Urban Resilience MArch Akshay Narwekar Kuber Patel Arpi Maheshwari aka Jagetiya Msc Hazar Karahan
Architectural Association School of Architecture Emergent Technologies and Design
Architectural Association School of Architecture Graduate School Programme Coversheet for Submission 2014-2016 Programme: Emergent Technologies and Design Term: 2014-2016 Student Name(s): Akshay Narwekar, Kuber Patel, Arpi Maheshwari aka Jagetiya Submission Title: Urban Resilience : A flood resilient approach for informal settlements
Course Title: Emergent Technologies and Design, Master in Architecture Course Directors : Michael Weinstock, George Jeronimidis Studio Tutors : Evan Greenberg, Manja Van De Worp, Mehran Gharleghi Submission Date: 05.02.2016 Declaration : “I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.�
Signature of Student(s):
Akshay Narwekar
Kuber Patel
Arpi Maheshwari aka Jagetiya
Acknowledgements We would like to thank the course directors at Emergent Technologies & Design - Michael Weinstock and George Jeronimidis for their invaluable guidance and critiques during this dissertation. Along with them, we would also like to express our gratitude to the course tutors - Evan Greenberg, Manja Van De Worp and Mehran Gharleghi for their continuous support and instilling faith and confidence in us. Lastly, we would be thankful to our family for supporting us as well as our friends at the EmTech studio for a constant exchange of knowledge and skills that would help us along our professional paths.
Table of Contents Abstract .....................................................................................................................09 0. Introduction ...........................................................................................................11 1. Domain ..................................................................................................................15 1.1 Global Scenario 1.2 Urban Flooding 1.3 Informal Settlements 1.4 Mumbai - Urban flooding and soil conditions 1.5 Flood Resilience 1.6 Resilient systems - Case Studies 1.7 Traditional Indian step wells 1.8 Housing in Mumbai - informal and low income groups 2. System Components .........................................................................................37 2.1 Branched morphology - Foundation System 2.2 The Chawls - Mumbai’s low income housing 2.3 Hydrological system - Retention Catchments 2.4 Critical Review 3. System Design Drivers - Global Scale ...........................................................65 4. Site ........................................................................................................................71 4.1 Mumbai : Areas exposed to floods 4.2 Site : Dharavi, Mumbai 4.3 Dharavi : Analysing the existing urban fabric 5. Hydrological System...........................................................................................93 5.1 System Approach 5.2 Calculations 5.3 System Logic 5.4 Method 5.5 Experiment Setup 5.6 Experiments : 1 5.7 Experiments : 2 5.8 Experiments : 3 5.9 System Development 5.10 Conclusion
6. Spatial Organization ........................................................................................121 6.1 Neighbourhood relationship 6.2 Open Space Analysis 6.3 Methods 6.4 Experiment 1 6.5 Experiment 2 6.6 Aggregation 7. System Integration..........................................................................................161 7.1 Experiment Setup and Evaluation Criteria 7.2 System Integration Experiment 8. Design Proposal.............................................................................................171 8.1 Overview 8.2 Density Gradient and Program Distribution 8.3 Design Development 8.4 Water Management 8.5 Design Details 9. Conclusion .......................................................................................................189 9.1 Overview 9.2 System Evaluation 9.3 Further Developments Appendix .................................................................................................................194 Bibliography ...........................................................................................................251
Abstract The global concern of flooding in metropolitan cities is a critical aspect laid down in this dissertation. The causes of such floods vary regionally but in an urban setting, the most affected sections of the city due to such natural hazards, are the informal settlements. The existence and growth of such informal communities in an urban scape can neither be controlled nor ignored. Due to lack of infrastructure and backing from the government, these communities are developed by substandard construction without safety and sanitary measures. In the metropolitan city of Mumbai (India) these informal settlements occupy a large footprint and are an integral part of the city’s industrial and economic growth. However, they are located in the most flood prone areas of the city, where urban flooding occurs due to the city’s reclaimed land and low lying topography. As much as they are a key to Mumbai’s industrial development, they are the root cause of aggravating floods as they block the natural discharge of storm water to the city’s outfall sources. Their haphazard planning as well as vulnerability thus becomes an issue to be addressed. Hence, a resilient approach in contrast to a resistant one is crucial in resolving the city’s problem of annual floods as well as rendering these informal settlements safe from flood exposure. This research aims at formulating strategies to develop the social and spatial organization of the informal communities with respect to the city’s geological and topographical conditions. It also takes into account, the integration of local level strategies with a global flood resilient system, to cope up with the annual flood scenario disrupting the city’s functionality.
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0. Introduction
Most of the metropolitan cities in the world are located in a way that enhances the scope for a global exchange, which boosts the economic growth of the city. Due to this, their geographical location makes them prone to climatic hazards. Cities located near the coasts or rivers and experiencing heavy annual rainfall have to withstand the adverse effects of flooding. As these cities grow, urbanization results into migration of rural population to the metropolitan cities. A high proportion of these migrants settle down into informal communities in the less developed areas of the city which are also prone to natural hazards. They lack the necessary infrastructure and construction materials which lead to a substandard living condition. These informal settlements are self organized without the government support which results into functional yet haphazard planning. There is a shortage of essential amenities such as water supply and sanitation. In Mumbai, the industrial capital of the country, a large proportion of rural migrants have led to the formation of extensive informal settlements. These informal settlements are the drivers of the industrial functionality of Mumbai. However, their existence has been adversely affecting the city’s resilience against natural calamities such as pluvial
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floods. Mumbai experiences an average annual rainfall of 1800 mm to 2400 mm during the months from June to September.[Ref 0.1] The natural topography of Mumbai is a low lying area formed by silty soil because of reclaimed land. This makes it difficult for the storm water to drain out from the city into the water bodies. At the same time, the informal communities which are set up near the water bodies create a barrier for the discharge of storm water to the natural outfall. Since it is essential for the informal settlements to be integrated into the urban fabric of Mumbai and rebuild its resilience against urban flooding, a set of strategies on reorganizing the informal settlements and resolving their issues relating to lack of basic amenities forms the basis of this research. In Mumbai, the ‘chawls’ have been a successful building system that have catered to provide housing for the migrants working in the textile mill lands and railway services. A strategy of reinventing the chawl based on its existing configuration and developing a building morphology for the informal communities based on their spatial organization is outlined. This building morphology is thought to be stabilized
for the low soil bearing capacity of the city by developing a foundation system.
issues faced by this informal community and their indirect impact on the city are also identified.
Since the resilient system deals with flooding, an approach of storm water collection, storage and distribution is adopted. The first step to this approach is collection of the incident storm water through a retention catchment strategy. Although this strategy does not directly help in discharging the storm water, it reduces the surface runoff through collection which in turn caters to the water management issues faced by the informal settlements.
The natural topography of the site is evaluated to identify the potential ways to collect storm water into catchments by means of a hydrological channel network. These storage devices, termed as detention catchments are intended to delay the storm water discharge during heavy rainfall. It would then channelize the stored water through a connection between watersheds and finally to its discharge outlets.
These three systems form a local part of a global system that deals with an approach of delaying and discharging the storm water to its outfalls. It also aims at integrating the informal community with the urban fabric by means of establishing a neighbourhood relationship and resolving the spatial organization for the redeveloped morphologies. Dharavi in Mumbai, one of Asia’s largest slum community is taken as the site for testing the system components. An analysis of the existing fabric of Dharavi is made to understand the positives that could be extracted from the current spatial organization that has enriched its socioeconomic life. At the same time, the root causes of several
Once this system is established from a top down approach, it would be essential to configure the built up and open spaces on the test patch by adopting a bottom up approach of formulating neighbourhood relationship rules. This would be strongly based on the spatial quality necessary for the live and work nature of the residents in Dharavi. These two systems would then be evaluated to resolve the components of a top down and bottom up approach in a way that the hydrological system integrates with the spatial qualities of the urban patch and is efficient in coping up with pluvial floods.
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1. Domain Global Scenario Urban Flooding Informal Settlements Mumbai - Urban flooding and soil conditions Flood Resilience Resilient systems - Case Studies Traditional Indian step wells Housing in Mumbai - informal and low income groups
“Urban flooding poses a serious challenge to the development and the lives of people, particularly the residents of the rapidly expanding towns and cities in developing countries.� -Abhas K Jha | Robin Bloch Jessica Lamond
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1.1 Global Scenario
Img [1.1.1] An aerial view of houses submerged in flood waters in Pengkalan Chepa, near Kota Bharu, Malaysia Photograph by : (Mohd Rasfan/AFP)
Vulnerability in global cities The strategic location of global cities near the coastal areas has played a major role in their development. At times, they are also located in the low lying areas, near the mouth of major rivers which enabled the growth of trade and commerce internally within the countries as well as for international exchange. This created a basis for such cities to develop as commercial capitals, enabling their own economic growth. [1.1.1] Because of this situation, the location of these cities have also exposed them to climatic hazards like cyclones, coastal flooding, excessive rainfall and sea level rise. Since it is said that the cities occupy a major position globally and are well developed, they also become centers of major population concentration. [1.1.2] According to Martin P. Brockerhoff, ‘‘just 25 years ago less than 2 percent of the global population resided in “mega cities” of 10 million or more inhabitants Today, the proportion exceeds 4 per cent and by 2015 it will top 5 per cent, when mega cities will likely house 400 million people’’. [1.1.3]
This very fact underlines the need of assessing the vulnerability of the population and the city’s assets and infrastructure exposed to the threats of climatic hazards. Moreover, in the urban scenario, most of the vulnerable population comprises of the economically backward people residing in the sub standard housing within the exposed locations of the city.
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1.2 Urban Flooding
Img [1.2.1] Mumbai, India 2005 Floods Photograph: Sebastian D’Souza
Effects of floods in cities Amongst the climatic hazards affecting global cities, the most frequent one addressed in this discussion is the threat posed by floods to the urban environment. Urban flooding has become a critical issue and a challenge to tackle for every vulnerable city. The cause of these floods keep accelerating and their impacts are increasing with the ever growing demographic pressure and urbanization. The impact of such floods result in intense damage to the city’s assets and dense population and is hence an expensive affair. The consequences are not just geographical but there is also a disruption in the social and economic aspects. [1.2.1] The issue of Urban Flooding is an aggravating problem mainly because of the percentage of population exposed to the natural risks in these urban areas. The existing and the predicted level of flood impacts should be given a priority in the formation of flood resilient infrastructure of the urban settlements. It is a necessity to understand the root cause of these urban floods and design and implement measures to 18 | Urban Resilience | Domain
curb them. The term ‘urban flooding’ encompasses a range of conditions causing floods [1.2.2] Causes and types of floods : The exposure of an urban block due to its vicinity of a river (fluvial), excessive rainfall (pluvial), coastal floods and floods due to rise in ground water level and flash foods are some of the factors posing threat to the cities. Pluvial Floods : Pluvial floods are caused by excessive rainfall and lack of a city’s ability to cope with the drainage of the excess water. This can be either because of the urbanization and demand for land, leaving no room for permeable soil which can hold the ground water. At times, it can be due to the drainage system failures. Fluvial Floods : These type of floods occur when the capacity of a river or water channel exceeds its capacity.
Diag [1.2.1] Countries affected by no. of flood events Flood Events, 1970-2011. Source: EM-DAT: The OFDA/ CRED International Disaster Database www. emdat.be - UniversitÊ Catholique de Louvain - Brussels - Belgium�
60 < 40-60 20-40 0-20 No data
The surface run off cannot be contained within the natural or artificial channel and typically, overflows in the adjacent low lying areas. Coastal floods : Usually, such type of floods occur when there is a sudden or brief attack of ocean water on the coastal areas of a city. This results because of a rise in the level of sea water caused by storms or seismic activity. During a storm, the high intensity winds cause the surface water of the ocean to gather and form a dome of water due to the low pressure suction inside the storm. If the storm approaches the coastal line of a city, the water dome is pushed on to the land forms causing these type of floods.
Flash Floods : Flash Floods are caused by a sudden outbreak of an upstream contained behind a dam, glacier or a landslide. Topographical conditions of a region contribute to the factors affecting these floods. Urban areas are more prone towards flash floods because of the impervious surface of streets, roofs and car parks where surface runoff occurs rapidly. Flash floods are a greater threat as their occurrence is unpredictable compared to other types of floods. [1.2.3]
Ground water floods : Excessive amount of rainfall in a particular region may cause the ground water in the sub surface layer to rise up and cause floods. This usually occurs in low lying areas with permeable rocks and also when the ground water enters the sewer systems of an urban area. Architectural Association | Emergent Technologies & Design | 19
1.3 Informal Settlements
Img [1.3.1] Image of Floods in Jhakarta, by a user at cintafoto.
Vulnerability in informal settlements Urbanization which is broadly defined as the transition from rural to largely urban societies puts more people and assets at risk. Urbanization in major cities is a phenomenon which is not controlled. To accommodate the situation of urbanization from rural towns to mega cities, these cities expand outwards. The planning and spatial expansion of such cities catering to the needs of ever growing population usually occurs in the flood plain areas or in the coastal or flood prone regions [1.3.1] ‘‘In the developing world, a very high proportion of urban population growth and spatial expansion takes place in the dense, lower-quality informal settlements that are often termed ‘slums’ These are located in both city-center and peripheral, suburban or peri-urban locations and are frequently at highest risk’’.[1.3.2] These informal settlements are mainly built out of substandard construction materials. They have the least ability and infrastructure to cope up with hazardous events. The fact that these settlements are built on risk prone areas 20 | Urban Resilience | Domain
of the city like low lying flood plains is because they have lesser scope of moving to risk free sites within the city. [1.3.3] The adjacent map shows the proportion of every country’s population living in these informal settlements. Out of these, the cases of informal settlements affected by floods in Capetown- South Africa, Manila- Philippines and Mumbai in India are shown as an overview before focusing on a detailed step by step analysis of selected case. The causes of these urban floods and what can be done to cope up with the natural scenario to keep the identity of the city intact will be a basis to formulate the research question of this dissertation.
Diag [1.3.1] Proportion of countryâ&#x20AC;&#x2122;s population living in informal settlements Data as per UN habitat definition.
Cape Town - South Africa
Mumbai - India
North Manila - Philippines
Informal Households : 370 shacks Population : 1357 people Low lying wetland High water table
Informal sector : 55% Population: 94,000 /sq km Low lying flood prone land High water table
Informal houses : 101,278 Population: 17,800 /sq km Flat land area Low water table
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Img [1.3.2] Image on left sideHeavy rain wreck in informal settlement Egoli, Cape Town. Image source :http://www.iol. co.za/ Img [1.3.3] Image on right sideHouses submerged in floods. Malabon City Image source : http://poleshift.ning. com/
Informal settlements in Asia & Africa Egoli, Cape Town- South Africa Egoli in Cape Town city, is one of the informal settlements in the Philippi area. This patch is sparsely populated as compared to the urban Cape Town district, but it appears as an island of informal settlements. This town was formed by most of the migrants who were brought to Cape Town and then abandoned their contracts due to bad working conditions. This settlement was formed on a private land which was originally a soccer field. About 36% of the people are unemployed out of a total 1357 people. The informal settlement lacked drainage facilities and the structural quality was substandard. Every winter, this settlement faces the problem of flooding and a substantial percentage, about 87.2 % of this settlement has experienced such disasters. Most of this patch is inhabited by population living in shacks. During monsoon in winter, the water level rises, covers the pathways and flows into the shacks. The settlement is located in a low lying area termed as a wetland. Due to the the clod climate, the flood water does not evaporate and neither does it flow out due to its topography.
