 63 100%

963 m 9.54 %

903 m 8.94 %

2,864 m 28.36 %

1,164 m 11.53 %

10,099 m 100%

urban balconies

horizontal connectors

Fig. 122 Paseo 21 de Mayo [21st May Promenade] source: Fernández J. [2015] Located next to Artillería Funicular built in 1893, this urban balcony is mainly used as a place to see the bay.

Fig. 122

‘m i r a d o r’ [urban balcony]

Fig. 123

As plazas develop mainly in the flat land of the city, ´Miradores´ are placed basically at two levels - 50m and 100 m above sea level. The former is part of the formal city and miradores here were built as part of the urban tissue. Due to their location and urban configuration they embody different characters. On the one hand, some have an urban balcony character, and on the other hand although the others keep urban balcony character their particularity is being a horizontal connection between stairways. ´Miradores´ at 100m in the present work are called ´openings´ considering that they are not proper places to stay, rest or see the urban landscape. They are a consequence of urban spaces where the street has one façade leaning to the hill and the other side of the street is empty land . This topographic condition creates 'openings' from which pedestrians can enjoy panoramic views of the urban landscape. This level of ‘openings’ at Alemania Avenue coincides with informal settlements growth.

Fig. 124-a ´Mirador paseo Gervasoni´ 123 m length

Fig. 123 ‘Mirador’ next to ‘Ascensor Espíritu Santo’ Funicular source: http://valparaiso.blogspot. co.uk/2015/01/cerrobellavista-parte-2.html Differently from 21 de Mayo Mirador, this urban balcony has a predominant function as a place to pass through when stepping Pasteur stairway next to it. Fig. 124 a - b a.‘Mirador’ diagram 1, a place to see the city. b.´Mirador´ diagram 2, stairway landing, a place to rest when climbing stairways.

‘m i r a d o r’ [urban balcony]

Fig. 124-b Fig. 125-a

´Mirador paseo Atkinson´ 200 m length

Fig. 125-b

Fig. 125-c

´Mirador ascensor Espíritu Santo´ 100 m length

Fig. 125 a,b,c a. ‘Mirador Paseo Gerbasoni´ isovists. b. ´Mirador Paseo Atkinson´ isovists.

c. ´Mirador ascensor Espíritu Santo´ isovists.

The Site

101

102

The Site

Fig. 126 ´Puppet show at El Mirador´ next to ´Ascensor Artillería´ Funicular Tong S. [May 22nd, 2017] Image source: https://www.scope. travel/destinations/ what-you-need-todo-in-valparaiso/ size [1920x1440]

Fig. 127

Fig. 128

Fig. 127 OjedadelRio, P. 2017 'Boy seeing the city through telescope, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken November 4th 2017, a typical Saturday afternoon at ´Paseo 21 de Mayo´ [mirador] permanently visited by citizens and foreing people to take a city overview at different height and location of Valparaíso. Fig. 128 OjedadelRio, P. 2017 'Urban artist carrying its selling trolley , Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken November 4th 2017, a typical Saturday afternoon at ´Paseo Atkinson´ [mirador] permanently visited by citizens and foreing people to take a city overview at different height and location of Valparaíso. The central figure of the picture is ´Patomadera´ who sells Kaleidoscope made by himself. Fig. 129 OjedadelRio, P. 2017 'Walking, sharing, buying arts and crafts, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken November 4th 2017, a typical Saturday afternoon at ´Paseo 21 de mayo´ [mirador] permanently visited by citizens and foreing people to take a city overview at different height and location of Valparaíso.

Fig. 129

The Site Fig. 126

103

4.8.2.2.3

Amphitheatre and ´spontaneous amphitheatre´

Fig. 130-a ´Cleaning the site for building the ravine´ [date] Image source: ´TAC Cordillera´

Valparaíso existing Typologies Open Space Social Active Spaces

Fig. 130-b ´Building the amphitheatre by the local community´ [date] Image source: ´TAC Cordillera´ Fig. 130-c ´Using the amphitheatre by the local community´ Image source: ´TAC Cordillera´

‘plazas’

‘miradores’

‘amphitheatre’

Fig. 130-a

Fig. 131

Fig. 131 Valparaíso plan, locating ´TAC Cordillera amphitheatre´ and four stairways used as ´spontaneous amphitheatre´

Fig. 130-b

Fig. 130-c

Besides open space types ´plazas´ and ´miradores´ being at different altitudes in the city they share the condition of being placed on flat terrains. The current open space analysed is different in that it has taken advantage of the natural Valparaíso steep topography for terracing the land and building an amphitheatre. Amphitheatres meet specific geographical conditions such as utilising a steep terrain to accommodate its tiered seats with a desirable viewpoint to the centre point of stage from different angle variations. Their natural spatiality to congregate an audience make them a welcome open-air-venue. In those open spaces can be observed a strong relation between form and function. As Hillier [1996] pointed out ´space is given to us as a set of potentials and we exploit these potentials as individuals and collectivities in using space ´ [Hillier, 1996]. This section ´Amphitheatre and ´spontaneous amphitheatre´ refers to two types of open spaces present in Valparaíso traditional area. The former, despite the fact that there is only one construction in the city formally defined as an amphitheatre, was selected due to its powerful social significance. The latter is an interesting example of what is described by Hillier as ´a spatial layout - a stairway in this particular case - can thus be seen as offering different functional potentials´. In the present work ´spontaneous amphitheatre´ defines a type of ephemeral venue that emerges in some stairways of the city as shown in figures 132 and 133. Cultural events usually take place in some of those urban spaces due to their natural suitability to hold spectators on the stairway-steps acting

104

The Site

as bleachers and with the proper urban ground as the stage. What makes these places propitious for such events is their spatial flexibility and consequently they ´become imbued with positive emotional associations, memories or aspirations. ´ [Jaffe and Koning, 2016] ´Taller de Acción Comunitaria´ [TAC] [Community Action Workshop] is a social organisation that started to work with ´Cordillera´ hill community in the nineties for recovering a steep terrain that had been a landfill for forty years. A first approach with the local community was cleaning the site, becoming an important process of mutual construction of trust. In this way TAC principles of local participation, voluntary action and punctual urban interventions that has been up to the present times are the basis that have given sustainability over time. After the initial process of cleaning the ravine and building mutual trust the community planned to ´build the ravine´ to transform that space into a place. This process exemplifies what Jaffe and Koning [2016] refer to as ´place-making´ - the construction of meaning in relationship to the physical environment. That engagement with the place was done by the fact that the local community was involved in the construction of the amphitheatre. The place became in an iconic communal venue both locally at the neighbourhood scale and globally at the city scale.

Fig. 132 ´Spontaneous Amphitheater in Bianchi Street´ Lazo E. [August 21st, 2016] Image source: https://twitter.com/ edgardolazo size [1200x674] Fig. 133 ´Invación Callejera´ Street invation in stairway next to ´Espíritu Santo´funicular, Acuña Bravo, [December 31st, 2011] Image source: https://www. rodrigoacunabravo. cl/tag/reportajegrafico/ size [640x425]

Fig. 133

Fig. 132

The Site

105

4.9

Fig. 134 Camp´s Population Piramid, The Cadastre made in 2011 did count 83.683 number of people living in Informal Urban Settlements called ´Camps´. This population presents a Demografic Structure younger than National Standards as it is showed in the graph structured by gender and age. Fig. 135 Camp´s Social Map Family Structure Type (%). This graph shows family structure composition in terms of the presence or not of the family members. source: Chilean Housing and Urbanis Ministry, publication "Camp´s Social Map" published Junary 2013

106

Valparaíso´s ravines social aspects and dynamics

A crucial aspect of the present research is Valparaíso´s ravines local social structure and the dynamics behind it. It is known that, Informal urban settlements due to the nature of their initial constitution, first, lack formal urban responses for providing shelter to people with limited economic resources and second, community self-organisation processes looking for better living conditions, have developed peculiar social networks. This will be the object of analysis in the present chapter. Social Network Analysis [SNA] where the context of the social actor and its relationships between and amongst actors are core, will be the type of sociological analysis for undertaking the study of Valparaíso´s ravines local social structure and dynamics. Relatively few uses of SNA in planning related applications have been documented. The first group of studies that apply SNA to planning issues is focused on understanding the spatial and social dimensions of community, to what extent social ´distance´ depends on spatial distance, and what the implications are for planning policy and practice. [Scott and Ward, 2011]. As Piselli pointed out, while the spatial and social dimensions of community ´condition and reinforce each other, ´ community must ultimately be considered as a network, not a place. [Piselli, 2007] Central to Piselli’s argument is the notion that relationships involve exchanges or flows of many different kinds, one of the most important being communication. Historically these exchanges have involved face-to-face contact and spatial proximity. However, there are an increasing number of examples where community is created and maintained, even over great distances. While making a compelling case for defining community as a network rather than a place, the author does not discount the importance of place in shaping and supporting many communities, [Scott and Ward, 2011]. Contrary to Piselli´s argument, informal land appropriation of Valparaíso´s ravines, was done over time from the fifties, by families that were individually arriving to self-build their own dwellings. The process of populating the area was done through invitations from those pioneers to other families or acquaintances to gradually colonise that territory.

Therefore, there is a high level of engagement with that territory making a powerful connection between community and place. Interaction delineates the local territory and these borders are in constant flux depending on where people interact. The built environment both is created by local inhabitants and shapes their interaction. ´Thus, the study of how interaction processes shape and are shaped by the local territory is an appropriate focus for the sociology of community´ [Wilkinson, 1991]. In fact, this study is focused on Urban Social Interactions specifically the ones having place in Open Spaces, that as a result of the steep topography of the land create a spatial configuration that is both influential in social interactions and the result of the local territory. Recent studies have examined the spatial impacts of networks, generally finding that community exists independent of space. However, social networks impact the planning of space in that they can be drawn upon in public participation and economic development activities. Although, social networks may exist independent of physical space; networks are often organized or based geographically. [Carpenter, 2013] This social research, following SNA process for investigating social structures through the use of networks and graph theory has been conducted interviewing people living in the area. Considering the small group analysed, it was targeted to select a social variety represented by gender, age, family structure and living location. Eight social networks were developed as Ego-Centric Networks treated as Global Networks, specifically five women and three men, from 20 to 76 years old. The hypothesis is that, from the study of this social variety, will be found on the one hand, particularities from each person and on the other hand, similarities and common factors which will set social patterns being a relevant result of the conclusions for further urban design logic definitions.

Camp´s (Informal Urban Settlements) Population Piramid

Family Structure 42 % Nuclear family with children

Men

Women

80 and more 75 to 79 70 to 74 65 to 69 60 to 64 55 to 59 50 to 54 45 to 49 40 to 44 35 to 39 30 to 34 25 to 29 20 to 24 15 to 19 10 to 14 5 to 9 0 to 4

80 and more 75 to 79 70 to 74 65 to 69 60 to 64 55 to 59 50 to 54 45 to 49 40 to 44 35 to 39 30 to34 25 to 29 20 to 24 15 to 19 10 to 14 5 to 9 0 to 4

Created by Marie Van den Broeck from the Noun Project

4 % Father Single Parent Family Created by Amandine Vandesteene from the Noun Project

10 % Childless family Created by Gan Khoon Lay from the Noun Project

20 % Non family relationship 83 % Individuals [16.6 % of the total] Created by Gan Khoon Lay from the Noun Project

17 % Group of people

[3.4 % of the total]

5000 4000 3000 2000 1000 0 1000 2000 3000 4000 5000 Fig. 134

The Site

24 % Mother Single Parent Family

Created by Gan Khoon Lay from the Noun Project

Created by Adrien Coquet from the Noun Project

Fig. 135

Fig. 137

Fig. 136 Summary map, showing street network and interviewed people living location represented in red dots

60-m 58-m

25-f

20-m

1 Ha 100 m

Fig. 136

Family group

Organisa�onal group

neighbours 1 familial ties + neighbours 2 familial ties 3 friendship 4 hobbies 5 educational 6 health care 7 occupational [collegues, clients, employees] 8 organisations 9

Neighborhood Familial + Neighbrhood

Fig. 137 Nuclear family with children diagram describes grafically how information is shown

< 30

1/30

4-8/30

Geographical Fig. 138

Emo�onal

Legend

Fig. 139

Legend, nodes and ties attributes

Fig. 138 Ego social relation types definition by colours and when a colour is added black in the perimeter means that ´alter´lives in the same neighbourhood

Family group

Familial Friendship Hobbies

Fig. 139 Nodes and Ties Attributes description and contact frequency categories

Educa�onal Func�nal

< 30

1/30

30/30

09 -f-12

alter-list number gender age family-children

�es contact frequency

52-f

nodes �es contact frequency man woman male female

+ 12-f-40

08 -m-15

69-f

32-f

family group parents marriage symbol

06-m-45

76-f

30/30

Legend, Nuclear family with children

4-8/30

The social networks analysed are visualised through sociograms and graphs. In terms of graphics representations, the Ego-node is represented in black at the centre of the network with the correspondent shape, circle or triangle, depending on its gender. Women nodes are represented by triangles and men nodes by circles. Two major groups were defined: the first group - Émotional Relationships´ contains four subgroups: Family Neighbours [family living in the same neighbourhood], Family [living outside the neighbourhood], Friendship and Hobby-Partners. The second major group is ´Functional Relationships´ which includes Educational, Health Care, Occupational and Organisational categories. Also in this second group, some people from a subgroup have the neighbour condition. For each Social Group, a specific colour has been assigned as shown in the legend. A third subjacent group dubbed ´Geographical group´ referring to 'living in the same neighbourhood' contains mainly Ńeighbours category´ and ´Family Neighbours category´. However, some others Social Relation Types such as ´Friendship category´ or Órganisation category´ are also included in the ´Geographical group´. As Social Network Analysis is mainly focussed on the overall structure of ties, consequently diagrams highlight frequency of contacts between the Ego and its alters into four frequency degrees: 30/30 seeing each other every day, 4-8/30 seeing each other 1 or 2 times a week, 1/30 seeing each other once a month and finally <30 seeing each other less than once a month. Among each Social Network, smaller groups were identified, for instance, ´Family groups´ meaning family members living in the same dwelling, Organisational groups, Educational groups and Hobby groups. The size of the nodes has been defined by Centrality Measure Ńodal Degree´ [d(ni)] that indicates the number of ties [connections] that are incident on it. Below each node there is a code [number-gender-age]. Two additional bar graphs summarise in percentages Ego Age-groups and Ego Social Relation types.

Healthcare Occupa�onal

Organisa�onal group

Organisa�ons

The Site

107

Formulas

PC = n (n-1) 2 number of alters including the Ego Potential Connections AC PC Potential Connections Actual Connections Network Density ND =

Fig. 140-a Ego Network Composition Main Network mesurements, such as Potential Connections Actual Connections Network density Standar deviation Average Degree Fig. 140-b Ego Age-groups, there were defined six Agegroup intervals 0 to 4 years old, 5 to 19 years old, 20 to 49 years old, 50 to 64 years old, 65 to 79 years old, and over 80 years old. This horizontal bar graph seeks to show which Age-group interval has more predominance for the Ego Social Network. Fig. 140-c The purpose of Ego Social Relation types, through this horizontal bar graph is showing the percentages related to each Social Relation Type group, such as Neighbours, Family Neighbours, Family, Friends, Hobbies, Educacional, Health care, Occupational, Organisacional.

The Ego interviewed is a 20-years-old man with 575 Social Network alters. 0 10 15 20 25 His nuclear family is composed by his mother [52] and girl-friend [20]. He was born in that urban area where his family arrived 60 years ago. 86.21 % of his Social Network lives in the same neighbourhood composed by [8.62 % Neighbour´s category, 41.38 % Family-neighbour´s category, 13.80 % Friendship category, 8.62 % Hobby Partners category, 6.90 % Educational category,1.72 Occupational category and finally 5 10 15 20 5.17 % Organisation category]. 13.79 % of0 his alters live outside the25 neighbourhood. In terms of age-group, 70.69 % of his alters are between 20 and 49 years old. With regard to the use of urban space, the Ego usually meets 44.83 % of his alters in the neighbourhood public spaces, such as the streets and the soccer field [figure 164 page 129]. There is no other possible urban encounter for social interaction in the area. 0 5 10 15 20 The frequency of his contacts is 63.16% 30/30, 14.04% 4-8/30, 19.30 %25 1/30, and finally 3.50 % <30. Most of his social encounters leaving the family apart, due to those being in-doors activities, belong to Friendship category and Neighbour’s category that happen in ´places of the neighbourhood´, as they were previously mentioned. The network density at 0.678, is the densest Social Network analysed. 0

Ego Network0 composition 5 10 40

Ego gender male Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree:

[ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4]

10 10

1520

2030

25 40

30 50

17 -m-70

40 0

18-f-65

0 10

02 -f-52

+ -f-59

23 -m-35 35

100

30 -m-40

5 20

10 30

35 -m-25

29 -m-40 25

34 -m-28 33 -m-30

43 -m-20

24 -m-66

36 -m-24

5 12-m-32 10 13 -f-32 15 01-m-20 20 08 -f-20 25

28 -m-35

25 -f-17 27 -f-14

50-m-20

21 -f-37 0 40

22 -m-23 52-m-20 53-m-20

Fig. 140-c

8.62 % Neighbours 41.38 % Family-neighbours 3.45 % Family 13.80 % Friends 0 10 10.34 % Hobbies 35 8.62 % 40Educational 0.00 % Health care 6.90 % Occupational 5.17 % Organisational

26 -f-43

Ego Social Relation types

03 -m-26

Fig. 140-a

32 -m-70 31 -f-70

19 -m-53

54-f-21

Ego age N° of alters 20 57 1.653 Average Distance 1.249 1.120 0.678 0.467 19.310

Fig. 140-b

20 years old - male - Ego Social Network

51-m-20

0.00 % 10.34 % 6.90 % 70.69 % 10.35 % 1.72 %

11-m-54

Ego Age-groups

37 -m-25 25

42 -m-20

38 -m-24

14-m-7 15-f-12 16-f-5

41 -m-18 40

40 -m-20

39 -m-26 46 -m-20

04-f-26 06-f-3 Fig. 140-d 20 years-old male Sociogram

108

05 -f-25

10 -f-20

09 -f-25+ 55 -m-30 35 40

07 -f-08

45 -m-21

44 -m-32

47 -m-25 48 -m-30 56-m-70 58-m-30 57-m-45

The Site

49-m-20

Fig. 140-d

The Ego interviewed is a 25-years-old woman with 58 Social Network 0 5 10 15 20 alters. Her nuclear family is composed by her husband [31] and daughter [3]. She was born in that urban area where her great grandfather arrived 100 years ago. 61 % of her Social Network lives in the same neighbourhood composed by 23.73 % Neighbour´s category, 28.81 % Family-neighbour´s category 0 5 10 15 20 and 8.46 % Friendship´s category 39 % of her alters live outside the neighbourhood. In terms of age-group, 64.41 % of her alters are between 20 and 49 years old. With regard to the use of urban space, the ego usually meets 32.75 % of her alters in the neighbourhood public spaces, such as the streets and the soccer field [figure 164 page 129]. There is no other possible urban 5 10 15 20 encounter for social interaction in the area. 0 The frequency of her contacts is 10.34 % for 30/30, 39.66 % for 4-8/30, 25.86 % for 1/30, and finally 24.14% <30. Most of her social encounters leaving the family apart due to those being in-doors activities, belong to Friendship´s category and Neighbour´s category that happen in ´places of the neighbourhood´, as they were previously mentioned. The network density is 0.434 0

Ego Network composition 0 5 10 15 20 40 Ego gender female Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree:

Ego age-groups [ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4] 25

36-f-26

0 5

10 10

20 15

2030

25 40

30 50

Fig. 141-b

100

Fig. 141-b 25 years old female, Ego Age-groups

Fig. 141-c

34-m-58 26-m-45 31-f-30 33-m-34 35-m-45

28 -m-25 30-f-26

23-m-38

12 -f-58

29 -m-25

0.00 % 6.78 % 13.56 % 64.41 % 11.86 % 3.39 %

28.81 % Family-neighbours 13.56 % Family 18.64 % Friends 0 0.00 % Hobbies 13.56 %40 Educational 35 1.70 % Health care 0.00 % Occupational 0.00 % Organisational

Fig. 141-a 25 years old female, Ego Network Composition

Ego Social Relation types

27 -m-26

Ego age N° of alters 25 58 1.711 Average Distance xxx 742 0.434 0.624 27.831

25 years old - female - Ego Social Network

Fig. 141-a 25

Fig. 141-c 25 years old female, Social Relation types

100

22 -f-45 15

32 -m-23

24-f-35 25-m-56 10 -f-77 52-f-31

04 -f-42+ 05 -m-43

55-f-23

51-f-27

11 -f-25 15-f-50

09 -f-56 0

56-f-48 53-f-31 54-f-40

06 -f-19 07- f-17 08-f-0

01-f-25 +

59-m-20

15 13-f-20

17-m-27

+ 19-f-40 20 18-m-40 25

48 -m-8

21-m-34 20-f-32

57-f-53

58-f-40

14-f-19

02 -f-31

03 -f-3

47-f-43

16-f-12

50-m-8 49 -m-8

39 -m-28 10

44-f-42 37-f-46

38-f-50 45-f-60 43-f-72

42-f-78

41-m-47

46-m-65

40-f-25

Fig. 141-d

The Site

Fig. 141-d 25 years-old female Sociogram

109

Fig. 142-a 32 years old female, Ego Network Composition

Fig.142-b 32 years old female, Ego Age-groups

Fig. 142-c 32 years old female, Social Relation types

The Ego interviewed is a 32-years-old woman with5 49 Social Network 0 10 15 20 25 alters. Her nuclear family is composed by her husband [32] and their four children [1, 7, 10, 12 years-old]. Concerning the Family Structure, they belong to ´Nuclear family with child´s category´ that represents 42% of Informal Urban Settlements population. She was born in that urban area where her family arrived 70 years ago. 44 % of her Social Network lives in the same neighbourhood composed 0 5 10 15 20 25 by 8 % Neighbour´s category, 30 % Family-neighbour´s category and 6 % Friendship´s category. 56 % of her alters live outside the neighbourhood, composed by Occupational category, most of Friend´s category, Educational and Health Care category. In terms of age-group, 38.00 % of her alters are between 20 and 49 years old. With regard to the use of urban space, the ego usually meets 10 % of 0 5 15 20 25 her alters in the neighbourhood public spaces, such as 10 the streets and the soccer field [figure 164 page 129]. There is no other possible urban encounter for social interaction in the area. The frequency of her contacts is 46.94% 30/30, 18.37% 4-8/30, 22.45% 1/30, and finally 12.2 % <30. The network density is 0.406 0

Ego Network composition 0 5 10 40 40

Ego gender 0 10 female Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree:

Ego Age-groups [ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4] 30

Fig. 141-a

Ego age N° of alters 90 30 40 50 60 70 80 32 49 1.225 Average Distance 1.594 497 0.406 0.491 19.88

10 10

1520

20 30

25 40

30 50

100

Fig. 142-b

2.00 % 4.00 % 32.00 % 38.00 % 22.00 % 2.00 % 0

EGO JESSICA 32

Fig. 142-c

40-m-50

12 -m-32

+ 43-m-50

37-f-40

42-f-50

34-f-26

35-f-26

07 -f-90

+ 1008 -f-73 15

11 -m-53

26-f-35

01- f-32

22 -m-18

10-f-52

16 -f-75

15 -m-53 14 - f-52

21 -m-43

02 - m-32 17 -f-50

18 -f-52

25-f-31 03- f-12

45-f-12

04 - m-10

05 - m-7

06 - m-1 20-f-20

27-f-32

29-m-33

Fig. 142-d 32 years-old female Sociogram

110

44-m-13

47-f-12

19-f-24

46-m-13

49 -m-7

50 -m-7

48-f-12 32-f-50 39-f-50

The Site

13 -m-24

09 -m-72

24-f-33

28-f-32

100

23 -m-35 36-m-26

41-f-50

32 years old - female - Ego Social Network

38-m-60

8.00 % Neighbors 30.00 % Family-neighbors 16.00 % Family 26.00 % Friends 0.00 % Hobbies 0 10 % Educational 3510.0040 2.00 % Health care 8.00 % Occupational 0.00 % Organisational

Ego Social Relation types

31-f-50

30-f-50

33-f-23

Fig. 142-d

The Ego interviewed is a 52-years-old woman with 52 Social Network alters. Her nuclear family is composed by her0 husband [60]. 6015years20ago, 25 5 10 her family moved to what was at that time a rural area of Valparaíso, after 8 years her parents were living there she was born in 1965. Concerning Family Structure, they belong to ´Nuclear family childless’s category´ that represents 10 % of Informal Urban Settlements population. 75.47 % of her Social Network lives in the same neighbourhood 35.85 % Neighbour´s category, 24.53 % Family-neighbour´s category, 5.66 % 25 0 5 10 15 20 Friendship´s category, 9.43 % Organisation category. 24.53 % of her alters live outside the neighbourhood. In terms of age-group, 37.74 % of her alters are between 50 and 64 years old. With regard to the use of urban space, the Ego usually meets 35.85 % [Neighbour´s category] of her alters in the neighbourhood public open spaces, such as the streets and soccer field [figure 161 page 127]. Additionally, she meets the Organisation group category in a public 0 5 10 15 20 25 enclosed space called ´Social Headquarters´. There is no other possible urban encounter for social interaction in the area. The frequency of her contacts is 36.54 % 30/30, 23.08 % 4-8/30, 19.23 % 1/30, and finally 21.15 % <30. Most of her social encounters leaving the family apart, due to those being in-doors, belong to neighbourhood´s category, Friendship´s category and Organisational´s category that happen in ´places of the neighbourhood´,0 as 5they 10were 15previously 20 25 mentioned. The network density is 0.429.

Ego Age-groups [ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4] 30

10 Ego gender 0 female Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree:

100

Fig. 143-a

Ego Network composition

90 30 age 40 50 60 N° of alters 70 80 Ego 52 52 1.378 Average Distance 1.587 591 0.429 0.495 22.302 30

Fig. 143-a 52 years old female, Ego Network Composition

100

Fig. 35 143-b40

5Fig. 143-b10

1.89 % 24.53 % 37.74 % 26.42 % 9.42 % 0.00 % 0

10 10

Ego Social Relation types

20 15

2030

25 40

30 50

100

Fig. 143-c

35.85 % Neighbors 24.53 % Family-neighbors 0.00 % Family 11.32 % Friends 0.00 % Hobbies % Educational 35 0.0040 7.55 % Health care 7.55 % Occupational 13.20 % Organisational

52 years old female, Ego Age-groups

Fig. 143-c 52 years old female, Social Relation types

EGO ROSA 52

52 years old - female - Ego Social Network

21-f-50 19-m-60

41 -m-66 27-f-55

42 -m-60 40 -m-25

32 -m-67

+ 39-f-26

37 -m-27

11 -f-8

15-f-64 18-f-51

10 -m-14 08 -m-17 09 -f-8

02 -f-52 29 -m-60

26-f-65

14-f-62

06-m-50 12-f-52

31-f-68

28-f-70

23-m-28 24-f-37

43-f-69

+ 07-m-56 13-f-58

38-f-30

15 20-f-60

25-f-65

30 -m-70

22-f-48

33-f-40

02 -m-60

16-f-86

17-f-66

04-m-35 05-f-37

34-f-25

03 -f-7

35-f-67 36-m-70 44-f-52

48-m-52 47-f-52

+46-f-42

45-m-45

49-m-52 50-f-44 52-f-65 51-f-60

Fig. 143-d 52 years-old female Sociogram

53-f-65

Fig. 143-d

The Site

111

Fig. 144-a 58 years old female, Ego Network Composition

Fig. 144-b 58 years old female, Ego Age-groups

Fig. 144-c 58 years old female, Social Relation types

The Ego interviewed is a 58-years-old man with 39 Social Network alters. He is a single man so concerning Family Structure, belongs 0 5 he 10 15 to ´Non20 25 family relationship category´ that represents 16.60 % of Informal Urban Settlements population [pie chart figure 135 page 106]. He arrived in that urban area 38 years ago in 1979, following his sister that had arrived 5 years before. 52.50 % of his Social Network lives in the same neighbourhood composed by 27.50 % Neighbour´s category, 25.00 % Family-neighbour´s category. 0 5 10 15 20 25 47.50 % of her alters live outside the neighbourhood. In terms of age-group, 37.74 % of her alters are between 20 and 49 years old. With regard to the use of urban space, the Ego usually meets 52.50 % of his alters in the street´s neighbourhood. Additionally, he meets the Friendship´s category and Hobby Partners category in open spaces outside the neighbourhood. 0 5 10 15 20 25 The frequency of his contacts is 48.72 % 30/30, 30.77 % 4-8/30, 2.56 % 1/30, and finally 17.95 % <30. Most of his social encounters leaving the family apart, due to those are in-doors, belong to Neighbourhood category, Friendship category and Hobby Partners category that happen for the former in ´places of the neighbourhood´ and for the latter outside the neighborhood. And the network density is 0.377. 0 5 10 15 20 25 0

Ego Network composition

0 0 5 10 40 Ego gender male Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree: 40

10 10

20 15

27.50 % Neighbors 25.00 % Family-neighbors 7.50 % Family 12.50 % Friends 0 10 12.50 % Hobbies % Educational 35 0.0040 0.00 % Health care 15.00 % Occupational 0.00 % Organisational

2030

100

Fig. 144-b

25 40

30 50

20 50

35 144-c40 Fig.

100

31-m-65 32-m-50

20 -m-35

17 -m-50

30-m-65

34-m-45

13 -m-60

40-f-56 02- f-34

15-f-50

21 -m-35

03 -f-35

23-f-80

14-f-90

35-m-70

16 -m-55 22-f-75

36-m-70

18 -m-35 01- m-58

37-m-60

19-f-35

38-m-60 08 -f-65

39-f-45

09 -f-59 12 -m-60 10 -m-56 05 -f-40

04 -f-35 Fig. 144-d 58 years-old female Sociogram

29-m-60

27-f-55

28-m-60 24-m-65 26-m-65 25-m-65

11 -f-35 07 -f-45

06 -f-35

Fig. 144-d

112

The Site

58 years old - male - Ego Social Network

33-m-80

7.50 % 22.50 % 32.50 % 37.50 % 0.00 % 0.00 %

Ego Social Relation types

Fig. 144-a 0 35 40 5 20 10 15 20 25 30 25 30 35 Ego age N° of alters 58 39 780 Average Distance 1.623 294 0.377 0.485 14.70

Ego Age-groups [ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4]

The Ego interviewed is a 60-years-old man with 60 Social Network alters. His nuclear family is composed by his0 wife5 [59] 10 and their son 15 20 [5].25 Concerning Family Structure, they belong to ´Nuclear family with child´s category´ that represents 42% of Informal Urban Settlements population. He arrived to live in that urban area 10 years ago, when he got married and moved to live in her wife´s urban plot, that belongs to her family since 1974. 68.85 % of his Social Network lives in the same neighbourhood composed 0 5 10 15 20 25 by 39.34 % Neighbour´s category, 29.51 % Family-neighbour´s category 31.15 % of his alters live outside the neighbourhood. In terms of age-group, 59.02 % of his alters are between 20 and 49 years old. With regard to the use of urban space, the Ego usually meets 39.34 % [Neighbour´s category] of his alters in the neighbourhood public open spaces, specifically the neighbourhood’s streets. Additionally, he meets 0 5 10 15 20 25 the Organisational group category in a public enclosed space called "Social Headquarters". There is no other possible urban encounter for social interaction in the area. The frequency of his contacts is 66.67 % 30/30, 10.00 % 4-8/30, 21.67 % 1/30, and finally 1.67 % <30. Most of his social encounters leaving the family apart, due to those being in-doors, belong to neighbourhood´s category and Organisational´s category that happen in ´places of the25 0 5 10 15 20 neighbourhood´, as they were previously mentioned streets and Social Headquarters. EGO NETWORK 69 The network density is 0.528. 5 10 15

Ego Age-groups [ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4] 30

Ego gender male Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree:

10 10

1520

20 30

25 40

30 50

04 -f-35

22-f-48

13 -f 33

+ 43 -m-35 42-f-33 44 -m-7 45 -m-3

12 -f-10 0

56-f-6 8

01-m-60

100

40 -m-49

23-m-63

14 -m-36

46-f-43

15 -f-15 160 -m-7 517-f-2 10 35 40

02 -f-59

Fig. 145-c 60 years old female, Social Relation types

24-f-81

25-m-45

26-f-42

2033-f-4225

27-m-30

32 -m-35

07 -m-5

38 -m-60

61-f-33 58-m-40

59-m-45

100

06 -m-42

37-f-57

60-f-43

Fig. 145-b 60 years old female, Ego Age-groups

31-f-30

54-f-37

57-m-65

100

41-f-33 29 -m-38

18-m-47

55-m-64

Fig. 145-b

08- m-14 09 -m-11 10 -f-5

53-m-45

Fig. 145-c

39.32 % Neighbors 27.87 % Family-neighbors 8.20 % Family 6.56 % Friends 10 0.00 % Hobbies 0 % Educational 35 9.8540 0.00 % Health care 0.00 % Occupational 8.20 % Organisational

Fig. 145-a 60 Ego Network Composition

Ego Social Relation types

+ 03 -f-40 05- m-41

Ego age N° of alters 60 60 1.830 Average Distance 1.472 967 0.528 0.499 31.705

11 -f-14

21-m-57

1.64 % 4.95 % 16.39 % 59.02 % 14.75 % 3.28 %

60 years old - male - Ego Social Network

20-f-61

19-f-78

Fig. 145-a

Ego Network composition

49-f-30 52-f-32

50-f-24

47-f-40 51-f-41

48-f-30

36-f-23

28-f-24 30-f-54 39 -m-56

34 -m-43

35-f-22 Fig. 145-d 60 years-old female Sociogram

Fig. 145-d

The Site

113

Fig. 146-a 69 years old female, Ego Network Composition

Fig. 146-b 69 years old female, Ego Age-groups

Fig. 146-c 69 years old female, Social Relation types

The Ego interviewed is a disabled 69 years-old woman with 46 Social 0 5 by her 10 husband 15 20[74].25 Network alters. Her nuclear family is only composed Concerning Family Structure, they belong to ´Nuclear family childless’s category´ that represents 10 % of Informal Urban Settlements population. She was born in that urban area in 1948, where her grandfather arrived in 1896. 68.85 % of her Social Network lives in the same neighbourhood composed by 39.34 % Neighbour´s category, 29.51 % Family-neighbour´s 0 5 10 15 category. 20 25 31.15 % of her alters live outside the neighbourhood. In terms of age-group, 50.00 % of her alters are between 65 and 79 years old. With regard to the use of urban space, the Ego usually meets 50.00 % of her alters in the neighbourhood public open space, specifically people from Neighbour´s category in neighbourhood’s streets and Organisational group category in a public enclosed space called ´Mother's Centre´. There 0 5 10 15 20 25 is no other possible urban encounter for social interaction in the area. The frequency of her contacts is 2.22 % 30/30 [just her husband], 24.44 5 of 10 15 % 4-8/30, 24.44 % 1/30, and finally 48.89 % <30. Most her social encounters leaving the family apart, due to those being in-doors, belong to Neighbourhood category and Organisational category that happen in ´places of the neighbourhood´, as they were previously mentioned. And the network density is 0.441. 0 5 10 15 20 25

Ego gender 0 10 female Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation Average degree

0 0

5 5

Ego Age-groups [ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4] 30

EGO ANA 69

0 composition 5 10 35Ego Network 40

10 10

15 15

20 20

25 25

30 30

10 10

20 15

2030

25 40

30 50

Ego Social Relation types 35 40 30

5 10 40-m-35

07-f-58 0 40

08-f-36

39-f-35

100

Fig. 146-c

12-f-54

100

14-f-72

15-f-68

16-f-74

18-f-70

17-f-75

26-m-76

20-f-46

19-m-69 27-f-46

22-f-64

21-f-70

34-f-41

23-f-62

42-f-55

The Site

100

24-f-80

35-f-52

43-m-35

114

Fig. 146-b

05 -m-75

+ 01 -f-69 02 -m-74

37-f-68

25-f-76

41-f-30 Fig. 146-d 69 years-old female Sociogram

04 -f-74

33-f-58

09-f-7

32-f-66

10-f-22

03 -f-72

31-f-66

13-f-69

06 -m-60

30-f-52

2.17 % 50.00 % 23.91 % 21.74 % 2.17 % 0.00 %

69 years old - female - Ego Social Network

38-f-70

100

35 35

11-m-56

36-f-60

Fig. 146-a

Ego age N° of alters 90 30 40 50 60 70 80 69 46 1.035 Average Distance 1.559 456 0.441 0.496 19.826

30.43 % Neighbors 23.91 % Family-neighbors 8.70 % Family 4.35 % Friends 0.00 % Hobbies 0 10 % Educational 35 0.0040 8.70 % Health care 0.00 % Occupational 23.91 % Organisational 25

29-f-65 44-m-35 45-f-66 46-f-66

28-f-72 Fig. 146-d

The Ego interviewed is a 76 years-old woman with 59 Social Network 5 10 [76], 15 daughter 20 25 alters. Her nuclear family is composed by 0her husband [40], son in law [30] and grandson [5]. Concerning Family Structure, they belong to ´Nuclear family with child´s category´ that represents 42% of Informal Urban Settlements population. She was born in that urban area in 1941 and has lived there her whole life. 68.33 % of her Social Network lives in the same neighbourhood composed by 40.00 % Neighbour´s category,025.005 % Family-neighbour´s 10 15 20 25 category, 3.33 % Friends´ category. 31.67 % of her alters live outside the 5 10 15 neighbourhood. In terms of age-group, 36.67 % of her alters are between 50 and 64 years old. With regard to the use of urban space, the Ego usually meets 50.00 % of her alters in the neighbourhood public open space, specifically people from Neighbour´s category in neighbourhood’s streets and Organisational group category in a public enclosed space called ´Social 0 5 10 15 20 25 Headquarters´. Also she meets friends and neighbours in a ´plaza´ next to her house [figure 158 page 125]. 5 10 15 Frequency of interactions with her contacts is 37.29 % 30/30, 35.59 % 4-8/30, 11.86 % 1/30, and finally 15.25 % <30. Most of her social encounters leaving the family apart, due to those being in-doors, belong to Neighbourhood category and Organisational category that happen in ´places of the neighbourhood´, as they were0previously mentioned. 5 10 15 20 25 The network density is 0.436.