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This causes the flood water to stay covered over the paths and flow into the households of the residents. The stagnant water causes a spread of diseases and also damages the already poor structural quality of the shacks. The rainfall also causes a leakage through the roofs of the shack due to lack of material and construction quality of the households. The residents of this area are fighting against eviction charges and are illegal. During floods, the residents are not aided by any centralized disaster relief organizations or the government. [1.3.4] Malabon city, Manila -Philippines This regions is comprised of small industrial estates in the coastal area and is proliferated by informal settlements. The topography of this patch is a flat land and it experiences perennial flooding due to the 14 open waterways and 5 rivers , 3 of which join the Manila Bay. The flood prone areas are the ones flanking the river banks. The settlements are informal with low housing rents and they have been densifying as they are affordable living places closer to the
Img [1.3.4] July 2005 floods Mumbai Image source : http://irevolution. net/
work places of their residents. The low economic standards of the residents constrain their ability to cope up with the floods. There is no access to proper drainage systems or flood control infrastructure. The residents affected by urban flooding themselves try and cope u with the damages and rebuild their shelters. This results in a cycle of the vulnerability of the people and their households in a loop. [1.3.5] Mumbai -India Mumbai is a city formed out of agglomeration of seven islands and most of it is a landfill. The low lying areas of Mumbai comprise of most of the informal settlements termed as squatter communities. About 55 % of the cityâ&#x20AC;&#x2122;s population live in these informal settlements. That counts for about 94,000 people per sq km and thus makes it the densest districts of Mumbai. The heavy rainfall during monsoons and a high water table intensify the problem of flooding in the low lying areas. The informal houses are built of substandard salvaged materials
and are single or two floor houses. They suffer from inadequate water management and sanitation facilities. The drainage system is poor or non existent. Some of these communities are located near the Mithi river banks and the over flowing of this water channel during heavy rains, also adds up to the critical scenario of urban flooding. The government has taken up actions to demolish these informal communities and relocate them with better facilities of water supply and drainage, but the typical attitude of these residents of renting out their provided shelters and returning to their original squatter systems still prevails. [1.3.6] Mumbaiâ&#x20AC;&#x2122;s characteristics of a low lying topography combined with the geological soil conditions and perennial rainfall have aggravated the problems of flooding in the city. The analysis of how these factors along with the issues of urban waste management and drainage systems puts forward the extremely vulnerable situation of the people and their substandard housing conditions exposed to these disasters.
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1.4 Mumbai - Urban flooding and soil conditions
Wetland Evaluation
0.0
2.0 m Postmonsoon water table
Fill
1.5 m 2.2 m
Clayey Sand
3.1 m
Soft Clay
4.4 m
Stiff Clayey Silt
5.0 m Premonsoon water table Diag [1.4.1] Reduction of Wetlands
Very Stiff Clayey Silt
7.2 m Soil Profile
1925
2015 Mangroves Water Bodies
Diag [1.4.2] Soil type of Mumbai
Causes of floods in Mumbai “Mangroves are large and extensive types of trees up to medium height and shrubs that grow in saline coastal sediment habitats in the tropics and subtropics—mainly between latitudes 25° N and 25° S.” [1.4.2] They are essential buffer between habitable land and the sea. As shown in the map, there has been a significant reduction in the Mangrove ecosystem from 1925- 2015. In a decade since 1995, approximately 40% mangrove ecosystems existing along the Mithi River and Mahim Creek have been destroyed by builders for land reclamation or by slum encroachment. [1.4.3] This has resulted into increased soil erosion and siltation which in turn has decreased the cities natural defences to 24 | Urban Resilience | Domain
cope with Flooding. As shown in the Diag[1.4.2], the reclaimed land of Mumbai consists of various soil type ranging from very stiff Clayey silt to clayey sand. This increases the rise in Water table during monsoon as the water penetrates not only from the top but also from the bottom. Soft soil is better for capturing water run-off in comparison to Hard soil. However the soft soil in Mumbai in most places is saturated and thus the water table is already high even before the Monsoon arrives. This reduces the absorption capacity of the soil and increases water run-off. Moreover paved surface does not allow water to penetrate in the soil and 85% of Mumbai Land is paved. In times of
B
A Hard Soil
Watersheds
Soft Soil Open Land Soil Profile Low Water Table (5-10 m)
Mud Flats Mangroves Water Bodies Natural Outfall
Zone 1 Zone 2 Zone 3 Zone 4
excessive rainfall, Mumbai land is incapable of water infiltration and results into huge amount of water being wasted from water-run off and accumulation of this water in low lying areas causes flooding. Mud Flats are usually created by deposition of clay and silt caused due to Siltation. These Mud-Flats help in coastal defence, reducing high wave energy .[1.4.1] However, when the outlet of Drains are falling into these Mud-flats, it causes disturbance in the drainage system by blocking the outflow of water. The increasing Mud-Flats in Mumbai have raised the flood risk during excessive rainfall.
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Diag [1.4.3] Soil Conditions in Mumbai Natural Outfall for water channels.
1.5 Flood Resilience
Urban Resilience to Floods ‘Resilience is the capacity of a system to absorb disturbance and reorganize while undergoing change so as to still retain essentially the same function, structure, identity, and feedbacks.’ [1.5.1] As discussed in the previous chapter, rapid urbanization has lead to the expansion of major cities. This phenomenon of urbanization results in ‘urban sprawl’ [resilience and urban risk management]. This urban sprawl develops a risk condition because the urban growth happens in the zones which are more vulnerable to natural hazards. Urban resilience to floods is termed as a city’s potential to withstand flooding and to cope up with the damage caused in terms of physical and socio economic conditions, with an ability to reorganize. This essentially applies to the fact that the city cannot completely shield itself from the effects of floods but its functionality is in a desirable shape during such a natural situation. The desirable condition in which the city functions despite the calamity is governed by a set of parameters such as security, economic performance 26 | Urban Resilience | Domain
dealing with the city’s socio economic identity.[1.5.2] Although this idea of the city’s potential to withstand floods can allow its regular functionality, it can only bear this till a limited threshold. Once the magnitude of the flood event surpasses this limit, the city’s situation shifts to an undesirable regime. This particularly involves threat and loss of human life as well as damage to the economic and social structure of the urban setting. The whole idea of a resilient city is to not resist such circumstances, but to accommodate the scenario of urban floods. In such a scenario, if the city is unable to cope with the disaster and there is an uncontrollable loss of socioeconomic assets, then it is up to the city’s ability to recover and reorganize from this event. Urban resilience to a flood scenario can be divided in to two categories as a self organizing city system and an adaptive system. The self organizing system copes up with damages and disruptions since it is a distributed system. On the other hand, the adaptive approach is a learning experience
Img [1.5.1] Aerial shot of Calgary city, Canada 3rd most flood resilient city in the world. Image Source : www.revitalizationnews.com Photo by offshootstudio.
and it grows with time. Thus its resilience to flooding increases over the years. [1.5.3] Self organizing systems being distributed systems, do not rely on a central source for back up during an event like flood. It is a strategy which is independent of the government or public organizations and is a faster way of coping up with the disruptions. It relies on its internal ability to fix and rebuild its identity after a flood. The approach of a resilient city is different from the one which deeply depends on a resistant system. A resistant system highly depends on flood control infrastructure and is intolerable to wet conditions prevailing on a greater extent. The notion of a flood resistant city is regarded as a temporary success by simply postponing the natural calamities and building up risks by avoiding the possibility of learning and improving from a disaster.
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1.6 Resilient systems - Case Studies
Img [1.6.1] Design Concept Plan of proposed resilient system Location : Qunli, Haerbin City , China Image Source : www.asla.org
Green Sponge for a Water-Resilient City : Qunli, China In the city of Qunli in China, existed a regional wetland which was listed as one of the protected zones in the urban region. In 2009, a project commissioned for a client was handed over to the landscape architect with the base guide line of preserving this wetland area. The area was 34 hectares, encompassed by a dense development and road networks on all four sides. This isolation deemed a threat to the connection of this wetland with its water source. The proposal made by the landscape architect went beyond the mere task of preserving this wetland by putting forth an idea of transforming it into a storm water park as a public space. The system comprised of a strategy of collecting, purifying and storing the storm water and enlivening the habitats for recreational purpose. This ecosystem, proposed a list of strategies to create a water resilient patch in the urban setting. The central part of this wetland was allowed to naturally evolve with its habitats with a series of mounds and pounds encircling the boundary. As an interface between this natural setting and the city, the combination of these 28 | Urban Resilience | Domain
mounds and ponds acted as a storm water system which developed as a buffer zone within the interface. This buffer zone allowed the filtration of water from the surrounding urban area, before it drained into the core wetland region. Regional silver and birch trees were grown on the mounds with varying heights to create a dense natural woodland. Around these ponds were grasses and meadows with varying depths. After setting out this initial strategy, a series of pathways was initiated linking the ponds and mounds to allow a dynamic public access in this area. This created an interactive experience for the visitors who accessed this area and have a contact with the natural setting in the urban context. A number of platforms and sit out spaces also enabled an intimate interaction with the eco system. A loop of sky walk over the mounds allowed the local residents to have a visual experience over the wetland and the landscape design. This sky walk loop connected 5 pavilions, 2 viewing towers and the designed platforms. [1.6.1]
Img [1.6.2] Image of a sky walk above the storm water filtrating and cleansing pond on the south eastern edge of the park created by excavations. Location : Qunli, Haerbin City , China Image Source : www.asla.org
Conclusions : This landscape proposal in Qunli, allowed the residents and visitors to have distant views for observation of nature within the central part of the park. Apart from the chief aim of preserving the wetland, the site performed several functions of collecting, cleansing and storing the excess storm water and replenishing the aquifers. This has incorporated the scenario of urban flooding as an aspect that contributes to the environmental and biological system of Qunli city. The study of this wetland as a natural patch in an urban setting sets out the idea of how a retaining catchment strategy could prove useful in tackling the issue of urban flooding in a city. The proposal is also intriguing in the sense of adding a public space dimension to the primary aim of preserving wetlands and curbing the flood problems by infiltrating storm water as a part of the resilient system. In a metropolitan city, a string of catchments linked with one another could contribute to evolve a global resilient system to address to the effects of urban flooding. Architectural Association | Emergent Technologies & Design | 29
Img [1.6.3] Yanweizhou Park, Jinuah City, China Image of the urban area in dry season Image Source : www.turenscape. com
Img [1.6.4] Yanweizhou Park, Jinuah City, China Image of the urban area in wet season Image Source : www.turenscape. com 30 | Urban Resilience | Domain
Img [1.6.5] Yanweizhou Park, Jinuah City, China Landscaped skywalks as a part of the resilient urban area Image Source : www.turenscape. com
Yanweizhou Park in Jinhua City, China Jinua city in China has a monsoon type climate and as a reason why, it suffers from annual flooding. In the riparian parts of the city, strong concrete walls of substantial height were built to resist the effects of the annual flooding. â&#x20AC;&#x2DC;Riparianâ&#x20AC;&#x2122; means a part which is an interface between the land and a water body. [1.6.2] Due to this strategy, the cohesive relationship between the land in Jinhua city, its vegetation and water bodies was severely strained which ultimately scaled up the problem posed by annual flooding. In the central part of Jinhua city ( population more than 1 million ) a sole part of natural riparian wetland over 26 hectare was underdeveloped. The convergence of the rivers, Wuyi and Yiwu forms the Jinhua river and this part is the wetland named Yanweizhou ( The sparrow tail ). Beyond this region, the natural wetlands were replaced by creating an opera house. In order to adapt to the floods during monsoon, plantations and a flood resilient terrain was generated. This included a development of a bridge and pathways that formed a system which helped tolerate the strong water currents as well as cater to the pedestrian movement. This
system also helped in bridging the citizens of Jinhua city with the natural land and its past to its current identity. The chief material use was gravel retrieved from the site which was used to create infiltration surfaces for the river water around a pond in the vicinity of the open air opera house. Bio swales also formed a part of this urban intervention which were woven around tree planters and concrete paving for vehicular access. [1.6.3] Conclusions : The urban landscape intervention helped create a resilient system, allowing the inland area to be permeable for the river water. This created a wet and dry seasonal character which curbed the annual flooding situation and integrated the river, flow of people and the dynamism of the landscape elements. It was placed well in the environmental setting by creating intimate public spaces and catering to the needs of intensive audiences for the opera house. This study brings to light, a technique of developing urban public spaces which are well integrated with a flood resilient system on regional scale. Architectural Association | Emergent Technologies & Design | 31
1.7 Traditional Indian step wells
Image 1.7.2 Sketch Plan of a typical step well
Img [1.7.1] Step-pond in Abhaneri, Rajasthan India
Fluctuating water level
Image source : www.rogerdhansen. wordpress.com
Image 1.7.3 Sketch section of a typical stepwell
Indian Step wells : Public spaces & Catchments The traditional step wells have been an subterranean architectural edifice in India. These step wells originated in the 2nd -4th century AD. Their evolution continued till the 11th century where they became astounding complex feats of art, architecture and engineering. The need for these step wells originated out of a necessity crisis in the hot and dry climate specially in northern parts of India like Gujarat. This climate prevailed over 8 months of the year followed by torrential rainfall in the monsoons for about 3-4 months. There was a need for a year round supply of water in these regions for drinking, washing, bathing and irrigation. The water table in such areas was fairly low and this factor, enabled the construction of step wells up to 10 stories below the ground level. This allowed the stored water to remain refreshingly cool due to the underground temperature difference. It not only involved the development of a sinking trench and a water cylinder from which the 32 | Urban Resilience | Domain
ground water could be retrieved, but it also involved lining the trench with a series of steps and side ledges which enabled access to the fluctuating level of water within the step wells. In the dry season, the steps allowed people to reach the bottom of the well to reach the water surface, while in the wet season, a part of these steps or the whole of them would be submerged under the water level. Some of these step wells in Gujarat had built pavilions in successive steps and ledges to provide shade for the stored water. The emulation of this step well featured gradually in many cities and public gardens across India. They also came to be known as â&#x20AC;&#x2DC;retreat wellsâ&#x20AC;&#x2122;. Their shapes varied from rectangular, circular or even L shaped in some places and were built from rubble, masonry or brick. They typically have 4 entrances which had their own unique character. These step wells took a place of pride in the Indian architecture as devices for water storage as well as functional public spaces. [1.7.1]
Img [1.7.4] Step-pond in Abhaneri, Rajasthan India Image source : http://www.lonelyplanet.com/india/ rajasthan/jaipur
Conclusion : Aspects of the system worth emulating The legacy of the Indian step wells seem to have faded, but the current water crisis in most cities in India could call for their redemption. In this dissertation, the study of these step wells becomes important from the point of view of storing water and emulating this architectural element as functional public space. Since the issue of urban flooding in Mumbaiâ&#x20AC;&#x2122;s low lying areas is addressed, the idea of creating catchments to store water could prove beneficial on an urban level. Since there is a seasonal character of the step well functionality, such catchments could be crucial to retain water in the wet season and can be used as public places like squares or plazas, on a micro level in the dry season. This could evolve into a global system which could contribute towards flood resilience for that specific region. On a local scale, this idea of having a dual character of public space functionality as well as water storage devices could prove useful for solving
water supply and storage crisis in the low income groups of the city facing these problems. Further, the key point of discussion will be, how these local level catchments could evolve as an integrated system to solve the crisis of water management from a regional to a global scale. The idea of incorporating such a system which can be flood resilient and incorporate water storage thus becomes intriguing as a strategy.