30 Ego

70 alters 80

100

40 age

N° of

100

1.770 Average Distance 1.564 772 0.436 0.496 25.733

Fig. 147-a

100

35 147-b40 Fig.

8.33 % 16.67 % 36.67 % 28.33 % 10.00 % 0.00 %

10 10

Ego Social Relation types

20 15

2030

25 40

30 50

Fig. 147-b 76 years old female, Ego Age-groups

100

Fig. 147-c

40.00 % Neighbors 25.00 % Family-neighbors 28.33 % Family 3.33 % Friends 0.00 % Hobbies % Educational 35 0.0040 0.00 % Health care 0.00 % Occupational 3.34 % Organisational

Fig. 147-a 76 years old female, Ego Network Composition

Fig. 147-c 76 years old female, Social Relation types

76 years old Ego Social Network

21-m-74 22-f-65

26-m-50 23-f-46

Ego gender Potential Connections (PC) Actual Connections (AC) Network Density (d) Standard deviation: Average degree:

[ < 80] [65 -79] [50 - 64] [20 - 49] [5 -19] [0 - 4]

EGO NETWORK AURORA 76

35 25 Ego Age-groups 30

Ego Network composition 0 5 10 40

24-f-44

25-f-37 56-f-80

18-f-61 17-m-58

19-m-42 20-m-39

46-f-60

60-m-67 59-f-82

16-f-92

52-f-42

40-m-76 41-f-64

09 -m-54

43-m-62

55-m-58

42-m-70

10-m-60 08-f-58

51-f-56

47-m-55 34-f-60

15-f-7

54-m-60 53-f-60

01 -f-76 02 -m-76

11-f-37 12-m-39 13-m-15 14-f-11

+ 03

05 -m-30

-f-40

39-f-50

27-m-76 28-f-65 29-m-44 31-m-17

30-f-38

49-f-58

33-f-55

04 -m-5

+07-f-56

06-m-46

35-f-62

50-m-60 48-f-85

45-f-75 44-f-64

36-f-86 37-m-38 38-f-30

32-m-15

57-m-44

58-m-46

Fig. 147-d 76 years-old female Sociogram

Fig. 147-d

The Site

115

Social Network Data Summary and Conclusions Fig. 148-a Centrality measures summary data table. Basic Social Network concepts definitions: Ego Alters: number of people that belong to the Ego Social Network. Potential connections: is a connection that could potentially exist between two “nodes” – regardless of whether or not it actually does. Actual Connections: is one that actually exists. This person does know that person. Average Degree: number of connections of a node. Degree Centralisation It measures the extent to which an individual interacts with other individuals in the network. The more an individual connects to others in a network, the greater their centrality in the network. Network Density: Measurement of network connectedness For binary data, density is simply the ratio of the number of adjacencies that are present divided by the number of pairs - what proportion of all possible dyadic connections are actually present. Average Distance: average number of steps to reach all network participants. Standard Deviation: is a measure that is used to quantify the amount of variation or dispersion of a set of data values. A low standard deviation indicates that the data points tend to be close to the mean (also called the expected value) of the set, while a high standard deviation indicates that the data points are spread out over a wider range of values.

Contact Frequency amongst alters is a relevant aspect in Social Networks, so that at this point it is important to analyse how and where these contacts take place. Contact between egos and alters depends on their social relation type. As this study is being done for setting up the urban logics for the emergence of Socially Active Spaces [SAS] types and system among them, direct contacts in public spaces are the ones to analyse in depth. Most of the Ego's Family Social encounters are in in-door places, as well as Organisational and Occupational activities. Therefore, urban social encounters are mostly reduced to Neighbour´s category, Family Neighbour’s category and Friends ‘category. Unfortunately, there are no places, other than the streets and soccer field, for social interaction and which in the case of the latter is restricted mostly to young males. The table below summarises Ucinet Centrality measures data that gives some lights for approaching conclusions. As has been shown in Sociograms, an important percentage of Ego´s Social Networks live in the same neighbourhood. This is a distinctive local aspect of Social Networks, that nowadays tend to be independent of neighbourhood and specific places. Although, the density factor is in general high that does not mean that between the highest and the lowest network density numbers there is a significant gap. The former belongs to the youngest person interviewed [20] and the latter belongs to a divorced single man [58]. The former almost doubles in network density the latter. This is due to most of his social type Categories living in the same neighbourhood, and also an important number of them knowing each other. Conversely, the latter

Centrality measures Years old Gender Years old Ego Alters Gender Poten�al connec�ons Ego Alters Actual connec�ons Poten�al connec�ons Average Degree Actual connec�ons Deg Centralisa�on Average Degree Network Density Deg Centralisa�on Avg Distance Network Density Standard Devia�on Avg Distance Mutuals Standard Devia�on Nulls Mutuals Nulls

Contact Frequency 30/30

4-8/30 30/30 1/30 4-8/30 >30 1/30 >30

Fig. 148-b Contact frequency summary data table

Fig. 148-a

Ego-20

Ego-25

Ego-32

Ego-52

20 Ego-20 male 20 57 male 1,653 57 1,120 1,653 19.310 1,120 0.334 19.310 68% 0.334 1.322 0.678 0.467 1.322 0.678 0.467 0.322 0.678 0.322 63.16% 14.04% 63.16% 19.30% 14.04% 3.50% 19.30% 3.50%

25 Ego-25 female 25 58 female 1,711 58 742 1,711 27.831 742 0.586 27.831 43.4% 0.586 1.566 0.434 0.624 1.566 0.434 0.624 0.566 0.434 0.566 10.34% 39.66% 10.34% 25.86% 39.66% 24.14% 25.86% 24.14%

32 Ego-32 female 32 49 female 1,225 49 497 1,225 19.88 497 0.619 19.88 40.6% 0.619 1.594 0.406 0.491 1.594 0.406 0.491 0.594 0.406 0.594 46.94% 18.37% 46.94% 22.45% 18.37% 12.24% 22.45% 12.24%

52 Ego-52 female 52 52 female 1,378 52 591 1,378 22.302 591 0.574 22.302 42.9% 0.574 1.587 0.429 0.495 1.587 0.429 0.495 0.571 0.429 0.571 36.54% 23.08% 36.54% 19.23% 23.08% 21.15% 19.23% 21.15%

58 Ego-58 male 58 39 male 780 39 294 780 14.7 294 0.656 14.7 37.7% 0.656 1.623 0.377 0.485 1.623 0.377 0.485 0.623 0.377 0.623 48.72% 30.77% 48.72% 2.56% 30.77% 17.95% 2.56% 17.95%

60 Ego-60 male 60 60 male 1,830 60 967 1,830 31.705 967 0.488 31.705 52.8% 0.488 1.472 0.528 0.499 1.472 0.528 0.499 0.472 0.528 0.472 66.67% 10.00% 66.67% 21.67% 10.00% 1.67% 21.67% 1.67%

69 Ego-69 female 69 46 female 1,035 46 456 1,035 19.826 456 0.585 19.826 44.1% 0.585 1.559 0.441 0.496 1.559 0.441 0.496 0.559 0.441 0.559 2.22% 24.44% 2.22% 24.44% 24.44% 48.89% 24.44% 48.89%

76 Ego-76 female 76 59 female 1,770 59 772 1,770 25.733 772 0.583 25.733 43.6% 0.583 1.564 0.436 0.496 1.564 0.436 0.496 0.564 0.436 0.564 Fig.37.29% 148-b 35.59% 37.29% 11.86% 35.59% 15.25% 11.86% 15.25%

59.62 %

79.49 %

76.67 %

26.66 %

72.88 %

77.20 %

116

has a small Social Network and each Social Group has a very well defined urban area for interaction. Thus, that network characteristic implies a non-existing relationship between different groups making the network density value the lowest of the group of people interviewed. Both these Ego networks have in common that, due to the lack of open space, leisure time and meeting with friends happens outside the neighbourhood. It is important to notice that, even though, there is no open space for leisure time, no social infrastructure and commerce, local people’s attachment to that urban area is mainly because of the powerful meaning that it has for them both in terms of the social and geographical environment. This does not mean that Socially Active Spaces and infrastructure associated with them will be an opportunity to strengthened social relations and more than that empower the population´s urban life. Notwithstanding, distances between ego and alters living in the same neighbourhood varying between 500 and 50 meters, the slope of the topography is a factor to strongly consider in terms of alleviating those trajectories trying to define a relation between distance and slope to decrease the physical effort to walk from one to another for improving their quality of life. Contact Frequency [CF] is another variable that indicates specific attributes of Ego´s network. Particularly the lowest percentage at 26.66 % of CF corresponds to a disabled female 69-year-old that struggles to frequently go out of her home to meet people for social reasons.

The Site

50.00 %

65.31 %

Ego-58

Ego-60

Ego-69

Ego-76

5.0

Site conclusions

Site analysis was done on two levels. In a global level traditional Valparaíso open spaces and network elements were analysed, such as stairways and funiculars absent in ravines informal settlements. At the local level four small open spaces previously selected for this research were analysed, located at different points of the Jaime´s ravine. The whole body of this chapter has been focused on the analysis of Valparaíso vernacular characteristics with computational tools for obtaining concrete data in order to develop a design strategy in the next chapter. What informal urban settlements and the most primitive hamlets have in common are their developmental processes that have evolved in a linear order beginning with land boundary definition, shelter self-construction, network connections, functional needs such as sanitation and electricity, infrastructure and so on. Lastly, when all these conditions are fulfilled other latent social needs appear with greater impetus. Informal urban settlements form part of Valparaíso scenery though this picture never admits that those settlements are much more than a backdrop to the formal city. Although there are powerful social networks within those settlements a lack of places for social interaction is a condition that had constrained higher levels of fulfilment. What have seemingly mitigated the need of open spaces is that the natural amphitheatre shaped by the geography creates a sense of spatial expansion since at any location there is a visual field that connects to the overall urban environment. However, that transcendent social need is far beyond the capabilities of the natural environment. Social needs such as to connect with others to share and exchange cultural values and common visions of the future validate the tacit truth of belonging to a society that has evolved based on the same roots. Psychologist Abraham Maslow in the mid- twentieth century developed the idea of a ´hierarchy of needs´ pyramid that organises human needs into an order

from the most basic needs to the most evolved needs that leads to positive mental health. As individuals have different levels of needs moving from the most basic to more elaborated needs, similarly societies also have different levels of needs, from the very basics to more elaborated needs. As nowadays most of the world population is living in cities, the scope of the argumentation is related to urban life. Housing, sanitation, electricity, transportation among others are considered basic needs in cities, and supposedly following Maslow´s theory before moving towards more refined levels of fulfilment, those have to be attained. However basic needs never end therefore the promotion to higher levels of development such as sense of belonginess [the third from the bottomup in Maslow pyramid] through the promotion of social interaction in open spaces most of the time is left apart But in social evolution all the needs have to be attended opportunely. Therefore, the role of individuals as part of a community is a task yet to be accomplished in terms of the definition of the surge of urban places for achieving the social aspect of human evolution is still unattended. Site analysis has given to the present research a rich number of variables [parameters] to use in design strategy and project design. Having analysed Valparaíso traditional patterns of social interaction in ´plazas´, ´miradores´ and amphitheatres the present work has found that there is a wide range of activities and behaviours connected to those urban places which can inform the development of a Social Active Space System in ´Jaime´s ravine´. Social Network Analysis made at the local level will be a powerful basis for the kind of open spaces and built environment to be proposed grounded on local social aspects and dynamics.

The Site

117

118

Design Development

05 DESIGN DEVELOPMENT & STRATEGY

Urban patch analysis Urban Network Analysis, Centrality Indices, 4 clusters 5.2.1 Analysis cluster 1 5.2.2 Analysis cluster 2 5.2.3 Analysis cluster 3 5.2.4 Analysis cluster 4 5.2.5 Cluster´s conclusion 5.3 Topography and solar radiation analysis and filtration strategy 5.3.1 Filtration process 5.3.1.1 Slope gradients on the terrain 5.3.1.2 Slope gradients patch analysis 5.3.1.3 Solar radiation patch analysis 5.3.1.4 Overlapping slope gradient and solar radiation 5.3.1.5 Synthesis map and diagrams and conclusion 5.3.1.6 Conclusive diagrams 5.4 Creation of Ravine Polygon 5.5 Polygon Elevation analysis 5.6 Polygon Aspect Analysis 5.7 Polygon Slope Analysis 5.8 Overlay strategy for elevation, aspect and slope 5.9 Elevation, Aspect and Slope program Strategy 5.10 Housing Logic 5.10.1 Housing Type Definition

5.1

5.2

5.10.3 Cluster´s

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5.1

Urban patch analysis

5.1.1

topography of the urban patch

Fig. 150-a Topographical plan of the selected patch highlighted in red ´Alemania´ Avenue at 125 contour level.

5.1.2

network and distances

5.1.3

inner connections

Alemania Road

Fig. 150-b Current street network of the patch highlighting four open spaces and their connections with ´Alemania´ Avenue. Fig.150-c Cluster´s open space connections highlighted in green.

Fig. 150-a

Fig. 150-b

An urban patch of 1.6 Km2 was defined for selecting four areas and analysing occupation patterns their open spaces and the relation between the built and the non-built environment. Additionally, the connection amongst these four open spaces is considered. The selection of the four areas has taken into account two variables: First, the identification of open areas [SAS] within the urban tissue in those informal urban settlements, and second the distance between those open areas and ´Alemania´ Avenue which as mentioned before is the urban boundary between the formal and the informal parts of the city. The closer the cluster is to that road, the older it is in terms of land occupation. Four open areas for social interaction were found in the whole patch, which with their surroundings will be treated from now as clusters. From those open spaces a length of 300 metres along the existent roads was defined, through the connection of their end points one polygon per cluster was created highlighted in figure 150-b. Following this logic, the resulting four polygons are different in size and shape. After clusters are individually evaluated there is a comparative analysis for obtaining patterns that will inform the research proposal and the creation of a new model of urban occupation for the patch.

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Fig. 150-c

Parameters to be analysed in each cluster are size of the polygon area, number of plots, total private land [number of plots and m2] versus public land [roads m2 and open space m2], population number and finally open area square metres per person which is a central aspect of the thesis. A first approach to analyse the whole patch is to apply the Urban Network Analysis [UNA] tool, measuring Centrality indices defining all the buildings as origin points and four open spaces as destination points. Although open spaces of each cluster were defined as destination points and presumably it could have been assumed that each of those open spaces will have a similar level of centrality, Centrality measure indicates that cluster 2 has higher levels of centrality and its location is literally at the centre of the patch. Cluster 1 shows a high level of centrality too though that is due to its location that places half of the area in the informal and the second half in the formal city. Clusters 3 and 4 have very low levels of Centrality. Topographic sections in Fig. 152-a and 152-b show the spatial relation between open spaces highlighting in blue the bottom of the ravine and in red dwelling´s settlements.

5.2

Urban Network Analysis, Centrality Indices 4 clusters

Formal city area contour level from 0m to 125m

Fig. 151 Urban Network Analysis [UNA] toolbox. Centrality indices, ran considering 4 open spaces as destination points [1,2,3,4] and dwellings as origin points.

Alemania Avenue

contour level125m

Informal city area

contour level from 125m to 350m

3 4

Fig. 151

Fig. 152-a Section between open space 4 and 3 passing through the bottom of the ravine [blue point]

+ 205

+250 +225

+130

ravine bottom 300

300

Fig. 152-a

300

Fig. 152-b Section between open space 1 and 2 passing through the bottom of the ravine [blue point]

ravine bottom 300

300

Fig. 152-b

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5.2.1

Analysis cluster 1 Plot sizes

Network analysis [UNA] b b a

44 96

105

116

214 131

143

92 539

131 70

312

118 86

100

95 66

betweenness

´Alemania´ Avenue 115m sea level

high 539

low 1

Fig. 153-a

Range of plots sizes

Fig. 153-a Cluster 1 plot size. Plot size has a decisive effect on the urban configuration.

Fig. 153-d Cluster 1 network, Urban Network Analysis [UNA], Betweenness. Fig. 153-e Topographic sections show the ground relief of the open space Cluster 1.

120

200

number 60 258 99 40 13 20 10 5 0

150 100 50 0

100

250

150

range [ m2] [ 28 - 200] [200 - 400] [400 - 600] [600 - 800] [800 - 3000]

Section 1.a

120

percentage = 66.84 % = 25.64 % = 3.37 % = 2.59 % = 1.30 %

public land

Fig. 153-c

Cluster 1 open space [in green] is the nearest to ´Alemania´ Avenue at a 10 distance of 142m, so that it has a higher density than clusters farther from that 0 0 urban boundary. 66.84 % of the plots vary in size between 28m2 and 200m2, and 25.64 % vary between 200m2 and 400m2. Consequently 92.48 % of the urban land is housing urban use, with sporadic local small shops. Its open space 20

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´Alemania´ Avenue 100m sea level

130 m

20 30 10

b 0

Section 1.b

0 25 50 75 100

plaza axis

N 40

roads

0 25 50 75 100

Fig. 153-e

10 comprises a soccer field and a green area surrounded by two schools. Open space m2 per person is very low 1.61 m2 per person while the World Health 0 Organisation [WHO] has suggested a minimum of 9m2 per person to ensure a healthy urban life.

a a

40 30

plaza axis

polygon plots private land roads 50 open space population open space 80 [m2] [number] [m2] [m2] [m2] number m2/person 70 40 [4.536x6] [1.186+1.208] [370x4] 60 98,260 m2 385 76,644 m2 27,220 m2 2,394 m2 1,480 1.61 30 50 100.00 % 72.13 % 25.62% 2.25 % m2b/ p 20

Fig. 153-b

Cluster urban data

130 m

130 m 130 m

Topographic sections

300

Fig. 153-b Cluster 1 plot size bar graph revealing that most common plot size is the range between [28 - 200 m2] with 66.84 %. Fig. 153-c Cluster 1 urban data. The table summarises urban data gathered from a population of 1,480 whith 1.61 m2 of open space per person.

Fig. 153-d

Network analysis, distances from Social Active Space

Remaping distances from Social Active Space [SAS] 3 2 2

SAS

53m 214m

45m 73m 274m

115m

91m

322m

218m 827m

Fig. 154-a

3 2 4

125m 116m

Fig. 154-b

From Betweenness analysis the roads that have higher levels of Betweenness were selected as is pictured in figure 153-d. Secondly, the length of each path from their end points to the Social Active Space was calculated. Finally, the number of intersections that are found along these routes [Fig. 154-a]. Routes sections are defined by intersections between routes. In other words, each time a route is intersected by another route a new section starts. Only two intersections between roads were found apart from four intersections at the open space itself. Length of road sections vary between 45m to 827m. Following this synthesis, as shown in figure 154-b road´s length is remapped into a range between 1 and 10. From this analysis both the number of steps from the end point of each road to the open space and the number of nodes [intersections] along the route are evaluated. Therefore, depending on the set of roads surrounding Social Active Spaces and no less important the number of road´s intersections, those spaces [SAS] will be more or less accessible from origin points equivalent to each dwelling in the neighbourhood. Specifically, although cluster 1 open space has four access points filled by eight roads which gives a high potential of connection with its surroundings the network just counts with two intersections making the other six roads very long walking routes, especially when the extra effort required to walk those distances on steep slope terrains is considered. All these factors decrease the success of the open space as a centre of the community´s social interaction. Fig. 155

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Fig. 154-a Cluster 1 network distance analysis. Distances taken from SAS as origin points of roads to road end points and the number of intersections that have each route, defining a radius of 300 metres. Fig. 154-b Remapped network values to make an analytical comparison between the four networks analysed.

Fig. 155 OjedadelRio, P. 2017 ´Open Space Cluster 1, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 3rd 2017, a typical Saturday afternoon at ´Plaza Esmeralda´ permanently visited by citizens and foreing people size: 7.9 MB dimensions [8278x3782]

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5.2.2

Analysis cluster 2 Network analysis [UNA]

plots sizes b b a

106 18

76 69

151

132 166

181

476 1

134

64 55 48 13 a

a b

betweenness high

476 2a section cluster 2-

Fig. 156-a

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Fig. 156-d Urban Network Analysis [UNA], Betweenness measurement is b applied to test Cluster 2 network. Fig. 156-e Topographic sections show the ground relief of the open space Cluster 2.

50 number 86 40 108 30 29 20 2 10 7

Cluster urban data

plaza axis

rangea [ m2] [ 67 - 200] [200 - 400] [400 - 600] [600 - 800] [800 - 3000]

percentage = 37.07 % = 46.55 % = 12.50 % = 0.86 % = 3.02 % Fig. 156-b

public land

60 space 50 polygon plots private land roads open [m2] [number] [m2] [m2] [m2] 50 40 [6.633x5] 40 88.003 m2 232 67.675 m2 20,328 m2 556 m2 30 100% 76.90% 23.10 % 30 20 20

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[223x4] 892

130 m

section cluster 2- 2b Section 2.b

plaza axis

0 25 50 75 100

population open space number m2/person

0.62 m2/p roads Fig. 156-c

Cluster 2 open space [in green] is 371m distance from ´Alemania´ Avenue. 10 10 46.55 % of the plots vary in size between 200m2 and 400m2, and 37.07 % vary b between 67m2 and 200m2. Consequently 83.62 % of the urban land is housing 0 0 urban use, with sporadic local small shops. Plots from 400m2 up to 3,000m2 correspond to empty land mostly due to the steep topography. Dwellings occupy part of the plots and the rest remain non-occupied with its original condition as hills so that a certain rural condition is still part of this urban configuration. Open space per person for social interaction is very low - 0.62 a

124

130 m

plot

west

Section 2.a

Fig. 156-b Cluster 2 plot size bar graph revealing that most common plot size is the range between [200 - 400 m2] with 46.55 %. Fig. 156-c Cluster 2 urban data table summarises urban data for obtaining open space m2 per person that for this cluster is 0.62 m2, over a population of 892 inhabitants.

150

Fig. 156-d

eastTopographic sections

Range of plots sizes Fig. 156-a Cluster2 plot size. Plot size has a decisive effect on the urban configuration.

low 1

0 25 50 75 100

Fig. 156-e

m2. Moreover, that open space is a soccer field next to a small ´plaza´ with the former being used only by part of the population [young and adult man mostly]. Consequently, open space per person average decreases if the calculation only took the small ´plaza´ into consideration because that is the only space open to a wide variety of visitors. Another aspect observed is that open space is surrounded only by dwellings and there is not another complementary activity that takes place there. Therefore, there is not another activity that appeals to passers-by to reach the place.

Network analysis, distances from Social Active Space

Remaping distances from Social Active Space [SAS]

4 52m 96m 128m 372m 243m

188m

SAS

5 6

Fig. 157-b

The same analytical process applied on Cluster 1 has been followed in the present urban network analysis. In diagram 156-d is shown Betweenness analysis and from its results the roads better ranked were taken. Those roads are analysed in diagram 157-a showing that only one intersection between roads was found apart from two intersections at the open space itself. Length of road sections vary between 52m and 372m, however most of them have a length longer than 128m with slope degrees varying between 15° to 30°. The diagram shows a very poor spatial connection between the open space and the surrounding built environment. Following this synthesis, as shown in figure 157-b the road´s length is remapped into a range between 1 and 10. The diagram shows that the roads surrounding Cluster 2 Social Open Space make this space less accessible from origin points equivalent to each dwelling in the neighbourhood.

Fig. 157-a Network analysis in a virtual circunference of 300 metres radius it is analysed the distances from the SAS to those end points and the number of intersections that have each route. Fig. 157-b Remapped network values to inform the research project.

Fig. 157-a

Fig. 158

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Fig. 158 OjedadelRio, P. 2017 ´Open Space Cluster 2, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 7th 2017. size: 8.4 MB dimensions [7994x3628]

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a b b

Analysis cluster 3

5.2.3

plot sizes

Network analysis [UNA]

1 23

14 45

135

142 41 3 2 347

32 45

156 61

93 23 b

20 5

high 347

Fig. 159-a

section cluster 3- 3a

Range of plots sizes Fig. 159-a Cluster plot size. Plot size has a decisive effect on the urban configuration. Fig. 159-b Cluster plot size bar graph revealing that most common plot size is the range between [200 - 400 m2] with 47.00 %. Fig. 159-c Cluster urban data table summarises urban data for obtaining open space m2 per person that for this cluster is 1.36 m2, over a population of 1,056 inhabitants. Fig. 159-d Urban Network Analysis [UNA], Betweenness measurement is applied to test Cluster 3 network. Fig. 159-e Topographic sections show the ground relief of the open space.

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150

70 120

number 50 98 40 133 30 33 20 10 10 9

range [ m2] [ 95 - 200] [200 - 400] [400 - 600] [600 - 800] [800 - 3000]

south + 255S [+255m] 90/600= 0.15

percentage = 34.63 % = 47.00 % 15% = 11.66 % = 3.53 % = 3.18 %

Fig. 159-b

Plot urban data public land

60 polygon plots private land roads open space population 50 m2 number m2 m2 m2 number 50 40 [2,582x5] [264x4] 98,611 m2 283m2 85,703 m2 12,908 m2 401,435 m2 1,056 30 100 % 86.91 % 13.09%

Fig. 159-d

Section 3.a

+ 165 north

N [+165m]

130 m

section cluster 3- 3b Section 3.b

soccer field axis

0 25 50 75 100

west

W [+230m]

east

E [+165m]

roads Fig. 159-c

130 m

soccer field axis

1.36 m2

Cluster 3 open space [in green] is 776m distant from Â´AlemaniaÂ´ Avenue. 47.00 % 10 10 of the plots vary in size between 200m2 and 400m2, and 34.63 % vary between 95m2 and 0200m2. Consequently 81.63 % of the urban land is housing urban use, 0 with sporadically situated local small shops. Plots from 400m2 up to 3,000m2 [18.37%] correspond to empty land mostly due to the steep topography. Open space per person for social interaction is very low - 1.36 m2. Moreover, as in

Design Development

low 1

Topographic sections

open space m2/p

30 20

betweenness

0 25 50 75 100

Fig. 159-e

cluster 2, that open space is a soccer field and for similar reasons as stated earlier open space per person could decrease to zero if it is considered that the rest of the population has no place to socially interact other than the streets. Another aspect is that open space is surrounded only by dwellings and there is not another complementary activity to do there.

Network analysis, distances from Social Active Space

Remaping distances from Social Active Space [SAS] 5

SAS 73m

4 4 4

12 10 9 11 10

119m Fig. 160-b

120m

112m 65m

64m

29m 67m

33m 28m 32m 77m 21m 24m 47m 49m 46m 46m

231m end points intersection points

The same analytical process applied on Cluster 1 has been followed in the present urban network analysis. In diagram 159-d is shown Betweenness analysis and from its results the roads better ranked were taken. Those roads are analysed in diagram 160-a showing that eight intersections between roads were found apart from two intersections at the open space itself. Length of road sections vary between 29m to 231m which is the only road that surpass 119m length. Following this synthesis, as shown in figure 160-b the road´s length is remapped into a range between 1 and 10. Particularly, this network shows the highest number of intersections between roads and consequently shorter road´s length in each section, allowing the open space to have a better connection with the neighbourhood as a whole.

Fig. 160-a Network analysis, in a virtual 300m radius circunference it is analysed the distances from the SAS to the end points of each path and the number of intersections that have each route. Fig. 160-b Remapping network values to inform the research project.

Fig. 160-a

Fig. 161

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Fig. 161 OjedadelRio, P. 2017 ´Open Space Cluster 3, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 7th 2017. size: 7.5 MB dimensions [8792x3822]

127

a b

5.2.4

Analysis cluster4

plots sizes

Network analysis [UNA]

6 7

215

120

140

121

140

248

8 21

betweenness high 248 a

Fig. 162-a

Range of plots sizes Fig. 162-a Cluster plot size. Plot size has a decisive effect on the urban configuration. Fig. 162-b Cluster plot size bar graph revealing that most common plot size is the range between [200 - 400 m2] with 52.21 %. Fig. 162-c Cluster urban data table summarises urban data for obtaining open space m2 per person that for this cluster is 1.92 m2, over a population of 476 inhabitants. Fig. 162-d Urban Network Analysis [UNA], Betweenness measurement is applied to test Cluster 4 network. Fig. 162-e Topographic sections show the ground relief of the open space.

128

Topographic sections

80 70 60

number 3 71 45 10 7

50 40 30 20 10 0

86.86940m2 100% 30 20

percentage = 2.21 % = 52.21 % = 33.08 % = 7.35 % = 5.15 %

roads open space m2 m2 [5,235x5] 60.696 m2 26,173 m2 918 m2 30.13%

population open space number m2/p [119x4] 476 1.92 m2/p

+ 248

soccer field axis

130 m

east + 241

E[+241m]

130 m 0 25 50 75 100

+ 312 south

S [+312m]

+ 196.50

+ 161 N [+161m] north roads

Fig. 162-c

Cluster 4 open space [in green] is 1500m distant from ´Alemania´ Avenue. 10 52.21 % of the plots vary in size between 200m2 and 400m2, and 33.08 % vary between 400m2 and 600m2. Consequently 85.29 % of the urban land is 0 housing urban use, with sporadically situated local small shops. In the eastern side of the area slope degrees range from 35° to 40° affecting plot sizes and position on the ground. The width of the plots ranges between 9m and 15m and length varies from 25m to 50m. Most frequent plots orientation pattern over the terrain - define the width in favour of contour lines while the length against the slope. Consequently, where plots are 40-50m length, two houses Design Development

Fig. 162-d

soccer field axis west + 268 W[+268m] section cluster 4- 4a 151/600= 0.25 25%

public land

plots private land number m2 136 69.87%

range [ m2] [ 67 - 200] [200 - 400] [400 - 600] [600 - 800] [800 - 3000]

Fig. 162-b

Plot urban data polygon m250

51/116= 0.44 44% low 1

0 25 50 75 100

Fig. 162-e

occupy the plot one next to the upper side and the other next to the lower side leaving a central private empty land that can be potentially occupied in the future by another house or by extensions of the original houses. Plots from 600m2 up to 3,000m2 [12.50%] correspond to empty land mostly due to the steep topography. This cluster has the lowest number of inhabitants indicating that it is the newest in occupation amongst the four. Open space per person for social interaction is very low - 1.92 m2 per person. Moreover, that open space is a soccer field with the same implications as stated earlier in cluster 3.