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1.8 Housing in Mumbai - informal and low income groups
Img [1.8.1] Informal Settlement Dharavi, Mumbai Photograph: Stefano, Runfox
Informal settlements in Mumbai Informal settlements are defined as places where people occupy the land and build housing units without any legal claim. Sometimes informal settlements are provided electricity and water. In Mumbai informal settlements are the major part of the total population. According to 2011 census done by government the total population of Mumbai is 12,442,373 and out of the total population, informal settlements population is 5,207,700. The map shown above indicates the total population vs informal settlement population by ward division in Mumbai . According to 2011 census ward S has the highest number of informal settlements population which is 537,900 and it has the highest informal settlements population density 72%. A map for vulnerable population under flood risk is prepared by combining information from flood risk zones and the graph of informal settlements in various wards. A significant change can be observed in the population under flood risk in different wards. It is also observed that the wards near Mithi River have the most vulnerable population. 34 | Urban Resilience | Domain
It is noted that the flooding caused due to overflowing of Mithi River is one of the major reasons for destruction in Mumbai. Especially the informal settlements near the rivers have low resistance to the flood and take more time compared to the rest of Mumbai to revive from the damage, suffering very high damage to human life and infrastructure. Comparing the extent of flood risk population in various wards, it is evident that in these areas the population under risk is between 60,000- 150,000. These figures reflect the high intensity of the damage faced by informal settlements across these areas. Most of the areas have informal settlements having population in the range of 10,000-60,000 under the flood risk, whereas only few areas have population lower than that under flood risk. This map demonstrates the overall flood risk faced by informal settlement across Mumbai. Thus it becomes essential to take sufficient and significant measures to ensure the safety against the flood risk faced by such a huge population residing in these informal settlements. [1.8.1]
Informal settlements Informal settlements exposed to flood Site boundary
Diag : [1.8.1] Development plan for Greater Mumbai - Municipal Corporation of Greater Mumbai
41%
Informal settlements
51%
Informal settlements exposed to flood risks
70%
Household income <300$ annually
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Diag [1.8.2] The above data is as per the percentage of informal settlements in comparison to the overall population of Mumbai.
2. System Components Branched morphology - Foundation System The Chawls - Mumbaiâ&#x20AC;&#x2122;s low income housing Hydrological system - Retention Catchments Critical Review
2.1 Branched morphology - Foundation System
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Image 2.1.1 Vertical Piles
Image 2.1.2 Branched Piles
Soil Bearing pressure
Fractal Growth Branching
L
Need for structural stability The reclaimed land in Mumbai has had major infill over the years resulting in the soil structure to have low bearing capacity. The infill ranges between debris, silt and garbage. The site experiences sinking and soil erosion caused due to the poor quality soil with a added disadvantage to being highly saturated. Also the post monsoon water table is particularly very high. This requires the necessity to design the foundation which can reduce the stresses in the soil up to its maximum stress bearing capacity. Strand 7 was used to determine these stresses in the soils. Strand 7 is a structural software where the models are simultaneously analysed and observed in a fullyintegrated visual platform. Thus initial tests for the optimum angle and the comparisons between various lengths of the branching of the foundation was conducted in Strand7. The observations formed the basis of further experiments for determining the depth and number of branching required.
Image 2.1.3 Vertical Piles
L/2
L/2
Image 2.1.4 Branched Piles
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Experiment 2.1a 8m
12m
L2
L1 8m
60o
18m
L1 = 1m L2 = 1m Diag 2.1a.1
L1 = 2m L2 = 2m Diag 2.1a.2 Plate Stress:XY(Pa) 40000.00 24000.00
0.00
-24000.00 -40000.00
Material Properties: 1. Soil: Modulus : 5 MPa Poisson’s Ration : 0.3 Density : 1.836 x 103 kg/m3 2. Building block: Modulus : 3 x 104 MPa Poisson’s Ratio : 0.3 Density : 5.0 x 102 kg/m3 3. Steel Beam: Modulus : 2.1 x 105 MPa Poisson’s Ratio : 0.3 Density : 7.8 x 103 kg/m3
L1 = 3m L2 = 3m Diag 2.1a.3
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8m
12m
L2
L1 8m
60o
18m
L1 = 2m L2 = 1m Diag 2.1a.4
The experiments for the foundation were carried out to study the stresses developed in the low stress bearing soil of Mumbai. The maximum allowable pressure for silty soil is considered as 287 KPa. [2.1a.1] However, considering that this allowable limit would reduce to 1/10th when the soil is wet, the allowable pressure is considered to be 28.7 KPa. To address this, the stress limits were set to 25 KPa to 40 KPa to study the results. The model was initialized with 3 materials -the soil, the building block and the steel beams for foundation. Soil properties are taken considering the Mumbai soil in saturated conditions. The hollow circular steel beam used for this branched foundation has outer radius of 10cm and thickness 2cm.
L1 = 2m L2 = 3m Diag 2.1a.5 Plate Stress:XY(Pa) 40000.00 24000.00
From the experiments, following observations are noted :Lesser stresses are observed in Diag 2.1a.3 , with L1=L2 = 3m, Diag 2.1a.5, with L1= 2m and L2 = 3m, and Diag 2.1a.6, with L1= 3m and L2 = 2m. Thus the foundation with these 3 types of branching lengths are more functional in soils with low stress bearing capacity. In the present document, the actual height of the morphology would never go beyond 9mts, making foundations even more efficient. With the variations in the length of vertical and angular members, it was observed that increase in length of angular member L2 had higher impact on reducing stresses in the soil compared to increase in the length of vertical member L1. This is clearly evident from Diag 2.1a.5 and Diag 2.1a.6.
0.00
-24000.00 L1 = 3m L2 = 2m Diag 2.1a.6
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-40000.00
Experiment 2.1b 16m
12m
L2
L1
8m
8m
28m
Diag 2.1b.1
L1 = 2m, L2 = 3m
Diag 2.1b.2
L1 = 3m, L2 = 2m
Diag 2.1b.3
L1 = 3m, L2 = 3m
Plate Stress:XY(Pa) 40000.00 24000.00
0.00
-24000.00 -40000.00
Material Properties: 1. Soil: Modulus : 5 MPa Poisson’s Ration : 0.3 Density : 1.836 x 103 kg/m3 2. Building block: Modulus : 3 x 104 MPa Poisson’s Ratio : 0.3 Density : 5.0 x 102 kg/m3 3. Steel Beam: Modulus : 2.1 x 105 MPa Poisson’s Ratio : 0.3 Density : 7.8 x 103 kg/m3
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Experiment 2.1c 16m
12m
L2
L1
8m
8m
28m
Diag 2.1c.1
L1 = 2m, L2 = 3m
Diag 2.1c.2
L1 = 3m, L2 = 2m Plate Stress:XY(Pa) 40000.00
From this experiment, it was derived, that like displacement in the previous experiment, stresses developed in the soil are lesser in the branching system spanned at 8m in comparison to the same in 16m. Thus 8, span is more effective to reduce both, the displacement and the stresses developed in the soil. In conclusion, various angular-vertical lengths can be used depending on the load of the built unit with a span of 8m. However, these experiments were carried out for single unit. Grid of these branched foundation can be used for the entire chawl morphology which needs to be tested.
24000.00
0.00
-24000.00 -40000.00 Diag 2.1c.3
L1 = 3m, L2 = 3m
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2.2 The Chawls - Mumbai’s low income housing
Img [2.2.1] left Source: www. ramblinginthecity. wordpress.com
Image [2.2.2] right Source : http://www.pixquarterly.in/ Photograph by Mark Philips
The Structure of Chawls The chawl is a prominent housing type that exists in Mumbai since the mid 19th century. It was mainly built for the migrant workers who worked in the mill lands in Mumbai. About 75% of the city’s population resided in the chawls till late 1989. Today, 20% of Mumbai’s population still lives in this mass housing type. The idea of this housing system is based on a sense of communal bond between the residents of these chawls. The chawls are owned by the government and private individuals. This results into two types in the design of these housing types. The private clusters of chawls typically have a courtyard in the central area which is enclosed by the built forms around it. Only the residents of the chawl use the central courtyard. The common corridors are overlooking this central area. On the other hand, the chawls owned by the government are usually linear built forms with the courtyard existing as a public space sandwiched between the linear 44 | Urban Resilience | System Components
structures. This public space is used by the residents as well as non residents of the chawls. They are used as market spaces. The corridors of such chawls do not overlook any private space such as a central court. In today’s scenario, these linear chawls have shops at the ground level and the residences are above them. The typical layouts of chawls [ diag 2.2.1 ] indicate the configuration of courtyard spaces within the built morphologies. Usually, this is one central space shared between 2 or more blocks enclosing its boundaries. The living, bedroom and kitchen facilities are configured within the private unit of each household while the bathing and washing facilities are usually shared spaces. Corridors are configured as a loop around the household units, giving access to the rooms and is overlooking the central courtyard termed as ‘chowk’. [2.2.1]
Image [2.2.3] Social activity in the courtyards of Mumbai Chawls Image source: indiadestinationsblo g.wordpress.com
Communal Living The social interaction and idea of sharing imbibed in the chawls, form the genesis of its architectural quality. This culture and its architectural elements are crucial, as a basis of its further development. The accomplishments of sustainable living within these communities, lies in all these factors. Due to the interaction between the residents of the chawls, there existed a sense of security in these communities. The attitude of its users have helped maintain this cultural image of the chawl structure. In order to preserve this community, it is necessary for the redevelopment proposals to identify the existing built environment along with its social structure. Likewise, these elements of the communal living can be identified as guidelines on which the design can be established. Such fundamentals can be crucial in maintaining the contribution of the communal factors in carrying forward the existing lifestyle of the people living in
chawls. It is essential to understand and study the spatial configuration of social and living spaces embedded within the built morphologies of these communities. The people belonging to the low income groups have the understanding of sharing social provisions and at the same time maintaining their private life. [2.2.2] The chawls in Mumbai were mainly of two types, the ones built by private enterprise and the ones developed by the government organizations. A typical layout of a chawl structure and case studies of the private and publicly owned chawls is discussed further ahead.
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Img [2.2.4] Source: www. ramblinginthecity. wordpress.com
46 | Urban Resilience | System Components
Typical Layout of Chawls
toilet
washing area kitchen/dining room bedroom/living room central courtyard corridor
corridor
The Layout of a â&#x20AC;&#x2DC;Câ&#x20AC;&#x2122; Shaped Chawl
bedroom/ living room
kitchen/ dining room
1m
1m
3m
bathroom Diag [2.2.1]
Plan of a Unit in a Chawl
Typical Layout of chawls in Mumbai
6m
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Diag [2.2.2] Chawls by private sector built in the urban setting, Mumbai
Chawls built by Private Enterprise The Haji Kasam Chawl - Parel, Mumbai. The Haji Kasam Chawl in was built in the mid 19th century when the cotton textile industry flourished in Mumbai. A number of textile mills were setup along with workshops for railways, trams, ship building, oil and chemicals as well as paper mills. During this period of industrialization, there was a lot of capital invested in these industries and resulted into a rise of job opportunities. Thus, there was a stream of migration of the working class people from the rural areas and interior regions of the state to Mumbai. Along with this, the need for affordable housing followed. The private entrepreneurs invested in agricultural lands and developed buildings of the one room tenement systems- the chawls. Parel area of Mumbai comprised of a large number of textile mills
48 | Urban Resilience | System Components
and resulted in the development of such affordable chawl systems around the mill lands. The Haji Kasam chawl is one such example. This chawl housed 2500 tenants in single room units. The room size of each of these units was 13 sq m. The configuration of the Haji Kasam chawl was slightly different from the usual configuration of units around a central courtyard. This housing type had two major blocks of residential units mirrored along a linear central space and each block had voids withs the room units strung around them. The ground coverage of this chawl was 2121 sq m with a total of 500 single room tenements. The average size of a family living in each unit was 5 persons. The construction type for this chawl was load bearing with wooden frames and pitched roofs. The maximum height
Connected courtyards
Disconnected courtyards
C
A
Diag [2.2.3] Central Courtyard and internal space configurations in the chawls, Mumbai
B D
Private enterprise chawls
Chawl building blocks
Chawls built by Government Organizations was 3 flood above ground level. The idea of the corridor loop around residential units and the voids still existed in this chawl. The size of the linear courtyard between the two blocks took away the traditional character of a central open space for communal activities. Therefore, most of the social interaction happened in the corridor spaces termed as â&#x20AC;&#x2DC;verandahsâ&#x20AC;&#x2122; . The voids in each block did create a positive impact in terms of lighting quality and social interaction between each individual block wing. These voids had a loop of the the verandah which also strung around the units and overlooked the central linear space between the two blocks. The formation of the units in both building blocks towards the east side near the main entrance resulted in an evolution of two other courts on the rear side of each block. These spaces were however, disconnected from the central linear area because of the existence of stairway blocks. [2.2.3]
BDD Chawls, - Lower Parel, Mumbai : The Chawls built by the Government organizations differed from the ones setup by private owners in a range of characteristics. The chawl systems developed by government agencies were mainly devised to overcome the problems of overcrowding in the existing chawls and their dilapidation and lack of infrastructure due to an increase in migration rate. The basic idea of having room tenements configured around a loop of corridor and a shared toilet system. In some cases, the common courtyard space in private chawls gets neglected when it is tucked on the rear side and disconnected where as the central space becomes linear and sandwiched between the built blocks. In these systems, the internal courtyards within individual blocks were quite
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Diag [2.2.4] Chawls built by government organizations, Mumbai
common. The publicly developed chawls eliminated the idea of the central courtyard space and incorporated a master plan of several blocks with adequate open space around them. The idea of internal courts was also erased and the spaces between different chawl blocks were used as public and semi public spaces. This space was setup as a market place in some areas where the ground level of the chawls were used for shops. The idea of government developed chawls was mainly helpful in generating mass housing on a master plan level. the configuration of the built blocks is fairly simple without and architectural characteristics from the private chawls [2.2.4] Conclusions : Proceeding from the spatial configuration of the informal sectors and identifying how they are also reflected in the low income housing type of the chawls in Mumbai, specific 50 | Urban Resilience | System Components
architectural qualities of how each tenement unit in the chawl is configured around social spaces for the success of a shared living system is studied. The chawls exhibit a better construction quality than the informal settlement- slums, but their condition has deteriorated over the years. In some chawls, the idea of a central courtyard is not fully functional because of a sandwiched linear space where as in some cases, it is disconnected from the other social spaces. In the publicly owned chawls, this court space is completely removed and is utilized as a public space for markets and shops. However, the structure of individual living spaces and how they coherently work with the shared facilities like the washing areas and corridors in a loop is a quality which can be extracted and utilized as a basis for redesigning a communal living block for the migrants in Mumbai.
2.2a Spatial Organization - Building Morphology
Corridor loop with terraces
Adding multiple levels
Cluster around courtyard
Single Unit Living
Diag [2.2.5] Components of a chawl morphology
Retaining social organization of chawls Mumbaiâ&#x20AC;&#x2122;s informal settlement deals with a very high population density and simultaneously very less open space per person. The open spaces available are usually too far away from the residential areas so they are not used much for social interactions. These informal settlements need semi-private open spaces for their social activities as well as for development of their economy, which is usually based on small-scale hand made product manufacturing that is carried out by a group of people belonging to the same or extended family. So the idea behind chawls is to distribute the larger open spaces equally amongst and closer to the residential units. This gives a rise to a new live-work-social urban fabric where
people migrating from rural areas can be accommodate. Providing them opportunities to develop their work would eventually give a rise to their economic structure and they would move on to bigger residences, evacuating these chawls which can further be used by the newer migrants. This would help improve the economic lives of people along with preserving and enhancing their social integration. The chawl typically comprises of a central open space surrounded by housing units which are connected to each other by corridors. The aim of the following experiments is to evolve a morphology with inherent social and spatial characteristics of a chawl with improved structural and urban living conditions.
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2.2b Chawl Morphology Experiment Experiment Setup Analysis ( Case Studies )
Experiments
Design Parameters
( Block Level )
( Inputs ) 8m
Unit Size
Design Decision
4m
4m x 8m= 32 m²
Average chawl height Mumbai Analysis Sky view factor (SVF) Mumbai Analysis Open space ratio per person (OSR)
Block Height Experiment of
9m
Courtyard size SVF evaluations
Courtyard Sizes
Experiment of Courtyard size OSR evaluations CS: 16 x 12 m² CS: 12 x 20 m² CS: 20 x 20 m²
Entry Size and Location
Design Decision Random location Entry size: 3 to 6 units
Upper Level Social Areas by Move Units
Experiment of Unit move SVF evaluations
0.5 m to 1m
Mumbai Analysis Chawl Case study (FSI) (OAR)
Experiment of Unit deletion OAR evaluations FSI evaluations
0.5 m to 2m
Upper Level Social Areas by Deletion Units
for 1st floor from 20% to 60% 52 | Urban Resilience | System Components
0.5 m to 2m
for 2nd floor from 10% to 30%
3(30-30-30 E 2(25-25-25-25) Fitness Criteria
1. Population Density (D)
2. Ground Space Index (G)
Maximize
Minimize
Population Density =
6875 metres 519 sq m/person op/sq m
Total Population
GSI =
Plot Area
Minimize GSI : 0.575 Maximize Distribution of open spaces : 0.171875 metres Maximize open space per person : 8.985156 sq m/person Maximize Population Density : 3.52 Pop/sq m
3. Average Distance Between Open Spaces (A)
4. Open Space Ratio (S)
Maximize
Plot Area
Minimize GSI : 0.575 Maximize Distribution of open spaces : 0.171875 metres Maximize open space per person : 8.844665 sq m/person Maximize Population Density : 3.52 Pop/sq m
Maximize
C3_G10_09
C3_G5_05
C3_G5_05
SVF: 0.56
C3_G10_03
Minimize GSI : 0.590278 Maximize Distribution of open spaces : 0.195313 metres C3_G10_03 Maximize open space per person : 6.027357 sq m/person Maximize Population Density : 3.377778 Pop/sq m
SVF: 0.59
SVF: 0.55 OSR =
Differential Weighing of Fitness Criteria
Evaluation Criteria
The parameters are used to generate 10 generations with
Sky View Factor
C2_G3_04
C3_G1_05 C3_G1_10 a population of 10 each. These morphologies are then
625 metres 35 sq m/person op/sq m
Ground Coverage Area
evaluated based on differential weighing of all these fitness GSI : 0.625 0.6 criteria and distributed into 3 Minimize : Maximize Distribution of open spaces : 0.16875 metres Morphology A : D (40%) + G (20%) + A (20%) + S (20%) Morphology B : D (25%) + G (25%) + A (25%) + S (25%) Morphology C : D (10%) + G (30%) + A (30%) + S (30%)
These morphologies are then evaluated for Sky View factor
C3_G1_10
C3_G3_09 Minimize GSI : 0.55 Maximize Distribution of open spaces : 0.1625 metres Maximize open space per person : 6.507624 sq m/person Maximize Population Density : 3.876923 Pop/sq m
Maximize open space per person : 6.599796 sq m/person Maximize Population Density : 3.762963 Pop/sq m
SVF: 0.56 SVF: 0.55 before selecting them for further aggregation.
Open Area Block population
C3_G3_09
SVF: 0.59 Minimum SVF required = 0.38
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G1_09
C1_G5_05
C1_G3_02
E 3(30-30-30-30) TYP Selected Chawl Morphologies CHAWL MORPHOLOGY A D(40%)
Minimize GS Maximize Di Maximize op Maximize Po
Minimize GSI : 0.659722 Maximize Distribution of open spaces : 0.321181 metres Maximize open space per person : 10.892649 sq m/person Maximize Population Density : 2.289655 Pop/sq m
CHAWL MORPHOLOGY B
G(20%) A(20%) S(20%)
C2_G7_10
D(25%)
G(25%)
A(25%)
S(25%)
CHAWL MORPHOLOGY C D G(30%) (10%)
A(30%)
Courtyard Size : 16 x 12 m² C_01A_06
C_01B_05
C_01C_03
S(30%)
C2_G5_10
(25-25-25-25) E 3(30-30-30-30) Minimize GSI : 0.59375
es: :0.15625 0.203125 metres Maximize Distribution of open spaces : 0.167969 metres es metres 9.595885sqsqm/person m/person Maximize open space per person : 7.454223 sq m/person 5.762552 53846 m Maximize Population Density : 3.847619 Pop/sq m Pop/sqPop/sq m
G3_10
Population : 273
Minimize GSI : 0.65625 Minimize GSI : 0.5625 Minimize GSI : 0.5625 Maximize Distribution of open spaces : 0.214844 metres Maximize Distribution of open spaces : 0.167969 metres Maximize Distribution of open spaces : 0.140625 metres Maximize open space person : 7.284925 sq m/person Maximize open space per person : 6.257491 sq m/person Maximize open space perper person : 2.956947 sq m/person Maximize Population Density : 2.530909 Pop/sq Maximize Population Density : 5.422222 Pop/sq m m Maximize Population Density : 3.795349 Pop/sq m
Population : 217
SVF : 0.56
C1_G10_08 C1_G1_08
SVF : 0.50
C1_G3_05
Courtyard Size : 12 x 20 m² C _02A_04
C_02B_03
Population : 174
Minimize GS Maximize Dis Maximize op Minimize Maximize Po Maximize Maximize Maximize
SVF : 0.53
C1_G3_09
C3_G5_08
C_02C_03
TYPE 1(20-2 TYPE
Minimize GSI : 0.590278 Minimize GSI GS : 0. Minimize GSI : 0.590278 Minimize GSI : 0.555556 Minimize GSI : 0.694444 Minimize Distribution of open spaces : 0.195313 metres Distribu spaces : 0.251736 metres Maximize Distribution of open spaces : 0.195313 metres Maximize Maximize Distribution of open spaces : 0.321181 metresMaximize Distribution of open spaces : 0.190972 metresMaximize Maximize Di open space perper person : 6.027357 sq m/person Maximize openop s son : 10.892649 sq m/personMaximize open space per person : 6.432695 sq m/personMaximize Maximize open space person : 7.616 sq m/person Maximize open space per person : 3.686357 sq m/person Maximize : 2.289655 Pop/sq m Maximize Population Density : 3.377778 Pop/sq mm Maximize Popula Maximize Population Density : 3.377778 Pop/sq m Maximize Population Density : 3.927273 Pop/sq m Maximize Population Density : 1.72973 Pop/sq Maximize Po
2_G1_06
Population : 368
SVF : 0.42
Population : 218
C2_G3_04 C2_G10_02
SVF : 0.51
C2_G3_06
Population : 179
SVF : 0.41
C2_G1_09 C2_G7_08
Courtyard Size : 20 x 20 m² C _03A_01
C _03B_01
C _03C_04
Minimize GS Minimize GSI : 0.59375 Minimize Minimize GSIof: open 0.575 spaces : 0.15625 metres Minimize G Minimize GSI : 0.55 Maximize Dis Maximize Distribution Maximize Distribution of open spacessq : 0.2 metres Maximize Maximize Maximize Distribution of open spaces : 0.16875 metres Maximize op Maximize open space per person : 6.485636 m/person Maximize Maximize open space per person sq m/person Maximize open space per person : 10.358519 sq m/person Maximize Maximize Po Maximize Population Density : 4.04 Pop/sq: 7.358708 m Maximize Maximize Population Density : 2.575 Pop/sq m Maximize Maximize Population Density : 3.496296 Pop/sq m
es : 0.167969 metres 5.792847 sq m/person 6512 Pop/sq m
Population : 320
SVF : 0.58
C1_G1_06 C3_G7_10
54 | Urban Resilience | System Components
Population : 268
SVF : 0.56
C3_G10_09
Population : 249
SVF : 0.57
C1_G7_06 C3_G7_04 C3_G7_08
Conclusion A set of analysis and initial experiments were carried out to derive the parameters for this experiment. Genetic Algorithm was used to obtain a wide range of chawl morphologies. 4 Fitness Criteria were used to obtain a variation in the population : Population Density to maximize the population, minimizing ground space index to obtain higher population with lesser built footprint, to increase the distance between the open terraces created on the upper floor to have a uniform distribution of these terraces which can be used as social spaces on the upper floors and maximizing the open space per person ratio to increase the chawl courtyard to carry out their occupational and social activities. Differential weighing is applied to the obtained population of the chawl morphologies and low, medium and high
density population chawls are identified accordingly. All these morphologies are evaluated based on the Sky View Factor(SVF) of 0.38 , which was derived from initial analysis on existing patches in Mumbai. It was observed that since the parameters were derived based on the evaluation criteria of SVF, the obtained set of generations of chawl morphology had almost the entire population having a higher SVF, thus creating an appropriate open space for â&#x20AC;&#x153;live-workâ&#x20AC;? type of community living. This experiment was run on 3 sizes of central courtyards and a low, medium and high population density chawl morphology was selected for each of the 3 types of courtyards for the further development of the urban fabric on the selected site patch in Mumbai. Architectural Association | Emergent Technologies & Design | 55
2.3 Hydrological system -
For water management
Retention catchments within communal courtyards Due to the rapid urbanization with a high density urban fabric, the city lacks adequate water collection areas ( i.e. lakes, rivers or water bodies) to deal with excessive incident rainfall. This is a major reason for the cause of annual pluvial floods. The cityâ&#x20AC;&#x2122;s natural defenses such as the surface infiltration and surface run-off cannot manage the cityâ&#x20AC;&#x2122;s storm water inflow during such circumstances. The informal settlements face the acute problem of water management. The drinking water supply provided by the government does not suffice the day to day needs of the residents of these informal communities. In addition to this, the water is stored in plastic barrels without taking proper care of the sanitation due to haphazard planning and lack of water supply lines to the residential units. Therefore, a collective water management strategy has been developed where the storm water would be stored in retention catchments at a local level. The building 56 | Urban Resilience | System Components
morphology allows to have a communal open space within each of the designed chawls. This communal courtyard being to open to sky, the incident storm water is intended to be stored in retention catchments within these communal spaces. The retention catchments are derived with this idea of having a social place integrated with the water collection units. Thus, on a local level, a percentage of the incident storm water is proposed to be stored in storm water collection devices termed as retention catchments, which could be used to cope up with the water management issues faced by the informal communities. The target of drinking water is 3 liters per person per day, where the catchment area is intended to provide storage for dry season. The courtyards with inlets are elevated from the ground level. The elevated inlets would allow additional seating as well as avoid contamination from surface run-off.
2.3a Retention catchment typology
Interconnected loop
Type : 1 Seating for social court Type : 2 No seating for work court
Type : 1 Courtyard inlet for social court Type : 2 Terrace inlet for work court
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2.3b Experiment [ Gene Pool ] Gene 4 _ Height 0.18m 1.0m Iterative height along z-axis keeping the minimum in mind so that the storage should not contamination by surface run-off.
Gene 5 _ Random reduce Seed : 1000 unit Number : [40%] , [50%] Elimination on the number provided with random seed to have randomness in the cells clusters.
Gene 6 _ Selective elimination The periphery around verandah and the centroid intersection path between the location finder gene shall be removed upon collision of cells.
Gene 1 _ Repelling point 2m 6m Eliminates cell based on the intensity of the radius of the repelling point. Usually the area center or location finder centroid.
Coherent and Cohesive gene vs criteria response As mentioned earlier the experiments carried out was set in 2 stages. The initial 2 experiments are evaluated to understand the appropriate criteria that can help with system intervention. Experiment uses the defined criteria with effective Gene Pool to add diversity. The kill strategy controls the catchment morphology from expanding beyond the minimum open space ratio per person and store atleast 50% drinking water. Criteria 3 maximizes average distance between entry points for accessibility to the chawl units from the courtyard. Genetic Algorithm (GA) method is helps obtain a Pareto Front between 2 contradicting criterias: Circulation area nad catchment area.
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Gene 2 _ Adjacent distance 2m 6m Calculate cell in a given range with distance amongst each other to region union into a catchment space.
Gene 3 _ Location finder 2 - Verandah to courtyard 1 - Entrance to courtyard Selects entry location in a list of points a total 3 with 1 point selected closer to the building entrance and 2 from verandah.
[ Fitness Criteria : 1 ]
[ Fitness Criteria : 2 ]
Circulation area (Maximize)
Catchment area (Maximize)
[ Fitness Criteria : 3 ] Entry prism distance from centroid (Maximize)
Target area :
Storage Calculation :
Storage provided Annual Rainfall Source Rainfall
2400mm
(a) x
(b) Catchment area
m2 y x
(c)
Population (d)
x
x
L
(e) Survival water supply
Dry seasons (279days) (f)
/
Annunal Rainfall (g)
3 ltrs
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Design development for system integration The population and given courtyard sizes were taken from the selected chawl morphologies. The water management system is highly influenced by the buildings. The development of retention catchment is as per population density of each Chawl morphology. Morphologies with maximum storage capacity were selected from the Pareto Fronts. A catalogue was prepared with the integrated retentions inside 3 different courtyard sizes of provided for Low, Medium and High Density Chawl Morphology to be used in the Design Development Stage. Due to the varying courtyard sizes, the requirement of storage with adequate circulation area was difficult to achieve. On an average the system could cope with 80% of the water requirement.