Network analysis, distances from Social Active Space

Remaping distances from Social Active Space [SAS]

8 240m

185m

SAS

21 16 15

7 7

160m

Fig. 163-b

93m 80m 66m

58m

18m 203m 128m

154m

86m 35m

end points intersection points

The same analytical process applied on Cluster 1 has been followed in the present urban network analysis. In diagram 162-d is shown Betweenness analysis and from its results the roads better ranked were taken. Those roads are analysed in diagram 163-a showing that five intersections between roads were found apart from two intersections at the open space itself. Length of road sections vary between 35m to 240m. Following this synthesis, as shown in figure 163-b the road´s length is remapped into a range between 1 and 10. This urban network has a variety of road lengths and its number of road intersections increase the potential success of the open space as a centre of community activity.

Fig. 163-a Network analysis, in a virtual 300m radius circunference it is analysed the distances from the SAS to the end points of each path and the number of intersections that have each route. Fig. 163-b Remapping Cluster 4 network values to inform the research project.

Fig. 163-a

Fig. 164

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Fig. 164 OjedadelRio, P. 2017 ´Open Space Cluster 4, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 3rd 2017. size: 2.3 MB dimensions [4032x3024]

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5.2.5

ClusterÂ´s conclusion

Fig. 165-a Cluster 1 open space built environment. Fig. 165-b Cluster 2 open space built environment. Fig. 165-c Cluster 3 open space built environment.

105

Fig. 165-d Cluster 4 open space built environment. Fig. 165-e,f,g,h Network synthesis. In red open space analysed. Fig. 165-i,j,k,l Remapped values for network structure Red dots denote intersections between roads, and sizes denote the distance from the intersection to the

Fig.165-a

Fig.165-b

Fig.165-c

Fig.165-d

Fig.165-e

Fig.165-f

Fig.165-g

Fig.165-h

SAS

Fig.165-i

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SAS

Fig.165-j

SAS

Fig.165-k

Fig.165-l

Socio-spatial configuration generative order III generative order II

dominant path

enslaved order

generative order I enslaved order

enslaved order

enslaved paths

enslaved order history / memory Fig. 165-m [a]

Fig. 165-m [b]

Fig. 165-n

The main focus of these clusters analysis is to understand how open spaces as Social Active Spaces are embedded into these urban configurations. Understanding that those urban places are the prime urban spatiality to produce social interaction, the way inhabitants arrive in to them is a consequence of the type of network into which they are inserted. The arrangement of streets acts as a flow of energy that has a decisive impact on spaces for social encounter. As described by Batty [2013] in the transfer of energy from some central source to many distant locations, it is more efficient to develop infrastructure that captures as much capacity for transfer as near to the source as possible, Â´if there are 16 points arranged around a circle, rather than build a link between the source and each of these 16 points, it is more efficient to group the links in such a way that the distance to these different locations is minimizedÂ´ Batty [2013], [see figure 165m a and b]. This demonstrates the important point: when resources are to be conserved space has to be filled efficiently. As shown in figure 165m-b through a system of bifurcation shorter paths emerge and the

Fig. 165-o

time t the structure of the system in time t

flow of energy reaches more efficient results. The network of street patterns was analysed to see how these open spaces interact with their building environment through the network. In conclusion, network morphology for clusters 1,2,3 and 4 are unique for each of them and the result of a bottom-up approach following topographic logic with the application of the efficiency in the transfer of energy as mentioned by Michael Batty however lack of intersection points decreases network flexibility affecting open spaces reachability. The streetÂ´s intersections constitute a crucial aspect in network systems since they incorporate flexibility to the system and facilitate shorter routes from origin points to destination points - the former understood as dwellings and the latter as Social Active Spaces allowing pedestrians from different age groups -children, elderly population, young people and adults- to reach those spaces at their own pace, considering the steep slopes as a factor that adds an extra value when distances increase.

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Fig. 165-m Literal Hierarchies: Transport from a central source. a - Each link is separate b - Arranging links into a moe efficient structure. Redraw from The New Science of Cities, Batty [2013] Fig. 165-n Street intersections diagram.

Fig. 165-o Furcative change diagram [SelfOrganization and the City] by Portugali. Form of change, with every stage of furcation the systems become more complex in the sense that the old or alternative states [or order parameters] do not die but continue to live, enslaved by the dominant order parameters.

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5.3

Topographical and solar radiation analysis and filtration strategy cell 21 x 21 m

Grid applied onto the land [28x43]

Fig. 166-a Valparaíso topographic map, including at 125m sea level ´Alemania´ Avenue [in red] for referencing the urban boundary between the formal and informal city. The segmented rectangle is a sample patch for studying the topography and solar radiation of the cells within it. 1204 cells will be analysed. The small black dots are a reference of the previous clusters analysed.

588 m

Pacific Ocean

Fig. 166-b Square grid [588mx903m] cell grid [21x21]

903 m

Fig.166-a 5.3.1

Fig. 167 Filtration process is the strategy applied to find homogeneous slope areas with solar radiation levels over the average.

Fig.166-b

Filtration process 1st result

total cells slope gradient

- = aisolated cells

ﬁltra�on process [fp] 1

grouped cells

2nd result

Solar Radia�on test

number average Solar Radia�on number each cell

[fp] 2

An urban patch representative of informal settlements occupation located in two hills separated by a natural barrier - Jaime´s ravine is selected. An initial strategy for identifying diverse areas on the urban patch is analysing slope gradient variations. Taking into consideration that the average plot size in the area is around 10 to 12 meters’ width by 20 to 22 meters’ length, the analysis has standardised a cell of 21 by 21 meters containing two possible plots of 10.50m width by 21m length. Clusters previously analysed are located around the selected urban patch for making the experiments. Onto the land surface is projected a cell grid of 903 meters’ length [21x43] by 588 meters’ width [21x28] resulting in a total area of 530,964m2 [53.09 hectares] equivalent to 1204 cells for individually analysis of their slope gradient. The main purpose of the study is to graphically identify the percentage of each slope gradient range and their location. The slope gradient of the area ranges from 1.05° to 54.04°. As can be seen in the slope gradient graph the slope was classified into eight ranges: 1° to 6.99° as flat, 7° to 12.99° as moderate, 13° to 18.99° as steep, 19° to 20.99°, 21° to 25.99, 26° to 30.99°, 31° to 35.99° and 36° to 54° as extreme.

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ﬁnal result

3rd result

[fp] 3

cell numbers over

grouped cells > 5.000 m2

cell numbers below

clustered cells > 5.000 m2 clustered cells < 5.000 m2

[fp] 4

Fig.167

Over a total of 1,204 cells the results show that the least percentages are present with 2.99 % [36 plots] at the steepest slope between 36° and 54°, followed by 4.23% [51 plots] at slope between 19° and 20.99° and finally 5.15% [62 plots] at almost flat slope between 1.05° and 6.99°. In terms of location most of the almost flat cells are spread individually along the patch however there is an area where they make a cluster. That area is located between 230m and 245m above sea level and in the midst of cluster's open spaces 2 and 3 analysed in the previous section. Cells with a degree between 7° and 12.99°, classified as moderate, account for 15.19% and their locations are along the patch nonetheless seemingly to cells in slope 1° to 6.99°, bigger clusters are located in the east side of the patch between contour level 210m to 240m. Slope gradient between 13° and 18.99° and between 21° and 26.99° represent 22.43% and 25.25% of the land area respectively. Considering as the main target the configuration of large homogeneous areas with good solar radiation for developing Social Active Spaces the criteria is developing a filtration process for the selection of the fittest cells which consider three conditions, first slope continuity, second solar radiation better ranked than the average and third extension of areas over 5000m2.

1.049°

Slope Gradients analysis

6°

8°

FLAT 0° to 6°

10°

12°

slope gradient: 1.05° to 3° = 5.24 %

1.049°

39 plots =

1.049°

2.88 %

slope gradient: 4.00° to 6° = 10.51 %

1.049° 6°

6° 1.049°

8° slope gradient: 7.00° to 8° = 14.5 % 8° 8° 8° 8° slope gradient: 9.00° to 10° = 17.63 % 8° 8°

16°

MODERATE 8° to 12°

6°

STEEP 14° to 18°

14°

1.049°

1.049° 6° 6°

18°

6°

10° 10°

205 plots = 15.19 % 20°

10°

10° 10° 10°

10°

12° slope 12° 12° 12° 12° 12°

gradient: 11.00° to 12° = 21.26 % 22°

14°gradient: 13.00° to 14° = 24.93 % slope 14° 12° 14° 14° 14° 14° 16° slope 16° 14°gradient: 15.00° to 16° = 28.67%

16° 16° 16° 18° 16° 18° gradient: 17.00° to 18° = 32.49% slope 16° 18° 18° 18° 18° 20° 18° 20°

24°

297 plots = 22.00 %26°

25% 28°

30°

20° gradient: 19.00° to 20° = 30.40% slope 20°

] 102 plots =

20° 20° 20°

20%

7.56 %

35°

22°

15%

22°

slope gradient: 21.00° to 22° = 40.40 % 22°

22° 22°

22°

EXTREME > 20°

5.3.1.1

24° 24° 24° 291 plots = 21.56 % slope gradient: 23.00° to 24° = 44.52% 24° 24° 24° 24° 26° 26° 26° 26° 26°gradient: 25.00° to 26° = 48.7725% slope % 25% 26° 25% 26° 25%

10%

22°

25% 25% 25%

28° 28°

28°

54.043°

1°/ 6° 7°/12° 13°/18° 19°/20° 21°/26° 27°/30° 31°/35° 36°/54°

Fig.168-b

28°

slope gradient: 27.00° to 28° = 53.17 %

20%

28° 28°

20% 30° 30° 20% 172 plots = 12.74 % 20% 30° 20% 30° 20% 30° 30° slope gradient: 29.00° to 30° = 57.7320% % 30°

28°

35° 15% 35° 15% 35° 15% 35° 15% 35° 15% 35° slope 35°gradient: 31.00° to 35° = 70.02%15% 15%] 185 plots = 54.043° 54.043°

54.043° 54.043° 54.043°

13.70 %

10% 10% 10% 10% 10% 10% 10%

54.043°

5% 5% 5% 5% 5% 5% 5%

slope gradient: 36.00° to 54.04°= 137.85% ] 59 plots =

0 0 0 0 0 0 0

4.37 %

For attaining these conditions, the research has established a set of rules for assessing all the cells of the patch selected. Each slope gradient is analysed individually for quantifying cells that belong to it and identifying their locations. Only cells forming groups of two or more will remain to the next step of analysis, thus individual cells [441 m2] are eliminated. Additionally, for being considered grouped cells they have to touch one another by their edges and not their vertices. Hence all grouped cells pass the next stage where they are assessed through solar radiation levels. The ones that obtain a level higher than the average will remain. In the graphs those are highlighted with a black perimeter. Finally, for passing to the last stage the requirement is forming groups bigger than 5.292 m2 [12 cells of 441m2 each] Finally, from this filtration process the original patch of 1204 cells is reduced to 231 cells distributed into five groups. These areas will act as attractor points for densifying areas around them.

Fig. 168 Slope Gradient analysis, assessment based on Tweed Shire Council www.tweed.nsw. gov.au From the sample patch analysed there were identified slope degrees from 1° to 54°.

Design Development

133

Fig. 169 Slope degree percentages bar graph. Indicates the number of cells in each slope degree range.

Fig.168-a

Fig. 170-a Cell´s filtration process on slope degree [1° - 6.99°] single cells: 19 grouped cells: 43 5 clusters were found 3 clusters of 882 m2 3 clusters of 1,323 m2 1 cluster of 12,348 m2

5.3.1.2 Patch

analysis, slope gradient and solar radiation Solar Radiation in cells at 1°-6°

5.3.1.2.1

Slope gradients

kWh/m2

Fig. 172-b X [SR] 2,128.49 kWh/m2 89c < X [SR]< 137c 4 groups of higher solar radiation from the average have a surface bigger than 5,292 m2 Fig. 173-a Cell´s filtration process on slope degree [19° - 20.99°] single cells: 33 grouped cells: 18 6 clusters were found 4 clusters of 882 m2 1 cluster of 1,764 m2 1 cluster of 2,646 m2

gradient+solar radiation kWh/m2 2251.74<=

Fig. 170-c 2116.00 kWh/m2 The diagram shows the 2251.74<= area1980.26 that accomplished: 2116.00 1844.51 1. grouped cells bigger than 1980.26 441 1708.77 m2 slope gradient 1844.51 [1° -1573.03 6.99°] 2. solar radiation bigger 1708.77 1437.29 than1301.54 2.124,94 kWh/m2 1573.03 3. Group area bigger in size 1437.29 1165.80 than1301.54 5.292 m2 1030.06 The plot area is 11.466 m2.

2157.47 kWh/m2 2075.61 2239.32<= 1993.75 2157.47

2075.61 1911.89 1993.75 1830.03 1911.89 1748.17 1830.03 1666.32 1748.17 1584.46 1666.32 1502.60

43 cells pass to SR analysis

Radiation Analysis [Antofagasta_CHL 1 JAN 1:10 - 31 DEC 24:00

1 JAN 1:10 - 31 DEC 24:00

30 plots remain as higher levels of solar radiation

1502.60

1030.06

<=1420.74

<=894.32

Fig. 170-c

30 plots remain as higher levels of solar radiation

Solar Radiation in cells at 7°-12°

198 cells [7°- 12.99°]

1165.80 <=894.32

1584.46 <=1420.74

Analysis Fig.Radiation 170-b [Antofagasta_CHL

Fig. 170-a

Solar Radiation in cells at 19°-20°

Solar Radiation in cells at 7°-12°

Solar Radiation in cells at 19°-20°

Fig. 171-c kWh/m2 The diagram shows the area2245.58<= that accomplished: 2196.20 kWh/m2 1. grouped cells bigger than 2245.58<= 441 2146.82 m2 in a slope gradient 2196.20 [7° -2097.44 12.99°] 2. solar radiation bigger 2146.82 2048.06 than1998.68 2.127,67 kWh/m2 2097.44 3. Group area bigger in size 2048.06 1949.30 than 5.292 m2 1998.68 1899.92 The plot area is 22.050 m2.

kWh/m2 2251.84<= 2139.33 kWh/m2 2026.81 2251.84<= 1914.29 2139.33

2026.81 1801.78 1914.29 1689.26 1801.78 1576.74 1689.26 1464.23 1576.74 1351.71

173 cells pass to SR analysis

Fig. 171-b

Fig. 171-a Solar Radiation in cells at 1°-6°

270 cells [13°- 17.99°]

Radiation Analysis [Antofagasta_CHL 1 JAN 1:10 - 31 DEC 24:00

Fig. 172-a [Antofagasta_CHL

Radiation Analysis 1 JAN 1:10 - 31 DEC 24:00

30 plots remain as higher levels of solar radiation

kWh/m2

1850.54 <=1751.78

1239.19 <=1126.67

Solar Radiation in cells at 13°-18°

1801.16

Fig. 171-c

<=1751.78

kWh/m2 2251.74<=

kWh/m2 2157.47

kWh/m2 2116.00

2239.32<= 2075.61

2251.74<= 1980.26

2157.47 1993.75

2116.00 1844.51

2075.61 1911.89

1980.26 1708.77

1993.75 1830.03

1844.51 1573.03

1911.89 1748.17 1830.03 1666.32

1708.77 1437.29 1573.03 1301.54

1748.17 1584.46

1437.29 1165.80

1666.32 1502.60

1301.54 1030.06

1584.46

1165.80 <=894.32

<=1420.74 266 cells pass to 1502.60 SR <=1420.74 analysis

b c

1030.06 <=894.32

Fig. 172-b

Solar Radiation in cells at 7°-12°

Fig. 172-c The diagram shows four areas that accomplished: 1. grouped cells bigger than 441 m2 in a slope gradient [13° - 18.99°] 2. solar radiation bigger than 2.128,49 kWh/m2 3. Group area bigger in size than 5.292 m2 Plot areas are [a] 6.174 m2 [b] 7.056 m2 [c] 11.025 m2 [d] 15.435 m2

Fig. 172-c

Solar Radiation in cells at 19°-20°

Solar Radiation in cells at 7°-12°

51 cells [19°- 20°]

Solar Radiation in cells at 19°-20° kWh/m2

kWh/m2

2251.84<=

2245.58<=

kWh/m2 2139.33

kWh/m2 2196.20

2251.84<= 2026.81

2245.58<= 2146.82

2139.33 1914.29

2196.20 2097.44

2026.81 1801.78 1914.29 1689.26 1801.78 1576.74 1689.26 1464.23

2048.06 1949.30 1998.68 1899.92

1576.74 1351.71

1949.30 1850.54

<=1126.67

Fig. 173-c The diagram shows that there is no area accomplishing grouped cells bigger than 5.292 m2 with solar radiation higher than the average from slope gradient [19° - 20.99°]

2146.82 2048.06 2097.44 1998.68

1464.23 18 cells pass to 1239.19 SR 1351.71 <=1126.67 analysis 1239.19

Design Development

1899.92 1801.16

1351.71 <=1126.67

2239.32<=

30 plots remain as higher levels of solar radiation

Fig. 173-a

1949.30 1850.54

1464.23 1239.19

Solar Radiation in cells at 13°-18°

Solar Radiation in cells at 1°-6°

Fig. 173-b X [SR] 2,112.18 kWh/m2 10c < X [SR]< 8c 0 group of higher solar radiation from the average have a surface bigger than 5,292 m2

134

5.3.1.2.3 Overlapping slope Solar Radiation in cells at 13°-18°

2239.32<=

Fig. 171-b X solar radiation [SR] 2,139.33 kWh/m2 c < X [SR]< c 1 group of higher SR from the average have a surface bigger than 5,292 m2 Fig. 172-a Cell´s filtration process on slope degree [13° - 17.99°] single cells: 44 grouped cells: 226 37 clusters were found 13 clusters of 882 m2 6 clusters of 1,323 m2 4 clusters of 1,764 m2 3 clusters of 2,205 m2 3 clusters of 2,646 m2 1 cluster of 3,528 m2 1 cluster of 3,969 m2 1 cluster of 5,262 m2 1 cluster of 5,292 m2 1 cluster of 7,497 m2 1 cluster of 7,938 m2 1cluster of 3,087 m2 1 cluster of 11,025 m2 1 cluster of 19,845 m2

Solar Radiation in cells at 13°-18°

radiation

62 cells [1°- 6.99°]

Fig. 170-b X solar radiation [SR] 2,124.94 kWh/m2 11c < X [SR]< 32c 1 group of higher SR from the average have a surface bigger than 5,292 m2 Fig. 171-a Cell´s filtration process on slope degree [7° - 12.99°] single cells: 27 grouped cells: clusters were found 10 clusters of 882 m2 2 clusters of 1,323 m2 5cluster of 12,348 m2

5.3.1.2.2 Solar

Solar Radiation in cells at 1°-6°

1899.92 1801.16 1850.54 <=1751.78 1801.16

Fig. 173-b

<=1751.78

Fig. 173-c

Solar Radiation in cells at 31°-35°

Slope gradients [5.3.1.2.1 second part] 304 cells [21°- 26.99°]

Solar Radiation in cells at 21°-26°

Solar radiation [5.3.1.2.2 second part]

kWh/m2

Solar Radiation in cells at 21°-26°

kWh/m2

2251.74<=

2250.86<=

2116.00 kWh/m2

2116.02 kWh/m2

1980.26 2251.74<=

1981.18 2250.86<=

1844.51 2116.00

1846.34 2116.02

1708.77 1980.26

1981.18 1711.50

1573.03 1844.51

1846.34 1576.65

1708.77 1437.29

1441.81 1711.50

1573.03 1301.54

1306.97 1576.65

1437.29 1165.80

1441.81 1172.13

1301.54 1030.06

1306.97 1037.29

<=894.32 1165.80

<=902.45 1172.13

262 cells pass to 1030.06 SR <=894.32 analysis

Solar Radiation in cells at 27°-30°

kWh/m2

Fig.2223.75<= 174-c The2091.66 diagram shows the kWh/m2 areas that accomplished: 1959.57 2223.75<= 1. grouped cells bigger than 1827.48 2091.66 441 m2 slope gradient 1959.57 [21°1695.39 and 26.99°] 1827.48 1563.30 2. solar radiation bigger 1695.39 than1431.21 2.044,80 kWh/m2 1563.30 areas bigger in 3. Group 1299.12 size1167.03 than 5.292 m2 1431.21 Plot1034.94 areas are 1299.12 [a] <=902.85 6,174 m2 1167.03 [b] 7,938 m2 1034.94 [c] 8,379 m2 <=902.85

Fig. 174-c

Solar Radiation in cells at 36°-54°

Solar Radiation in cells at 27°-30°

Solar Radiation in cells at 36°-54° kWh/m2

Fig. 175-c kWh/m2 The diagram shows that 2159.41<= there is no grouped cells kWh/m2 that2023.12 have accomplished 2159.41<= the 1886.83 two conditions, 1750.55 solar radiation over the 2023.12 average and area bigger 1614.26 1886.83 than1477.97 5.292 m2 in slope 1750.55 gradient [27° to 29.99°]

2245.58<=

2237.04<=

2196.20 kWh/m2

2135.68 kWh/m2

2146.82 2245.58<=

2034.32 2237.04<=

2097.44 2196.20

1932.96 2135.68

2048.06 2146.82

1831.60 2034.32

1998.68 2097.44

1730.23 1932.96

1949.30 2048.06

1628.87 1831.60

1899.92 1998.68

1527.51 1730.23

1477.97 1205.40

1850.54 1949.30

1426.15 1628.87

1069.11 1341.69

106 cells pass to 1801.16 1899.92 SR 1850.54 analysis

Fig. 175-a

1037.29

151 cells [27°- 29.99°] kWh/m2

[ 5.3.1.2.3 second part]

<=902.45

Fig. 174-b

Fig. 174-a

Overlapping Solar Radiation in cells slope at 31°-35° gradient + solar radiation

1801.16

Fig. 175-b

1614.26 1341.69

1324.78 1527.51

932.83 1205.40

1426.15 <=1223.42

<=796.54 1069.11

1324.78 <=1223.42

Fig. 175-c

932.83 <=796.54

Solar Radiation in cells at 31°-35° Solar Radiation in cells at 21°-26° Solar Radiation in cells at 31°-35° kWh/m2

Solar Radiation in cells at 21°-26°

132 cells [30°- 35.99°]

kWh/m2

kWh/m2 2250.86<=

2223.75<=

kWh/m2 2116.02

kWh/m2 2091.66

2251.74<= 1980.26

2250.86<= 1981.18

2223.75<= 1959.57

2116.00 1844.51

2116.02 1846.34

2091.66 1827.48

1980.26 1708.77

1981.18 1711.50

1959.57 1695.39

1844.51 1573.03

1846.34 1576.65

1827.48 1563.30

1708.77 1437.29 1573.03 1301.54

1711.50 1441.81

1695.39 1431.21

1576.65 1306.97

1563.30 1299.12

1437.29 1165.80

1441.81 1172.13

1431.21 1167.03

1301.54 1030.06

1306.97 1037.29

1299.12 1034.94

1172.13

1167.03 <=902.85

2251.74<= kWh/m2 2116.00

1165.80 <=894.32

<=902.45 114 cells pass to 1037.29 SR <=902.45 analysis

1030.06 <=894.32

Fig. 176-c

Solar Radiation in cells at 36°-54°

Solar Radiation in cells at 27°-30°

36 cells [36°- 54°]

Solar Radiation in cells at 27°-30°

1034.94 <=902.85

Fig. 176-b

Fig. 176-a

Solar Radiation in cells at 36°-54°

kWh/m2

2245.58<=

2237.04<=

2159.41<=

kWh/m2 2196.20

kWh/m2 2135.68

kWh/m2 2023.12

2245.58<= 2146.82

2237.04<= 2034.32

2159.41<= 1886.83

2196.20 2097.44

2135.68 1932.96

2023.12 1750.55

2146.82 2048.06

2034.32 1831.60

1886.83 1614.26

2097.44 1998.68

1932.96 1730.23

1750.55 1477.97

2048.06 1949.30

1831.60 1628.87 1730.23 1527.51

1614.26 1341.69 1477.97 1205.40

1628.87 1426.15

1341.69 1069.11

1998.68 1899.92 1949.30 1850.54

29 cells pass to 1527.51 1324.78 SR 1426.15 <=1223.42 analysis 1324.78

1899.92 1801.16 1850.54 1801.16

Fig. 177-a

Fig. 176-c The diagram shows one area that have accomplished: 1. grouped cells bigger than 441 m2 slope gradient [30°- 35.99°] 2. solar radiation bigger 1.809,33 kWh/m2 3. Group areas bigger in size than 5.292 m2 Plot area is 6.174 m2

<=1223.42

Fig. 177-c The diagram shows that there is no area accomplishing grouped cells bigger than 5.292 m2 with solar radiation higher than the average from slope gradient [36° - 54°]

1205.40 932.83 1069.11 <=796.54 932.83

Fig. 177-b

<=796.54

Fig. 177-c

Design Development

Fig. 174-a Cell´s filtration process on slope degree [21° - 26.99°] single cells: 42 grouped cells: 262 39 clusters were found 15 clusters of 882 m2 5 clusters of 1,323 m2 3 cluster of 1,764 m2 3 cluster of 2,205 m2 1 cluster of 2,646 m2 2 cluster of 3,087 m2 2 cluster of 4,410 m2 1 cluster of 5,292 m2 3 cluster of 6,174 m2 1 cluster of 8,379 m2 1 cluster of 10,584 m2 1 cluster of 11,907 m2 1 cluster of 13,230 m2 Fig. 174-b X solar radiation [SR] 2,044.80 kWh/m2 111c < X [SR]< 151c 3 groups of higher SR from the average have a surface bigger than 5,292 m2 Fig. 175-a Cell´s filtration process on slope degree [27° - 29.99°] single cells: 45 grouped cells: 106 24 clusters were found 7 clusters of 882 m2 6 clusters of 1,323 m2 4 clusters of 1,764 m2 1 cluster of 2,205 m2 2 clusters of 2,646 m2 1 cluster of 3,087m2 2 clusters of 3,969 m2 1 cluster of 7,056 m2 Fig. 175-b X [SR]1,903.90 kWh/m2 54c < X [SR]< 52c 0 group of higher solar radiation from the average have a surface bigger than 5,292 m2 Fig. 176-a Cell´s filtration process on slope degree [30° - 35.99°] single cells: 18 grouped cells: 114 19 clusters were found 8 clusters of 882 m2 2 clusters of 1,323 m2 1 cluster of 1,764 m2 2 clusters of 2,646 m2 2 clusters of 3,969 m2 1 cluster of 5,292 m2 1cluster of 5,733 m2 1 cluster of 7,056m2 1 cluster of 7,497 m2 Fig. 176-b X [SR]1,809.33 kWh/m2 54c < X [SR]< 60c 1 group of higher solar radiation from the average has a surface bigger than 5,292 Fig. 177-a Cell´s filtration process on slope degree [36° - 54°] single cells: 7 grouped cells: 29 3 clusters were found 1 cluster of 2,205 m2 1 cluster of 3,528 m2 1 cluster of 7,056 m2 Fig. 177-b X [SR]1,629.25 kWh/m2 17c < X [SR]< 12c 0 group of higher solar radiation from the average have a surface bigger than 5,292 m2

135

5.3.1.5

Synthesis map , diagrams and conclusion

Slope gradient continuity and high solar exposure groups

Fig. 178 Cell´s filtration process final result, ten areas constitute five groups accomplishing slope gradient continuity and high solar exposure.

+200

A3 +200

+180 +200

+150

+200

+175

A9 +200

+225

+175

+225 +235

+245

+250

Legend 1° to 6° slope

1 plot = 11.466 m2

7° to 12° slope

1 plot = 22.050 m2

A1 A2

1 plot = 1 plot = 1 plot = 1 plot =

A3 A4 A5 A6

13° to 18° slope +240

+200

19° to 20° slope

0 plot > 5.000 m2

21° to 26° slope

1 plot = 6.174 m2 1 plot = 7.938 m2 1 plot = 8.379 m2

27° to 30° slope

0 plot > 5.000 m2

31° to 35° slope

1 plot = 6.174 m2

36° to 54° slope

0 plot > 5.000 m2

+175

A10 +200 +225 +225

6.174 m2 7.056 m2 11.025 m2 15.435 m2

A7 A8 A9 A10

Fig. 178

From the filtration process the diagram illustrates the areas that accomplished rules set at the initial point. Those rules respond to the research goal of finding extensions of land for developing Social Open Spaces and their surrounding built environment. Three conditions will define the formation of the areas pursuit: first slope gradient continuity, second good solar exposure and third a minimum size. As a result, nine areas are identified forming five groups described as follows: group 1, is composed by three areas [7,938+8,379+15,435] = 31,752 m2, two of them with a 21° to 26° slope gradient and one in between them with a 13° to 18° slope gradient at 175m to 250m above sea level; group 2, is composed by four areas [6,174+11,025+22,050+11,466] = 50.715 m2, two of them with 13° to 18° slope gradient, one with 7° to 12° slope gradient and one with 1° to 6° at 200m to 245m; group 3, is a 6,174 m2 area with 21° to 26° slope gradient, 150m to 175m; group 4, is a 7,056 m2 area with 13° to 18° slope gradient at 200m to 225m; group 5, is a 6,174 m2 area with 31° to 35° slope gradient at 175m to 225m. The addition of the five areas reach 101,871m2 [10.18 hectares]. Two groups [1 and 2] are composed by areas with variation in slope gradient, the other three groups are isolated areas with homogeneous slope located in different parts of the patch. Interestingly, groups 1 and 2 locations are at the west [1] and east [2] sides of the ravine. This confirms one of the primary design ambitions of the present study that is to connect them through the ravine

136

Design Development

territory to achieve an urban integration considering the topography as part of the urban system. The section in figure 176-b has been drawn from group 1 centre point to group 2 centre point passing through Group 3. Through ´Line of Sight´ analysis intervisibility between defined points at the top of those hills is evaluated. This determines what is visible and what is hidden along the lines between these points. For observer 1 [O1], the surface at the eastern side of the bottom of the ravine is hidden, a similar condition happens to observer 3 [O3] for which the surface at the western side of the bottom of the ravine is hidden, though less so. The position of observer 2 [O2] has no hidden surfaces, which makes that position a privileged condition in terms of the visual field at the ravine land. Although there is a high level of intervisibility urban settlements remain functionally unconnected as a consequence of the steep topography with only one pair of roads that pass through the ravine to connect both sides of it. At this particular topographic section the ravine due to an isolated hill is divided into two, and group 3 is located at the top of that isolated hill in between those ravine branches [R1 and R2]. Connection patterns are then a subject of experiments for attaining a wide range of possible urban continuity in an east-west direction and vice-versa.

Fig. 179-a Top view three groups obtained through filtration process: Group 1 [G1] ravine[R] west side , group 2 [G2] R east side and group 3 [G3].

Fig. 179-a

+228

+227 21°-26°

13°-18° 21°-26°

R1 28°

+160

170m

O1 O2 O3

21°-26° +148

E +229 7°-12°

29°

Fig. 179-b Section illustrates three groups G1, G2 and G3, additionally three observer points O1, O2, O3 that show the visibility between observer´s points. At this point of the ravine the topography develops a small hill that divides the ravine into two branches. Legend Line of sight

235m

Fig. 179-b

Visible surface Hidden surface

1Km

Design Development

137

5.3.1.6

Conclusive diagrams

R Y

Formal city

Fig. 180 Urban and Geographical boundaries separating areas found after filtration process.

N B A U R

Informal city

Group 2

E O G R A P H A

R Y e D A U N B O

Group 1

Area 4

Fig. 180

138

Design Development

vin

Group 3

Area 5

formal city urban boundary

sea [north] Fig. 181-a Urban [Â´AlemaniaÂ´ Avenue] and Geographical [ravine] existent boundaries between informal urban settlements [IUS] and between them and the formal city, Synthesis diagram.

IUS

spatial connection between the formal and inforal city

Fig. 181-b Conceptual intention for connecting IUS and connection between them and the formal city Synthesis diagram.

top of the hills

Fig. 181-a

hills [south]

bottom o

f the ravin

bottom o f

the ravin

top of the hills

Fig. 181-b

Design Development

139

5.4 Creation

of ravine polygon

Fig. 182-a Jaime´s ravine natural topography. Fig. 182-b Square grid over Jaime´s ravine natural topography. Fig. 182-c Dot grid over Jaime´s ravine natural topography. Fig. 182-d Collection of dots following the bottom of Jaime´s ravine.

Fig. 182-a

Fig. 182-b

After filtering for gradient continuity and high solar exposure the result has given 5 groups of land in different locations of the urban patch. Those portions of land have confirmed that the ravine is a subject of division between the hills studied. Therefore, from now on the research will be focused on the ravine as the site of intervention for improving the urban integration between informal settlements. Hence a procedure of defining a terrain for the project is illustrated from figures 182-a to 182-g. In figure 182-a the natural topography of the area is shown highlighting ´Alemania´ Avenue as an urban element for orientation. A first approach is to project a square grid of cells [21x21] as was done previously with the patch analysed. From the square grid is obtained a grid of points

140

Design Development

Fig. 182-c

Fig. 182-d

[centre points] in figure 182-c, and points at the bottom of the ravine are connected to create the prime trace of the ravine in figure 182-d. Following that trace points at the top of the east and west sides of the hills are identified to create a polygon that includes the bottom of the ravine the foot of the hills and the summit of each ravine side. The initial square grid in figure 182-b has changed culling all the cells outside the polygon created. Figure 183 from a to f are sections of the ravine to illustrate how its spatiality changes from north to south. North and South sections reveal a narrower spatiality than sections such as 3W-3E and 4W-3E which reveal a wider spatiality and also more gentle slopes.