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Selected Retention Catchment
C_01A_06_Population : 273
Generation 4.3 Catchment area : 64 m2 0% Circulation area : 137 m2
100% 105%
0%
100%
57%
Sum distance from entry centroid : 42m 2.0 Liters per capita per day (LPCD)
C_01B_05_Population : 217
Generation 6.3 Catchment area : 56 m2 0% 74% Circulation area : 140 m2
100%
0%
100%
58%
Sum distance from entry centroid : 37m 2.2 Liters per capita per day (LPCD)
C_01C_03_Population : 174
Generation 4.3 Catchment area : 64 m2 0% Circulation area : 137 m2
100% 105%
0%
100%
57%
Sum distance from entry centroid : 42m 5.0 Liters per capita per day (LPCD)
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2.4 Critical Review
Critical reflection on Local Scale system components The initial phase of this dissertation was based on developing a flood resilient system and improving the current scenario of the informal sectors in Mumbai. This also addressed the social and economic aspects of the existing population of the informal settlements. The site conditions of Mumbai, the causes and effects of floods were taken into consideration and 3 strategies were generated and tested to achieve the set aims. Foundation Strategy : This strategy was devised to address the issues of silt soil conditions caused because of the reclaimed landforms in Mumbai. The reclaimed land resulted into low bearing capacity of the soil and led to soil erosion. Biologically, the roots of plants like mangroves, have grown in a way that they hold the soil together creating compactness and reduce soil erosion. Also, the roots are morphologically developed so as to carry the weight of the tree structure above ground. Emulating this, a branched foundation morphological system was developed and tested for the existing soil conditions in Mumbai. A set of parameters exploring the lengths, angle of branching and spans of these foundations were derived to test the stress on low soil bearing capacity giving a better understanding of these parameters with the bearing capacity.
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However, these experiments were tested in a 2-dimensional plane generating a homogeneous grid of foundations for the designed morphologies. A 3-Dimensional approach allowing for a non homogeneous grid could be more beneficial to reduce the mass of these foundations at unnecessary points. Also, a maximum load of the morphologies was consistently assumed. There was a lack of tests and iterations to cater to a change in the number of floor levels of the designed morphologies. Thus, these two systems could have been more informed by one another. Building Morphology : The strategy of building morphology design was incorporated to accommodate the high density population of the informal settlements. The main idea of this was to preserve the social organization of these informal communities. As a part of the research, traditional chawls in Mumbai were considered as a case study to extract the aspects worth preserving and devise certain parameters on which the new design could be based upon. A set of experiments was conducted based on certain design decisions derived from the initial case study. However, it lacked the analytical sense to form a strong base for the building morphology design. Although, it led to an acquisition of some skills which were helpful to formulate a better analytical approach and generate a new set of rules in the consequent iterations. In the end, there could have still
been a series of more experiments with iterations and added fitness criteria for the genetic algorithm. The output however, provided with a wide variation to derive a catalogue of morphologies from the Genetic Algorithm which catered to varying population densities with enhanced spatial organization and architectural qualities. These units provided for a central courtyard which formed a crucial factor in designing the water management strategy for storing excess storm water. Catchment Strategy : Annual rainfall and average rate of rainfall was used to estimate the depth and the surface area required for these catchment basins. Almost 50% of water required for drinking could be obtained, However, these experiments lacked the analytical research and the derivations from the step-wells could not be quantified. The parameters used for these retention catchments were mostly based on design decisions from the understanding of these step-wells, but they lacked the analysis and test experiments which should have been based on the step-wells or flood resilient systems. The system was intended to be integrated at different scales for collection, storage and distribution of water delaying fluvial flooding. However, the strategy did not reflect this idea clearly at the beginning nor was their any understanding of resilience stated.
For further development, a focus on the water management issues of chawl would be thought about in relation to the global system for flood resilience. A relation between the built morphologies and the water catchments spread across the city for integrated hydrological connections needs to be established. The water catchments should also be designed considering the urban aspect and to develop means or strategies for collection, storage and distribution of rain water. This will help in reducing the excess rainwater getting collected in the streets and causing floods. The aim is to have an integrated urban-water system which allows to use the excess water for the benefits of chawl dwellers, thereby improving resilience and social character of these informal settlements in Mumbai.
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3. System Design Drivers - Global Scale
3. System Design Drivers
Open Space Connectivity : Local Level
Open Space Connectivity : Global Level
Open Space Built up
Spatial connectivity The building morphology system characterizes a catalogue of chawls with three courtyard sizes, each catering to three different population densities in a gradient from high to low. The central courtyards, open to sky, are considered as the chief communal spaces for each of these chawls while the corridors overlooking these communal spaces formed a loop to connect terraces on each level as semi private areas. This connectivity for communal interchange to maintain the social character of the chawls, was established on a local level. As this system is taken ahead, an aggregation of these chawls would define the urban fabric to accommodate the residents of an informal settlement. The idea of social interaction thus, needs to be propagated from a local to a global scale, where the interaction between several such chawl units would enrich the socio-cultural image of the informal communities.
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For this, the connectivity between the communal courtyards of chawls needs to be established. At the same time, the connectivity between these courtyards to the contextual open spaces also becomes critical. This gives rise to the aspect of resolving neighbourhood relationships between built and open spaces. This design driver focuses on the need for analysing the existing urban fabric of the informal settlement that needs to be redeveloped as well as incorporating the key principles of its spatial organization to configure a relationship between built and open spaces.
Surface water flow
Detention catchments
Discharge channels
Hydrological system For the system to be resilient to urban flooding, a global strategy of a hydrological network system is proposed. This system driver is based on retaining the existing topography of the selected site patch. On this site, the lower topographic points are identified as the potential locations in low lying areas prone to urban flooding. The global hydrological system is intended to function with a collection, storage and distribution strategy. For this, the initial step would be to place storm water detention catchments in the low lying areas, which would collect and store the incident storm water as well as surface run off from the surrounding areas. These catchments would then be connected by a channel network to distribute the stored storm water from one catchment to another based on a hierarchy. The final discharge outlets to drain the excess storm water would be identified as the end points of this
hydrological network. The channels designed to connect such catchments would function as storm water distribution networks that would also collect and channelize the surface run off. These would be designed as a part of the urban networks while resolving the connectivity and spatial organization of the site.
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Lower topographic points Higher topographic points
Low impact zone
System failure strategy The hydrological system strategies have been directed towards coping up with the annual rainfall and discharging the storm water through channels by means of collection, storage and distribution. However, the system cannot be entirely reliable before conducting several experiments to test and validate its efficiency. Hence, a third system driver is formulated. The idea deals with retaining the existing site contours and identifying the base surface flow of rainwater. The lowest topographic points on the site patch would be identified, where the storm water would naturally flow with respect to its topography. The program distribution of the selected site patch would help to develop an alternate strategy to direct the flood water to a temporary zone termed as a â&#x20AC;&#x2DC;low impact zoneâ&#x20AC;&#x2122; near these low lying areas. These low impact zones would comprise of the social provisions that do not affect the overall functioning of the city, in case of a flood scenario.
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This back up strategy would help in delaying the storm water discharge, by allowing the zones to temporarily retain the storm water. The adaptable programmatic functions would make this zone feasible to be non functional in such a scenario of system failure.
Cluster 1
Cluster 2
Cluster 3
Connectivity in Retention Basins Retention basins incorporated within the courtyards of the building morphology clusters provide for the demand of drinking water within these individual clusters. On a local scale, it aims to resolve the water management issue of the residential units. However, when these block morphologies are aggregated on a global level, the courtyards of these chawls could potentially be used as work yards for the economic benefits of the residents. This implies the fact that, it is not feasible for every chawl block to have its own retention catchment as an integrated social space in the courtyard. It would be possible for such blocks to have an underground storage device to cater to the water requirements but this effectively cancels out the idea of storing water from the incident rainfall through the openings in the retention catchments. This system driver intends to achieve an establishment of connections between the water storage devices between
an aggregation of chawls. This connection is thought to be worked in a loop, which controls the flow of water between these retention catchments depending upon the requirement of water in each chawl. Thus, the water management would be tackled by an interdependent strategy between these chawls aggregated as a part of the redeveloped fabric for the informal settlements.
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4. Site Mumbai : Areas exposed to floods Site : Dharavi, Mumbai Dharavi : Analysing the existing urban fabric
4.1 Mumbai : Areas exposed to floods
Criteria Total population Site area Flood exposure
3
2
1
Site selection criteria
Diag [4.1.1] Flood risk zones and informal settlements of Mumbai
Flood Risk Zones Vulnerable informal sectors Informal sector boundary Ward Boundary
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Mumbai has been pointed out as one of the global cities posed by the threat of urban flooding because of its annual rainfall and topographical conditions.54% from the total population of Mumbai lives in informal settlements spread across the city. [4.1.1] As discussed in the domain, informal settlements are the most vulnerable sections of the city under the risk of urban flooding due to the lack of infrastructure and poor living conditions. After analysing the flood risk zones in Mumbai [Diag 4.1.1] the informal settlements lying within these zones have been categorized into a range of low, medium and high density population scales and their locations have been identified based on their ward category. It is essential to identify the demographics of these informal settlements to understand their exposure to the risk of flooding. The adjacent map of Mumbai points out 9 potential informal settlements, 3 from each density type, which are analysed
1- Dharavi Flood exposure : 83 % Site area : 204 Ha Total population : 180,336 Population Density : 884 p/Ha
2- Kajupada, Mohli & Asalfa village Flood exposure : 5 % Site area : 306 Ha Total population : 183,600 Population Density : 600 p/Ha
3- Unknown Flood exposure : 2 % Site area : 292 Ha Total population : 125,400 Population Density : 430 p/Ha
further on the basis of their total population, their site area and the percentage of population in these communities exposed to the risk of urban flooding. The percentage of flood exposure is mainly determined by the siteâ&#x20AC;&#x2122;s topographical conditions such as location near a water body and the low lying areas of the city which are most susceptible as flood prone regions. For determining the site patch to design and test a global resilient system, 3 of the high density informal settlements are compared in order to choose a site with the highest percentage of population exposed to the flood risk. Patch 2 ( Kajupada, Mohli and Asalfa village ) shows the highest population and site area as compared to patch 1 ( Dharavi ) and patch 3. But the percentage of population within this community exposed to the flood threat is merely 5 % as compared to a major 83% of Dharavi which has a lesser site area but a larger density of population. Also, Dharavi is
located in a low lying mangrove swamp area, adjacent to the south side of the Mithi river. Therefore, for a site case to test the initial components of the resilient system, a detailed analysis of the urban fabric of Dharavi and its demographics is carried out in the further chapters.
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Diag [4.2.1] Informal settlement boundaries Source: Development plan for Greater Mumbai - Municipal Corporation of Greater Mumbai
4.2 Site : Dharavi, Mumbai
Image [4.2.1] Source : www.static. panoramia.com
Dharavi : Overview Dharavi is the largest informal settlement in the central part of Mumbai and one of the 30 largest slum settlements in the world. It was established in the early 19th century by migrants from different parts of the country and it evolved over the late 90’s after 1960. The migrants from the state of Gujarat first set up their houses in Dharavi, which was a land covered with mangrove swamps, scavenged scrap and coconut tree leaves. These migrants were mainly involved with the fishing industry. Subsequently, migrants from the state of Tamilnadu ( South India ) whose main occupation was leather tanning settled here. Once the leather tanning and textile industry was set up, people from all parts of India shifted to Dharavi to engage with the flourishing textile industry. Dharavi was morphologically configured with a high density urban setting of small workshops, live and work housing units as well as high rise structures along the peripheral roads. The informal community was divided into various districts called ‘Nagars’ based on the social and religious beliefs of its inhabitants as well as the type of occupation and industry followed by the migrants. These migrants carried their lifestyle, food and clothing ethics 74 | Urban Resilience | Site
and religious practices from their root villages. The evolution of Dharavi can be studied in 3 phases. The colonial period, the post independence period and the period post 1981. Colonial phase : During this phase, there was a gradual transformation of an active fishing industry at the boundary of the city into an informal community of fisherman housing. This process enabled the reclamation of the islands towards the north of Dharavi, thereby cutting of the inlets from Mithi river and the fishing community. This was a period when the fishing industry developed along with the subsequent leather tanning industry. During this time, the northern part of Mumbai was undeveloped which made Dharavi, an informal community at the northern edge of the city. Post Independence phase : The critical development that took place in the post independence period was the shift in Dharavi’s location from the edge of the city to its centre. This was mainly because of Mumbai’s development in the northern part near and beyond the Mithi river. The laying of roads lead to the relocation of the residents into an area called Transit Camp which was the southern part of Dharavi.