Fig. 183-b

Fig. 182-f Hills´ highest points connection for creating a polygon for the project.

Fig. 182-g Square grid of the polygon created.

Fig. 183-a

Fig. 182-e Dots identifying highest points of Jaime´s ravine hills.

2W 1E

3W 2E

Fig. 183-c 3E 4W

5E 4W

5W 6W

Fig. 183-d

7W 8W

Fig. 183-e

Fig. 182-e

6E Fig. 183-f

Fig. 182-f

Fig. 183-a Ravine section 1W-1E Fig. 183-b Ravine section 2W-2E Fig. 183-c Ravine section 3W-3E

Fig. 180-a Ravine section 1W-1E

Fig. 183-d Ravine section 4W-3E Fig. 183-e Ravine section 5W-5E Fig. 183-f Ravine section 6W-6E

Fig. 182-g

Fig. 184 Jaime´s ravine relief.

Fig. 184

Design Development

141

5.5

Elevation Analysis

Fig. 185-c

SECTORS

elevation [252m to 210m] [210m to 168m] [168m to 126m] [126m to 84m] [84m to 42m] remapped values 2 1 3 4 5

NE SE NW SW R whole terrain

[42m to 0m]

6% 31% 0% 33% 9%

35% 53% 10% 38% 9%

36% 16% 54% 29% 42%

14% 0% 20% 0% 16%

6% 0% 13% 0% 10%

3% 0% 3% 0% 14%

13%

28%

37%

12%

4% Fig. 185-d

Fig. 185-a Elevation analysis top view of the terrain highlighting ravine sector. Fig. 185-b Sub-sector polygons Northeast [NE] Southeast [SE] Northwest [NW] Southwest [SW] Ravine [R]

NE 350 cells

Fig. 185-c Broken down elevation remapped values.

SW 162 cells

Fig. 185-d Summary table elevation numbers, remapped values and percentages of each of them in the five sectors of the patch analysed. Additionally percentage of each elevation remapped value on the whole terrain.

142

R 185 cells

NW 231 cells

SE 137 cells

Fig. 185-a

Design Development

Fig. 185-b

For analysis purposes the terrain has been divided into five sectors: the north-eastern [NE] sector, the south-eastern [SE] sector, the north-western [NW] sector, the south-western [SW] sector and finally the ravine [R] sector that composes these sectors into a unified region. As part of a set of hills that form a bay running from 20m to 400m elevation, the patch analysed develops levels from 20m to 250m elevation along JaimeÂ´s Ravine. The methodology of analysis proceeded by calculating the height for each of the cells of the patch and grouping them by ranges of height. Those ranges were remapped into values from 0 to 5. Later the percentage of cells in each sector was calculated as can be seen in the table figure 182-d. As the ravine runs from the lowest to the highest part of the land there is height variation along its path to the southern part of the patch. The NE and NW sectors contain almost all the height ranges, whereas the SE and SW sectors develop height from 126m to 250m elevation.

Aspect Analysis

Fig. 186-c

o N

[22.5° to 67.5]

a s NW

o r i e n NE [& W] E [& SW]

a t SE

[337.5° to 22.5] [292.5° to 337.5] [22.5° to 67.5] [67.5° to 112.5] [112.5° to 157.5][157.5° to 202.5] orientation [°] remapped values 2 1 5 4 3 0

SECTORS

5.6

NE SE NW SW R whole terrain

18% 13% 7% 9% 13%

27% 40% 2% 6% 25%

26% 43% 24% 18% 30%

28% 4% 43% 67% 20%

0% 0% 23% 0% 7%

1% 0% 1% 0% 5%

12%

20%

27%

33%

[157.5° to 202.5] [157.5° to 202.5]

Fig. 186-d

N NW 337.5° 315°

0° 22.5°

292.5°

67.5° 90° E

W 270° 247.5°

Fig. 186-a

NE 45°

112.5°

225° SW 202.5°

157.5° 180°

135° SE Fig. 186-b

As the ravine runs along a north-south axis the hills that surround it are at its eastern and western sides, therefore orientation of the cells shows a variety of slope aspect angles that range from 0° to 360° illustrated in figure 183-d. Consequently, possible orientations are to the north, northeast, east, south east, south, southwest, west, northwest. Continuing with the analysis by sectors, it can be seen that they have a direct influence of surfaces orientation. Sector NE and SE have 6% and 17% of their cells northeastern oriented respectively. In the NW sector 43% of the cells have an eastern orientation and 7% northern orientation. In SW sector cells are 67% eastern oriented and 18% northeastern oriented. As the ravine develops from north to south it has different cell orientations, 21% facing in a northeastern direction and 13% to the east. When the remapping values were elaborated south west, west, and northwest directions were given remapped values 2, 3 and 4 respectively. As can be seen in the top view in figure 186-a northeastern and northwestern sectors have a wide proportion of the cells eastern and south western oriented, 28% and 23% respectively, which implies that 51% or the land has poor levels of sun exposure. In contrast northern [remapped value 5] and northeastern [remapped value 3] orientations account for 12% and 15% respectively, in total 27% of the land with good sun exposure condition.

Design Development

Fig. 186-a Aspect analysis top view of the terrain highlighting ravine area [red] Fig. 186-b Compass indicating cardinal directions on the patch analysed. Fig. 186-c Broken down aspect remapped values. Fig. 186-d Summary table aspect degrees numbers, remapped values and percentages of each of them in the five sectors of the patch analysed. Additionally percentage of each aspect remapped value on the whole terrain.

143

5.7

Polygon Slope Analysis

Fig. 187-b

SECTORS

slope [°] remapped values

i e

[7° to 12.99°]

2% 0% 5% 1% 13%

8% 2% 10% 5% 34%

14% 20% 14% 15% 33%

18% 38% 20% 28% 16%

21% 24% 11% 22% 3%

37% 16% 40% 29% 1%

12%

18%

22%

16%

28%

NE SE NW SW R whole terrain

[13° to 19.99°]

[1° to 6.99°]

[20° to 26.99°] [27° to 30.99°]

[31° to 54°]

Fig. 187-c

Fig. 187-a Slope gradient analysis top view of the terrain highlighting ravine sector [white]. Fig. 187-b Broken down slope remapped values. Fig. 187-c Summary table slope gradient numbers, remapped values and percentages of each of them in the five sectors of the patch analysed. Additionally, percentage of each slope remapped value on the whole terrain.

Fig. 187-a

144

Design Development

Jaime´s ravine develops a rich variety of gradients ranging from 1° to 54°, however nearly a third of the territory has gradients above 31° which is categorised as extreme following the assessment based on Tweed Shire Council p. 133 figure 168-a. Slope gradients are analysed in the five ravine sectors defined previously. Northeastern and northwestern sides have the largest proportion of steep slopes. This ravine´s sides face each other and spatially is the connection between the ravine and the formal city passing through ´Alemania´ Avenue. The bottom of the ravine [sector called R as ravine] has 47% of its slope gradients between 1° and 13° and 33% between 13° and 20° which allows continuity along its development. At the top of the NE sector there is a wide area with gradients ranging between 1° and 20° at the top of the hill. The SW sector has 49% of the slopes with gradients between 1% and 27° and 51% between 27° and 54°. As shown in the table [figure 187-c] although each sector has a characteristic relief shaped by the slope angles present in them almost all the slope degrees are present in each sector allowing multiple possible land uses suggesting a heterogeneous urban program for the territory. Remapped values were given following the logic of flatter terrains would be suitable for housing use and open spaces too. However, only 16% of the land analysed has a slope gradient between 1° and 13°, meeting the requirement for developing strategies for land occupation with the steepest slopes covering the remaining 84% of the land.

5.8

Overlay strategy for elevation, aspect and slope

elevation [m] [252m to 210m] [210m to 168m] [168m to 126m] [126m to 84m] [84m to 42m] remapped 2 1 3 4 5 values

13%

N Fig. 188

Fig. 189

28%

37%

12%

[42m to 0m]

elevation

N NW NE [& W] E [& SW] SE S orientation [°] [337.5° to 22.5] [292.5° to 337.5] [22.5° to 67.5] [67.5° to 112.5] [112.5° to 157.5][157.5° to 202.5] remapped compass orientation 2 1 5 4 3 0 values

slope [°] remapped values

13%

[1° to 6.99°]

[7° to 12.99°]

12%

34%

[13° to 19.99°]

18%

35%

17%

[20° to 26.99°] [27° to 30.99°]

22%

16%

[31° to 54°]

slope gradient

28%

52%

Fig. 190

The main purpose of the topographical analysis is to identify local conditions [cell´s unit] of elevation, aspect and slope that were given remapped values, 0-1-2-3-4-5. The elevation factor fittest cells were defined at the elevation that represent the centre of the ravine with remapped values 4 and 5. For the aspect factor the fittest values were defined as the angles ranging from 337.5° to 67.5° meaning northwest, north and northeast oriented cells remapped values 4, 5, and 3 respectively. Slope factor has defined the fittest values between 7° to 30°, with 4, 3 and 2 remapped values respectively. In each cell the three factor´s analysis were summed thus the results were sorted to rank them and identify the fittest. The synthesis map shows the fittest cells for each factor. Exceptionally, in some cells the fittest values for elevation, aspect and slope meet. Cells that accomplish at least 2 factors are spread along the whole patch however the central area of the patch has a major concentration of fittest cells. From these land properties the following research stages will focus on elaborating a strategy for the land use distribution on the urban patch in accordance with the natural properties of the patch.

Fig. 188 Elevation fittest cells synthesis map and remapped values table. Fig. 189 Aspect fittest cells synthesis map and remapped values table. Fig. 190 Slope fittest cells synthesis map and remapped values table. Fig. 191 Elevation, aspect and slope remapped values synthesis map.

Fig. 191

Design Development

145

Elevation 0 1 2 3 4 5

Program Built environment

A s p e c t 0 1 2 3 4 5

stilt

7° - 13°

1° -7°

20° - 27° 13° - 20°

27° - 31°

flat 31° - 54°

292.5°-337.5°

Dwellings & Infrastructure 337.5°-22.5°

67.5°-112.5° 202.5°-247.5° 22.5°-67.5° 247.5°-292.5°

112.5°-157.5°

157.5°-202.5°

168m - 210m

126m -168m

84m - 126m 210m - 252m

42m - 84m

Elevation, Aspect and Slope, urban program strategy

0m- 42m

5.9

Slope 0 1 2 3 4 5

dwellings stairways funiculars ´plazas´ ´miradores´ amphitheatre

Fig. 192

Remapped 0-1-2-3-4-5 values Elevation 4-5 Aspect 0-1-2-3-4-5 Slope 2-3-4 Fittest cells 372 Fig. 193-a

The graph above shows a synthesis of the strategy for running the next experiment seeking the fittest cells for specific land uses. According to this, the results become a guide to develop the occupation of the urban patch. The criteria is to defined the constraints and opportunities for each land use. A criterion that drives most of the land uses is elevation at the altitude ranging from 126 to 210 due to the purpose of occupying the central area of the patch as an initial population strategy. Five out of six land use define a suitable aspect remapped values of 3,4 and 5. However, for the built environment aspect factor is related with the development of architectural elements for controlling façades sun exposure. Differently is the criterion for slope which has particular remapped values for diverse land uses. Elevation, aspect and slope values vary from cell to cell, however what is happening in the neighbouring system is what further experiments will evaluate for finding areas instead of individual cells well ranked. As Harold [2001] has pointed out ´The complex architecture of living forms requires more than the simple coexistence of many cells in close proximity; the spatial organisation of many cells has to enable interactions of each celll to its immediate neighbours, and it is the interaction that iniciates the processes that produce hierarchical order and morphological complexity.´ This inspiring idea will be a guideline for the next experiments.

Fig. 192 Elevation, aspect and slope urban program strategy. Fig. 193-a Fittest cells for housing land use.

146

Design Development

Criteria for finding the fittest land cells for dwelling location have taken into consideration that the main purpose of the present work is to propose an urban model that makes of the ravine an urban connector between existent informal settlements. Hence, the urbanisation of the centre of the ravine is a priority that puts elevation factor as a first condition for housing population placing them in altitudes between 126m and 210m. A second condition is slope ranging from 7° to 27°. Therefore, with this two conditions the result is a wide range of cells for placing the built environment. Regarding to aspect factor, architectural elements such as balconies, terraces and eaves will have a range of lengths for defining larger length to the northern and western facing façades.

Open Space Social Active Spaces

Network Elements funiculars

staiways

‘plazas’

‘miradores’

‘amphitheater’

Remapped 0-1-2-3-4-5 values Elevation 0-1-2-3-4-5 Aspect 0-1-2-3-4-5 Slope 1-2-3 Fittest cells 552

Remapped 0-1-2-3-4-5 values Elevation 0-1-2-3-4-5 Aspect 0-1-2-3-4-5 Slope 0-1 Fittest cells 154

Remapped 0-1-2-3-4-5 values Elevation 0-1-2-3-4-5 Aspect 3-4-5 Slope 4-5 Fittest cells 126

Remapped 0-1-2-3-4-5 values Elevation 3-4-5 Aspect 0-1-2-3-4-5 Slope 3- 4-5 Fittest cells 169

Remapped 0-1-2-3-4-5 values Elevation 0-1-2-3-4-5 Aspect 0-1-2 Slope 0-1 Fittest cells 262

Fig. 193-b

Fig. 193-c

Fig. 193-d

Fig. 193-e

Fig. 193-f

Criteria for stairways distribution have taken into consideration that at any location of the patch will be necessary to develop a pedestrian network. The requirements for the development of urban stairways are related with the slope which range between 7° to 31°, see page 96 figure 106. In terms of aspect and elevation there is no requirement since their functionality do not rely on these other two factors. The map shows all the fittest cells that have scored the slope remapped values between 1 and 3.

The search is seeking slopes from 27° to 54° [page 96 figure 106]. since these are the slopes attended by funiculars ´ascensores´. This result will have further development when a pedestrian network integrated by stairways and funiculars is developed along the urban patch. Funicular location requirement in only related with the slope in terms of aspect and elevation there is no constraints since their functionality do not rely on these other two factors. The map shows all the fittest cells that have scored the slope remapped values 0 and 1.

The search for Social Active Spaces focusses the constraints in aspect and slopes factors. For aspect factor it has been defined as optimal remapped values 3-4-5 which range from 292.5° to 67.5°, northwestern to northeastern orientations respectively. Since ´plazas´ main purpose is the promotion of social encounter they need a minimum surface extension which consider an average slope between 1° and 13° that allow a surface treatment for attaining flat surfaces. However, elevation have no incidence in the success of these urban spaces, additionally, it is expected to place ´plazas´ at different altitudes.

Differently from ´plazas´, ´miradores´ constraints are elevation and slope factors. Elevation from 126m to 252m sea level are suitable for the allocation of ´miradores´ since they are open areas devoted to sightseeing the surrounding natural and urban environment and platforms for connecting steep element of the network such as stairways and funiculars. Due to the transient character of the pedestrian using them aspect factor have no limitations. Similarly, to ´plazas´ they need a minimum surface extension which consider an average slope between 1° and 13°.

For amphitheatres, constraints are aspect and slope factors. Elevation is not considered a limitation inasmuch as this factor does not define the success of these spaces that can be located at any altitude that accomplishes aspect and slope factors. Aspect has been defined seeking slightly sunny and shaded areas which have a neutral sun light consequently remapped values of 0-1-2 that are cells oriented to the east, south and southeast cardinal directions. Steep slopes are a precondition for amphitheatre development considering slopes from 27° to 34°.

Design Development

Fig. 193-b Fittest cells for stairways land use. Fig. 193-c Fittest cells for funiculars land use. Fig. 193-d Fittest cells for ´plazas´ land use. Fig. 193-e Fittest cells for ´miradores´ land use. Fig. 193-f Fittest cells for amphitheatre land use.

147

5.10

Housing Logic 5.10.1 Housing

Type Definition

Created by Kris Prepiakova from the Noun Project

inhabitants

Fig. 194-a Family structure and number of family members. Fig. 194-b Housing room and size requirements per family size.

42 % Nuclear family with children

Created by Marie Van den Broeck from the Noun Project

Created by Gan Khoon Lay from the Noun Project

Legend

Lounge

K B

Kitchen & Bath

Bed-room

Circulation

24 % Mother

2,940 [1,680]

Single Parent family

4 % Father

Single Parent family

[280]

1,960

980 / [2 + 1] = 980 / [2 + 2] =

327

980 / [2 + 3] =

196

653 / [1 + 1] = 653 / [1 + 2] =

327 218

654 / [1 + 3] =

164

700 / 2

350

1,162 / 1

= 1,162

245

9.00 m2

9.00 m2 9.00 m2

K B

BR1

K B

BR1

BR2

K B

BR1

BR2

BR3

K B

BR1

BR2

BR3

9.00 m2

BR4

Created by Amandine Vandesteene from the Noun Project

Fig. 194-c Minimum inhabitable surface per family size - Healthy Housing a Practical Guide published by World Health Organisation [2005]

700

10 % Childless family Created by Gan Khoon Lay from the Noun Project

Created by Gan Khoon Lay from the Noun Project

20 % Non family relationship 1,162 83 % Individuals [16.6 %] 17 % Group of people [3.4 % ] 238

238 / 3

Fig. 194-a

Fig. 194-b

Created by Adrien Coquet from the Noun Project

Fig. 195 Neighbourhood units top view location. Legend Population density according to Census 2012, data source: ValparaĂso Municipality: Neighbourhood unit 66, population of 2,648 in 59 hectares 45 inhabitants/ha

Neighbourhood unit 15, population of 1,180 in 27 hectares 43 inhabitants/ha Neighbourhood unit 18, population of 2,218 in 20 hectares 110 inhabitants/ha For 106 hectares there is a population of 6,046, with an average density of 57 inhabitants/ha. Over a population of 7,000 inhabitants considering a annual growth rate of 0.8 % projected in 20 years.

148

Minimum inhabitable surface per family size Room Index of capacity * [in m2]

Room Kitchen-dining Dinning room Living room Bedroom parents Bedroom for 1 child Bedroom for 2 children Bedroom for 3 children Bedroom for 4 children Total area

[R]/[P]

2/3 2/4 3/4 3/5 3/6 4/6 4/7 4/8 5/8 6 5 13 14 8 46

7 7 5 5 13 13 14 14 12 8 - 8 - - 51 55

8 6 14 14 12 8 - 62

8 6 16 14 12 12 68

8 6 16 14 12 8 8 - 72

8 7 17 14 12 12 8 78

8 8 18 14 12 12 12 - 84

8 8 18 14 12 12 8 8 88

* The first figure refers to the number of bedrooms [R], the second to the total number of persons [P] normally accomodated

Fig. 195

Design Development

Fig. 194-c

[47ha] Created by Kris Prepiakova from the Noun Project

T1 & T2

= 36m2

1065 cells [21x21] 469,665m2 44.34% of 106ha

1.512

= 45m2

544

= 63m2

487

= 81m2

327 2,870 [106ha]

8% = 28 cells 14% = 49 cells 18% = 63 cells NE SE NW SW R

350 cells 137 cells 231 cells 162 cells 185 cells

140 cells

221 80 71 47 87 31 28 19

[1 - 2 p] [3p] [4p] [5p] [1 - 2 p] [3p] [4p] [5p]

Created by Kris Prepiakova from the Noun Project

419

dwellings

2% = 2 cells 20% = 27 cells 38% = 52 cells

81 cells

10% = 23 cells 14% = 32 cells 20% = 46 cells

101 cells

147 53 48 32

[1 - 2p] [3p] [4p] [5p]

280 dwellings

77 cells

101 36 32 22

[1 - 2p] [3p] [4p] [5p]

191 dwellings

155 cells

114 41 37 25

[1 - 2p] [3p] [4p] [5p]

217 dwellings

5% = 8 cells 15% = 24 cells 28% = 45 cells 34% = 63 cells 33% = 61 cells 16% = 30 cells

Housing cells availability 544

Fig. 196 Number of house type per ravine sector.

165 dwellings

TOTAL: 1,272

dwellings to allocate in the polygon Fig. 196

2012 census data gathered from Valparaíso Municipality informs the project in terms of the population density in existent sectors analysed. The research have taken the information of the sectors that coincide with the polygon of the project. Neighbourhood-unit 66 and 15 have a similar population density with an average of 45 and 43 inhabitants/ha respectively. Neighbourhood 18 has 110 inhabitants/ha. The average density for the three neighbourhood-units is 57 inhabitants/ha. Nowadays, the Chilean population growth rate is 0.8% so that from a present population of 6,046 inhabitants -summing the three sectors - it is projected in 20 years that it will reach a population of 7,091. The calculations for the development of the project have taken an average population of 7,000 inhabitants. The first step is dividing the population number [7,000] with the percentages received from family structure in ´The Site ‘chapter section 4.9 ´Valparaíso´s ravines social aspects and dynamics´ for obtaining how many people belong to each family group. Secondly, for two-parent and single parent families the total number of people is divided into three subsets, parents with only child, parents with two children, parents with three children. Childless families, individuals and groups of people living together do not have subset groups. The results are oriented to find the number of houses needed for each group and their spatial requirements. For instance, two-parent family groups need 326 houses for only-child families, 245 houses for two children families, and 196

houses for three children families. The same logic is applied to single-parent families. The definition of the basic spaces and sizes of dwellings are illustrated in figure 194-b that is an adaptation of the information given in Healthy Housing a Practical Guide [2005] published by the World Health Organisation. In the table the numbers that have a relation with Valparaíso demographic data is highlighted., For example the local maximum number of children per family is 2 or 3, exceptionally 4. For that reason, housing standards were defined for a maximum family member of 5. From the four family structure five house types are defined: type 1 [T1] is a house for individuals and for childless families in a one storey floor with 36 m2, type 2 [T2] is a house for childless families [two people] that has been aggregated with another house for the same group. In other words, for optimising land occupation in the same plot a two-storey floor volume will be developed considering two dwellings for the two families. Type 3 [T3] is dedicated to families with three members and groups of friends or acquaintances composed of three people. This house considers 45 m2 in a one storey floor. Type 4 [T4] developed for a four-member family structure, is 63 m2 in two storey floors. Finally, type 5 [T5] allocates a five-member family structure, with 81 m2 developed in three storey floors. The total number of houses are proportionally distributed in the five polygon sectors of the project. Figure 196 last part indicates in detail those numbes which will be in slope degrees from 7° to 27°.

Design Development

149

floors

21.0

36m2

Created by Gan Khoon Lay from the Noun Project

individual 01/35

36m2 x 2

Created by Gan Khoon Lay from the Noun Project

Created by Adrien Coquet from the Noun Project

individual 01/35

45m2

Created by Gan Khoon Lay from the Noun Project

Created by Adrien Coquet from the Noun Project

Created by Amandine Vandesteene from the Noun Project

individual 01/35

Created by Marie Van den Broeck from the Noun Project

21.0

Fig. 197-a Cell grid 21x21 divided into 4 plots for four dwelling occupation. At the centre an empty space defined as the nearest dwelling open space.

Fig.197-a

198-a 1 floor T1 - 36m2- fittest individual dwelling.

Ground floor [T1+T2+T3+T4] 3.0

3.0

north facade surface balcony surface area inhabitable area perimeter 1 floors family 3 people open area / garden T3

198-c T3 -45m2- fittest individual dwelling.

north facade surface balcony surface area inhabitable area perimeter open area / garden

individual 09/35

individual 04/35

north facade surface 33.83m2

north facade surface balcony surface area inhabitable area perimeter open area / garden

62.69m2 88.39m2 74.89m2 75.27m2 37.68m2

north facade surface balcony surface area inhabitable area perimeter open area / garden

40.14m2 59.76m2 49.09m2 51.52m2 22.91m2

individual 03/35

3 floors

21.0 Ground floor [T1+T2+T3+T5] 3.0

1 floors family 3 people balcony surface area 71.52m2

1 individual

198-b T2 -36m2x2- fittest individual dwelling.

30.85m2 60.57m2 36.04m2 44.46m2 35.95m2

21.0

197-b Dwelling types, T1, T2, T3, T4 and T5 1 floor distrubution. All the cells have the same density 1 however individual with different family type structures. For that reason it is illustrated two types of ground floor plan, one with T4 and the other with T5. one [ Considering 2 peopleonly each] big family per cell, either four member family or five member family.

21.0

3.0

families

Fittest individual dwellings

21.0

floors

5.10.2

T35floors

inhabitable area perimeter open area / garden

41.76m2 42.40m2 30.24m2

individual 12/35

72.41m2 87.65m2 73.22m2 78.11m2 35.02m2

north facade surface balcony surface area inhabitable area perimeter open area / garden

36.39m2 57.77m2 48.24m2 50.60m2 23.76m2

individual 17/35

individual 08/35

north facade surface 43.15m2 balcony surface area 95.76m2 inhabitable area 72.81m2 perimeter 78.55m2 open area / garden 33.71m2

north facade surface 35.69m2 balcony surface area 57.69m2 inhabitable area 49.73m2 perimeter 46.00m2 open area / garden 22.27m2

individual 22/35

individual 13/35

families [ 2 people each]

1 floor

3.0

First floor [T2+T4] 21.0 3.0

T13 floors family 3 people

21.0

1 individual

floors

T5 3 floors or T4

families [ 2 people each]

north facade surface balcony surface area inhabitable area perimeter open area / garden

32.71m2 62.02m2 42.40m2 43.20m2 29.60m2

individual 18/35

1 floor

3.0

First floor [T2+T5] 21.0 3.0

T13 floors family 3 people

T1 21.0

1 individual

floors

3 floors

families [ 2 people each] Fig.197-b

150

Design Development

north facade surface balcony surface area inhabitable area perimeter open area / garden Fig.198-a

31.20m2 48.75m2 37.05m2 36.96m2 34.94m2

north facade surface balcony surface area inhabitable area perimeter open area / garden Fig.198-b

54.69m2 89.39m2 75.56m2 79.92m2 37.05m2

north facade surface balcony surface area inhabitable area perimeter open area / garden Fig.198-c

42.47m2 60.02m2 49.73m2 46.92m2 22.27m2

T4 - 63m2 individual 05/35

T5 - 81m2 Created by Marie Van den Broeck from the Noun Project

north facade surface 47.51m2 balcony surface area 107.62m2 inhabitable area 63.20m2 perimeter 67.14m2 open area / garden 42.40m2 individual 06/35

individual 16/35

kceorB ned naV Created eiraMCreated ybby deMarie taeby rCMarie Van den VanBroeck den Broeck tcejorfrom P nuo the from N eNoun hthe t moNoun Project rf Project

north facade surface 75.50m2 balcony surface area 91.97m2 inhabitable area 95.08m2 perimeter 120.89m2 open area / garden 37.92m2 individual 19/35

T1 T2 T3 T4 T5 north facade surface 50.66m2 balcony surface area 115.87m2 inhabitable area 73.68m2 perimeter 81.49m2 open area / garden 36.43m2 individual 16/35

north facade surface balcony surface area inhabitable area perimeter open area / garden

individual 23/35

50.27m2 130.93m2 67.24m2 73.70m2 33.12m2

individual 17/35

north facade surface 68.91m2 balcony surface area 106.80m2 inhabitable area 63.27m2 perimeter 69.16m2 open area / garden 39.80m2 Fig.198-d

north facade surface 105.01m2 balcony surface area 80.37m2 inhabitable area 101.18m2 perimeter 117.02m2 open area / garden 35.75m2

north facade surface 93.26m2 balcony surface area 104.81m2 inhabitable area 94.02m2 perimeter 126.31m2 open area / garden 37.37m2 individual 29/35

north facade surface 70.59m2 balcony surface area 81.59m2 inhabitable area 91.60m2 perimeter 100.39m2 open area / garden 40.80m2

Created by Kris Prepiakova from the Noun Project

m2 number of inhabitants 36 1 [36X2] [2+2] 45 3 63 4 81 5

storey floors 1 2 1 2 3 Fig.199

Since the previous calculation results indicate the number of dwelling per type and slopes to be allocated, the present section seeks to find the fittest dwelling for each family group. The first aspect to set their design requirements is the square meters needed per group illustrated in figure 194-a. The goal is to allocate four houses per cell [21mx21m] leaving an empty area at the centre, that becomes the nearest dwelling open space. Four houses main entrance are connected to that space. Since there is five types and four plots per cells, dwelling type T4 -four-member family and T5 -five-member family are alternated cell by cell, this based on having a big family group in each cell. Valparaíso social structure and dynamics has brought to the study as an important social parameter which is the tight relation amongst family members and neighbours materialised in the housing proximity from one another. Thus, in each cell there is a heterogeneity in terms of family structure. Architectural parameters extracted from both existent housing type in page 88 and 89 and social characteristics extracted from Valparaíso´s ravines social aspects and dynamics page 106 were evaluated to define the variables which contribute to a better spatial quality: north facade surface, balcony surface area, inhabitable area, perimeter and ground open area. Experiments for house type, T1, T2, T3, T4 and T5 have been run individually with the goal of finding the fittest solutions for each. Based on the original set of eight cells per plot the number storey floors is defined by the total square meters to achieve for each house type. In the experiments ran in Octopus there was defined a population of thirty-five individuals and thirty-five generations for each house type that following simple rules such as moving and scaling the original cells, and making balconies’ extension to the north, allowed to reach a wide variety of volumes that attaint the goals originally set. Four dwellings were selected from each group after applying a quantitative and qualitative analysis of these results. The next experiments are related to combine these 20 solutions into single cells that have different environmental conditions that will affect next results.

198-d T4 -63m2- fittest individual dwelling. 198-e T5 -81m2- fittest individual dwelling. 199 Housing type summary table, square meters, number of inhabitants and storey floors. Octopus evaluation process: Elitism: 0.6 Mutation probability: 0.1 Mutation rate: 0.6 Crossover rate: 0.8 Population size: 35 Max Generations: 35 Record interval 1 Average evaluation time solution: 651ms Number of genes: 11 Number of objectives: 5

Fig.198-e

Design Development

151

5.10.3 Cluster´s

Definition

individuals north facade m2

Fig. 200 Octopus´ results Summary table. highlighled in red fittest individuals, and in blue secondary fittest.

mean values

1213

1216

1190

1183

1208

1222

1221

1251

1213

isovist perimeter 718 isovist area 2718

640

617

708

681

639

642

700

668

2703

2217

2192

2734

2730

2789

2661

2593 Fig.200

Since the fittest dwelling types were selected in previous experiments the present search refers to the definition of objectives to create clusters of four houses as the smallest aggregation unit. Goals for finding the fittest housing type combination are focused on both the individual need for good levels of sunlight and the collective need for a central open area shared by four dwellings, which is analysed through isovists area and perimeter. The houses exposure to sunlight is affected by contradictory forces, on the one hand ideal conditions for optimal solar radiation are related to the distance between dwellings and their orientation. On the other hand, due to dwelling proximity as a consequence of the densifying purpose in order to leave open space for social interaction, taller volumes depending on their position shade smaller volumes conditioning levels of solar radiation of the latter. Slope factor influences these goals case by case, for instance on 7° slope terrains smaller dwellings are prone to shading by taller houses. However, on a 20° slope smaller houses located down from north facing plots are not affected by taller houses located in the upper part of the same plot. While this example is given in an abstract situation of four houses analysis, the fact is that urban blocks are composed by a variety of housing aggregations, and for this urban configuration each dwelling has a range of one to eight possible neighbours to affect or be affected by. Thus, the experiment run in Octopus with the objectives mentioned returns a variety of clusters compositions. From those results the fittest six dwelling groups are selected to populate the territory.

152

Design Development

individual 05/30

individual 00/30

north facade SR 1208 m2 isovits perimeter 681 m isovist area 2734 m2

north facade SR 1213 m2 isovits perimeter 718 m isovist area 2718 m2 individual 06/30

individual 02/30

north facade SR 1220 m2 isovits perimeter 639 m isovist area 2730 m2

north facade SR 1216 m2 isovits perimeter 640 m isovist area 2703 m2 individual 08/30

individual 03/30

north facade SR 1221 m2 isovits perimeter 642 m isovist area 2789 m2

north facade SR 1190 m2 isovits perimeter 617 m isovist area 2217 m2 individual 10/30

individual 04/30

north facade SR 1183 m2 isovits perimeter 708 m isovist area 2192 m2

north facade SR 1251 m2 isovits perimeter 700 m isovist area 2661 m2

Design Development

Fig.201 ClusterÂ´s individuals, fittest highlighted in red, secondary fittest highlighted in blue.

153

5.11

Network Generation Logic

Fig. 202-a

Fig. 202 Patch Network generation diagram [47Ha] Fig. 202-a Socially Active Spaces location [green dots] alongside the polygon. Fig. 202-b Dot´s connection 42m distance. Fig. 202-c Dot´s connection 63m distance. Fig. 202-d Dot´s connection 84m distance. Fig. 202-e Dot´s connection 105m distance. Fig. 202-f Dot´s connection

154

Fig. 202-b

Fig. 202-c

As the main purpose of the present work is urban social interaction through the promotion of urban social encounters in Socially Active Spaces, the primary action to initiate the process of network generation is the location of Social Active Spaces [green dots] on the terrain. This initial recognition of the land properties and the urban program studied in Elevation, Aspect and Slope, program strategy [page 146 figure 192] returns suitable places [cells] for being developed as SAS. When possible location for ´ plazas‘ and for ´miradores´ coincide, the project prioritises ´miradores´ over ´plazas‘ since the placement of the former is limited by altitude and ´plazas´ can be developed at any level of the terrain. A next stage on network generation is making connections [lines] between these dots differentiating distances between them. The diagrams above illustrate those distances showing from left to right dot´s distances of 42m, 63m, 84m, 105m, and 126m. As distance increases between points the network becomes more complex and embraces a bigger portion of land. The emergent network after connecting 126 meters distant points evidence a denser area at the centre of the ravine where east and west connections are aimed to be intensified for creating more interactions between these two sides of the ravine. After this preliminary approximation a strategy of integration of both the existent street and stairways network and a new pedestrian network composed by pedestrian paths, stairways and funiculars is undertaken.