Site access points
Diag [4.2.1] Selected Site area in Dharavi
The occupation of land around Dharavi increased because of the influx of migrants and it came to be recognized as the largest slum community in India. The government declared the emergence of Dharavi as a slum in 1971 and an act to provide the residents with basic amenities was passed. Post 1981 phase : This was a phase where Dharavi flourished as an industrial hub within the city. It was recognised as the largest live and work informal community and plans for undertaking a redevelopment program were established. The first step towards the development involved the provision co-operative housing schemes in the residual areas of Dharavi. The tanneries were also relocated and about 85 new buildings were developed in this area. The parts of southern Dharavi near the railway line and major roads were developed with industrial areas as well as housing societies. The Transit Camp grew into an area of
relocation for the inhabitants and emerged as a linear grid of houses abutting next to each other along the major road network. Over the years post 1990, the informal community has shown a growth in because of constant migration and the residents have adapted to the growing family structure by self organizing and extending their existing houses. Further analysis on how this process proves as a problem to the existing urban fabric is elaborated along with the identification of what positives could be extracted from this emergent development. The area of the entire Dharavi is 204 Hectares. The peripheral units consist railway quarters, bus station and other low economy housing. However, our main concentration is the informal settlements highlighted in the Diag 4.2.1 with the site area of 113.2 Hectares and population density of Dharavi is 884 p/ha which is approximated to 900p/ha. Architectural Association | Emergent Technologies & Design | 75
Image [4.2.2] cdn.theatlantic.com
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Image [4.2.3] Source: cdn.lightgalleries.net
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Image [4.2.4] Source : trip.me/blog - By Thomas Luthard
Image [4.2.5] Source : shutterstock1 78 | Urban Resilience | Site
Urban Flooding Natural outfall blocking and storm water discharge by dumping barriers
Growth and Congestion Narrow lanes and lack of ventilation by household extensions and encroachment
Lack of amenities & construction quality - Limited water supply and essentials giving rise to risk of diseases. - Threat by adverse weather conditions because of poor construction quality
Dharavi - Issues faced in the existing scenario Dharavi is a manifest representation of an informal settlement, that has survived and evolved through the ages. As discussed in the criteria for site selection, urban flooding remains a dominant issue faced by the residents in this informal community. The root cause of this is because of the dumping barriers created by the residents of Dharavi near the natural outfalls of the Mithi river that blocks the discharge of storm water. The industrial nature of the settlement also gives rise to possibilities of harmful toxic waste mixing with the discharge water. Beyond this, the squatter community faces several acute issues that need to be addressed. The dwellings in Dharavi have an incremental growth pattern. The region is divided into several communities based on their occupational, religious and social character. Few of these communities have set up base since the migrants first shifted and initiated their occupations, while the rest have followed path subsequently. The extents of Dharavi are confined but the influx of migrants still continues gradually. This results in a modification and extension of the communityâ&#x20AC;&#x2122;s existing built fabric. Predominantly, this extension of the built fabric results in a part of the dwelling spilling out in the open areas. Although, it is a restricted process since the open areas are used as work yards for occupational purposes and public gathering spaces during the time of festivals and other social events. This leads to the
extension of the dwellings into the narrow lanes which are transitional routes between dwellings within a community sector. Owing to this situation, the transition lanes are further reduced in their widths and become narrow, leading to lack of natural light and ventilation. The extensions to the built fabric are inadequate in terms of their construction quality and materials which make them susceptible to deterioration in adverse weather conditions. Apart from these conditions, the aid from the Government to these informal communities is scarce due to the illegal nature of these settlements. The water supply, electricity and sanitation facilities in Dharavi are limited and the water necessary for domestic purposes is stored in plastic barrels and containers in the housing clusters. Apart from the grave fact of water supply shortage, this increases the chances of the water being contaminated and intensifies the risk of spreading diseases. On the whole, the issues faced by the informal settlements in Dharavi deal with the adequate supply and discharge of water, the lack of infrastructure to cope up with the growing migration rate and congestion as well as proper organization of the existing ideas of communal live and work scenario. Along with these issues faced by the residents, there are certain aspects of the community worth considering as positives, that can be retained and improved to enhance the social live and work quality. Architectural Association | Emergent Technologies & Design | 79
Open spaces / work yards Hierarchy of open spaces and work yards for occupational needs
Adaptive dwelling clusters Dwelling units catering to family structure and formation of clusters around open yards
Communal connectivity Connectivity between cluster of dwellings in one sector based on occupations and religion
Positive aspects worth retaining in Dharavi As mentioned in the discussion above, the social organization and the live and work ideology is an aspect worth retaining in the informal settlements in Dharavi. Every dwelling that has been established over a period of time in this informal settlement, has its roots based on a certain occupational and social character. A migrant family settles in a particular place which caters to their occupational and economic necessities. Initially, this family may be comprised of two or three individuals. But over a range of years, the family gradually increases and the residents upgrade their spatial requirements by adding an extra floor or allowing their habitat to grow outwards by taking up neighbouring open space. Essentially, the compromise of extensions, as mentioned earlier, is not made on the open spaces necessary for work yards and social gatherings. These growing residential units flank such open spaces or work yards. People belonging to similar work and religious ethics tend to form a cluster and share such open work yards for their industrial needs. These works yards and open spaces are manifested in the urban fabric of the informal settlement in Dharavi. A wide range of occupations define the character of work yards that are created over a period of time. Large scale industries requiring the process of sun drying and outdoor manufacturing tend to have larger open work yards. Occupations that require small scale machinery 80 | Urban Resilience | Site
feasible within the interiors of the live and work units have comparatively smaller or no work yards. Such units may or may not have a social space around which they are clustered. These open spaces are used for religious festivals and social gatherings. Although the urban growth in Dharavi is not adequate in terms of its construction quality, the spatially adaptive nature of its dwellings allows the family structure to grow and gradually become a permanent part of the community in a period of time. A single floor living unit has a live and work space on the same level. As the family grows, an additional floor is provided and the live and work functions are segregated into different levels. As the community clusters have also grown over the years, the spatial hierarchy of open spaces also starts differentiating on the basis of public, semi public and private spaces and whether they are defined as work yards or communal spaces for social meetings.
Image [4.2.6] Source: realitytoursandtravel.com
Image [4.2.7] Source: thepoliticalbouillon.com
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4.3 Dharavi : Analyzing the existing urban fabric
Slums Cemetery Commercial
Diag [4.3.1] Programme Distribution of Dharavi
Public amenities Govt. schemes Industrial
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Most Integrated
Least Integrated
Diag [4.3.2] Syntactical analysis of network integration of Dharavi
Dharavi - Existing program distribution Dharavi is a coherent mix of live and work dwellings as well as dedicated industrial units. A large percentage of the site is mainly occupied by the slums which are either completely residential units or units which cater to a live and work life style. The configuration of the morphologies in Dharavi is such that the residential, live-work and commercial programs are all coherent. The figure ground map [Diag [4.3.2]] of Dharavi shows the distribution of retails and commercial spaces along the major networks in a consistent way. Also, the industrial functions and public amenities are well connected to the primary networks. It is evident that, as one goes towards the interior parts from these major networks, the programmatic functions of the morphologies convert mainly to the informal dwellings. The width of the networks connecting the clusters and open spaces also decreases significantly. Although Dharavi is mainly an
informal community with slum dwellings, the Government has taken up initiative to execute redevelopment schemes in some parts of the site. Each sector in Dharavi shows 7-8_% of government schemes and redeveloped structures. The figure ground data map of Dharavi gives an understanding of the percentage of variation and the configuration of programs within the overall site. It is visually possible to identify which programs are well connected owing to their location in the vicinity of major networks. But the syntactical analysis of network integration (Diag [4.3.2]) helps evaluate the overall connectivity between open spaces and public facilities by primary, secondary and tertiary networks. Further, the distribution of religious spaces as well as the connectivity of open spaces is studied by an agent based syntactical analysis.
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Diag [4.3.3] Religious Space Distribution of Dharavi
Temple Church Mosque
Distribution of religious spaces ‘There are a number of religious places, spread throughout Dharavi. Since a large part of this neighbourhood was developed relatively recently (in the late nineteenth century and after the 1950s), the dense development patterns and continual migration of residents reveal how religious sites function in unplanned and ethnically diverse urban neighbourhoods.’ [ 4.3.1] A majority of the migrants in Dharavi were lower cast Hindus who were not allowed to enter the temples in Mumbai. These residents took the liberty to develop their own religious entities within their neighbourhoods. Such entities varied from a range of small scale stone carvings and votives to bigger organizations within a communal space. The Muslim community in Dharavi is represented by mosques, which are evenly distributed across the neighbourhood. Similar to their Hindu counterparts, these religious places range in size from small scale storefronts to large courtyards. The oldest mosque in Dharavi, which is the Sunni Jama Masjid, is located near the transit camp area 84 | Urban Resilience | Site
along the main road. The Muslim residents hold their prayer gatherings along this street. However, the ratio of these mosques is less as compared to the number of temples in Dharavi due to the majority of residents being Hindu. A majority of the population in Koliwada, in the northern part of Dharavi are Christians. The fishermen community known as Kolis, who are the original inhabitants of the area, have had a long relationship with Catholicism, from their first encounters with Portuguese colonizers. ‘Religious sites in the Koliwada area have a characteristically Indian flavor with small shrines dedicated to the virgin Mary that are virtually indistinguishable from Hindu religious sites in other areas. The area has its share of large churches and small shrines as well as numerous small Christian worship spaces that exist inside of the homes of local residents. These informal religious spaces have no signage or indication of their dual purposes.’ [ 4.3.1] The religious spaces would be configured as a part of communal spaces in the Designed Site.
Image [4.3.1] kemmannu.com
Image [4.3.2] Source: farm6. static.flickr.com
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d
Roa
Ro
ad
Area test patch (A) : 4800 sq m Number of agent locations : 6 B
Bounding primary networks : 2
C
A
Open Spaces - Connectivity Within the site, 3 local scale patches involving primary open spaces were extracted and analyzed to understand their connectivity with the existing networks and how they are integrated within the communities. For this, an agent based syntactical analysis was conducted with DepthMap. Within each patch, a series of agents are released from predetermined locations; along the streets flanking the primary open spaces. With the agents restricted to a maximum of five steps before change in movement direction, the most covered public areas are mapped in a gradient indicating its integration with neighboring spaces. The primary open spaces in the test patch a,b & c are bound by streets on one or two sides and show greater penetrability from these streets into the open public spaces. From this existing scenario of spatial configuration within Dharavi, it is implied that the primary public spaces
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exist within a close range of the secondary networks. Test patch (a) consists of 3 opening lanes through the existing morphologies leading directly into the public area while test patch (b) has 4.These public spaces are bound by the primary roads on one side. On the other hand, test patch (c) which has primary roads on 2 sides of its perimeter with no physical barriers. This study helps in formulating parameters for configuring the primary open spaces in the urban setting. The ratio of the open lanes to the built morphologies from a minimum to maximum extent can be taken as an average to achieve a permeable pedestrian circulation from primary networks into these public spaces. This also gives a limit for a minimum area of a primary public space and suggests that its closeness to a secondary network route establishes maximum access to the open areas.
Road
Area test patch (C) : 7593 sq m Number of agent locations : 4
Road
Bounding primary networks : 1
Area test patch (B) : 1550 sq m Number of agent locations : 6 Bounding primary networks : 1
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6
3
2 4 5
1-Pottery
1
2-Washing & Broom making
Diag [4.3.4] Sectors and occupations of Dharavi
3-Leather and Textiles
Source: http://mythologiesofmumbai. pukar.org.in/dharavi
5-Factory garments and sewing
4-Leather tanning
6-Fisheries
Social structure : sectors and occupations Essentially, Dharavi is divided into 6 major sectors based on the type of occupations followed by the residents. The first residents of Dharavi were believed to be the fisherman folk known as ‘Kolis’ who settled in the patch on the north and north western part of Dharavi adjacent to the Mithi river. Since then, catching fish, drying and selling them has been the chief occupation in this patch of Dharavi which is known as ‘Dharavi Koliwada’ (sector 6). The subsequent migrants in Dharavi were the community of potters from northern part of India. They established their occupational territories in the southern part of Dharavi . These residents had a peculiar characteristics of their live and work spaces. They set up their housing units with a workspace in the rear side of the livable space and a retail area outside the front access. Their community has evolved over a period of time due to the migration of the family members involved with the pottery industry. This area came to be known as ‘Kumbharwada’ (sector 1). The central district of Dharavi is inhabited by the 88 | Urban Resilience | Site
migrants involved with leather tanning. A part of sector 3 is also involved with the leather industry. This makes the community, the largest section of migrants in Dharavi. The sector is famously known as ‘Chamda Bazaar’ for its industry of salting leather/hide and leather products. These residents have the bigger work yards as compared to other work yards and open spaces in communities within Dharavi. Sector 5 comprises of the migrants whose major occupation is producing factory garments and sewing. These communities mainly have their production indoors, thus requiring lesser outdoor work yards. Sector 2 mainly consists of the washer men called ‘Dhobis’ who indulge in laundry services along with a section of the residents engaged in the broom making activity. Each of the occupations in Dharavi has a direct impact and relationship with the type of open spaces and work yards associated with it.
Image [4.3.3] https://images.trvl -media.com Pottery work yards in shared open spaces destination /6292781/Dharavi-97735.jpg
Image [4.3.4] mythologiesof mumbai .pukar.org.in Indoor broom making
Image [4.3.5] iwepindia.tumblr. com Leather tanning
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Image [4.3.6] www.flickr.com Factory garment sewing industry in the work & retail units
Image [4.3.7] http-//s3-ap-southeast-1.amazonaws. com/.jpg Food product drying
Image [4.3.8] http-//www.thehindubusinessline. com/.jpg Dharavi fisheries Fish selling in Dharavi Koliwada
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5. Hydrological System System Approach Calculations System Logic Method Experiment Setup Experiments : 1 Experiments : 2 Experiments : 3 System Development Conclusion
5.1 System Approach
Top down approach to a hydrological system Mumbaiâ&#x20AC;&#x2122;s major reason for urban floods is due to the pluvial flooding and lack of natural defences. Haphazard planning of squatter communities around urban neighbourhoods results in water clogging, which leads to a delay in the discharge of storm water. The settling time as per first hand local survey, takes up to 1 week in some parts of Mumbai after a 24 hour rain cycle with an intensity as high as 944 mm. The city relies on 70% of its flood water to be drained through surface runoff, while the remaining through infiltration and evaporation respectively. As the cityâ&#x20AC;&#x2122;s drainage system was designed almost 70 years ago it fails to cope up with the current scenario. [5.1.1] The idea of this hydrological network system is a top down approach, to target majority of the flood water through surface runoff. It also acts as a major component of the whole system, with the main driver as topography of the site 94 | Urban Resilience | Hydrological System
in deriving networks; for both collection of surface runoff as well urban networks. The system is intended to run parallel to the urban networks as drainage along the natural topographical gradient of the site. The final proposal would serve as a hydrological system in a hierarchy of three orders, to cope with flooding in the most resilient way possible. Rain water through surface runoff is intended to be channelized by drain networks which allow the storm water to flow onto the secondary detention catchments as storage units. These catchments delay the water flow before channelizing it into the primary outlets of each sub-watershed. The primary catchments of each watershed within the site are intended to be interlinked with one another till a final discharge outlet point. The system works as a collection, storage and distribution strategy.
Existing flood scenario
Drainage : 25mm/hr Highest rainfall : 180mm/hr
TIME : 6 hours
TIME : 12 hours
TIME : 18 hours
TIME : 24 hours
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System Strategy
Channel along roads
Stage : 1 Collection
Channels connected to the primary and secondary catchment.
Stage : 2 Storage
Primary catchments connected between watershed for discharge.
Stage : 3 Distribution
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5.2 Calculations :
Determining the requirement for detention and retention basins
Built up spaces BUILT UP
DETENTION BASIN Detention basins RETENTION BASIN Retention basins OPEN SPACE Open spaces
CALCULATION FOR DETENTION BASINS
CALCULATION FOR RETENTION BASINS
Area of the Dharavi = 1132408.5 sqm Rainfall intensity = 180 mm/hr = 0.18m/hr Run-off coefficient = 0.9 [5.2.1] Capacity required for Detention basin for 1hr rainfall = C
Population density of Dharavi = 900 persons/hectares Area of the selected Site = 1132408.5 sq meters Thus, Total Population of the selected Site = 102106 persons Drinking water required per day per person is considered 3 liters. Number of days of dry season = 240 days Following is the calculation of Drinking water required to be stored for use during the Dry season.
C = Area (sq m) x intensity (m/hr) x runoff coefficient = 1132408.5 x 0.18 x 0.9 = 183450 cum Therefore, Capacity = 183450 cum Area required = Capacity / depth = 183450 / 2 = 91725 sq m
Water required = population x per person water req (3) x days (240) = 73516320 liters = 73516.32 cum Area required = water required / depth = 36758.16 sqm
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Diag 5.2.1 Basic distribution of spaces
5.3 System Logic Diag 5.3.1 : Dendritic pattern
Diag 5.3.2 : Parallel pattern
Diag 5.3.3 : Rectangular pattern
Diag 5.3.4 : Trellised pattern
Diag 5.3.5 : Radial pattern
Diag 5.3.6 : Annular pattern
Dia : 5.3.0 Source : River Morphology and Channel Processes, Iware Matsuda, College of Economics, Page 8
River system As mentioned in the domain chapter, resilience is the systemâ&#x20AC;&#x2122;s ability to change and adapt through natural processes. In order to develop a highly effective system, the logic of river morphologies and channel patterns were investigated. Every channel in a drainage basin has its own shape depending on the geology and the predominant slope. The shapes of such natural processes are hard to be defined by a deterministic approach. However, a quantitative analysis has identified three basic river morphologies found in alluvial plains.(I.e. meander, braided & straight). The hydrological networks function as an open system, mimicking the bifurcated patterns found in the delta region which is mostly straight. [5.3.1] The geometric structure of these bifurcated drainage patterns are classified as per Howard (1967), which mostly differ at the angle at which two streams meet and based on the length, width and depth ratio. The major reason for such transformation in patterns is due to the texture in geology. The illustrations above show the following patterns : 98 | Urban Resilience | Hydrological System
A dendritic pattern (Diag 5.3.1) composed on homogeneous rock. A parallel pattern (Diag 5.3.2) composed on strata of sedimentary rocks with unidirectional slope. A rectangular pattern (Diag 5.3.3) due to faults joints on rocks. A trellised pattern (Diag 5.3.4) formed in basins composed of alteration of hard and soft strata. A radial pattern (Diag 5.3.5) appears on fresh volcanic areas. A annular pattern (Diag 5.3.6) is found commonly in an area with less gradient where the land surface is very gentle. While running the experiments, the emergence of dentritic and annular pattern was observed as the major force driving the movement of agents to mimic water patterns based on the topographical gradient of the site. The algorithm works as a stochastic approach; However, as the surface texture was not considered while running these experiments, certain angular constraints were required. The research carried out in this chapter suggests that certain formations could not be possible due to such constraints.