Design Development

Fig. 202-d

Fig. 202-e

Fig. 202-f

The main goal to achieve with this new composite network is to ease the accessibility to SAS through a heterogeneous urban network that allows different age-groups access to these urban spaces. 5.11.1

Route inclination differentiation defined by slope degree

As the topography encompasses a variety of slopes the new pedestrian network includes walking paths ranging from 0% to 17%, stairways ranging from 12% to 60% and funiculars ranging from 36% to 60%. Although, this network flexibility offers a variety of walkable paths, due to the steep slopes some areas are only reachable by stairways or steep paths above 10°. Taking this fact into account stairway landings are included as small areas to rest along the paths. A specific type of these areas are open spaces at the centre of dwelling plots. The results of slope factor input on ´Path Finding´ methodology outputs vary as follows: walking paths from point to point, and walking path combined with stairway or funiculars. As discussed on page 96 figure 107-a and 107-b distances and slopes are crucial factors to define alternative routes from place to place in this urban scenario. Indicating slope limitations into path finding experiments alternative routes are accurately informed delivering shorter paths that accomplish the slope degree indicated.

Route inclination differenciation defined by slope degree

21°

16°

13°

1°

17°

10°

37.1

7.0

16.8

32.9

19.5

12.9

107.7

24.0

8.3

Fig. 203-a

8.8

42.0

5.9

29°

5°

4.6

5.8

8°

16°

35°

21.6

23.4

18.8

8.4

84.0

7.9

15.6

10.0

Fig. 203-b

Fig. 203 Route inclinations differentiation by slope degree top view. Fig. 203-a Section 107 meters. Fig. 203-b Section 84 meters. Fig. 203-c Stairway development for section 84 meters and slope degrees varying from 5° to 35°.

Fig. 203-c

Fig. 203-d Section 42 meters slope degrees from 8° to 25°.

Legend 11°

24°

> 9% 9% to 17% 17% to 23% 23% to 36% 36%° to 75° 75% to 100% < 100%

10.6

5.11.1

8°

10°

25°

3.1

4.6

19.5

42.0

8.8

5.9

Fig. 203-d Fig. 203

Design Development

155

degrees for pedestrian, handic walking behaviour, pedestrians 36°, under those urban condi�o thus as it was men�oned befo less likely to make that eﬀort to

social events, and shorter distances for recrea�on than respondents with higher household income and those who lived in less-urban areas. These diﬀerences have implica�ons for developing strategies to increase physical ac�vity through walking. The number, dura�on, and distance of realised walking trips is itself a func�on of social and environmental features; thus, the pa�erns observed by sociodemographic factors may largely reﬂect the environmental constraints

5.11.2

Walking distance and slope degree W = 6e

-3.5

dh + 0.05 dx

dh = S = tan 0 dx where W = walking velocity [km/h] dh = eleva�on diﬀerence dx = distance S = slope θ = angle of slope (inclina�on).

Fig. 204-a Walking distance and slope degree and speed diagram illustrating slope angle difficulty degrees. 1:100 - 1.00 % -0° 1:50 - 2.00 % -1.15° 1:20 - 5.00% -2.86° 1:12 - 8.75 % -5° 1:8.14 - 12.28 % -7° 1:5.67 - 17.63 % -10° 1:4.33 - 23.09 % -13° 1:4 - 25.00 % -15° 1:2.75 - 36.40 % -20° 1:1.96 - 51.00 % -27°

fig. ﬁg.205-a XXX walking speed [Km/h] maximum speed at -2.86° slope [6 Km/h]

Fig. 205-a Tobler´s Hiking function. Tobler's hiking function is an exponential function determining the hiking speed, taking into account the slope angle.It was formulated by Waldo Tobler and published in 1993. This function was estimated from empirical data of Eduard Imhof [1950]. Fig.205-b Tobler´s Hiking graph Fig. 205-b1 Turtle created by Y,KR from the Noun Project Fig. 205-b2 Rabbit created by Tatiana Belkina from the Noun Project

156

54 °

235.1

323.6

b-2

5 4.7

c-2

1 0.8 0.55 0.4

100.0

397.0 400.0 2.2 Km/h

400 m / 10.90 min

3.3 Km/h 5 Km/h

400 m / 7.27 min

-60 -50 slope degree speed

b-1

1.2 1 0.8 0.55 0.4

c-1

1.2

ﬁg. XXX

1.3 Km/h

3.3 3.2 3 2.2 2

2.2 2

400 m / 18.46 min

a-1

181.6

90.0 136.8

48.7

7°

282.8

45° 27°

20°

13°

5 4.7

3.3 3.2 3

Fig. 204

-60

d-2 e-2 400 m / 400 m / 400 m / 400 m / 18.46 min 10.90 min 7.27 min 4.8 min -40 -30 -20 -10 0 10 20 -36 -27 -13 -7 -2.86 7 13

d-1

e-2 -50 -36

e-1

30 40 27 36

400 m / 4.8 min

As has been mentioned before, due to Valparaiso’s ravines topographical condition, with slopes ranging mostly from 7° to 36°, the relation between distances and slope degree is a crucial element for informing the definition of a flexible urban grid made from interconnected points following walking distance logic. The concept of walking distance denotes the distance that can be travelled by walking in a fixed amount of time. A distance of 0.25 miles [402.33 meters] is often used as an acceptable walking distance in U.S. research studies. [Yang & Diez-Roux, 2012]. A survey undertaken in 2011 studying population subgroups found large variations among various purposes in both distance and duration. On the one hand, walking varied by purpose with the lowest probability of walking observed for work and the highest observed for recreation and petrelated activities. However, on the other hand, people with lower household income walked longer distances for work but shorter distances for recreation. Also, factors such as the level of urbanization impact walking activity. Respondents with lower household income and those who lived in more urban areas walked longer distances for work, shopping and social events, and shorter distances for recreation than respondents with higher household income and those who lived in less urban areas. These differences have implications for developing strategies to increase physical activity through walking. The number, duration, and distance of realised walking trips is itself a function of social and environmental features; thus, the patterns observed by sociodemographic factors may largely reflect the environmental constraints of areas where

Design Development

36°

Fig. 204 b, c, d, e b: Walk created by Ani from the Noun Project c: Daytime cycling created by Kathleen Black from the Noun Project d: Wheelchair created by Jens Tärning from the Noun Project e: Child Running created by Gan Khoon Lay from the Noun Project.

fig. 205-b

walking speed [Km/h] maximum speed at [6 Km/h]

diverse groups live. The commonplace unit of measurement in the planning profession was often represented by a radius measuring 400 meters [Olson, 2010]. The velocity on flat terrain is 5 km/h, and the maximum speed of 6 km/h is achieved roughly at a gradient of -2.86°. Regarding the urban patch analysed, the whole public system such as source work, institutional, educational and health infrastructure, commerce and cultural activities is located outside the neighbourhood, hence the population depends on public transportation called ´colectivos´ that are cars [four people capacity] that make a circuit around each neighbourhood located over 120m sea level. As long as the centre of the present research is a proposal of a new urban model centred on a system of Social Active Spaces, walkability along the system is a subject that will be deeply studied. In that regard slope, distances and walking speed is the technical basis to address the feasibility of the research proposal. Tobler´s Hiking function is an exponential function determining the hiking speed, taking into account the slope angle. Following ´the acceptable walking distance of 400 meters´ from U.S. research studies as a point of reference, a diagram was done to illustrate the constrains in terms of distances and slope degrees for pedestrians, the handicapped, and bicycle riders. Although in terms of local walking behaviour pedestrians are used to walk along roads with slopes up to 36°, under those urban conditions trips are done strictly for functional purposes. Thus, as was mentioned before the population from informal settlements is less likely to make the effort to visit recreation areas.

-27

5.11.3

Stairways and Funiculars

Fig.206 Staiway and funicular basic elements. Fig. 207 Funicular´s locations. Legend Funicular SAS Integrated network existent and proposed ´Alemania´ Avenue

2 fig. 206

From previous experiments to find path inclination differentiation, connections between 7° and 33° are the slopes developed as stairways. Applying path finding with different slope degrees returns alternative routes for connecting two points. These routes consist of different slopes that indicate the type connection to be developed. Furthermore, one route can be composed by walkable paths and stairway sections. Following this methodology the location for stairways has emerged as an element for connecting slopes from 7° to 27° that also in the same stair degrees of inclination can change due to slope variations along the route as shown in figure 203-d. Funiculars operate to reach steep slopes and distances between two points faster and more easily. The strategy applied for their location follows creating strategic network connections. Funicular 2 and 3 connect the bottom of the ravine with the upper parts of amphitheatres which are Social Active Spaces visited by large numbers of people. Funicular 3’s upper station is a ´mirador´ building that can become an iconic building to reach that social urban place. Funicular 1 located at the entrance of the ravine connects ´Alemania´ Avenue with the upper part of the northeaster side of the ravine developed as housing land use. Funiculars 4, 5 and 6 connects SAS located at the bottom and upper part of the ravine surrounded by dwellings. Funicular Technical description: Funicular 1, north-south orientation, starts at 100m above sea level, has 103m horizontal trajectory and 50m height with 25° of inclination. Funicular 2, east-west orientation, starts at 120m above sea level, has 80m horizontal trajectory and 41m height with 27° of inclination. Funicular 3, northeastern-southwestern orientation, starts at 150m above sea level, has 86m horizontal trajectory and 54m height with 32° of inclination. Funicular 4 north-south orientation, starts at 150m above sea level, has 73m horizontal trajectory and 28m height, with and inclination of 21°. Funicular 5, northwestern-southeastern orientation, starts at 170m above sea level, has 77m horizontal trajectory and 30m height, with an inclination of 21°. Funicular 6, northeastern-southwestern orientation, starts at 150m above sea level, has 72m horizontal trajectory and 42m height, with 30° of inclination.

6 5

fig. 207

Design Development

157

5.12

Socially Active Spaces Logic 5.12.1'Plazas'

emergence process

10.5

21.0

7.0

3.5

N Fig.208-a ´Plazas´ fittest location.

Fig. 208-b ´Plazas´ geometry and rotation following slope aspect. Legend Platforms levels

10.5

21.0

7.0

3.5

N Intersection areas ´Plazas´ Green areas ´Plazas´ Blue areas

´Plazas´ small shops

W W N

Fig. 208-b

N ´Plazas´ location is driven by slope and aspect factors. However, since the intention is to develop a Socially Active Space system the methodology has intentionally set aside the elevation factor. Thus, from the analysis of fittest W as shown on page 147 figure 193-d, 126 cells have cells for ´plazas´ location, achieved conditions to develop these open spaces. However, some of those cells’ location is also suitable N for developing ´miradores´ hence when cells are located in elevations above 150m they are developed as ´miradores´ that rely on E height to perform their main activity which is having a view of the surrounding area. Then plazas are spread alongside the territory from elevations of 20m to 250m. Particularly the bottom of the ravine has a wide number of cells accomplishing slope and aspect requirements that reinforce the concept of transforming the axis of Jaime´s ravine into an urban place devoted to promote social encounters. This sector will be the main open green area of the project. The geometry of the primitive of plazas is a composition of square platforms forming a final square 21x21m [441m2]. This body plan is composed by nine independent platforms joined by four intersection elements. The biggest square [10.5x10.5] is in charge of finding the slope aspect for rotating the whole

Design Development

N E

Fig. 208-a

158

cell to that direction. Four aspect orientations were defined; therefore, cells rotate following the nearest cardinal orientation, north, east, south and west. For instance, for a slope aspect of 30° the cell will rotate to the north [0°], for a slope aspect of 50° to the east [90°]. There are two concepts to develop this composition. First creating areas of different size predicated on different agegroups and consequently different activities. Second slope factor determines the level where each platform will reach the terrain, giving more flexibility to find the average slope of the particular location for each platform illustrated in figure 208-d. Each primitive [21x21] counts with intersection elements such as green and blue areas and a small shop or kiosk. As can be seen in figure 208-a different group of cells emerge from the experiment to find the fittest cells for ´plazas´ location. Here three types of groups are illustrated, two-cells group [882m2], four-cells group [1764m2] and six-cells group [2646m2]. Shape variations are the result of cell group types and intersections with the street and stairway network. These factors make of each ´plaza´ a result of local conditions from a basic initial geometric primitive.

E N

S N E

N W

Fig. 208-c

Fig. 208-d

Fig. 208-d 2-cells ´Plaza´ perspective view. Fig. 208-e ´Plazas´aggregation process - 4 cells, top view. 1764 m2 plaza capacity: 588 inhabitants [3.00 m2 / inh.]

Fig. 208-f ´Plazas´aggregation process - 4 cells, perspective view.

Fig. 208-e

Fig. 208-c ´Plazas´aggregation process - 2 cells. 882 m2 plaza capacity: 294 inhabitants [3.00 m2 / inh.]

Fig. 208-f

Fig. 208-g ´Plazas´ aggregarion process - 6 cells, top view. 2646 m2 plaza capacity: 882 inhabitants [3.00 m2 / inh.] Fig. 208-h ´Plazas´ aggregarion process - 6 cells, perspective view.

Fig. 208-g

Fig. 208-h

Design Development

159

7.00

17.50

4.00

.00 17.50

21.00

7.00

13.00

17.50

21.00

3.50 7.00 12.00 7.00 7.00 3.00 3.00 3.00 3.00

8.50 4.00 8.50 7.00

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7.00

7.00 4.00 13.00 7.00 8.50

4.00

7.00

14.00 10.50

22.75

35.00

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7.00

Fig. 211

17.50

8.75

7.00

8.75

3.50

6.73 7.64

3.50

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3.50

0.8 1.63 2.45 3.28 4.12 4.98 5.85

8.50

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6.73 7.64

0.8 1.63 2.45 3.28 4.12 4.98 5.85

7.00 17.50

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6.73 7.64

8.75

0.8 1.63 2.45 3.28 4.12 4.98 5.85

4.00

35.00

3.50

7.00

7.00 4.00 13.00 7.00 8.50

4.00

3.50

Design Development

8.50 35.00

17.5

3.50

Fig. 210-c

35.0022.75 Valparaíso ‘miradores’ are in essence a type of urban balcony, that have two main and 14.00 22.75 7.00 7.00 3.50 10.50 14.00 associated functions. First a place to see the landscape when having social7.00 interchanges, 8.50 4.00 10.50 8.50 3.50 4.00 13.00 4.00 and second a place to rest when climbing stairways. The present project research has 8.75 found that this local urban element can develop as urban hubs incorporating social infrastructure as a localizing point for social interaction. Their location is the result of experiments run in section 5.8 ´Elevation, Aspect and Slope, urban program strategy´. Factors such as housing density and aspect orientation drive the development of these places. The geometry of the primitive is contained in 21x21m cells where a 2° 21.00 body plan is defined that17.40 develops according to factors mentioned before. The body 4.00 4.00 12.00 4° 21.00 4.00 4.00 plan has firstly17.40 been structured defining mass and void sectors. The former to develop 7° 9° infrastructure buildings and the latter for developing ´miradores´ - as places to see 11° 7.00 7.00 - that are in between the buildings. In specific cases these buildings 7.00 are developed 13° 16° as funicular´s departure and arrival stations. Solar illumination of this inner space 18° between buildings is controlled by Aspect orientation, which determines the distance 20° between buildings. A wider opening indicates an eastern or southern orientation, conversely, a narrow opening indicates a northern and western orientation.

160

7.00

7.00 7.00 8.50 4.00 7.00 4.00 13.00

17.50 1.60

individual´s range

12.00

7.64

3.00

7.00 8.50 4.00

Fig. 210-b

4.00

3.50

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21.00 21.00 17.50 17.40 3.50

1.60

7.64

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4.00 21.00 17.50 7.00 8.50 4.00

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Fig. 209-c

14.00

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6.73 7.64

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3.00 12.00 3.00 3.00 3.00 7.64

4.00

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17.50

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P L A N

top view

B O D Y P L A N

7.00

Fig. 209-b 8.50

3.50

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7.00 7.00

21.00 4.00 8.50 210-a Fig.

4.00

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21.00 17.50 3.50

21.00 17.50

3.50

8.50 primitive 21.00 geometry

17.50

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7.00

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14.00

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2° 4° 7° 9° 11° 13° 16° 18° 20°

3.50 3.50

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front view

7.00

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closed

21.00 7.00

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7.003.50

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B O D Y

3.00

14.00 17.50 21.00 3.50

21.00 17.50

21.00 7.00 7.00 7.00

8.50

7.00

Fig. 209-a

3.50

8.50 8.50

17.50 7.00 21.00 3.50

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circulation

building slope adjustement

3.50

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´mirador´ inner space

8.75

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35.00

8.75

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S & E oriented 7.00

´mirador´ width

4.00

22.75

3.50

7.00 8.50 4.00

7.00 4.00 13.00

3.50

Fig. 211 ´Mirador´ primitive geometry section.

7.00 8.50

4.00

8.75

Fig. 210-c ´Mirador´ front view, widen façade for southern and eastern orientations.

14.00 10.50

17.50

building length

Fig. 210-b ´Mirador´ front view, tighten façade for northern and western orientations.

17.50 1.60

´mirador´ outdoor

circulation 35.00

6.73 7.64

8.75

Fig. 210-a ´Mirador´ primitive geometry Body Plan front view.

0.8 1.63 2.45 3.28 4.12 4.98 5.85

3.50

Fig. 209-c ´Mirador´ widen façade 6.73 7.64 for southern and eastern orientations.

22.75

2.45 3.28 4.12 4.98 5.85

14.00 10.50

Fig. 209-b ´Mirador´ tighten façade for northern and 0.8 western orientations.1.63

17.50 1.60

´mirador´ width domain

building width open

closed

1.60 7.64

14.00

3.50

14.00

building width closed open closed N & W oriented

Fig. 209-a ´Mirador´ primitive geometry Body Plan top view.

7.00

4.00

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building width closed open closed

7.00

17.50 1.60

7.00

'Miradores' emergence process facade proportion

7.00

4.00

5.12.2

7.00

3.50

7.00

´Mirador´ Goals

Fitness Criteria

Tighten open facade

0°

NW 315°

NE 45°

1. Courtyard Sunlight control

Opennes facade degree Widen open facade 17.50

multi-optimisation generative process

90°

270°

Taller buildings 225° SW

135° SE

14.00

Building heights

180°

3.50

Shorter buildings 4.00

13.00

4.00

17.50

storey floor increment / loss

21.00

building m2 increment / loss

3.50 7.00

´miradores´ court yard

Fig. 212-a Mirador Fitness criteria and goals: Courtyard sunlight control.

7.00

´miradores´[m2]

Fig. 212-b Mirador Fitness criteria and goals: Social Active Space area [m2] compensation.

variable

open area [m2] outside ´miradores´

7.00

variable

1.60

14.00

2. Area [m2] compensation

3.50

8.50

4.00

8.50

21.00 17.50 3.50

7.00

Design Development

161

5.12.2.1

Fig. 213 Fittest ´miradores´ individuals, M1 type.

M1 Fittest ´Miradores´

kWh/m2 4.75 4.25 3.75 3.25 2.75 2.25 1.75 1.25 0.75

M1 ´Miradores´ Solar Radiation Analysis Summer Solstice

Winter Solstice

4.12

1.04

1.60

0.51

4.16

1.06

1.55

0.46

4.24

1.04

1.97

0.61

4.17

1.09

2.18

0.66

4.34

1.08

2.51

0.72

4.2

1.04

2.14

0.67

individual 01

Fig. 214 M1 type ´mirador´ Solar Radiation Analysis: a. Summer Solstice 21 December. b. Winter Solstice 21 June Legend

5.12.2.2

ilumination control 1: 17 m2 ilumination control 2: 6 m social provision building: 2441 m3 miradores area: 98 m2 individual 02

ilumination control 1: 18 m2 ilumination control 2: 3m social provision building: 2100 m3 miradores area: 49 m2

individual 03

ilumination control 1: 16 m2 ilumination control 2: 3 m social provision building: 1968 m3 miradores area: 117 m2

individual 04

ilumination control 1: 15 m2 ilumination control 2: 3 m social provision building: 1903 m3 miradores area: 196 m2

individual 05

ilumination control 1: 15 m2 ilumination control 2: 0 m social provision building: 1522 m3 miradores area: 196 m2

individual 06

Fig. 213

162

Design Development

ilumination control 1: 14 m2 ilumination control 2: 6 m social provision building: 2205 m3 miradores area: 245 m2

Fig. 214

5.12.2.3

M4 Fittest ´Miradores´

5.12.2.4

M4 ´Miradores´ Solar Radiation Analysis Summer Solstice

Winter Solstice

individual 01

ilumination control 1: 33 m2 ilumination control 2: 21 m building volume: 6.720 m3 ´miradores´ area: 1959 m2

4.05

4.07

4.13

4.02

1.04

1.03

1.02

Fig. 215 Fittest ´miradores´ individuals, M4 type.

3.36

3.44

3.58

3.37

0.69

0.75

0.74

0.71

Fig. 216 M4 type ´mirador´ Solar Radiation Analysis: a. Summer Solstice 21 December. b. Winter Solstice 21 June

individual 02

Legend

ilumination control 1: 31m2 ilumination control 2: 21 m building volume: 6575 m3 ´miradores´ area: 2118 m2

4.11

4.14

4.20

4.02

1.03

3.42

3.48

3.57

3.38

0.72

0.74

0.70

4.18

4.06

1.06

1.05

1.04

1.02

3.50

3.57

3.53

3.28

0.73

0.75

0.73

0.69

4.75

4.25

4.18

4.12

4.18

4.07

1.05

1.03

3.58

3.48

3.58

3.54

0.74

0.75

0.72

0.73

4.30

4.28

4.08

4.30

1.06

1.04

1.05

3.70

3.84

3.49

3.67

0.75

0.59

0.64

kWh/m2 4.75 4.25 3.75 3.25 2.75 2.25 1.75 1.25 0.75

individual 03

ilumination control 1: 33 m2 ilumination control 2: 18 m building volume: 6418 m3 ´miradores´ area: 1939 m2

individual 04

ilumination control 1: 32 m2 ilumination control 2: 18 m building volume: 6378 m3 ´miradores´ area: 2028 m2

individual 05

ilumination control 1: 30 m2 ilumination control 2: 18 m building volume: 6195 m3 ´miradores area´: 1905 m2

individual 06

Fig. 215

ilumination control 1: 72 m2 ilumination control 2: 3 building volume: 5145 m3 ´miradores´ area: 1527 m2

Design Development Fig. 216

163

04 06 03 5.12.2.5

Fig. 217 Fittest ´miradores´ individuals, M5 type.

M5 Fittest ´Miradores´

05 03 02

individual 01

Fig. 218 M5 type ´mirador´ Elevation that illustrates the width of ´miradores´ platforms and heights variations.

ilumination control 1: 86 m2 ilumination control 2: 24 m building volume: 11445 m3 miradores area: 441m2

04 06 02 01 05 03 01 06 04 02 05 03 01 04 06 02

individual 02

ilumination control 1: 85 m2 ilumination control 2: 21 m building volume: 10946 m3 miradores area: 487 m2

individual 03

ilumination control 1: 89 m2 ilumination control 2: 18 m building volume: 10841 m3 miradores area: 294 m2

individual 04

ilumination control 1: 84 m2 ilumination control 2: 9 m building volume: 9266 m3 miradores area: 530 m2

individual 05

ilumination control 1: 89 m2 ilumination control 2: 9 m building volume: 9607 m3 miradores area: 294 m2

individual 06

Fig.217

164

Design Development

ilumination control 1: 84 m2 ilumination control 2: 3 m building volume: 8452 m3 miradores area: 539 m2

Fig.218

05 03 01

5.12.2.6

M5 ´Miradores´ Solar Radiation Analysis Summer Solstice

Winter Solstice

4.08

4.16

4.08

4.14

0.92

0.98

0.97

0.95

0.96

2.33

2.96

2.51

2.23

2.56

0.37

0.52

0.43

0.37

0.43

Fig. 219 M5 type ´mirador´ Solar Radiation Analysis: a. Summer Solstice 21 December. b. Winter Solstice 21 June Legend

4.12

4.15

4.10

4.12

4.17

0.94

0.97

0.98

0.99

1.00

2.26

2.57

2.36

3.13

2.47

0.38

0.43

0.39

0.54

0.41

4.25

4.15

4.18

4.09

1.00

0.97

0.98

2.59

2.57

2.30

2.33

2.46

0.41

0.43

0.38

0.37

0.39

4.27

4.25

4.37

4.27

4.16

0.99

1.00

1.04

1.01

1.00

2.55

2.67

2.80

3.25

2.58

0.42

0.43

0.47

0.58

0.41

4.29

4.35

4.25

4.16

4.19

1.00

1.03

0.98

0.95

0.96

2.42

2.85

2.53

2.05

2.26

0.42

0.46

0.41

0.36

0.38

4.24

4.26

4.30

4.22

1.01

1.02

1.01

0.97

2.53

2.80

2.09

3.24

2.52

0.42

0.47

0.60

0.42

kWh/m2 4.75 4.25 3.75 3.25 2.75 2.25 1.75 1.25 0.75

Fig.219

Design Development

165

5.12.3

Â´AmphitheatreÂ´ emergence process

Fig. 220 Suitable cells for developing amphitheatres in the whole polygon. Legend: 262 Cells accomplishing first conditions for developing amphitheatres, in terms of slope and aspect factors. 32 cells accomplishing concave contours condition for visual angle requirement. Black dots illustrate convex curves. Red dots illustrate convave curves.

Fig. 221 Selection of grouped cells accomplishing conditions required for developing amphitheatres. Fig. 222 Detail of six grouped cells accomplishing slope, aspect and contour concavity for developing amphitheatres.

Fig. 221

Fig. 220

166

Design Development

Fig.222

Fig. 223-a Concave curve top view.

Fig. 223-b Concave curve section.

v e

top view

Fig. 223-a

Fig. 224-a

Fig. 224-a 3/4 arena amphitheatre. Advantages: improves the hearing and visual contact between spectator and performer. Disadvantages: film presentations is almost out of the question source: http://www. theatresolutions.net/ auditorium-seating-layout/ Fig. 224-b Wide fan amphitheatre. Advantages: brings distant spectator closer to the performer Disadvantages: limits space usage to primarily to speech related activities.

c o n c

e section Fig. 223-b

Although there is only one example of a proper amphitheatre [TAC Cordillera] analysed on page 104, ´spontaneous amphitheatre´ types [page 105], that occur in urban stairways, manifest the populations willingness to use such open areas allowing social gathering for participating in cultural expression. The particular topography of the polygon analysed comprises 262 cells out of 1072 with slopes over than 27° [remapped values 0 and 1] and lower levels of sun exposure reflected in aspect factor remapped values 0, 1 and 2 that are the defined conditions for developing amphitheatres as shown in figure 193-f on page 147. As an open-air venue used for entertainment such as musical and theatrical performances the evaluation of contour curvature of these cells constitutes a fundamental step of analysis for filtering these cells into the ones where it can be developed as amphitheatres. Two evaluative analysis have to be ran, the curvature in XY plane, and the curvature in XZ or YZ plane. The tiered seats of amphitheatres require them to be developed in a concave curve that takes into consideration acceptable angles of vision to the stage. Similarly, concave curvature is needed in the XZ

Fig. 224-b

or YZ plane. Figure 211 illustrates the process of the recognition of the cells that accomplish the land requirements for the development of these public structures. Also shown in figure211 it can be seen that groups of cells with the same conditions are located at the top left side of the ravine that correspond to NW sector, in SE and SW sectors these conditions are present in individual cells. Figure 212 illustrates a zoom-in into that area which is next to ´Alemania´ Avenue. Considering that one of the thesis ambitions is to develop a connection between the formal and the informal areas of the city, proximity to the urban and spatial node that connects ´Jaime´s ravine and the formal city suggests that this place can promote social integration because of its strategic location. Figure 213 is an enlargement of the top view of the selected area where is differentiated convex and concave contour curvature. Two amphitheatres are developed alongside the urban patch, developing different density scales, and also their location attains different urban impacts. Amphitheatres located at the northwestern sector and next to ´Alemania´ Avenue appeal to social encounters between local and foreign people. However, the amphitheatre located at northeastern sector impacts its surrounding urban area.

Design Development

167

06 DESIGN PROPOSAL Fig. 225 Proposal view.

168

6.1 Design

proposal 6.1.1 Design proposal, general view 1. [from western to south eastern side] 6.1.2 Design proposal, general view 2. [from sourthern to northern side] 6.1.3 Design proposal, view 3, Socially Active Spaces, Amphitheatre and ´Miradores´. 6.1.4 Design proposal, view 4, Socially Active Spaces, ´Plazas´ and Funiculars. 6.1.5 Design proposal, view 5, Housing, Stairways and Socially Active Spaces, ´Miradores´. 6.1.6 Design proposal, view 6, Housing and Socially Active Spaces, ´Miradores´. 6.2 Design evaluation 6.2.1 Urban Network Analysis [UNA], test patch. 6.2.2 Urban Network Analysis [UNA], cluster 1. 6.2.3 Urban Network Analysis [UNA], cluster 2. 6.2.4 Urban Network Analysis [UNA], cluster 3. 6.3.1 Isovist Analysis, cluster 1. 6.3.2 Isovist Analysis, cluster 2. 6.3.3 Isovist Analysis, cluster 3.

169

6.1.1

Design proposal, general view 1.

Fig. 226 Proposal general view.

170

Design Proposal

[from western to south eastern side]

Design Proposal

171

6.1.2

Design proposal, general view 2.

[from sourthern to northern side]

Amphitheatre

Â´MiradoresÂ´

Fig. 227 Proposal general view.

172

Design Proposal

Â´PlazasÂ´ Amphitheatre Funiculars Housing

Design Proposal

173

6.1.3

174

Design proposal, view 3, Socially Active Spaces, Amphitheatre and Â´MiradoresÂ´.

Design Proposal

175

6.1.4

176

Design proposal, view 4, Socially Active Spaces, Â´PlazasÂ´ and Funiculars.

Design Proposal

177

6.1.5

178

Design proposal, view 5, Housing, Stairways and Socially Active Spaces, Â´MiradoresÂ´.

Design Proposal

179

6.1.6

180

Design proposal, view 6, Housing and Socially Active Spaces, Â´MiradoresÂ´.

Design Proposal

181

6.2

Design evaluation 6.2.1

Urban Networks Analysis [UNA] test patch

6.2.2

Urban Network Analysis [UNA], cluster 1.

Fig. 232 UNA analysis, betweenness test patch.

361 318

Fig. 233 Cluster 1, UNA analysis, betweenness.

93 168

betweenness high 347

233

152

low 1

254

309

343 788

The hybrid network achieved by adding to the existent network a new network is analysed through Urban Network Analysis an later an analysis of the slope of every segment of it.

692

108

709

598

887

281

909

952

423

248

305

452 4.6 ha

Fig. 232

182

Design Evaluation

123

448

79 30

616 154

403 509

791

818

669 758

523 177

261

193

70 543

707

391 Fig. 233

6.2.3

Urban Network Analysis [UNA], cluster 2. 504

810

664

411

599

586

670

262 550

193

110

1006

703

Fig. 234 Cluster 2, UNA analysis, betweenness.

562

812

Urban Network Analysis [UNA], cluster 3.

+200

291

585 435

6.2.4

191 44

816

181

+200

602

574

709

111

271 346

222

150

249

120

196

258

288

253

290

Fig. 235 Cluster 3, UNA analysis, betweenness. Fig. 235-a Cluster 3, RoadÂ´s sections Length and Height from origin point to destination point.

656

+150

238

371 512

Fig. 234-a Cluster 2, RoadÂ´s sections Length and Height from origin point to destination point.

260

57 300

172

249

39 +225

263 3.57 ha

Fig. 234

L 58.17m H 2.17m L 90.79m H 7.50m 170

L 22.50m H 0.21m

2.8 ha

Fig. 235

L 43.51m H 2.76m

175

L 11.96m L 38.24m H 4.56m H 5.88m L 21.18m H 2.12m

L 50.73m H 16.02m

L 247.42m H 5.31m

150

L 72.54m H 3.48m L 23.05m H 1.78m

L 218.58m H 1.53m

L 18.57m H 7.47m

190

155

L 72m H 0.40 m

160

L 35 m H 1.7 m

175

L 23.21m H 9.31m

L 57.67m H 4.32m

L 39m H 1.68m 200

L 49m H 0.12 m

165 205

L 34.63m H 7.26m

L 65.27m H 11.95m

L 79.47m H 6.42m

L 22.78m H 7.97m L 22.35m H 7.22m

L 25 m H 2.35m

195

175

L 12.41 m 50m L 46.54 m H 3.32 m L H 13m H 12 m

L 32.80m H 2.76 m L 78.13m H 0.39 m

L 21.49m H 4.59 m

L 51.18m H 16.6m

L 24m H 6.42m

L 44m H 9.28 m

L 44.76m H 12.49m

185

180

L 26m H 3.94m L 28m H 7.54m

190

210

L 151.83m H 16.85m

L 52.42m H 4.51m

180

175

L 65.48m H 9.57m

L 52.95m H 4.03m

L 40.41m H 20.22m 185

L 45.87m H 2.08m

160

170

L 61 m H 0.44 m

165

195

L 22.17m L 34.63m H 6.01m H 8.72m

L 23.32m H 7.54m L 48.15m H 11.18m

200 205

L 45.44m H14.61m

210

L 43.05m H 8.75m

215 220

Fig. 234-a

Fig. 235-a

Design Evaluation

183

0 6.3.1

Isovist Analysis, cluster 1.

2 1

A spatial aim of the project is to achieve visual field connections between open spaces as illustrated in the following diagrams. Three areas of the urban patch are analysed, cluster 1, cluster 2 and cluster 3. Spatial differences between these clusters are reflected on the type of isovists that have emerged from them. Local clusters at the centre of four-housing plots visually connect with neighbourÂ´s courtyards, which create a spatial experience of being seduce by the next open space. This is predominantly illustrated in cluster 2 and 3. Cluster 1, shows a variety of Â´PlazasÂ´ isovists that overlap one another. Interestingly, in a wide range or cases isovist are stopped by the hills of the land that0 intensify visual fields to opposite directions. Since the built environment was defined as detached houses visual fields develop a wide range of singular cases. Although the results bring visual field variation in shape, size and number, a further development could refine the management of the built environment opacity to achieve more intentioned points of view of the urban landscape.

3 1 2

0 Fig. 236-a

3 2

IC1-0

IC1-1

IC1-2

IC1-3

IC1-4

IC1-5

IC1-6

Fig. 236-b

3 Fig. 236-a Cluster 1, Isovist spatial analysis, general view. Fig. 236-b Cluster 1, Isovist spatial analysis, individuals. Fig. 236-c Cluster 1, Isovist data.

3 Isovist data cluster 1 open space number: open space m2: isovists m2: perimeter: shortest line: longest line:

IC1-0 1214 m2 2220 m2 292 m 12 m 86 m

IC1-1 284 m2 3337 m2 597 m 7 m 100 m

IC1-2 398 m2 2892 m2 315 m 6 m 100 m

IC1-3 433 m2 23781 m2 391 m 7 m 100 m

IC1-4 479 m2 3124 m2 328 m 9 m 100 m

IC1-5 1214 m2 2220 m2 292 m 12 m 86 m

IC1-6 251 m2 3170 m2 436 m 43 m 100 m

IC1-7 335 m2 6 7469 m2 895 m 3m 100 m

IC1-8 345 m2 9254 m2 779 m 2m 100m Fig. 236-c

184

Design Evaluation

7 8

6.3.2

Isovist Analysis, cluster 2.

6.3.3

Isovist Analysis, cluster 3. 1 0

1 1 0

0 Fig. 237-a

Fig. 238-a

IC1-7

IC1-8

IC2-0

IC2-1

IC2-2

IC2-3

IC3-0

IC3-1

Fig. 237-b

IC3-2

3 Fig. 237-a Cluster 2, Isovist spatial analysis, general view.

IC3-3

Fig. 238-b

Isovist data cluster 2 open space number: open space m2: isovists m2: perimeter: shortest line: longest line:

IC2-1 428 m2 645 m2 150 m 3m 31 m

IC2-2 2403 m2 4790 m2 397 m 5m 100 m

IC2-3 690 m2 6036 m2 443 m 11 m 100 m Fig. 237-c

open space number: open space m2: isovists m2: perimeter: shortest line: longest line:

Fig. 237-c Cluster 2, Isovist data. Fig. 238-a Cluster 3, Isovist spatial analysis. general view.

Isovist data cluster 3 IC2-0 357 m2 3595 m2 389 m 2m 100 m

Fig. 237-b Cluster 2, Isovist spatial analysis, individuals.

IC3-0 441 m2 1699 m2 258 m 12 m 46 m

IC3-1 441 m2 558 m2 120 m 6m 21 m

IC3-2 3 441 m2 723 m2 324 m 6m 100 m

Fig. 238-b Cluster 3, Isovist spatial analysis, individuals.

IC3-3 441 m2 551 m2 210 m 6m 31 m Fig. 238-c

Fig. 238-c Cluster 3, Isovist data.

8 Design Evaluation

185

07 CONCLUSIONS 7.1 7.2

7.3

186

Conclutions Appendix 7.2.1 Excel Spreadsheet, Social Network Analysis Matrices. 7.2.2 Housing type Octopus experiments. 7.2.3 ClusterÂ´s type Octopus experiments. References 7.3.1 Reference List. 7.3.2 Books. 7.3.3 Videos. 7.3.4 Publications. 7.3.5 List of Figures.

Conclusions

187

7.1

Conclusions

Scope and aims of the project

The scope of this work is aimed at investigating the possibilities and constraints that pose both individual and collective contradictory forces at informal land occupation developing an urban model that promotes land appropriation in such a way that social interaction and environmental shelter are enhanced across public and semi-public areas. The thesis has developed an argument that underlines the importance of Socially Active Space in defining urban configurations, since these open spaces can guarantee the flow of social activity along the urban system that is in essence the leitmotif of open urban areas. Although the essential need for shelter pressures individuals for land appropriation, the collective need for spaces for social interaction has to be taken into account from the very beginning of the colonisation process. Besides, social diversity access to those places is a key factor in making these areas urban catalysts of social integration. Throughout the research it has been concluded that the challenging scenario for developing a new urban model for informal urban settlements is to respond to individual and collective needs, within the constraints of land configurations, which requires a comprehensive architecture and an urban design strategy that includes different scales of intervention. Such scales are related to the different stages of informal urban development, considering that it has to simultaneously manage both local responses for housing types definitions and also a global strategy for open spaces patterns of occupation. The goal of the project is to address the promotion of social integration through transforming natural barriers, such as steep terrains in Valparaíso Jaime´s ravines, as opportunities to incorporate a Socially Active Space system into the new urban fabric. Furthermore, developing an urban proposal that enables the understanding of the urban patch as an INCLUSIVE model that fulfils the integration of housing, with areas for social interaction connected by a hybrid pedestrian network.

While the population living in formal city areas receives from authorities and planners an urban configuration without being involved in its development, conversely, being part of an informal settlement communities´ encourages inhabitants to be key participants in their urban configuration development. Such condition considers implicitly the engagement of external cooperation and involvement in matters that could not be achieved without this participation. As J. Minnery has pointed out, pivotal to improving the social, economic and environmental needs of those living in informal settlements are the policies and strategies of city governments [Minnery et. al, 2013]. Minnery´s major contribution is the encouragement of local community and formal organisations to develop dialogues from which possible interventions for settlement´s improvements represent a bottom-up emergent process. Considering that valuable contribution, the research has considered the study of ´Social Networks and Dynamics´ [Chapter 04, 4.9] through Social Network Theory. Quantitative interviews were applied to a sample of eight local inhabitants [considering variation in gender, age and activity] which represent a social network of around 450 locals, to engage the project´s urban strategy with inhabitant´s social and urban patterns. This social network analysed during the fieldwork in Valparaíso provided valuable input to this research. Local people have developed land occupation patterns showing that geographical proximity to family members and friends for selecting a piece of land has been prioritised over the selection of land with better environmental and urban conditions, however far. As proximity eases social collaboration and interaction, this factor sets Housing Logic, Housing types and clusters definition developed in Chapter 05 pages 148 to 153. A heterogeneous family structure configuration of plots has been defined recognising the local social structure based on collaboration between family and acquaintances.

Two Local Forces Informing the Project, Geography and Social Ground aspects Based on the conceptual scope of the Socially Active Space, the research project has been informed by the analysis of two main forces - topography and social networks - to generate a contextualised approach for the development of the new urban proposal for informal urban settlements located in Jaime´s ravine [Valparaíso]. As concluded from case studies the initial organisation of the territory in informal taken land has a large impact on the type of social interaction that will arise from it. Valparaíso and Grotao cases studies share consequences of being located in steep topography that dramatises the relationship between land availability and the housing aggregation process. The heterogeneity of the ground with slope degrees ranging from 1° to 54° indicates that a deep analysis of the terrain considering slope, aspect and elevations has to be run to find out the fittest places [cells] to develop the urban program defined for this urban patch. The synthesis of the three factors analysed illustrated in section 5.8 [Overlay strategy for elevation, aspect and slope, page 145] demonstrates that suitable land cells decrease land availability significantly. Such a condition determines the land use strategy to increase the densification level while maintaining housing heights at a maximum of three storey since self-built construction constraints the vertical growth at that level.

188

Conclusions

Social Aspects

Socially Active Space Urban Order driver

Testing the Urban Model for Informal Settlements in Similar Realities

As this urban order gives prominence to social interaction, the project has prioritised the development of Socially Active Spaces that consolidate social capital [Putnam, 2000] and place attachment, which are considered fundamental aspects of urban quality of life. These urban elements constitute the principal spatial organisers of the urban patch, and considering Ben-Jacob ideas about complex system, developed in Chapter 02 Domain, that individual colonies variety and flexibility strengthen network adaptability [Ben-Jacob, 2011], the project has developed a range of open spaces oriented to play different roles in the urban Socially Active Space System. Furthermore, the Grotao case study reveals that variation in size, shape, location, slope degree and visual fields of open areas enriches the urban space as a whole, illustrated through isovist´ analysis. Stiles has pointed out that despite the importance of considering individual open spaces as urban individualities, it is also important to think of them as nodes within a wider open space system in the form of a network of inter-connected spaces [Stiles, 2013]. In that respect the project proposes a network of varied open spaces that benefit different aspects of urban social life. Local court yards at the centre of each four house-plot represent the smallest socially active space unit for social interaction. Other expressions of open areas are ´Plazas´, ´Miradores [associated to social infrastructure] and amphitheatres that represent larger spaces for socially interaction, having a capacity that vary from 147 to 882 inhabitants [3 m²]. These urban areas are spatially and functionally connected. The former by a series of isovists and the latter by a hybrid pedestrian-oriented network that adapts to slope variation composed by pedestrian paths, stairways and funiculars, reinforcing Socially Active Space System reachability for ultimately attaining social interaction for social integration.

Highlighting Allen´s [2016] statement that we can never really say that one system or city is ‘like’ another since their histories cannot be identical, we cannot build up a set of ‘exemplars’ of like systems, whose observed behaviour can safely and certainly be assumed for the next ‘case’. Everything we do is an experiment [Allen,2016]; according to this the major contribution of the urban model developed in Valparaíso Jaime´s ravine is related to its territorial and social approximation methodology and its capabilities rather than a fixed-end result. This makes the proposal a sample of a continual iterative experimentation over time since successful urban solutions are shaped through time and are the result of endless interventions. This evolving methodology can be refined through, first, the discussion on the generative design approximation into informal urban settlement and second, to test the urban model proposed in similar geographical, urban and social conditions in Latin American cities for finding its opportunities and constraints. This could be used as a sting tool to drive experiments in search of new proposals for urban development’s considering that 90% of LA population will be living in cities by 2050 [UN 2012], and a considerable part of this percentage will in part or wholly selforganise its own urban settlements. From this point onwards, there is potential to improve and develop the proposal by taking into consideration discussions between local inhabitants of occupied land, planners and authorities having as main motivation Socially Active Spaces as means of urban configuration for social integration.

Generative Design

Final words

Generative design methodology through advanced computational tools has allowed the research process to approach the project throughout as an explorative design. This approach brings into discussion social, urban and architectural parameters and criteria to elaborate a set of hierarchical relationships between them that focus the purpose into finding multiple optimal solutions to the same set of goals. For instance, at the local level, a resignification of ´miradores´ with multiple resulting phenotypes, as shown in Design Development Chapter, 5.11.2 'Miradores' emergence process, has been the result of a search for integrating a functional aspect to places that traditionally have been only used for encounter and sightseeing, attaining a hybrid building that incorporates varied land uses currently non-existent into the local neighbourhoods. On a global level, through a series of experiments, parameters and objectives are refined to approximate the fittest results that integrate into the new urban order a built environment, Socially Active Spaces developed as ´plazas´, ´miradores´ and ´amphitheatre interconnected by an active connective tissue.

The Socially Active Space System is a mere intention of developing a new urban order following a generative design approach that has revealed, throughout the research process, its endless possibilities. It can be argued that constraints are mostly related to the capability of designers to adequately read the variables related to specific urban and architectural problematics. It should be noted that ´Read the variables´ is intended not as isolated elements which could be part of an initial approximation, nonetheless read and understand their relationships and hierarchical organisation seems to be a leading aspect to start experimentation in search of novel structures. This new paradigm challenge to engage in this field as Allen [2016] has mentioned in relation to complex systems ´in a way complexity is telling us that we can never know for sure that a new ‘case’ will behave as expected on the basis of previous examples´ [Allen, 2016]. However, as cities are recognised as evolving complex systems, generative design brings immense possibilities to experiments and refine the professional approach to them until a solution emerges that best fulfils all the variables involved.

Conclusions

189

7.2

APPENDIX 7.2.1 7.2.2 7.2.3

190

Excel Spreadsheet, Social Network Analysis Matrices. Housing type Octopus experiments. ClusterÂ´s type Octopus experiments.

Appendix

191

8.62%

0.00%

6.90%

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

1 1 1

1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 41

1 4

1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

1 1 1 1 1 1 1 1 1

1 1 1 1

1 1 1 8

elena

dagoberto

macarena

joselin

martina

amanda

damaris

<30

1 1

1 1 1 1 1

1/30

4-8/30

30/30

yes

>80

65-79

50-64

5-19

20-49 1

1 1

3.51%

19.30%

1 1

14.04%

1 1 1

contact frequency

63.16%

1 1

0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 8

13.79%

1 1 1 1 4

10.34%

1 1 1 1 1

neighbour´s organisations neighbour´s organisations neighbour´s organisations friend friend friend friend friend friend friend friend hobbie hobbie hobbie hobbie hobbie hobbie educacional educacional educacional educacional educacional work work work / ´don lalo´s son work / ´don lalo´s son

1 1 1

1 1 1 0 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 50

86.21%

1 1 1 1 1 1

13.79%

1 1 1 1 1 1 1 1

1 1 1

neighbour, margarita´s husband

0.00%

girl friend uncle uncle father elder brother alex2´s wife nephew niece niece paternal grandfather maternal grandmother paternal uncle maternal aunt maria´s daughter maria´s grandson cousin, maria´s son mauro´s father cousin, carlo´s daughter carlos´s wife calos´s daughter neighbour-driver neighbour-driver neighbour-driver neighbour shop owner

10.34%

niece, dagoberto´s daughter

6.90%

niece, dagoberto´s daughter

1 1 1

70.69%

10.34%

ex partner´s dago amanda´s mum

age 20 52 26 26 25 3 8 20 25 20 54 32 32 7 12 5 70 65 54 59 37 23 35 66 17 43 14 35 40 40 70 70 30 28 25 24 25 24 26 20 18 20 20 32 21 20 25 30 20 20 20 20 20 21 30 70 45 30

0-4

organisations

occupational

health care

educational

hobbies

ex partner´s dago martina´s mum

Fig. 239

192

relationship description ego mother brother

1.72%

1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

5.17%

friendship

1 1 1

3.45%

alters bastian elena dagoberto macarena joselin martina amanda damaris nicole camila alex alex2 victoria joan bianca valentina hernan julia hernan2 maria romina patricio sergiomauricio sergio2 carla angelica angelica2 guardiola gino checho sramargarita donmilo alexis manzana caramelo oscar michael hugo jordan esteban alejandro ariel cesar lalo kevin marco pepe michael2 franco diego manuel renzo matias marianela andres donlalo francisco javier

43.10%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

lives in the neighbourhood

age group

8.62%

years old male social network matrix

neighbours

family

ego social relation types

bastian

Excel Spreadsheet, Social Network Analysis Matrices

family-neighbours

7.2.1

1 0

1 1 0

1 1 1 0

1 1 1 1 0

1 1 1 1 1 0

1 1 1 1 1 1 0

1 1 1 1 1 1 1 0

1 1

1 1 1 1 1

1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

1 1 1 1 1 1 1 1 1

1 1 1 8

19.30%

3.51%

1 1 1 1

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0

1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

javier

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

francisco

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

donlalo

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

andres

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

marianela

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

matias

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

renzo

1 1 1 0 1 0 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

manuel

1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

diego

1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

franco

1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

michael2

1 1 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

pepe

1 1 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

marco

1 1 1 0 1 0 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

kevin

1 1 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

lalo

1 1 1 0 1 0 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

cesar

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

ariel

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alejandro

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

esteban

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

jordan

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

hugo

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

michael

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

oscar

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

caramelo

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

manzana

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alexis

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

donmilo

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

sramargarita

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

checho

1 1 1 1 1 1 1 1 1 1 1 1 1 0

gino

1 1 1 1 1 1 1 1 1 1 1 1 0

guardiola

1 1 1 1 1 1 1 1 1 1 1 0

angelica2

1 1 1 1 1 1 1 1 1 1 0

angelica

1 1 1 1 1 1 1 1 1 0

carla

1 1 1 1 1 1 1 1 0

sergio2

1 1 1 1 1 1 1 0

sergiomauricio

damaris

1 1 1 1 1 1 0

patricio

amanda

1 1 1 1 1 0

romina

martina

1 1 1 1 0

maria

joselin

1 1 1 0

hernan2

macarena

1 1 0

julia

dagoberto

1 0

hernan

elena

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58

valentina

bianca

joan

victoria

alex2

alex

camila

nicole

bastian

<30 1

1 1

1/30

4-8/30

30/30 1 1

14.04%

13.79%

0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 1 1 8

contact frequency

63.16%

he ood

1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

193

194

1 1 1 1 1 1

1 1 1

1 1 1 1

1 1 1 1 1 1

1 1 38

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1

1 1 1 1 1 1

1 1

1 1 1 1

1 1

1 1 1 1 1

1 1 1

1 1 1 1

1 1

1 1 1

1 1

1 1 14

1 1 1 1

nicole

andrés

greta

marta

ernesto

camila

leandro

jade

1 0

1 1 0

1 1 1 0

1 1 1 1 0

1 1 1 1 1 0

1 1 1 1 1 1 0

<30

1/30

4-8/30 1

1 1

1 1 1 1 1 1 1 1 1 23

30/30

no 1 1

1 1

24.14%

1 1 1 1 1 1 1 1

1 1

25.86%

1 1 1 1 1 1 1 1

yes

>80

65-79

5-19

0-4

50-64 1

1 1 1 1

39.66%

1 1 1 1 1 1 1

10.34%

39%

0.00%

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

61%

0.00%

1 1

contact frequency

1 1

6.78%

1 1 1 1 1 1 1 1 1 1

13.56%

1 1 8

13.56%

18.64%

1 1 1 1 1 1

1 1

64.41%

1 1 1 1 1 1 1 1 1 1 1 1 1 1

age 25 31 3 42 43 19 17 0 56 77 25 58 20 19 50 12 27 40 40 32 34 45 38 35 56 45 26 25 25 26 30 23 34 58 45 26 46 50 28 25 47 78 72 42 60 65 43 8 8 8 27 31 31 40 23 48 53 40 20

11.86%

1 1 1 1 1 1 1 1 1 1 1

relationship description ego husband daughter mother father sister brother sister mother in law husband´s grandmother cousin paternal grandmother sister in law sister in law husband´s aunt paternal cousin cousin paternal uncle césar´s partner paternal aunt joselín´s partner maternal aunt patty´s partner maternal aunt fabiola´s partner friend friend friend friend friend friend friend friend friend friend friend neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour greta´s teacher (school) school director school parent´s cent. Presi. school treasurer school secretary school PC greta´s pediatrician ex school classemate ex school classemate

3.39%

organisations

occupational

health care

educational

hobbies

friendship

1 1

0.00%

1 1

0.00%

1 1 1 1

1.69%

family

family-neighbours 1 1 1 1 1 1 1 1 1 1

13.56%

Fig. 240

neighbours

alters nicole andrés greta marta ernesto camila leandro jade abigail damarís macarena alicia valentina maría josé gina masiel jovani césar karen joselín reinaldo patty pablo fabiola wille jorge dago jordan oscar valeria carla diego reinaldo ruben eduardo jenifer daniela rosa fabian valeria mauricio sra isabel sra eliana sra vero sra luisa don enrique patty serrano miguel angel franco vicente nicole carolina maca marcia camila sra ortensia sra liliana sandra césar

28.81%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

23.73%

years old female social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 1 1 1 1 1 1 0

1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0

1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0

1 1 1 1 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 1 0 1 0

1 1 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 0

1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 0

1 1 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 0

1 1 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 0

1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 1 1 1 1 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 1 1 0

1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 0

39.66%

25.86%

24.14%

1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0

1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0

1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

césar

1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 0 0 1 1 1 1 0 1 0 0 0 0 1 0 0

sandra

1 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

sra liliana

1 1 1 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

sra ortensia

1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

marcia tesorera

1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 1 1 0 1 0 0 1 0 0 0 0 0 0 0 0

camila secreatria

1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

maca presidenta

1 1 1 1 1 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

nicole

1 1 1 1 1 1 1 1 1 1 1 0

carolina

1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 1 1 0 0 0

miguel angel

1 1 1 1 0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0

don enrique

1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 0

patty serrano

1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 0 1 1 0

sra vero

1 1 1 1 1 1 1 0 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 0 1 0

sra luisa

1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 0 0

sra isabel

1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 1 1 1 1 0 1 0 0 0 0

sra eliana

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 0

valeria

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0

jenifer

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0

daniela

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0

ruben

1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0

dago

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0

jordan

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0

wille

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0

fabiola

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0

patty

1 1 1 0 1 1 1 1 1 1 1 1 0 0 1 0

pablo

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

joselín

1 1 1 0 1 1 1 1 1 1 1 1 1 0

reinaldo

1 1 1 0 1 1 1 1 1 1 1 1 0

césar

maría josé

alicia

valentina

camila

leandro

damarís

ernesto

macarena

greta

marta

jade

nicole

1 1 1 1 1 1 1 1 1 1 0

franco

1 1 14

1 1 1 1 1 1 1 1 1 0

vicente

1 1

10.34%

1 1 1

1 1 1 1 1 1 1 1 1 23

1 1 1 1

1 1 1 1 1 1 1 1 0

rosa

39%

1 1 1

1 1 1 1 1 1 1 0

reinaldo

1 1 1

1 1 1 1 1 1 0

mauricio

1 1 1 1 1 0

fabian

1 1 1 1 0

<30

1/30 1

1 1 1 0

eduardo

1 1

1 1 1 1

1 1

1 1 0

carla

1 0

diego

1 1 1 1

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59

oscar

valeria

1 1

1 1 1 1 1

jorge

karen

1 1

jovani

1 1 1 1

gina

masiel

1 1

abigail

1 1 1 1

1 1 1 1 1 1 1

andrés

1 1

4-8/30

contact frequency

30/30

he ood

1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

195

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

1 1 1 1 1 1 1 11

1 1 1

1 1 1 1 1

1 1 1 1

1 1

1 1 1 1

1 1

1 1 1 1

1 22

1 1 1 1 1 6

jessica

marcelo

marcela

ariel

gabriel

alonso

abuela

abuelo

<30

1 1 1

1/30

4-8/30

30/30

yes 1 1 1 1

>80

65-79

5-19

50-64 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1

12.24%

1 1 1

1 1 1 1 1

22.45%

1 1 1 1 1

18.37%

1 1 1 1 1 1 1

1 1

1 0

1 1 0

1 1 1 1 0

1 1 1 1 1 0

1 1 1 1 1 1 0

1 1 1 1 1 1 1 0

46.94%

0.00%

26.00%

8.00%

2.00%

10.00%

1 1 1 1 1 1 1 13

16.00%

1 1 1 1

0.00%

1 1 1 1 1

1 1 1 1

husband paternal uncle friend friend friend friend friend friend boss1 boss2 boss3 boss3´s daughter children´ teacher children´ teacher children´ teacher School Director School supervisor heathcare neighbour neighbour neighbour, shop neighbour, shop marcela´s friend marcela´s friend marcela´s friend marcela´s friend marcela´s friend gabriel´s friend gabriel´s friend

1 1

1 1 1 1 1 1 1 1

56.00%

1 1 1 1 1 1

1 1

contact frequency

44.00%

husband´s aunt husband cousin, eugenio´s son

1 1 1

1 1

2.00%

1 1 1

4.00%

husband cousin, dora´s daughter husband´s cousin, dora´s daughter

1 1

1 1 1

32.00%

husband´s aunt, elena´s daughter

1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

38.00%

husband´s aunt, elena´s daughter

0-4

organisations

occupational

father in law husband´s maternal grandmother

Fig. 241

196

health care

educational

hobbies

friendship

mother in law, elena´s daughter

age 32 32 12 10 7 1 90 73 62 52 53 32 24 52 53 75 50 52 24 20 43 18 35 33 31 35 32 32 33 50 50 50 23 26 26 26 40 60 50 50 50 50 50 13 12 13 12 12 7 7 36

22.00%

1 1

1 1 1 1 1

relationship description ego partner daughter son son son paternal grandmother paternal grandfather father aunt uncle, children driver cousin cousin

2.00%

1 1 1 1 1 1 1 1 1 1 1 1 1

family

family - neighbors

neighbors

alters jessica marcelo marcela ariel gabriel alonso abuela abuelo papá maría pedro juan pablo abraham mirta marcelo elena cecilia dora javiera rocío letty daniel eugenio ana jenifer joanna karen alice oscar carolina nelly ema camila susana valesca daniel gisseta luis eliana oscar bruna rita fernando hernán alondra fabián gabriela anaís felipe sebastián

30.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

8.00%

years old female social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 1

1 1 1 0

18.37%

22.45%

12.24%

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 0

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 1 1 1 1 0

sebastián

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

gabriela

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

fabián

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

fernando

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

rita

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0

bruna

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

luis

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

oscar

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

gisseta

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

daniel

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

valesca

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

susana

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

carolina

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

ema

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

nelly

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

oscar

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

ana

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

eugenio

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

daniel

javiera 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alice

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

felipe

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

anaís

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alondra

1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

hernán

1 1 1 1 1 6

46.94%

1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 0

eliana

1 1 1 1 1 1 1 1 1 1 1 1 0

camila

1 1 1 1 1 1 1 1 1 1 1 0

karen

1 1 1 1

1 1 1 1 1 1 1 1 1 1 0

joanna

1 1 1 1

1 1 1 1 1 1 1 1 1 0

letty

1 1 1 1 1 1 1 1 0

dora

abuelo 1 1 1 1 1 1 1 0

cecilia

abuela 1 1 1 1 1 1 0

marcelo

alonso 1 1 1 1 1 0

mirta

gabriel 1 1 1 1 0

1 1 1 0

abraham

ariel

1 1 0

juan pablo

marcela

1 0

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

pedro

maría

marcelo

<30

1 1

1/30 1

56.00%

1 1 1

jenifer

1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1

rocío

1 1 1 1 1

elena

1 1 1 1 1

papá

1 1

jessica

1 1 1 1 1 1 1 1

4-8/30

contact frequency

30/30

he ood

1 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 0

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

197

7.55%

1 1 1 1

1 1

1 1 13

1 1 1

rosa

ricardo

kiara

marcelo

claudia

pedro

daniel

variña

<30

1 1 1 1

1 1

1 1 1

1 1 1 1 13

1/30

4-8/30

30/30

yes

>80

65-79

5-19

0-4

50-64 1

1 1

1 1 1 1

1 1 1 1 1

1 1 1 1 1 12

21.15%

0.00%

1 1 1

1 1

1 1 1 1

1 1

1 1 1 1 1 1

19.23%

0.00%

1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1

1 1

23.08%

11.32%

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

1 1

36.54%

1 1 1 0

1 1 1 1 0

1 1 1 1 1 0

1 1 1 1 1 1 0

1 1 1 1 1 1 1 0

1 1

24.53%

1 1 1

1 1 1 1

contact frequency

1 1 1 1

75.47%

1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1.89%

1 1 1 1

1 1

24.53%

1 1

37.74%

13.21%

1 1 1 1 4

0.00%

1 1 1 1 1 1

neighbour, shop neighbour, shop neighbour, Com Las Cañas neighbour, Com Las Cañas neighbour, Com Las Cañas neighbour neighbour neighbour, School Dir neighbour, healthcare friend friend friend, mauricio´s wife friend ex school classmate friend ex school classmate friend ex school classmate ex work colleague ex work colleague ex work colleague teacher

1 1

26.42%

neighbour, playgroup cleaning

age 52 60 7 32 37 50 56 17 8 14 8 52 58 62 64 86 66 51 60 60 50 48 28 37 65 65 55 70 60 70 68 67 40 25 67 70 27 30 26 25 66 60 69 52 45 42 52 52 52 44 60 65 65

9.43%

1 1

relationship description ego, mother´s centre president husband, driver granddaughter son dauther in law brother - dir NA brother pedro´s son pedro´s daughter daniel´s son daniel´s daughter sister in law, pedro´s wife sister in law, daniel´s wife treasurer Mother´s Centre secretary MC MC MC MC kiara´s doctor nurse social assistant psychologist council coord sector 4 council coord neighbour neighbour, shop neighbour, dressmaker neighbour neighbour neighbour, ice cream seller neighbour neighbour, cars´ mechanic neighbour, play group dir

0.00%

1 1 1 1 1

1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

organisations

occupational

1 1 1 1 1 1 1 1 1 1 1 1 1

Fig. 242

198

health care

educational

hobbies

friendship

family

family - neighbors

neighbors

alters rosa ricardo kiara marcelo claudia pedro daniel variña emilia said jade tirsa janette laura cristina maría elena julia santiago sra cecilia miriam pía daniel gilda paulina patricia juana yolanda organillero santo delia mauricio tía valeria solange olivia amable mauricio oriana sra mauricio jesús pastor evangelico fernando (dir silvia pinto silvia mauricio flores paola blanca torrejon valencia manuel gallardo leticia flores ana maría ana orrego gabriela

24.53%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

35.85%

years old female social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 0

1 1 0

1 1 1 1 1 1 1 1 0

1 1 1 1 1 12

36.54%

23.08%

19.23%

21.15%

1 1 1 1

1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 1 0 1 1 1 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

gabriela

leticia flores

manuel gallardo

valencia

blanca torrejón

paola

mauricio flores

silvia

silvia pinto

fernando (dir

pastor evangelico

jesús

sra mauricio

oriana

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0

mauricio

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0

amable

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0

olivia

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0

solange

organillero

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0

yolanda

1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0

juana

gilda

daniel

1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

pía

1 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

tía valeria

1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

miriam

1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

ana orrego

1 1 1 1

1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

ana maría

1 1

1 0 0 0 0 0 0 0 0 0 0 0 0 1 0

mauricio

1 1 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0

delia

1 1 1 1 1 1 1 1 1 1 1 1 0

santo

1 1

1 1 1 1 1 1 1 1 1 1 1 0

patricia

1 1 1 1 1 1 1 1 1 1 0

sra cecilia

1 1

1 1 1 1 1 1 1 1 1 0

santiago

emilia 1 1 1 1 1 1 1 1 0

julia

variña 1 1 1 1 1 1 1 0

elena

daniel 1 1 1 1 1 1 0

maria

pedro 1 1 1 1 1 0

cristina

claudia 1 1 1 1 0

1 1 0

laura

marcelo 1 1 1 0

1 0

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53

janette

tirsa

jade

said

kiara

<30

paulina

1 1

24.53%

1/30 1

1 1 1 1 13

1 1

1 1 1

ricardo

1 1

1 1 1 1 1 1

1 1 1

1 1

1 1 1 1 1 1

rosa

1 1 1 1

4-8/30

contact frequency

30/30

he ood

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

199

1 13

1 1

vladimir javier

juvenal

jenifer

carmen

julia

maria verónica

maria ester

teresa

<30

4-8/30

30/30

1/30

1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 19

1 7

17.95%

1 1

2.56%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 0

1 1 0

1 1 1 0

1 1

30.77%

1 1

contact frequency

48.72%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 19

1 1 1

yes

>80

65-79

5-19

0-4

50-64

47.50%

0.00%

1 1 1

52.50%

15.00%

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

7.50%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

22.50%

1 1 1 1

32.50%

1 1 1

37.50%

1 1 1 1 1 1

0.00%

age 58 34 35 35 40 35 45 65 59 56 35 60 60 90 50 55 50 35 35 35 35 75 80 65 65 65 55 60 60 65 65 50 80 45 70 70 60 60 45 56

0.00%

organisations

occupational

health care

educational

hobbies

family

friendship 5

Fig. 243

200

relationship description ego son daughter niece, julia´s daughter niece, julia´s daughter niece niece sister sister brother sister brother in law neighbour neighbour neighbour neighbour neighbour neighbour, shop neighbour, shop neighbour neighbour neighbour neighbour colleague colleague colleague client client client domino group domino group domino group domino group domino group friend friend friend friend friend daughter and son´s mother

0.00%

1 1 1 1 1

0.00%

1 3

1 1 1 1 1

1 1 1 1 1 1

12.50%

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1

1 1

12.50%

7.50%

family-neighbours

neighbours

alters vladimir javier juvenal jenifer carmen julia maria veronica maria ester teresa julia vicente verónica alex juan nelly jannette luis david david paola sandro rodrigo juana brigida jorge antonio piña maria jean nelson gutierrez gastón salinas flores nelson jorge castillo lara antonio lucy maria gladys

25.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

27.50%

years old male social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 1 1 1 0