Image [5.3.1] Source : www.bergoiata.org Straight Colorado river.
Image [5.3.2] Source : nexus-wallpaper. com Meander Alaska mountain range river.
Image [5.3.3] Source : patternsofnatureblo g.com Braided Rakkaia river.
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1
1
1
1
1
1
1 2
2 1
1
1
2 1
1
3
1
3 2
2
3 3
Stream (Drain) order While running the experiments, identifying the order of the drains becomes important as the increase or decrease in discharge can be controlled by modulating the volume of water flowing through these channels. The order of drains as per Strahler (1957) suggested that it increases from its upper to the lower reach ( or the mouth of the drain ) where it opens into a catchment. The rule to derive orders of stream is identified as follows : When two first order drains join together, a second order drain is born and similarly, a third order. [5.3.1] There can be more than three orders of such drain channels but as the scale of the drainage basin or sub-watershed on the selected test patch is relatively small as compared to a river system, we have limited the identification of the order of drains to three in total. The channel cross section greatly depends on the water volume for a given drainage basin ( sub water shed ) which it covers and the rainfall intensity
100 | Urban Resilience | Hydrological System
of the site. A channel to be designed in area where the water carries suspension load, the width to the depth ratio is smaller. Such channels are generally found to be narrow and deep with a concave-upward channel profile. [5.3.2] This logic would be used to determine the initial channel profile, to test its flow rate against the water volume that it should carry once the gradients of hydrological networks on site are generated. [Diagram 5.3.7]
w3a
d3 w3b 3rd order drain
w2a
d2 w2b 2st order drain
w1a
d1 w1b 1st order drain
Sub-Watershed
wa - upper width wb - lower width d - depth
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Diagram 5.3.7 Order of drain based on agent trail.
5.4 Method
Agent movement using vector field An Agent Based Model (ABM) is one of the widely used computational models to simulate the behaviour of natural systems. These autonomous agents work with simple rules of interaction & change in actions upon contact with its environment which can be used to assess its effects on the collective behaviour of the whole system. As discussed in the system approach an agent based model is used, which interacts with the siteâ&#x20AC;&#x2122;s topography and changes its decision in movement using a vector field. The process is mostly stochastic with the gradient of the site dictating the formation of patterns. The first 3 set of experiments is run by giving the agents a range to turn within an angle of 180 degrees. However, angular constraints were introduced for further experiments in system development since the agents seemed to form a closed loop. [Refer conclusions] Rules for autonomous agents are as follows : 1) Every agent finds its initial vector field by identifying the position of its nearest neighbour and lowest to its current position. 102 | Urban Resilience | Hydrological System
2) The initial vector field is used by the agents to determine its next position with least angular movement. 3) Every agent is allowed to have a total of 180o angular turn in its complete life. This rule was introduced as a constraint as discussed in the chapter-system logic. 4) An Agent stops its life cycle when it finds that all its neighbours are topographically higher than its current elevation. [Diagram 5.4.1.] The system works with agents that were generated from the siteâ&#x20AC;&#x2122;s spot elevation points, informing the agents its realtime location in X,Y,Z direction. The agents represent water drop patterns and mimic their behaviour on the site surface. The step depth of the agent movement cumulatively tells the density of agent (flow) accumulation of all such agents to a particular point which can be identified as potential detention catchment location. This also helps in dividing the site into its sub-watershed area by clustering of agents that end their life at a common location on site. [Diagram 5.4.3]
Stop agent life
1
Vector Field
0
Agent
3
2 x=0
6
4 5
True (if x > y)
8
7
y =1 False (if x < y)
Angel of rotation -90o to 90o
9 Diagram 5.4.2 Gradient step depth
Diagram 5.4.1 Rule for agent movement.
Note : Number indicates topographical ranking of points.
Diagram 5.4.3 Agent descends from higher to lower topographic points.
0
1
2
3
4 5 7
6 8
9
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Catchment location 1st order drain 2nd order drain 3rd order drain AIM : Effective Run-off using water drop pattern
Catchment location 1st order drain 2nd order drain 3rd order drain AIM : Effective Run-off using water drop pattern
Rule 1: Find closest point lower than the current height Selected lower points Initial agent position
5.5 Experiment Setup
Rule 2 : Select points within the angle domain.
Catchment location 1st order drain 2nd order drain 3rd order drain AIM : Effective Run-off using water drop pattern
Selected lower points Initial agent position
Rule 1: Find closest point lower than the current height Selected lower points Initial agent position
Rule 2 : Select points within the angle domain. Selected lower points Initial agent position
3 : Move agent to the location with partial angle difference. Rule 3 : Move agent to the location with partial angle difference.RuleSelected lower points
Selected lower points Initial agent position Current agent position
Initial agent position Current agent position
Rule 4 : Agent stops moving when neighbours are higher than current altitude Selected lower points Initial agent position Current agent position
Parameters :
Rule 1: Find closest point lower than the current height Selected lower points Initial agent position
Diagram [5.5.1] Direction range of vector field
Rule 4 : Agent stops moving when neighbours are higher than current altitude Selected lower points Initial agent position Current agent position
Rule 2 : Select points within the angle domain. Selected lower points Initial agent position
Rule 3 : Move agent to the location with partial angle difference. Selected lower points Initial agent position Current agent position
Rule 4 : Agent stops moving when neighbours are higher than current altitude Selected lower points Initial agent position Current agent position
3 directions
8 directions
Evaluation criteria : Diagram [5.5.3] Slope : 1:80 to 1:110 Water flow
Water channel Fall
Diagram [5.5.2] Step length of agent ( 40m to 110m )
Distance ( m )
Coherent parameter relation and GA optimization The experiments were setup to study the relationship of the two parameters used to create a variation in the networks. The first two were tested by keeping one parameter constant. Experiment : 1 with fixed number of directions and varying step length while ; Experiment : 2 with fixed step length and varying number of directions. Experiment : 3 was based on a random combination of both parameters. The criteria for each set of experiments were compared to make conclusions and changes required in the algorithm for system development shown in Experiment : 4. The hydrological system was optimized by using Genetic algorithm with a percentage of gradient that fulfils the slope requirement in experiment : 4. The slope domain acceptable for the drain networks generated by agent movement is in between 1:80 to 1:110. [Diagram 5.5.3] The slope was decided to be in between this domain so that its not too 104 | Urban Resilience | Hydrological System
steep or flat for the water to flow with adequate velocity. [5.5.1] The step length parameter determines at what distance is an agent is allowed to travel for every iteration while moving on the surface. The domain of the parameter is between 40 and 250 meters with a range of 15 steps in multiples of 5. [Diagram 5.5.2.] The minimum value was determined from the Chawls proposed for the community living typology of the informal settlements. The number of direction allows the agents to choose its next direction of movement out the available neighbours at a low elevation point. The domain for number of directions is between 2 to 8. [5.5.1] The experiment runs in stages generating probable run-off on site, the possible subwatershed and the catchment locations by recording the flow accumulation from Agent trail. [Diagram : 5.5.4]
Flow Accumulation
Controlled Run - off
Sub - Watershed
Diagram : 5.5.4 Hydrological system.
Existing Topography
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5.6 Experiments : 1
Fixed direction varying length
Iteration : 1 Order
No.
3rd
190
800 m
1 : 56 to 1 : 184
2nd
151
400 m
1 : 48 to 1 : 184
1st
217
200 m
1 : 37 to 1 : 91
Max Length Slope
Parameters : Number of direction : 3 Step length : 50.0m Criteria : Segments with adequate slope : 34%
Iteration : 5 Order
No.
rd
3
50
490 m
1 : 110 to 1 : 298
2nd
71
420 m
1 : 53 to 1 : 82
1st
115
337 m
1 : 73 to 1 : 252
Max Length Slope
Parameters : Number of direction : 3 Step length : 70.0m Criteria : Segments with adequate slope : 38%
Iteration : 7 Max Length Slope
Order
No.
rd
3
84
560 m
1 : 48 to 1 : 472
2nd
48
273 m
1 : 48 to 1 : 472
1st
88
240 m
1 : 45 to 1 : 1289
Parameters : Number of direction : 3 Step length : 80.0m Criteria : Segments with adequate slope : 29% 106 | Urban Resilience | Hydrological System
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
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5.7 Experiments : 2
Fixed length varying direction
Iteration : 1 Max Length Slope
Order
No.
3rd
88
360 m
1 : 104 to 1 : 288
2nd
164
180 m
1 : 67 to 1 : 529
1st
219
180 m
1 : 108 to 1 : 125
Parameters : Number of direction : 2 Step length : 45.0m Criteria : Segments with adequate slope : 22%
Iteration : 3 Max Length Slope
Order
No.
rd
3
180
630 m
1 : 67 to 1 : 74
2nd
255
315 m
1 : 43 to 1 : 43
1st
218
243 m
1 : 44 to 1 : 124
Parameters : Number of direction : 4 Step length : 45.0m Criteria : Segments with adequate slope : 48%
Iteration : 7 Max Length Slope
Order
No.
rd
3
156
588 m
1 : 101 to 1 : 113
2nd
238
505 m
1 : 20 to 1 : 1016
1st
267
352 m
1 : 32 to 1 : 74
Parameters : Number of direction : 8 Step length : 45.0m Criteria : Segments with adequate slope : 45% 108 | Urban Resilience | Hydrological System
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
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5.8 Experiments : 3
Random combination
Iteration : 1 Max Length Slope
Order
No.
3rd
32
706 m
1 : 185 to 1 : 945
2nd
70
600 m
1 : 40 to 1 : 48
1st
126
362 m
1 : 30 to 1 : 50
Parameters : Number of direction : 8 Step length : 75.0m Criteria : Segments with adequate slope : 46%
Iteration : 3 Max Length Slope
Order
No.
3rd
120
627 m
1 : 47 to 1 : 106
2nd
150
385 m
1 : 48 to 1 : 4409
1st
179
517 m
1 : 43 to 1 : 164
Parameters : Number of direction : 6.0 Step length : 55m Criteria : Segments with adequate slope : 45%
Iteration : 6 Max Length Slope
Order
No.
rd
3
35
406 m
1 : 185 to 1 : 382
2nd
68
481 m
1 : 90 to 1 : 748
1st
101
362 m
1 : 43 to 1 : 50
Parameters : Number of direction : 4.0 Step length : 75m Criteria : Segments with adequate slope : 49% 110 | Urban Resilience | Hydrological System
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
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5.9 System Development
with Genetic Algorithm
Experiment :4 Generation : 0(6) Max Length Slope
Order
No.
3rd
311
473 m
1 : 47 to 1 : 117
2nd
142
96 m
1 : 28 to 1 : 29
1st
233
160 m
1 : 44 to 1 : 385
Parameters : Number of direction : 6 Step length : 40.0m Criteria : Segments with adequate slope : 52%
Experiment : 4 Generation : 1(2) Max Length Slope
Order
No.
3rd
58
706 m
1 : 51 to 1 : 185
2nd
41
181 m
1 : 56 to 1 : 90
1st
87
181 m
1 : 40 to 1 : 43
Parameters : Number of direction : 4.0 Step length : 75m Criteria : Segments with adequate slope : 51%
Generation : 1(6) Max Length Slope
Order
No.
rd
3
86
520 m
1 : 47 to 1 : 90
2nd
55
130 m
1 : 41 to 1 : 135
1st
110
156 m
1 : 30 to 1 : 101
Parameters : Number of direction : 4.0 Step length : 65.0m Criteria : Segments with adequate slope : 51% 112 | Urban Resilience | Hydrological System
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
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Generation : 1Population : 7 Max Length Slope
Order
No.
3rd
114
828 m
1 : 47 to 1 : 91
2nd
61
155 m
1 : 34 to 1 : 53
1st
187
233 m
1 : 32 to 1 : 106
Parameters : Number of direction : 7 Step length : 55.0m Criteria : Segments with adequate slope : 51%
Generation : 2 Population : 1 Max Length Slope
Order
No.
3rd
220
667 m
1 : 67 to 1 : 74
2nd
116
135 m
1 : 43 to 1 : 176
1st
207
135 m
1 : 44 to 1 : 87
Parameters : Number of direction : 6.0 Step length : 45.0m Criteria : Segments with adequate slope : 53%
Generation : 4 Population : 3 Max Length Slope
Order
No.
rd
3
247
585 m
1 : 67 to 1 : 74
2nd
119
90 m
1 : 43 to 1 : 71
1st
177
108 m
1 : 44 to 1 : 87
Parameters : Number of direction : 4.0 Step length : 45.0m Criteria : Segments with adequate slope : 54%
114 | Urban Resilience | Hydrological System
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
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5.10 Conclusion
System evaluation and further developments Comparing graphs for Experiment :1 and 2 it is evident that the parameters for number of direction plays a dominant role in providing optimal results. [Diagram 5.11.2] & [Diagram 5.11.3] While running the Experiments : 1,2,3 the patterns formed by the agent trail ran into a close loop where the agents continue to move even though they find their neighbours with the same elevation from its current location. In System development the rules for angular movement were added as stated in methods. This resulted in a diversity of the patterns obtained using a Genetic Algorithm. The criteria after using GA and changing the rule set helped achieve population over 50%. However the population showed higher convergence.[Diagram 5.11.4] One of the major reasons for restricting the number of angular turns that an agent makes, to a total of 180o goes back to the research discussed in system logic where the idea was to have axial drainage system which could result in adequate flow velocity. After ranking the population as per the criteria, it can be 116 | Urban Resilience | Hydrological System
seen that the step length and number of direction have no relationship. But the top 5 ranked population suggest that decrease in step length does increase the accuracy of agent movement resulting in better results. The optimal ranked in the top 5 will be used as a potential for hydrological network variations during its integration with spatial organization. However, all the optimal generated from the last Experiment did not reach more than 60%. of the required slope. A system manipulation can therefore be done by designing the cross section of the channel as per the required slope, length & order of drain. The drainage basin ( sub water shed ) of each channel system tells us the volume of water that is required to flow through these channels. This could be tested for further development of the system. Each hydrological network stores the drained water in its detention catchments working independently, making the system resilient. This implies that, in case of a system failure in one of the sub watersheds the neighbouring areas do not get affected.