1 1 1 1 1 0

1 1 1 1 1 1 0

1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 0

30.77%

2.56%

1 7

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

maria gladys

lucy

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

antonio

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

lara

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

castillo

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

jorge

gutierrez

nelson

jean francisco

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

maria

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

piña

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

antonio

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

jorge

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

brigida

1 1 1 1 1 1 1 1 1 1 1 1 1 0

juana

1 1 1 1 1 1 1 1 1 1 1 1 0

rodrigo

teresa

1 1 1 1 1 1 1 1 1 1 1 0

sandro

maria ester

1 1 1 1 1 1 1 1 1 1 0

paola

maria verónica

1 1 1 1 1 1 1 1 1 0

david

julia

1 1 1 1 1 1 1 1 0

nelson

1 1 1 1 1 1 1 0

flores

1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 0

salinas

48.72%

1 1 1 1 1 1

1 1 1 1 1 0

gastón

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 19

1 1 1 1 0

david

carmen 1 1 1 0

luis

jenifer 1 1 0

jannette

juvenal 1 0

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

nelly

juan

alex

verónica

vicente

julia

vladimir javier

<30

1/30

4-8/30

1 1

1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

17.95%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

47.50%

1 1

contact frequency

30/30

he ood

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

201

1 1 1 1 1 1 1 1 1

1 1 1 1 1

1 1 1 13

nelson

julia

julia hija

carmen

eugenio

patricio

martín

bastián

diego

<30

1/30

1.67%

4-8/30

30/30

yes

>80

65-79

1 1 1 1 1

21.67%

1 1

1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 19

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

10.00%

1 1 1 1 36

1 1 1

contact frequency

66.67%

1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

31.15%

1 1 1 1

68.85%

1 1 1 1 1 1

1 1 1 1

1.64%

1 1 1 1 1

50-64

5-19

1 1

4.92%

8.20%

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

friend friend valín´s wife friend

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

16.39%

hospital health council, tresurer

1 1

59.02%

hospital health council hospital health council

1 1 1 1 1 1

1 1 1 1

14.75%

hospital health council, director hospital health council, sub-dir

age 60 59 40 35 41 42 5 14 11 5 14 10 33 36 15 7 2 47 78 61 57 48 63 81 45 42 30 24 38 54 30 35 42 43 22 23 57 60 56 49 33 33 35 7 3 43 40 30 30 24 41 32 45 37 64 68 65 40 45 43 33

0-4

organisations

occupational

0.00%

6.56%

0.00%

9.84%

1 1 1 1 1

0.00%

1 1 1 1 4

8.20%

1 1 1 1 1 1

Fig. 244

202

health care

educational

hobbies

friendship

1 1 1 1 1

relationship description ego wife wife´s daughter wife´s daughter julia´s husband carmen´s husband nelson+julia´s son nefew, carmen+patricio nefew, carmen+patricio niece, carmen+patricio niece, julia+eugenio niece, julia+eugenio julia´s daughter paulina´s husband paulia + javier´s daughter paulina + javier´s son paulina + javier´s daughter family´s friend mother sister brother sister neighbour neighbour neighbour neighbour - alex´s wife neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour, luz m´s husband neighbour neighbour neighbour neighbour neighbour, marido maribe neighbour, hijo maribe neighbour, hijo maribe neighbour, almacén son´s school son´s school son´s school son´s school son´s school director son´s school

3.28%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

family

family - neighbors 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

27.87%

alters nelson julia julia2 carmen eugenio patricio martín bastián diego antonia camila jasmín paulina javier javiera cristóbal montserrat miguel ana marisol francisco anita juanito sra nelly alex esposa alex boris paz alex 2 verónica marcelita esposo marc paola david vanesa danixa luz maría jorge blado sandro daniela maribe ruben christopher stevens paola angelina gloria ema stefania deisy claudia soto pedro nancy serapio alicia américo iván zúñiga valín maría faviola

neighbors

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

39.34%

years old male social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 0

1 1 0

1 1 1 0

1 1 1 1 0

1 1 1 1 1 0

1 1 1 1 1 1 0

1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 19

1 1 1 13

66.67%

10.00%

21.67%

1 1 1 1 1

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

faviola

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

maría

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

valín

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

iván zúñiga

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

américo

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alicia

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

serapio

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

nancy

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

pedro

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

claudia soto

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

deisy

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0

stefania

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0

ema

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0

gloria

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0

angelina

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0

paola

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0

stevens

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 0

christopher

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 0

ruben

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0

maribe

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 0

daniela

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 0

sandro

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 1 0

blado

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 0

jorge

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0

luz maría

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

danixa

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

vanesa

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

david

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

paola

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

esposo marcelita

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

marcelita

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

verónica

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alex 2

1 1 1 1 1 1 1 1 1 1 1 1 1 0

paz

1 1 1 1 1 1 1 1 1 1 1 1 0

boris

1 1 1 1 1 1 1 1 1 1 1 0

esposa alex

1 1 1 1 1 1 1 1 1 1 0

alex

1 1 1 1 1 1 1 1 1 0

sra nelly

1 1 1 1 1 1 1 1 0

juanito

1 1 1 1 1 1 1 0

anita

bastián

1 1 1 1 1 1 0

francisco

martín

1 1 1 1 1 0

marisol

patricio

1 1 1 1 0

ana

eugenio

1 1 1 0

miguel

carmen

1 1 0

montserrat

julia hija

1 0

cristóbal

julia

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61

javiera

javier

paulina

jasmín

camila

antonia

diego

nelson

<30

1/30 1 1 1 1 1

31.15%

1 1 1 1 1 1 1 1 1

4-8/30

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1.67%

1 1 1 1

contact frequency

30/30

he ood

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

203

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

ana

ernesto

lucy

teresa

angel

omar

maritza

<30

1/30

4-8/30

30/30

1 1 1 1 1 1 1 1 1

1 1 11

1 1 1

1 1 1 1 1 1 1

1 1 1 1 1 1

1 1 1 1 1

48.89%

1 1

1 1 1 1 1 1 1 1 12

1 1 23

1 1

24.44%

1 1

yes

>80

1 1

24.44%

1 1

1 1 1 1

2.22%

1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 0

1 1 1 1 0

1 1 1 1 1 0

1 1

26.09%

1 1

1 1 1

1 1

1 1 1 1 1 1 1

contact frequency

73.91%

1 1

65-79

50-64

5-19

0-4

1 1 1 1 1 1 1 1 1 1

2.17%

1 1

50.00%

23.91%

21.74%

1 1 1 1 1 1

2.17%

23.91%

Primary health care, staff

friend, ex school classmate friend, ex school classmate

age 69 74 72 74 75 60 58 36 7 22 56 54 69 72 68 74 75 70 69 46 70 64 62 80 76 76 46 72 55 52 66 66 58 41 52 60 68 70 35 35 30 55 35 35 66 66

0.00%

organisations

occupational

Primary health care, staff

0.00%

4.35%

Primary health care, director

8.70%

relationship description ego husband cousin cousin brother in law brother in law sister in law, omar´s wife niece, omar´s daughter ingrid´s daughter niece, omar´s daughter brother in law sister in law, damaso´s wife sister in law sister in law sister in law neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour neighbour mother´s centre, director mother´s centre, participant mother´s centre, participant mother´s centre, participant mother´s centre, participant mother´s centre, participant mother´s centre, participant mother´s centre, participant mother´s centre, participant CENAMA architect Primary health care social assistant

0.00%

1 1 1 1 1 1 1 1 1 1 1

1 1 1 1

Fig. 245

204

health care

educational

hobbies

friendship

1 1 2

8.70%

1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1

0.00%

1 1 1 1 1 1 1 1 1 1

family

family-neighbors

neighbors

alters ana ernesto lucy teresa angel omar maritza ingrid sofía yara damaso miriam ana nina clari ester sra olga sara carlos alicia gloria clara carmencha dominga victor manuel marisol sra irma gloria 2 rosa lala elena cecilia grecia julia juanita 1 juanita 2 olvido CENAMA eduardo loreto miriam pancho josé maría sigris

23.91%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

30.43%

years old female social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 0

1 1 0

1 1 1 1 1 1 0

1 1 1 1 1 1 1 0

24.44%

1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 0

1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0

1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0

1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 1 1 1 0 1 0 0

1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 1 0 0 0 1 0

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0

josĂŠ

1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0

pancho

1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

miriam

1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

loreto

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0

eduardo

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0

olvido

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0

juanita 2

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0

julia

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0

juanita 1

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0

grecia

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0

cecilia

1 1 1 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 0

elena

1 1 1 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 1 0

lala

1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 1 1 1 1 0

rosa

yara

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0

victor

sofĂa

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 0

dominga

ingrid

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0

CENAMA

1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 0

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0

sigris

1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0

marĂa

1 1 11

24.44%

2.22%

1 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

gloria 2

1 1 1 1 1 1 1 1 1 1 1 1 1 0

sra irma

1 1

1 1 1 1 1 1 1 1 1 1 1 1 0

marisol

1 1

1 1 1 1 1 1 1 1 1 1 1 0

manuel

1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 0

carmencha

maritza

1 1 1 1 1 1 1 1 1 0

clara

omar

1 1 1 1 1 1 1 1 0

gloria

angel

1 1 1 1 1 1 1 0

1 1 1 1 1 1 0

alicia

teresa

1 1 1 1 1 0

carlos

lucy

1 1 1 1 0

1 1 0

sara

ernesto

1 1 1 0

1 0

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46

sra olga

ester

clari

nina

ana

miriam

damaso

ana

1 1 1 1

1 1 1

48.89%

1 1

26.09%

1 1 1 1 1 1 1 1 12

<30

1/30

4-8/30

contact frequency

30/30

he ood

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

205

206

0.00%

3.33%

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

1 1

1 1 1 1 1 1

1 1

1 1 1 1 1 1

1 1

1 10

1 5

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1

1 1 1

1 1 1 1

1 1

1 1 1 1 1 1

1 1 1 1

1 1 41

1 21

1 1

1 1 1 7

aurora

osvaldo

nery

sebastián

fernando

fabián

lorena

antonia

<30

1/30

1 1 1

1 1

1 1 1

1 1

4-8/30

30/30

yes

>80

65-79

5-19

50-64 1 1 1

1 1 1 1 1 1 1 1

15.25%

1 1

11.86%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

35.59%

1 1

37.29%

1 1 1

1 1

1 1 1 1 1 1 1 1 1

1 1 1 1 1

31.67%

0.00%

1 1 2

3.33%

1 1

neighbour - shop, NA neighbour neighbour neighbour neighbour neighbour maría´s husband neighbour, silvia´s son neighbour, lucho´s mother council staff fco´s secretary friend, osvaldo´s sister friend, alicia´s brother

1 1

1 1 1 1

contact frequency

68.33%

nelly´s husband, german´s brother

1 1

1 1 1 1 1 1

8.33%

neighbour, ramírez lucho´s sister

1 1

16.67%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1

36.67%

antonia´s husband luis+antonia´s daughter vanessa´s husband vanessa +leandro´s son vanessa +leandro´s son vanessa +leandro´s son antonia and julio´s mother antonia´s son mario´s wife ninoshka´s son ninoshka´s son aurora´s brother sister in law, juan´s wife juan serey´s daughter juan serey´s daughter juan serey´s daughter juan serey´s son cousin leonel´s wife leonel´s son marco antonio´s wife marco antonio´s son marco antonio´s son neighbour, NA neighbour neighour, NA elena´s mother elena´s son nelly´s daughter neighbour - shop, NA neighour neighbour, germán´s wife neighbour neighbour, policeman neighbour, mireya´s sister neighbour - shop

1 1

28.33%

paternal cousin - antonia´s sister

0-4

organisations

occupational

health care

educational

hobbies

friendship

paternal cousin-julio´s daughter

age 76 76 40 5 30 46 56 58 54 60 37 39 15 11 7 92 58 61 42 39 74 65 46 44 37 50 76 65 44 38 17 15 55 60 62 86 38 30 50 76 64 70 62 64 75 60 55 85 58 60 56 42 60 60 58 80 44 46 82 67

10.00%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

relationship description ego husband daughter grandson son in law husband´s nephew fabián´s wife

0.00%

1 1 1 1 1 1 1 1 1

family

family - neighbors 1 1 1 1 1 1

28.33%

Fig. 246

neighbors

alters aurora osvaldo nery sebastián fernando fabián lorena antonia julio luis vanessa leandro christopher devora sofía mercedes mario ninoshka rodrigo david juan serey sarita isabel maria eugenia priscila leonardo leonel ester marco antonio viviana lucas alan alma nelly elena rosita86 reinaldo helen clara germán maría nono juan javier verónica sra mireya jackeline patricio rosita85 olguita julio maría santander elenita q. maría luchito ramírez lucho silvia francisco robinson alicia osvaldo

25.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

40.00%

years old female social network matrix

lives in the neighbourhood

age groups

20-49

ego social relation types

1 0

1 1 1 0

1 1 1 0 0

1 1 1 1 1 0

1 1 1 1 1 1 0

1 1 0

1 1 1 1 1 1 0 0

1 21

31.67%

37.29%

35.59%

1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0

1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0

1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0

1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 0

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0

1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 0 1 0 0 0 1 1 1 1 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

1 1 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

osvaldo

1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

alicia

1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

silvia

1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0

ramírez lucho

1 1 0 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 0

maría santander

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 1 1 1 0

julio

1 1 1 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0

sra mireya

1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

verónica

1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0

javier

1 1 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

nono juan

1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

maría

1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 1 0

germán

1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 0

reinaldo

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 0

viviana

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0

marco antonio

leonardo 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 0 0 1 1 1 1 1 0

clara1

1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 1 0

helen

1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 1 0

priscila

1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 0 1 1 0

maría eugenia

1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0

rosita

1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0

elena

1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0

juan serey

david

rodrigo 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0

nelly

1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 0

alma

1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 0

ninoshka

mario

mercedes 1 1 1 1 1 0 0 1 1 1 1 1 1 1 1 0

alan

1 1 1 1 1 0 0 1 1 1 1 1 1 1 0

ester

1 1 1 1 1 0 0 1 1 1 1 1 1 0

isabel

1 1 1 1 1 0 0 1 1 1 1 1 0

sofía

antonia

1 1 1 1 1 0 0 1 1 1 1 0

devora

lorena

1 1 1 1 1 0 0 1 1 1 0

christofer

fabián

1 1 1 1 1 0 0 1 1 0

leandro

fernando

1 1 1 1 1 1 0 1 0

vanessa

sebastián

1 1 1 1 1 1 0 0

luis

nery

1 1 1 1 1 1 0

julio

osvaldo

1 1 1 1 1 0

robinson

1 1 1

1 1 1 0 0

francisco

1 1 1 1

1 1

1 1 1 0

1 1 0

luchito

1 0

maría

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

elenita

olguita

1 1 1

rosita

1 1

patricio

jackeline

1 1 1 1

lucas

1 1 1

1 1 1 1

leonel

1 1

<30

4-8/30

1/30

1 1 1

1 1

1 1 1

1 1

1 1 1 1 1 1 1 1

sarita

aurora

15.25%

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

1 1 1 1 1

11.86%

contact frequency

30/30

he ood

1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0

Appendix - Excel Spreadsheet, Social Network Analysis Matrix

207

7.2.2

Housing type Octopus experiments

7.2.3

Cluster´s type Octopus experiments

Fig. 247 Solution space navigation for Housing type Octopus experiments. Objectives: 1. Maximise north façade surface. 2. Maximise balconies´ northern surface area. 3. Maximise inhabitable area. 4. Maximise perimeter. 5. Maximise shared open area [garden].

Fig. 248 Solution space navigation for Cluster´s type Octopus experiments. Objectives: 1. Maximise solar radiation northern façade. 2. Maximise isovist perimeter. 3. Maximise isovist area.

Fig. 249 Solution space navigation for Cluster´s type Octopus experiments. Pareto Front results. Objectives: 1. Maximise solar radiation northern façade. 2. Maximise isovist perimeter. 3. Maximise isovist area.

208

Appendix

Reference List

209

7.3

REFERENCES

Reference List Reference List 7.3.3 Publications Reference List 7.3.4 List of Figures 7.3.4.1 01 Introduction List of Figures. 7.3.4.2 02 Domain List of Figures. 7.3.4.3 03 Methods List of Figures. 7.3.4.4 04 Site List of Figures. 7.3.4.5 05 Design Development & Strategy List of Figures. 7.3.4.6 06 Design Proposal List of Figures. 7.3.4.7 Appendix List of Figures. 7.3.4.8 Back and Cover Pages, List of Figures.

210

7.3.1 Books

7.3.2 Videos

Reference List

211

7.3

REFERENCES

7.3.1

Books Reference List

7.3.2

Videos Reference List

Batty, B., [2013] ´The New Science of Cities´. Benedikt, M.L. [1979] To take hold of space: isovist and isovist fields. Borgatti, S., et al. [2002] Ucinet 6 for Windows - Software for Social Network Analisis. User´s Guide Borgatti, S., et al. [2013] Ánalizing Social Networks´ Camazine, S., et al. [2001] ‘Self-Organization in Biologic System’. Freeman, L. C., [1978] Ćentrality in Social Networks: Conceptual Clarification. Social Networks´. Hillier, B., Hanson, J., [1984] ´The Social Logic of Space´ . Hillier, B., [1999] ´Space is the machine´. Jaffe, R., De Koning, A., [2016] Íntroducing Urban Anthropology´. Marshall, S. [2009] ‘Cities Design and Evolution’. Moholy, N.L. [1928] The new vision. Portugali J. [1999] ‘Self-Organisation and the City’. Portugali, J [2011] Ćomplexity, cognition and the City´. Sabidussi, G. [1966] The centrality index of a graph. Psychmetrika. Sevtsuk, A. [2015] © City Form Lab, 2015, cityformlab[at]mit.edu Urban Network Analysis Toolbox for Rhinoceros 3D. Wasserman, S. & Faust, K. [1994] ´Social Network Analysis, Methods and Applications´. Wellman, B., [1997] ´Social structures: a network approach´.

Ben-Jacob, E., [2011] ´Learning from bacteria about social networks´. video recording, YouTube, viewed 1 August 2016, <https://www.youtube.com/watch?v=yJpi8SnFXHs. [Accessed: 1 August 2016]. https://www.youtube.com/watch?v=y Jpi8SnFXHs https:// www.youtube.com/watch?v=yJpi8SnFXHs>.

212

7.3.3

Publications Reference List

Allen, P., [2016] Editorial [17.4]: ´The wonderful world of complexity. Emergence: Complexity and Organization´. 2015 Dec 31 [last modified: 2016 Feb 29]. Edition 1. doi: 10.emerg/10.17357.c2a3ed3047b8c5f2335cc2f33c1179fe. Alvarez, L. [2001] Órigen de los espacios públicos en Valparaíso´ - ‘Origin of the public space in Valparaíso’. Revista de Urbanismo, N° 4. Universidad de Chile, Santiago, 2001, Profesor at the Geography Institute Catholic University of Valparaiso. Makki M., et al., [2015] ´The Evolutionary Adaptation of Urban Tissues through Computational Analysis´ Batty M. [2011] ‘Urban Regeneration as Self-Organisation’ Centre of Advanced Spatial Analysis [CASA] University College London [UCL] Carr, S., et al. [1992] ´Public Space´. Cambridge University Press. Chilean Ministry Housing and Urbanism publication, [2011] Cadastre Hanneman, R., et al., [2005] Íntroduction to social network methods´. Hernández-García, J. [2013] ‘Public Space in Informal Settlements: The Barrios of Bogotá’ Cambridge Scholars Publishing. Jones, P., [2016] ´The Emergence of Pacific Urban Villages: Urbanization Trends in the Pacific Islands´; Asian Development Bank: Manila, Philippines. Klaufus, C., CEDLA [2012] ‘Informal Housing: Latin America’ Center for Latin American Research and Documentation, Amstedam, The Netherlands van Lindert P., Utrecht University, Utrecht, The Netherlands Magalaes F., editor Slum Upgrading and housing in Latin America Minnery, J., et al. [2013] ´Slum upgrading and urban governance: Case studies in three South East Asian cities´. Habitat Int. 2013, 39, 162–169. [CrossRef] images. Pino, A. [2012] PhD thesis ´Habitat informel dans les quebradas de Valparaíso dynamiques d’appropriation´, Université de Bretagne Occidentale. Piselli, F. [2007] Ćommunities, Places and Social Networks´. American Behavioral Scientist 50:867–78. Polenske, K. 2007. The Economic Geography of Innovation. Cambridge: Scott Dempwolf,C. et al., [2012] ´The Uses of Social Network Analysis in Planning: A Review of the Literature´. Journal of Planning Literature. 27. 3-21.10.1177/0885412211411092. Sheng, Y.K., [2010] ´Good Urban Governance in Southeast Asia´. Environ. Urban. ASIA 2010, 1, 131–147. [CrossRef]. Stiles, R., [2013] Á Guideline For Making Space´ Joint Strategy Activity 3.3 Guideline is part of the project “UrbSpace” [www.urbanspaces.eu] that is implemented through the CentraL eUroPe Programme cofinanced by the erDF. Turner, JC. [1967] ´Barriers and Channels for Housing Development in Modernizing Countries´ Journal of the American Institute of Planners, 33:3, 167-181, DOI: 10.1080/01944366708977912, To link to this article: http://dx.doi.org/10.1080/01944366708977912 Wolch, J., et al., [2014] Úrban Green Space, public health and environmental justice: The challenge of making cities just green enough´. Yang, Y., et al., [2012] ´Walking Distance by Trip Purpose and Population Subgroups´. American Journal of Preventive Medicine, 43(1), 11–19. http://doi.org/10.1016/j.amepre.2012.03.015 Zhu, J., [2017] ´Symmetric Development of Informal Settlements and Gated Communities: Capacity of the State—The case Study of Jakarta, Indonesia´; Asia Research Institute Working Paper Series No. 135; National University of Singapore: Singapore, 2010. Available online: http://www.ari.nus.edu.sg/wps/wps10_135.pdf [accessed on 31 May 2017].

7.3.4

List of Figures

7.3.4.1

01 INTRODUCTION, LIST OF FIGURES

Fig. 01 Google Earth [2012] 'Natural opened areas, used as soccer field' 014 source: Google Earth, imagery Date: 02.2012 Image © 2018 Google 33°03´25.41" S, 71°36´30.90" W, Elev. 11 m, eye alt. 3 m. Fig. 02 OjedadelRio,P. [2017], 'Urban Social Dancing & Happiness' [photograph] 016 Valparaíso´s own private collection. Spontaneous Social gathering in an open space in Valparaiso. The dancing Group 'Swim Valpo' and a music band meet at that point to appeal Social interaction. Location: Lautaro Rosas Street in ´Cerro Alegre´ - Valparaíso. Fig. 03

South America Map highlighting [orange] Chile and Valparaiso City Port [white dot].

018

Fig. 04

Valparaíso´s Regional map highlighting [grey Valparaiso District.

018

Fig. 05

Valparaíso District highlighting [red] Valparaiso City Port. Source: Valparaíso Municipality.

018

Fig. 06

´Techo´ [2013], Valparaíso informal settlements spreading in the upper part 019 of the mountains. source: http://www.laotravoz.cl/columna-lov-tras-la-reproduccion-so cial-de-los-campamentos/

Fig. 07

Valparaíso´s informal urban settlements distribution, size of dots is related with the number of families by settlement [see the Legend] Source: Document ‘Catastro 2011: Mapa Social de Campamentos’ resultados generals Housing and Urbanism Ministry.

020

Fig. 08

Chilean Region´s number of informal settlements bar graph. Source: 'Catastro 2011: Mapa Social de Campamentos' [Informal settlements Cadastre] published by 'Ministerio de Vivienda y Urbanismo' [Chilean House and Urbanism Ministry]

020

Fig. 09

Chilean Region´s number of households living in Informal settlements bar graph. Source: 'Catastro 2011: Mapa Social de Campamentos' [ Informal Settlements Cadastre] published by 'Ministerio de Vivienda y Urbanismo' [Chilean House and Urbanism Ministry]

020

Fig. 10

Rigid urban network connection between Valparaíso informal urban settlements.

021

Fig. 11

Flexible urban network, conceptual proposal for connecting Valparaíso informal urban settlements.

021

7.3.4.2

02 DOMAIN, LIST OF FIGURES

Fig. 12

La Vega aerial photograph ‘Unidades de Planificación Física 10 La Vega’ [Physical Planning Units No 10 La Vega] Source: Image © Enlace Arquitectura https://www.plataformaarquitectura.cl/cl/789996/ 48-anos-de-asentamientos-informales-en-caracas/ 5772a744e58ececfd8000017-48-anos-de-asentamientos-informalesen-caracas-foto

022

Fig. 13 Social Active Space absence in Latin American Informal Urban Settlements. Fig. 14 vectordragonfly [2015], ´Petare´ Neighbourhood, Caracas – Venezuela [size: 1200x674] Available at: http://www.dronestagr.am/favelas-of petare-neighborhood-caracas-venezuela-5/ Fig. 15 Unknown author, [2014], A soccer field in Brazil. Available at: http://all-that-is-interesting.com/brazil-favelas#5

025

Fig. 16

Unknown author, Comunas de Medellín Colombia. [size: 1556x1000] Available at: https://spark.adobe.com/page/ogHrs/

027

Fig. 17

Ben-Jacob [2006], Vortex Green 2. Ben-Jacob is a physicist leader in 028 Self-Organisation Theory and pattern formation in open system. Available at: https://imgur.com/gallery/idkEd

Fig. 18

Ben-Jacob [2006], Self-engineering capabilities of bacteria. Published 22 February 2006.DOI: 10.1098/rsif.2005.0089 Available at: http://rsif.royalsociety publishing.org/content /3/6/197

028

Fig. 19

Freel [2016], Visualising the evolution of Bacteria resistance on a ´Mega Plate´ Petri Dish. [Kishony Lab] Available at: http://www.molecularecologist.com/2016/09/ https://hms.harvard.edu/videos/bugs-screen

029

Fig. 20

Stockler [2014], work titled ´Terrão de Cima´ southern and western fringes of Sao Paulo, Brazil. Available at: http://rebrn.com/re/a-soccer-field-inbrazil-508535/

030

027

Fig.21 South America map highlighting Brazil and São Paulo. 032 Fig. 22 Sao Paulo map highlighting Paraisópolis Favela and Grotao Neighbourhood. 032 Fig. 23 Paraisópolis Favela urban perimeter highlighting Grotao Neighbourhood. 032 Fig. 24 Factors affecting Social Active Space in highly dense populated areas. 032 Fig.25 Gustavo Dudamel free Concert celebrating the 442 years of Caracas’ 033 foundation. Available at: http://parroquia-lavega.blogspot.co.uk/2010_12_01_archive.html http://www.noticias24.com/actualidad/noticia/71443/dudamel-y-la orquesta-sinfonica-estremecieron-a-la-vega/ Fig. 26

Grotao topographic site plan, identifying topographic profiles A, B, C, D, E, and flooding’s higher levels area.

Fig. 27 Topographic profiles B, C, D, E. Fig. 28 ‘Paraisópolis Urban Area suffers severe flooding due to heavy rainfall and lack of adequate runoff systems’. Image source: Google Earth, imagery Date: 19.04.2015 Image © 2017 DigitalGlobe 23°37´14.89" S 46°43´33.21" W, Elev. 0 m, eye alt. 515 m.

034 034 035

Image List

213

Fig. 29

Topographic profile A.

035

Fig. 42

Isovist Open Space 1 and 1a.

044

Fig. 30

Grotao Urban Network Breakdown.

036

Fig. 43

Isovist Open Space 0 and 2.

044

Fig. 31

Stairway Grotao - Paraisópolis Favela. Image source: Wagner Rebehy Urban ThinkTank Partner.

036

Fig. 44

Isovist Open Space 9, 9a and 9b.

045

Fig. 32

Pedestrian ways Grotao - Paraisópolis Favela. Image source: Wagner Rebehy Urban ThinkTank Partner.

036

Fig. 45

Isovist Open Space13 and 13a.

045

Fig. 33

Cull-de-sac streets within Grotao - Paraisópolis Favela. Image source: Wagner Rebehy Urban ThinkTank Partner.

036

Fig. 46

Grotao Urban Patch plan, highlighting houses in danger due to steep slope, 046 floods, landslides.

Fig. 34

Grotao urban patch identifying urban spaces variety. The plan was elaborated from information received from Urban-Think-Tank architect Wagner Rebehy.

037

Fig. 47

Ducci [2015], Grotao future social provision location. [image, size: 2000x1312] 046 Available at: http://www.archdaily.com.br/br/760744/grotinho-deparaisopolis-construindo-espaços-de-convívio-boldarini-arquitetos-associados.

Fig. 48

Grotao Urban Patch plan highlighting the area prepared for receiving the Social Infrastructure [1] and the terraced land for developing Urban Agriculture [2].

Fig. 49

Lafarge Holcim Foundation [2011], Urban Think Tank architectural and 047 urban proposal for Grotao - Paraisópolis Favela. [image-size: 2800x2100] Available at: https://www.lafargeholcim-foundation.org/awards/3rd-cycle/lat in-america/Winners The figure shows Urban Think Tank proposal for Grotao - Paraisópolis Favela.

Fig. 35-a Centralised Open Space diagram 038 Fig. 35-b Open Space [1] distances to nearest open spaces and entrances to the patch. 038 Fig. 36-a Exposed Open Space diagram

038

Fig. 36-b Open Space [2] distances to nearest open spaces and entrances to the patch. 038 Fig. 37-a Connective Open Space diagram

039

Fig. 37-b Open Space [9] distances to nearest open spaces and entrances to the patch. 039 Fig. 38-a Centralised with extensions Open Space diagram

039

Fig. 38-b Open Space [13] distances to nearest open spaces and entrances to the patch.039

214

Fig. 35-c Open Space [OS-1] metric distances diagram

040

Fig. 36-c Open Space [OS-2] metric distances diagram .

040

Fig. 37-c Open Space [OS-9] metric distances diagram .

040

Fig. 38-c Open Space [OS-13] metric distances diagram.

040

Fig. 35-d Open Space [01] synthesis diagram.

041

Fig. 36-d Open Space [02] synthesis diagram.

041

Fig. 37-d Open Space [09] synthesis diagram.

041

Fig. 38-d Open Space [13] synthesis diagram.

041

Fig. 39

Grotao Urban Patch, Convex Isovist map Sao Paulo, Brazil latitude: 23°37´4.51"S longitude:46°13´32.78"W

042

Fig. 40

Rocco R., [2011] Jardim Angela favela [image - size:853x1280] Available at: https://www.domusweb.it/en/architecture/2009/01/16/ urban-age.html.

043

Fig. 41

Rocco R., [2011] Favela Paraisópolis Side street Sao Paulo [image - size:685x1024] Available at: https://www.flickr.com/photos/ robertorocco/6256501245.

043

Image List

047

7.3.4.3

03 METHODS, LIST OF FIGURES

Fig. 50

Research Methodology diagram.

052

Fig. 63

Genetic Algorithm development diagram.

062

Fig. 51

Radiation Analysis SANTIAGO _CHL1 JAN 1:00 - 31 DEC 24:00 Source: https://energyplus.net/weather-location/south_america_wmo_ region_3/CHL//CHL_Santiago.855740_IWEC

054

Fig. 64

Initial stage, a regular grid [21x21] projected on the terrain.

064

Fig. 65

Cell subdivision into four plots for future housing development

064

Fig. 52

Sun path diagram for Latitude 33°3´and Longitude 71°36´showing Sun angle 054 degree in Winter Solstice [33°] [21 June] Sommer Solstice [85°][21 December].

Fig. 66 Sub-cells motion definition. Fig. 67 Cell´s range of movements. Fig. 68 Cluster four cells after cell´s rotation.

064

Fig. 69

065

Fig. 70-a Definition of points and connections between them.

066

Fig. 70-b Agent based path finding diagram.

067

Fig. 53-a Aspect analysis on the northeastern side of the polygon.

055

Fig. 53-b Aspect degree calculation for each cell of the Northeastern side of the polygon. 055 Fig. 53-c Northeastern side of the polygon aspect cell orientation summary table.

055

Fig. 53-d Aspect directions. Source: Burrough, P. A., and McDonell, R. A., 1998. Principles of Geographical Information Systems [Oxford University Press, New York], p.190

055

Fig. 53-e Aspect algorithm. Source: Burrough, P. A. and McDonell, R.A., 1998. Principles of Geographical Information Systems [Oxford University Press, New York], p. 190.

055

Fig. 53-f

Aspect compass orientation.

055

Fig. 53-g Legend of the weight given to Aspect degree ranges.

055

Fig. 54-a Slope angle analysis of a selected patch in Valparaíso´s ravines. Latitude: 33°3´34"W, Longitude: 71°36´60"S

056

Generative process concluded for three storey floor house.

065 065

Fig. 55-a Surface area increases proportionally when inclination increases. 057 Use surface area for realistic calculations Source: http://www.innovativegis.com/basis/mapanalysis/topic11/topic11.htm Fig. 55-b Surface area increment.

057

Fig. 56

Closest Facility measure - Valparasíso informal settlements located above Alemania Avenue.

058

Fig. 57

Betweenness measure - Valparasíso informal settlements located above Alemania Avenue.

058

Fig. 58

Grasshopper isovist component description.

059

Fig. 58-a Construction process of three neighbours´ open space isovist.

059

Fig. 58-b Three overlapped isovists ´polygon.

059

Fig. 59

Grandjean, 2014, Graph representing the metadata of thousands of archive 060 documents, documenting the social network of hundreds of League of Nations personals. Source: Grandjean, Martin [2014] ´La connaissance est un réseau´ Les Cahiers du Numérique 10[3]: 37-54. DOI:10.3166/LCN.10.3.37-54

Fig. 60

Ucinet 6 for Window, Version 6.636 Software environment.

061

Fig. 61

Ucinet 6 DL Editor, Import Text Data via Spreadsheet Interface ´1´ indicates relationship between two alters and ´0´ indicates no relation. The diagonal with zeros [red] corresponds to alters´ itself relation.

061

Fig. 62 Social Network Basic elements, nodes are connected by ties or linkages.

061

Image List

215

7.3.4.4

04 SITE, LIST OF FIGURES

Fig. 71

OjedadelRio, P. 2017 ´René Lagos ´ Road, 225m level in ´Monjas´hill. Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection.dimensions: [4032 x 3024] size 3.3 MB, date 03.06.2017

Fig. 72

Official map of the city with main basins. Base map: Municipality of Valparaiso, 070 redraw highlighting Jaime´s ravine, research project site.

Fig. 73

Jaime´s ravine nolli map.

070

Fig. 74

OjedadelRio, P. 2017, ´Carlos Pezoa Veliz´ Road, 300 m level in ´Miraflores´hill. Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions: [9350 x 3720] size 9.2 MB, date : 04.11.2017

070

Fig. 75

Jaime´s ravine aerial view, Source: Municipality of Valparaíso.

071

Fig. 76

Valparaíso topography, highlighting the site location. Contour lines source: Municipality of Valparaíso.

072

Fig. 77

Valparaíso topography, site location detail. Contour line source: Valparaíso Municipality.

072

Fig.78

Valparaíso topography cross sections: A, B, C, D, E, F.

073

Fig. 79

Valparaíso Camp´s Social Map publication. Land property distribution [%] pie chart [´Mapa Social de Campamentos´]. Source: Housing and Urbanism Ministry- Executive Secretary. [Secretaría Ejecutiva de Campamentos Ministerio de Vivienda y Urbanismo.] Publication date: 2013.

074

Fig. 80

Topographic north-south sections highlighting in red Informal Urban Settlement location.

074

Fig. 81

Valparaíso Hydrological system. source: Ministry of Public Works, Valparaíso Headquarter, information received in May 2017.

075

Fig. 82

Valparaíso Annual Precipitation rate, 372.5mm per year. Source: www.climatemps.com

075

Fig. 87

Valparaíso City longitudinal section. Relation between the topography and urban development and the location of informal settlements highlighted in green. ´Alemania´ Avenue and ´Colón´ Avenue are urban delimitations between the three urban strips.

080

Fig. 88-a Precariousness of some streets from Álemania Ávenue.Ćerro Toro´ hill. ´Móvil Arquitectosárchitectural practicesource: http://mobilarquitectos.cl/ urbano/cerro-toro. size: [1300x731]

080

Fig. 88-b

OjedadelRio, P. 2017 ´Calle Urriola´ Road, typical street from the second strip between 25m and 100m sea level in ´Alegre´ hill. Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions [6794 x 3830] size 6.3 MB, date: 04.11.2017

080

Fig. 88-c

Fong, C., 2014, ´Trolley buses´ [photograph]. Belongs to the first strip – flat urban area. This is one of the means of transportation of that part of the city. Image source: http://fongandcarolyn.blogspot.cl/2014/04/enjoyingsantiago-valparaiso-partying.html, size: [1600x1066]

081

Fig. 89

Informal settlements initial patterns of land occupation. Main roads of the 082 formal city cross Alemania Avenue, urban boundary that divides the city into two, the formal and the informal. The pioneers of land appropriation followed those traces to maintain connection with the previous urbanised area.

Fig. 90

Three types of Informal Settlement configurations highlighting in red main roads of each type. [1,2,3]

082

Fig. 91

Two directions for land appropriation: a. first movement direction: North to South, b. second movement direction: East-West.

083

Fig. 92

Land occupation pattern section.

084

Fig. 93

Informal settlement´s land occupation process.

084

Fig. 94

Informal settlement´s network branch type.

085

Fig. 83-a Valparaíso [Latitude: -33.0482, Longitude: -71.6035] Radiation Annual variation 076 Source: Energy Ministry, http://www.minenergia.cl/exploradorsolar/

085

Fig. 83-b Valparaíso Radiation Daily cycle variation [Latitude: -33.0482, Longitude: -71.6035] Source: Energy Ministry, http://www.minenergia.cl/exploradorsolar/

076

Fig. 95-b OjedadelRio, P. 2017 'Miguel Angel Road' [n°514] Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. dimensions: [4032 x 3024] size 2.5M, date: 04.11.2017

085

Fig. 84-a Sun path from Winter solstice [WS] - 21 June to Summer solstice [SS] - 21 December in 33°3´ South Latitude, for surfaces with a slope degree from 0° to 54°.

077

Fig. 95-c Samuel, 11.02.2006, 'Aquiles Ramirez 11th passageway' San Juan de Dios hil. [photograph] Image source: blogspot http://valparaisoenfotos.blogspot.cl/ 2006/02/ size: [640x 480]

085

Fig. 84-b Sun Path and the Slope degree summary table, highlighting the period of the year when each slope degree receives Sun rays at 90°.

077

Fig. 96

OjedadelRio, P. 2017, 'Ravine Stilt housing' [photograph] Valparaíso´s own private collection.

087

Fig. 85-a Valparaíso aerial photograph, Illustrate Valparaíso urban morphology. 078 Image source: Google Earth, imagery Date: 13.08.2017, Image © 2017, CNES/ Airbus 33°03´20.71" S - 71°38´01.25" W - Elev. 0 m - eye alt. 4.45 Km. Fig. 85-b Valparaíso three urban strips. 078

Fig. 97

Cut and fill technical procedure example redrew from original image in publication ´Steep Slopes Guide / Model Regulations´ - Lehigh Valley Planning Commission, November 2008. Source: http://www.lvpc.org/pdf/SteepSlopes.pdf

088

Fig. 86

216

068

Image List

Overlapping natural and urban layers diagram, four stages.

079

Fig. 98-a Flat House 1, 0° slope gradient, 33°04´05.53"S, 71°36´36.76"W, contour level +300 Image source: Google Earth.

088

Fig. 98-b Flat House 2, 5° slope gradient, 33°03´39.76"S, 71°36´43.95"W, contour level +225. Image source: Google Earth

088

Fig. 108 OjedadelRio, P. 2017 'Valparaíso traditional funicular, Ascensor Barón' [photograph] Valparaíso´s own private collection.

094

Fig. 99-a Flat house,1 elevation .

088

095

Fig. 99-b Flat house 2 elevation.

088

Fig. 100

Flat house 1 and 2 summary table.

088

Fig. 101

Stilt house technical information from LOW-VOLUME ROADS ENGINEERING Best Management Practices Field Guide By Gordon Keller & James Sherar USDA Forest Service/USAID

089

Fig. 110

Valparaíso places for urban social encounter mapped in the city plan. 096 Criterion for the definition of categories was defined by Size in m2. 7 categories were defined and ranked.

Fig. 102-a Wooden stilt house 1, 15° slope gradient, 33°04’02.15’’S 71°36’39.72’’W, contour level Image source: Google Earth.

089

Fig. 111

Valparaíso original flooding areas at the first occupation of the territory. 096 The diagram shows four original basins of Valparaíso bay. Later, those flooding areas were transformed into its main ´plazas´.

Fig. 102-b Concrete stilt house 2 30° slope gradient, 33°03’46.36’’S 71°37’05.04’’W, contour level +245. Image source: Google Earth.

089

Fig. 112

Valparaíso green areas comparative table.

Fig. 102-c Concrete stilt house 3, 14.04° slope gradient, 33°03’46.45’’S, 71°36’27.57’’W, Contour level 225m. Image source: Google Earth.

089

Fig. 103-a Stilt house 1 elevation.

089

Fig. 103-b Stilt house 2 Elevation.

089

Fig. 103-c Stilt house 3 Elevation.

089

Fig. 104

089

Stilt house 1, 2 and 3 summary table.

Fig. 105-a Overlapping housing and slope gradients. 48.39% of housing in slope gradient 1° to 6.99°.

090

Fig. 105-b 48.99% of housing in slope gradient 7° to 12.99°.

090

Fig. 105-c 58.51% of housing in slope gradient 13° to 18.99°.

090

Fig. 105-d 50.98% of housing in slope gradient 19° to 19.99°.

090

Fig. 105-e 57.24% of housing in slope gradient 20° to 26.99°.

090

Fig. 105-f 45.70% of housing in slope gradient 27° to 30.99°.

090

Fig. 105-g 34.10% of housing in slope gradient 31° to 35.99°.

096

Fig. 113 Campos M., 2016, ´Plaza Echaurren´ [photograph]. 097 Image source : http://img.soy-chile.cl/Fotos/2016/05/26/file_ 20160526182758.jpg Fig. 114 Anonymous, 2013, ‘Invación callejera 2013’ [photograph]. Summer Urban 097 Festival, took place in different places of the city and ´Plaza Aníbal Pinto´ was one of them. Image source: http://festivalinvasion callejerablogspotco. uk/2012/12/lista-la-programación-de-invasion.html Fig. 115

OjedadelRio, P. 2017 'Children playing at ´Plaza Victoria´, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. Date: 04.06.2017

098

Fig. 116

Sepúlveda C., 2010 ´El club de la brisca´ [photograph]. This picture was taken at Plaza O´Higgins a well-known plaza where retired men meet every day to play cards and sharing with friends and hobbies partners.

099

Fig. 117 OjedadelRio, P. 2017 'Readers at Plaza Victoria, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. Date: 04.06.2017

099

Fig. 118

099

OjedadelRio, P. 2017 'Jugglers at ´Plaza Victoria´, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. Date: 04.06.2017

Fig. 119 ´Plaza Victoria‘ plan.

099

090

Fig. 120

Valparaíso plan four ´miradores´ location: 1. ´21 Mayo Mirador ´ 2. ´Gerbasoni Mirador ´ 3. ´Atkinson Mirador´, 4. ´Espíritu Santo Mirador´.

100

Fig. 105-h 33.33% of housing in slope gradient 36° to 54°.

090

Fig. 121

´Miradores´ and openings data summary table.

100

Fig. 105-i Housing land occupation key map highlighting the area analysed.

091 091

Fernández J. [2015], ´Paseo 21 de Mayo´ [21st May Promenade], [photograph] next to ´ Ascensor Artillería´ funicular.

101

Fig. 105-j Housing land occupation summary diagram.

Fig. 122

Fig. 105-k Housing land occupation summary table.

091

Stairways and funiculars angle variations.

092

‘Mirador’ next to ‘Ascensor Espíritu Santo’ Funicular Source : http://val-paraiso.blogspot.co.uk/2015/01/cerro-bellavistaparte-2.html

101

Fig. 106

Fig. 123

Fig. 107-a Alternative routes diagram from origin to destination point at ´Mariposa´ funicular.

093

Fig. 107-b Alternative routes comparative table.

093

Fig. 124a ‘Mirador’ diagram 1, a place to see the city.

101

Fig. 124b ´Mirador´ diagram 2, stairway landing, a place to rest when climbing stairways. 101 Fig. 125a ‘Mirador Paseo Gerbasoni´ isovists.

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Image List

217

218

Fig. 125b ´Mirador Paseo Atkinson´ isovists.

101

Fig. 140-d 20 years-old male Sociogram.

108

Fig. 125c ‘Mirador ascensor Espíritu Santo´ isovists.

101

Fig. 141-a 25 years old female Ego Network Composition.

109

Fig. 126

Tong S. [May 22nd, 2017] ´Puppet show at El Mirador´, [photograph] next to ´Ascensor Artillería´ funicular. Image source: https://www.scope.travel/destinations/what-you-need-todo-in-valparaiso/ size [1920x1440]

103

Fig. 141-b 25 years old female Ego Age-groups.

109

Fig. 141-c 25 years old female Ego Social Relation Type group horizontal bar graph.

109

Fig. 127

OjedadelRio, P. 2017 'Boy seeing the city through telescope, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. Date: 04.11.2017

103

Fig. 141-d 25 years-old female Sociogram.

109

Fig. 142-a 32 years old female Ego Network Composition.

110

Fig. 128

OjedadelRio, P. 2017 'Urban artist carrying its selling trolley, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. Date: 04.11.2017

103

Fig.142-b 32 years old female, Ego Age-groups.

110

Fig. 142-c 32 years old female Ego Social Relation Type group horizontal bar graph.

110

Fig. 129

OjedadelRio, P. 2017 'Walking, sharing, buying arts and crafts, Social encounter 103 series' [photograph] Valparaíso´s own private collection. Date: 04.11.2017

Fig. 142-d 32 years-old female Sociogram.

110

Fig. 143-a 52 years old female Ego Network Composition.

111

Fig. 143-b 52 years old female Ego Age-groups.

111

Fig. 143-c 52 years old female Ego Social Relation Type group horizontal bar graph.

111

Fig. 143-d 52 years-old female Sociogram.

111

Fig. 144-a 58 years old female Ego Network Composition.

112

Fig. 144-b 58 years old female Ego Age-groups.

112

Fig. 144-c 58 years old female Ego Social Relation Type group horizontal bar graph.

112

Fig. 144-d 58 years-old female Sociogram.

112

Fig. 145-a 60 Ego Network Composition.

113

Fig. 145-b 60 years old female Ego Age-groups.

113

Fig. 145-c 60 years old female Ego Social Relation Type group horizontal bar graph.

113

Fig. 145-d 60 years-old female Sociogram.

113

Fig. 146-a 69 years old female Ego Network Composition.

114

Fig. 146-b 69 years old female Ego Age-groups.

114

Fig. 146-c 69 years old female Ego Social Relation Type group horizontal bar graph.

114

Fig. 130-a TAC Cordillera´ [Date] ´Cleaning the site for building the ravine´ Image source: ´TAC Cordillera´

104

Fig. 130-b ´TAC Cordillera´, ´Building the amphitheatre by the local community´ [Date] Image source: ´TAC Cordillera´

104

Fig. 130-c ´TAC Cordillera´, ´Using the amphitheatre by the local community´ Image source: ´TAC Cordillera´

104

Fig. 131

Valparaíso plan, locating ´TAC Cordillera amphitheatre´ and four stairways used as ´spontaneous amphitheatre´

104

Fig. 132

Lazo E. [August 21st, 2016] ´Spontaneous Amphitheater in Bianchi Street´ 105 [photograph]. Image source: https://twitter.com/edgardolazo, size [1200x674]

Fig. 133

Acuña, [2011] ´Invación Callejera´ [photograph]. Street invasion in stairway next to ´Espíritu Santo´funicular, size [640x425], Image source: https://www.rodrigoacunabravo.cl/tag/reportaje-grafico/

105

Fig. 134

Camp´s Population Pyramid, The Cadaster made in 2011 did count 83.683 number of people living in Informal Urban Settlements called ´Camps´. Source: Chilean Housing and Urbanism Ministry, publication "Camp´s Social Map" published January 2013.

106

Fig. 135

Camp´s Social Map Family Structure Type [%]. Source: Chilean Housing and Urbanism Ministry, publication "Camp´s Social Map" published January 2013.

106

Fig. 136

Interviewed inhabitants living location summary map.

107

Fig. 146-d 69 years-old female Sociogram.

114

Fig. 137

Nuclear family with children diagram.

107

Fig. 147-a 76 years old female Ego Network Composition.

115

Fig. 138

Ego social relation types.

107

Fig. 147-b 76 years old female Ego Age-groups.

115

Fig. 139

Nodes and Ties Attributes description and contact frequency categories.

107

Fig. 147-c 76 years old female Ego Social Relation Type group horizontal bar graph.

115

Fig. 140-a 20 years old Ego Network Composition.

108

Fig. 147-d 76 years-old female Sociogram.

115

Fig. 140-b 20 years-old male Ego Age-groups horizontal bar graph.

108

Fig. 148-a Centrality measures data summary table.

116

Fig. 140-c 20 years-old male Ego Social Relation Type group horizontal bar graph.

108

Fig. 148-b Contact frequency data summary table.

116

Image List

7.3.4.5

05 DESIGN DEVELOPMENT AND STRATEGY, LIST OF FIGURES

Fig. 150-a Topographical plan of the selected urban patch.

120

Fig. 159-e Cluster 3 topographic sections.

126

Fig. 150-b Selected urban patch current street network, four open spaces and their connections with ´Alemania´ Avenue.

120

Fig. 160-a Cluster 3 network distances analysis.

127

Fig.150-c Cluster´s open space connections.

120

127

Fig. 151

121

Fig. 160-b Cluster 3 remapped network distances values. Fig. 161 OjedadelRio, P. 2017 ´Open Space Cluster 3, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 7th 2017. size: 7.5 MB dimensions [8792x3822] -33.0608 latitude, -71.6067 longitude Fig. 162-a Cluster 4 plot´s size.

128

Fig. 162-b Cluster 4 plot´s size bar graph.

128

Fig. 162-c Cluster 4 urban data summary table.

128

Fig. 162-d Cluster 4 Urban Network Analysis [UNA], Betweenness.

128

Fig. 162-e Topographic sections show the ground relief of the open space Cluster 4.

128

Fig. 163-a Cluster 4 network distances analysis.

129

Fig. 163-b Cluster 4 remapped network distances values.

129

Fig. 164

OjedadelRio, P. 2017 ´Open Space Cluster 4, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 3rd 2017. size: 2.3 MB dimensions [4032x3024] -33.0630 latitude, -71.6178 longitude.

129

Fig. 165-a Cluster 1 open space built environment.

130

Selected urban patch Urban Network Analysis [UNA] centrality indices.

127

Fig. 152-a Section between open space 4 and 3.

121

Fig. 152-b Section between open space 1 and 2.

121

Fig. 153-a Cluster 1 plot´s size.

122

Fig. 153-b Cluster 1 plot´s size bar graph.

122

Fig. 153-c Cluster 1 urban data summary table.

122

Fig. 153-d Cluster 1 Urban Network Analysis [UNA], Betweenness.

122

Fig. 153-e Cluster 1 topographic sections.

122

Fig. 154-a Cluster 1 network distances analysis.

123

Fig. 154-b Cluster 1 remapped network distances values.

123

Fig. 155

OjedadelRio, P. 2017 ´Open Space Cluster 1, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 3rd 2017, a typical Saturday afternoon at ´Plaza Esmeralda ‘permanently visited by citizens and foreign people size: 7.9 MB dimensions [8278x3782], -33.0535 latitude, -71.6161 longitude.

123

Fig. 156-a Cluster 2 plot´s size.

124

Fig. 165-b Cluster 2 open space built environment.

130

Fig. 156-b Cluster2 plot´s size bar graph.

124

Fig. 165-c Cluster 3 open space built environment.

130

Fig. 156-c Cluster 2 urban data summary table.

124

Fig. 165-d Cluster 4 open space built environment.

130

Fig. 156-d Cluster 2 Urban Network Analysis [UNA], Betweenness.

124

Fig. 165-e Cluster 1 network synthesis. In red open space analysed.

130

Fig. 156-e Cluster 2 topographic sections.

124

Fig. 165-f Cluster 2 network synthesis. In red open space analysed.

130

Fig. 157-a Cluster 2 network distances analysis.

125

Fig. 165-g Cluster 3 network synthesis. In red open space analysed.

130

Fig. 157-b Cluster 2 remapped network distances values.

125

Fig. 165-h Cluster 4 network synthesis. In red open space analysed.

130

Fig. 158 Fig. 159-a

OjedadelRio, P. 2017 ´Open Space Cluster 2, Valparaíso, Social encounter series' [photograph] Valparaíso´s own private collection. This picture was taken June 7th 2017, size: 8.4 MB dimensions [7994x3628] -33.0585 latitude, -71.6151 longitude Cluster 3 plot´s size.

125

Fig. 165-i Remapped values for network structure Cluster 1.

130

Fig. 165-j Remapped values for network structure Cluster 2.

130

Fig. 165-k Remapped values for network structure Cluster 3.

130

Fig. 159-b Cluster 3 plot´s size bar graph.

126

Fig. 165-l Remapped values for network structure Cluster 4.

130

Fig. 159-c Cluster 3 urban data summary table.

126

131

Fig. 159-d Cluster 3 Urban Network Analysis [UNA], Betweenness.

126

Fig. 165-m Literal Hierarchies: Transport from a central source. a - Each link is separate b - Arranging links into a more efficient structure. Redraw from The New Science of Cities, Batty [2013]

126

Image List

219

Fig. 165-n Street intersections diagram. Fig. 165-o Furcative change diagram re-draw from ´Self-Organization and the City´ Portugali [1999]

131

Fig. 176-a Cell´s filtration process on slope gradient [30° - 35.99°]

135

131

Fig. 176-b X [SR]1,809.33 kWh/m2 for cells in slope gradient [26° - 29.99°]

135

Fig. 166-a Valparaíso topographic map, ´Alemania´ Avenue [in red] and urban patch selected

132

Fig. 176-c Fittest cells accomplishing, slope gradient [26° - 29.99°] continuity, solar radiation above mean value and minimum polygon size.

135

Fig. 166-b Square grid [588mx903m], cell grid size: [21x21]

132

Fig. 177-a Cell´s filtration process on slope degree [36° - 54°]

135

Fig. 167

132

Fig. 177-b X [SR]1,629.25 kWh/ m2 for cells in slope gradient [36° - 54°]

135

Slope and Solar radiation filtration process strategy.

Fig. 168-a Diagram of Slope gradients assessment based on Tweed Shire Council www.tweed.nsw.gov.au.

133

Fig. 177-c Fittest cells accomplishing, slope gradient [26° - 29.99°] continuity, solar radiation above mean value and minimum polygon size.

135

Fig. 168-b Slope range percentages bar graph.

133

Fig. 178

136

Fig. 169 Fig. 170-a Cell´s filtration process on slope degree [1° - 6.99°]

133 134

Fig. 179-a Top view three groups obtained through filtration process: Group 1 [G1] ravine[R] west side, group 2 [G2], R east side, group 3 [G3]

137

Fig. 170-b X solar radiation [SR] 2,124.94 kWh/m2 for cells in slope degree [1° - 6.99°]

134

Fig. 179-b Section illustrates spatial relation between three groups G1, G2 and G3.

137

Fig. 180

138

Fig. 170-c Fittest cells accomplishing, slope degree [1° - 6.99°] continuity, 134 solar radiation above mean value and minimum polygon size. Fig. 171-a Cell´s filtration process on slope gradient [7° - 12.99°] 134 Fig. 171-b X solar radiation [SR] 2,124.94 kWh/m2 134 for cells in slope gradient [7° - 12.99°]

Urban and Geographical boundaries separating areas found after filtration process.

Fig. 181-a Urban [´Alemania´ Avenue] and Geographical [ravine] existent boundaries 139 between informal urban settlements [IUS] and between them and the formal city, synthesis diagram. Fig. 181-b Conceptual intention for connecting IUS and connection between them 139 and the formal city synthesis diagram. Fig. 182-a Jaime´s ravine natural topography. 140

Fig. 171-c Fittest cells accomplishing, slope gradient [7° - 12.99°] continuity, solar radiation above mean value and minimum polygon size.

134

Fig. 172-a Cell´s filtration process on slope gradient [13° - 17.99°]

134

Fig. 182-b Square grid over Jaime´s ravine the Natural topography.

140

Fig. 172-b X [SR] 2,128.49 kWh/m2 for cells in slope gradient [13° - 17.99°]

134

Fig. 182-c Dot grid over Jaime´s ravine natural topography.

140

Fig. 172-c Fittest cells accomplishing, slope gradient [13° - 18.99°] continuity, solar radiation above mean value and minimum polygon size.

134

Fig. 182-d Collection of dots following the bottom of Jaime´s ravine.

140

Fig. 182-e Dots identifying highest points of Jaime´s ravine hills.

140

Fig. 173-a Cell´s filtration process on slope gradient [19° - 20.99°]

134

Fig. 182-f Hill´s highest points connection for creating a polygon for the project.

140

Fig. 173-b X [SR] 2,112.18 kWh/m2 for cells in slope gradient [19° - 20.99°]

134

Fig. 182-g Square grid of the polygon created.

141

Fig. 173-c Fittest cells accomplishing, slope gradient [19° - 20.99°] continuity, solar radiation above mean value and minimum polygon size.

134

Fig. 183-a Ravine section 1W-1E.

141

Fig. 183-b Ravine section 2W-2E

141

Fig. 174-a Cell´s filtration process on slope gradient [21° - 25.99°]

135

Fig. 183-c Ravine section 3W-3E.

141

Fig. 174-b X solar radiation [SR] 2,044.80 kWh/m2 for cells in slope gradient [21° - 25.99°]

135

Fig. 183-d Ravine section 4W-3E.

141

Fig. 183-e Ravine section 5W-5E.

141

Fig. 183-f Ravine section 6W-6E.

141

Fig. 184

141

Fig. 174-c Fittest cells accomplishing, slope gradient [21° - 25.99°] continuity, 135 solar radiation above mean value and minimum polygon size. Fig. 175-a Cell´s filtration process on slope gradient [26° - 29.99°] 135

220

Cell´s filtration process final result, ten areas constitute five groups accomplishing slope gradient continuity and high solar exposure.

Fig. 175-b X [SR]1,903.90 kWh/m2 for cells in slope gradient [26° - 29.99°]

135

Fig. 175-c Fittest cells accomplishing, slope gradient [26° - 29.99°] continuity, solar radiation above mean value and minimum polygon size.

135

Image List

Jaime´s ravine relief.

Fig. 185-a Elevation analysis top view of the terrain highlighting ravine sector.

142

197-b

Dwelling types, T1, T2, T3, T4 and T5 distribution.

150

Fig. 185-b Sub-sector polygons, Northeast [NE], Southeast [SE], Northwest [NW], Southwest [SW], Ravine [R]

142

198-a

T1 - 36m2- fittest individual dwellings.

150

Fig. 185-c Broken down elevation remapped values.

142

198-b

T2 -36m2x2- fittest individual dwellings.

150

Fig. 185-d Summary table elevation numbers, remapped values and percentages of each of them in the five sectors of the patch analysed.

142

198-c

T3 -45m2- fittest individual dwellings.

150

198-d

T4 -63m2- fittest individual dwellings.

151

Fig. 186-a Aspect analysis top view of the terrain highlighting ravine sector [red].

143

198-e

T5 -81m2- fittest individual dwellings.

151

Fig. 186-b Compass indicating cardinal directions on the patch analysed.

143

Housing type summary table, m2, number of inhabitants and storey floors.

151

Fig. 186-c Broken down aspect remapped values.

143

199 200

Octopus´ results Summary table for clusters´ generation

152

201

Fittest Clusters individuals.

153

Fig. 202

Patch Network generation diagram [47Ha]

154

Fig. 202-a Social Active Spaces location [green dots] alongside the polygon.

154

Fig. 202-b Dot´s connection 42m distance.

154

Fig. 202-c Dot´s connection63m distance.

154

Fig. 186-d Summary table aspect degrees numbers, remapped values and percentages 143 five sectors of the patch analysed. Fig. 187-a Slope gradient analysis top view of the terrain.

144

Fig. 187-b Broken down slope remapped values.

144

Fig. 187-c Summary table slope gradient numbers, remapped values and percentages 144 of each of them in the five sectors of the patch analysed. Fig. 188

Elevation fittest cells synthesis map and remapped values table.

145

Fig. 202-d Dot´s connection 84m distance.

154

Fig. 189

Aspect fittest cells synthesis map and remapped values table.

145

Fig. 202-e Dot´s connection 105m distance.

154

Fig. 190

Slope fittest cells synthesis map and remapped values table.

145

Fig. 202-f Dot´s connection 126m distance.

154

Fig. 191

Elevation, aspect and slope remapped values synthesis map.

145

Fig. 203

Route´s inclinations differentiation by slope degree top view.

155

Fig. 192

Elevation, aspect and slope urban program strategy.

146

Fig. 203-a Section 107meters.

155

Fig. 193-a Fittest cells for housing land use.

146

Fig. 203-b Section 84 meters.

155

Fig. 193-b Fittest cells for stairways land use.

147

Fig. 203-c Stairway development for section 84 meters, slope degrees from 5° to 35°. 155

Fig. 193-c Fittest cells for funiculars land use.

147

Fig. 203-d Section 42 meters, slope degrees from 8° to 25°.

155

Fig. 193-d Fittest cells for ´plazas´ land use.

147

Fig. 204-a Walking distance, slope degree and speed diagram.

156

Fig. 193-e Fittest cells for ´miradores´ land use.

147

Fig. 204-b Walk created by Ani from the Noun Project.

156

Fig. 193-f Fittest cells for amphitheatre land use.

147

Fig. 204-c Daytime cycling created by Kathleen Black from the Noun Project.

156

Fig. 194-a Family structure and number of family members.

148

Fig. 204-d Wheelchair created by Jens Tärning from the Noun Project.

156

Fig. 194-b Housing room and size requirements per family size.

148

Fig. 204e: Child Running created by Gan Khoon Lay from the Noun Project.

156

Fig. 194-c Minimum inhabitable surface per family size – Healthy Housing a Practical Guide published by World Health Organisation [2005]

148

Fig. 205-a Tobler´s Hiking function.

156

Fig. 195

Neighbourhood units top view location.

148

Fig. 204-c Daytime cycling created by Kathleen Black from the Noun Project.

156

Fig. 196

Number of house type per ravine sector.

149

Fig. 204-d Wheelchair created by Jens Tärning from the Noun Project.

156

Fig. 197-a Cell grid 21x21 divided into 4 plots for four dwelling occupation.

150

Fig. 204e: Child Running created by Gan Khoon Lay from the Noun Project.

156

197-b

150

Dwelling types, T1, T2, T3, T4 and T5 distribution.

Image List

221

Fig. 205-a Tobler´s Hiking function.

156

Fig. 218

M5 type ´mirador´ Elevation.

164

Fig.205-b Tobler´s Hiking graph.

156

Fig. 219

M5 type ´mirador´ Solar Radiation Analysis:Winter & Summer Solstice

165

Fig. 205-b1 Turtle created by Y,KR from the Noun Project

156

Fig. 220

Suitable cells for developing amphitheatres in the whole polygon.

166

Fig. 205-b2 Rabbit created by Tatiana Belkina from the Noun Project Fig. 206 Staiway and funicular basic elements.

156

Fig. 221

Selection of grouped cells accomplishing concave curvature required for developing amphitheatres.

166

Fig. 207 Funicular´s locations.

157

Fig. 222

Detail of six grouped cells accomplishing slope, aspect and contour concavity for developing amphitheatres.

166

Fig.208-a ´Plazas´ fittest locations.

158

Fig. 223-a Concave curve top view.

167

Fig.208-b ´Plazas´ geometry and rotation following slope aspect.

158

167

Fig.208-c ´Plazas´aggregation process - 2 cells, 882 m2.

159

Fig. 223-b Concave curve section. Fig. 224-a 3/4 arena amphitheatre.

Fig.208-d 2-cells ´Plaza´ perspective view.

159

Fig. 224-b Wide fan amphitheatre.

167

Fig.208-e ´Plazas´aggregation process - 4 cells, top view, 1764 m2

159

Fig.208-f ´Plazas´aggregation process - 4 cells, perspective view.

159

Fig.208-g ´Plazas´ aggregarion process - 6 cells, top view, 2646 m2

159

Fig.208-h ´Plazas´ aggregarion process - 6 cells, perspective view.

159

Fig. 209-a ´Mirador´ primitive geometry Body Plan top view.

160

Fig. 209-b ´Mirador´ tighten façade for northern and western orientations.

160

Fig. 209-c ´Mirador´widen façade for southern and eastern orientations.

160

Fig. 210-a ´Mirador´ primitive geometry Body Plan front view.

160

157

Fig. 210-b ´Mirador´ front view, tighten façade for northern and western orientations. 160 Fig. 210-c ´Mirador´ front view, widen façade for southern and eastern orientations.

160

Fig. 211

´Mirador´ primitive geometry section.

160

Fig. 212-a Mirador Fitness criteria and goals: Courtyard sunlight control.

161

Fig. 212-b Mirador Fitness criteria and goals: Socially Active Space area compensation. 161

222

Fig. 213 Fittest ´miradores´ individuals, M1 type. Fig. 214 M1 type ´mirador´Solar Radiation Analysis: a. Winter & Summer Solstice

162

Fig. 215

163

Fittest ´miradores´individuals, M4 type.

162

Fig. 216 M4 type ´mirador´Solar Radiation Analysis: Winter & Summer Solstice.

163

Fig. 217

164

Image List

Fittest ´miradores´ individuals, M5 type.

167

7.3.4.7

APPENDIX LIST OF FIGURES

168

Fig. 239

20 years old male social network matrix.

192

Fig. 226 Design proposal general view 1. [from northwestern to southeastern side].

170

Fig. 24

25 years old female social network matrix.

194

Fig. 227 Design proposal general view 2. [from southern to northern side].

172

Fig. 241

32 years old female social network matrix.

196

Fig. 228 Design proposal, view 3, ´Socially Active Spaces, Amphitheatre and ´Miradores´. Fig. 229 Design proposal, view 4, Socially Active Spaces, ´Plazas´ and Funiculars.

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Fig. 242

52 years old female social network matrix.

198

Fig. 243

58 years old male social network matrix.

200

Fig. 244

60 years old male social network matrix.

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178

Fig. 245

69 years old male social network matrix.

180

Fig. 246

76 years old male social network matrix.

Fig. 232

Urban Network Analysis [UNA], test patch. 182

Fig. 247

Solution space navigation for Housing type Octopus experiments.

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Fig. 233

Urban Network Analysis [UNA], cluster 1.

182

Fig. 248

Solution space navigation for Cluster´s type Octopus experiments.

208

Fig. 234

Urban Network Analysis [UNA], cluster 2.

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Fig. 249

Solution space navigation for Cluster´s type Octopus experiments. Pareto Front results.

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Fig. 235

Urban Network Analysis [UNA], cluster 3.

183 7.3.4.8

BACK AND COVER PAGES LIST OF FIGURES

7.3.4.6

06 DESIGN PROPOSAL LIST OF FIGURES

Fig. 225

Proposal view.

Fig. 230 Design proposal, view 5, Housing, Stairways and Socially Active Spaces, ´Miradores´. Fig. 231 Design proposal, view 6, Housing and Socially Active Spaces, ´Miradores´.

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Fig. 236-a Cluster 1, Isovist spatial analysis, general view. 184

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Fig. 236-b Cluster 1, Isovist spatial analysis, individuals.

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Fig. 236-c Cluster 1, Isovist data.

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Fig. 237-a Cluster 2, Isovist spatial analysis, general view.

185

Fig. 251

Fig. 237-b Cluster 2, Isovist spatial analysis, individuals.

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Fig. 237-c Cluster 2,Isovist data.

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Fig. 238-a Cluster 3, Isovist spatial analysis, general view.

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Fig. 238-b Cluster 3, Isovist spatial analysis, individuals.

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Fig. 238-c Cluster 3, Isovist data.

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224

Cover page, Dot´s connection 126m distance.

Image List

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´Socially Active Space´

a proposal for Ravine´s Self-Organised Urban Settlements, Valparaíso - Chile

´Socially Active Space ´

a proposal for Ravine´s Self-Organised Urban Settlements, Valparaíso - Chile Patricia Ojeda del Rio [M Arch]

P. Ojeda del Rio

´socially active space´ a proposal for ravine´s self organised urban settlements, valparaíso chile

Articles inside

the importance of urban social interaction for creating better cities