10 9 8
Step length
7
4 3
Rank
2
0-5
45m - 75m
4-7
> 50%
1
6 - 16
45m - 100m
4-8
< 50% to >40%
17 - 30 45m - 100m
2-3
< 40%
1
2
3
4
5
6
7
8
9
10
Number of direction
Diagram 5.11.2 Experiment : 1
Step Length Number of direction Slope
Table 5.11.1
Diagram 5.11.1 Parameter relation
Diagram 5.11.3 Experiment : 2
4
3
3
2
5 2 38% 32% 34%
6
30% 31%
7
48%
4
35%
46%
35% 34%
29%
22%
1
1
44%
30% 34% 34%
5
45%
11
45%
8
7 10
9 Diagram 5.11.4 Experiment : 3
3
45%
4
35% 22%
3.0
45%
2
44%
22% 46%
49% 5
Diagram 5.11.5 System Development
45%
22%
0.0
6
2.0
1.0
48%
.0
5
0
.0
45%
6
4.0
49%
6.0 6
3
2
1.0 44% 1
51%
46%
0.0
4
51%
51%
52%
1
49%
45%
49% 5.0
7
45%
53%
54%
6.0 5
6
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6. Spatial Organization Neighbourhood relationship Open space Analysis Methods Experiment 1 Experiment 2 Aggregation
6.1 Neighborhood relationship
Distance between Built to open space (Avg : 82.36 sq m)
Diag [6.1.1] Distribution of existing Open Spaces
Overview After devising the hydrological system to cope up with the urban flooding scenario in Dharavi, it is essential to extract key parameters from the analysis of Dharaviâ&#x20AC;&#x2122;s existing urban fabric, for configuring the spatial organization of the informal settlement. The site analysis of Dharaviâ&#x20AC;&#x2122;s current scenario in chapter 4 gives an overview of the program distribution, network connectivity, occupational structure and the configuration of open spaces within its urban fabric. However, to set up rules for establishing a neighbourhood relationship to achieve an efficient spatial organization on the selected site, it is further necessary to understand the area and distribution of open spaces in relation to the built morphologies. This would help in deriving the parameters on which the design principles could be set during the automation rules. The following chapters explain this detailed analysis and the method used to set up and run experiments to achieve the desired neighbourhood relationships. 120 | Urban Resilience | Spatial Organization
Distance between open spaces (Avg : 80.25 sq m)
The above diagram [6.1.1] shows the distribution of open spaces within the selected patch of Dharavi. To understand the relationship of these open spaces with their contextual built morphologies, the distance between each built block to its closest open space is identified. Also, the distance between one open space to the other closest to it, is studied. This gives an understanding of how close or far these spaces are configured from one another. Further, the areas of these open spaces are calculated and categorized in three types based on a range of area sizes. These analyses would form a basis of extracting the required parameters, to fulfil the minimum requirements of spatial distribution while configuring neighbourhood rules.
open are
Diag [6.1.2] Open Space Distribution of Dharavi
Open space variation and distribution A study on the spatial organization of Dharavi gives an understanding of the influence of socio-economic and cultural beliefs of the communities on its urban configuration. As discussed earlier, Dharavi is composed of migrants who have settled there, over a period of time. These migrants tend to organize their communities based on certain occupations which they are involved in. Open spaces in Dharavi are important elements which support the social and economic needs of the residents. A lot of craft based activities which are essential for trade are carried out within these open spaces. The range of open spaces vary from small scale work yards and private/semi public spaces to larger communal areas. Essentially, the work yards are spaces outside the live and work housing units and are shared between the residents involved with the same type of occupation. At times, they are also retail spaces to display the products while the other functions
include activities like drying in case of pottery yards and units manufacturing food products. Bigger open spaces are usually meant for social gatherings and communal events. Many of these spaces are typically configured around religious places to cater to the gatherings related to those following similar religious ethics. But certain occupations involving leather tanning and salting hide have comparatively larger work yards. Public amenities like government schools are also provided with mid scaled open spaces. The map above identifies the open spaces within the urban fabric of Dharavi. It varies from a range of open spaces lesser than 1000 sq m area to those which are larger than 5000 sq m. The mid scaled open spaces lie between 1000 sq m to 5000 sq m. ( Areas smaller than 10 sq m are considered as transition spaces )
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open area 1000-5000 open area
Sector 6
Sector 3,4 & 5
Sector 1& 2
122 | Urban Resilience | Spatial Organization
Patch 1 Patch 2 Patch 3
Redefining site division based on Open space areas From the analysis of the existing range of open spaces, it is evident that sectors 1 and 2 have a similar proportion of open space areas while sectors 3,4 and 5 have a range of large scaled open areas. Sector 6 on the other hand has medium scale open spaces. The main aim of this analysis is to identify the sectors in Dharavi which have a similar area proportion of open spaces. This would allow the sectors to be grouped together based on similar spatial characteristics which in turn would provide a basis for establishing neighbourhood relationships between built and open spaces for an urban intervention. After the analysis, the sectors are clubbed together into 3 distinct patches which would define the boundaries for allocating and running the automation rules of neighbourhood relationship. Patch 1 denotes the group of sectors 3,4 and 5 with larger open spaces. Similarly, patch 2 denotes sector 6 with medium scale spaces and patch 3
denotes a group of sector 1 and 2 with the smallest open spaces in the spatial hierarchy. Further after this analysis, a detailed study of the character of open spaces in each patch, with their individual areas, proportions and built edges is studied. This would give an idea of calculating the average size of open spaces for work yards and social interaction required in the automation rules for the urban intervention on a neighbourhood basis. The underlying principle for this study was to take the positive aspects of the spatial organization of the existing urban fabric of Dharavi.
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Diag [6.1.3] Site division
6.2 Open Space Analysis Patch 1
Analysis
Conclusion
This part of Dharavi is mainly dominated by people who are involved in the occupation of leather tanning and textile works. Most of these people who live here, used to have vast open spaces outside their residences where they could keep their cattle, tools and materials. So, even after migration from their villages, they maintained these large open spaces necessary to carry out their economic activities. Lack of overall areas resulted in people living in small narrow rooms while maintaining the open area required for their work. These spaces are often multi functional and are also used for social and community gatherings apart from their primary use as work places. The size of open area varies across the patch, however the average size of this patch consisting of sectors 3,4 and 5 is much larger compared to that of the other patches. Each open area serves as the working place for its surrounding built neighbourhood. So an analysis of the sizes of open spaces across this patch is made. The area of the open spaces range from 2000sqm to around 10552sqm.
Every occupation based community requires a certain area of open spaces which they use for carrying out their occupations and also use it for their social and communal gatherings. The occupation of leather tanning and textiles requires considerably larger open areas for carrying out their daily works. The average areas of these open spaces is found to be approximately 5800sqm. So, the aim is to maintain this average along this patch while implementing the neighbourhood relationship rules.
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Sample 1
Sample 5
Open Space area : 2683 m2
Open Space area : 10552 m2 Sample 2
Sample 6
Open Space area : 2432 m2 Open Space area : 9030 m2 Sample 3
Sample 7
Open Space area : 2720 m2 Open Space area : 7216 m2 Sample 4
Open Space area : 5434 m2
Patch 1
Open Space Area(OS) m2
Sample 1
10552
Sample 2
9030
Sample 3
7216
Sample 4
5434
Sample 5
2683
Sample 6
2432
Sample 7
2720
Average
5724
Architectural Association | Emergent Technologies & Design | 125
6.2 Open Space Analysis Patch 2
Analysis
Conclusion
Mithi river lies to the north of Dharavi, adjacent to the marshy land above this particular patch. Since fishery was the predominant occupation in of the residents who migrated and settled here in groups to form this community, it was termed as â&#x20AC;&#x2DC;Koliwadaâ&#x20AC;&#x2122;. For fisheries, these residents required open areas for drying the fish and to sort them. The residents began constructing their housing units around such open spaces which were essential for their fishing industry. As the community grew, instead of expanding their houses into these open areas, the residents of Koliwada constructed additional floors to their existing houses to leave the open space intact. This phenomenon is observed throughout Koliwada where people have retained the value of open spaces for their economic value and adapted to the growth by vertical expansion.
The average ratio of the open space to the neighbouring built up for the selected patches is 0.84. Some of the samples used for analysis have a higher ratio, however it is balanced by another patch which has a lower ratio. Thus, overall the average ratio of built to open spaces for the Koliwada community living is identified to be about 0.8.
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Sample 1
Sample 5
Open Space area : 9250 m2
Open Space area : 3276 m2 Sample 2
Sample 6
Open Space area : 2257 m2
Open Space area : 2666 m2 Sample 3
Sample 7
Open Space area : 1977 m2
Open Space area : 4418 m2
Sample 4
Open Space area : 1696 m2
Patch 2
Open Space Area(OS) m2
Sample 1
3276
Sample 2
2666
Sample 3
4418
Sample 4
1696
Sample 5
9250
Sample 6
2257
Sample 7
1977
Average
3648
Architectural Association | Emergent Technologies & Design | 127
6.2 Open Space Analysis Patch 3
Analysis
Conclusion
This patch consisting of sectors 1 and 2 mainly comprise of the pottery community and the migrants who are engaged with the broom making and laundry services. A section of this patch is the Transit Camp of relocated migrants. The pottery communities termed as â&#x20AC;&#x2DC; Kumbharwadaâ&#x20AC;&#x2122; have a live and work ethic which consists of a small work place in the rear side of their living units and a front work yard used for drying and displaying pots for retail purposes. These work yards are some what a transitional space between the networks connecting the housing clusters. The broom makers utilise the indoor work space in their households for manufacturing brooms and most of the residents of the Transit Camp work outside Dharavi. This has lead to a configuration of only small open spaces which are either work yards for the potters or merely transitional spaces from one cluster to another. The government schools have relatively larger open spaces but on an average, this patch is mainly characterized by open areas lesser than 1000 sq m.
Excluding the largest open area in this patch which is a cemetery, the average size of an open space either for social purposes or as a functional work yard is about 500 sq m. Since the Transit Camp residents have jobs outside Dharavi, there has not been any provision for communal spaces or spaces for industrial purpose. They are morphologically configured as rows of individual housing units abutting parallel to one another. Compared to the 2 patches studied earlier consisting of sectors 3,4,5 and 6, this patch has the least average of open space area.
128 | Urban Resilience | Spatial Organization
Sample 1
Open Space area : 501 m2 Sample 1
Open Space area : 483 m2 Sample 1
Open Space area : 348 m2 Sample 1
Open Space area : 704 m2 Sample 1
Open Space area : 122 m2 Sample 1
Open Space area : 349 m2 Sample 1
Open Space area : 486 m2
Patch 3
Open Space Area(OS) m2
Sample 1
501
Sample 2
483
Sample 3
348
Sample 4
704
Sample 5
122
Sample 6
349
Sample 7
486
Average
428
Architectural Association | Emergent Technologies & Design | 129
6.3 Methods 6.3a Cellular Automata
Moore Neighbourhood Centre Cell + 8 neighbouring cells
Cellular Automata : Urban patch Generation Built vs open spaces Built spaces
Von Neumann Neighbourhood Centre Cell + 4 neighbouring cells
Open spaces
Diag [6.3.1] Cellular Automata neighbourhood
Generating urban neighbourhood relationships. Cellular Automata (CA) is a “bottom-up” approach which is based on the concept that simple decisions or activities of single units or individuals can manifest into a complex global system. [6.3.1] ‘The growth of informal settlements can be considered a similar phenomenon where a high dense complex system is evolved based on interactions and decisions of the individuals which are based on the considerations of only their immediate neighbourhood.’ [6.3.2] Cellular Automata is defined as a collection of ‘coloured cells’ which evolves over a series of discrete time steps based on neighbourhood relationship of adjacent cells. This relationship is input as a set of rules that govern the states of an initial cell and its neighbouring cells. Cellular automata are models for systems in which simple components act together to generate complex patterns of behaviour. ‘ One of the most fundamental properties of a cellular automaton is the type of grid on which it is computed. The simplest such “grid” is a one-dimensional line.’ [6.3.2]In two dimensions, square, triangular, and hexagonal grids may be considered. 130 | Urban Resilience | Spatial Organization
In addition to the grid on which a cellular automaton lives and the colours its cells may assume, the neighbourhood over which cells affect one another must also be specified. The simplest choice is “nearest neighbours,” in which only cells directly adjacent to a given cell may be affected at each time step. Two common neighbourhoods in the case of a two-dimensional cellular automaton on a square grid are the so-called Moore neighbourhood (a square neighbourhood) and the Von Neumann neighbourhood (a diamond-shaped neighbourhood). Diag [ 6.3.1] This method of cellular automation is used to generate an urban patch with a neighbourhood relationship between open spaces and built up spaces based on an analysis of the existing site to retain and improve certain aspects of its spatial structure.
1: Survive Rule condition 1 Live cell survives when it has 2 live neighbour cells.
Live cell dies when it has less than 2 live neighbour cells.
Live cell dies when it has more than 2 live neighbour cells.
2: Birth rule : Cell comes to life when it has 4 live neighbour cells.
Diag [6.3.2] Cellular Automata Rules
Cellular Automata : Rules for automation In a 2 dimensional CA, a matrix of 9 cells is considered. Each cell has a possibility of having 2 states - either 0 or 1. In this case 0 means a ‘dead cell’ and 1 means ‘live cell’. In each time step of the automaton, the new state of a cell can be expressed as a function of the number of adjacent cells that are in the alive state and of the cell’s own state; that is, the rule is outer totalistic. The neighbourhood is comprised of 9 cells which means the central cell is the one which is evaluated based on the conditions of its 8 adjacent cells. For all our experiments we considered the “built cells” to be “alive” and the “open space cells” to be “dead” For setting rules of automation, in Diag [6.3.2] the survive rule is taken into account. In this case, the state of the centre cell is 1 which means it is alive. This cell will retain its state when it has 2 live neighbouring cells. If It has less than 2 live neighbour cells, then under this condition, it dies i.e.: its state becomes 0. This also means that, if the cell has more than 2 alive neighbours, then it dies and its state becomes 0.
The birth rules in the above example suggests that when a dead cell ( state 0 ) has 4 live neighbours, its state changes to 1 which means it becomes a living cell. The grid for a 2 dimensional Cellular Automata can be expanded 16 or 25 or more and evaluation rules for 2, 3 or more cells could be set. On an urban scale, this grid matrix of cells can be configured with a set of rules based on existing site analysis and design criteria to derive a relationship between spaces or designated cell properties to achieve desired spatial organization.
Architectural Association | Emergent Technologies & Design | 131
rise
6.3b Compound Topographic Index
θ run Slope analysis
Diagram [6.3b.1] A representation of DEM of model using spot height method.
10%
0%
345
345
345
340 336
Flow accumulation model
Diagram [6.3b.2] A representation of Flow accumulation using D8 algorithm in ArcGis.
100%
340 334
335
Elevation
100% Single - flow
0%
23%
49%
28%
Multiple - flow
Flow accumulation The catchment strategy is used as a part of the resilient system to collect and store fresh water for drinking purposes on a micro scale. On a macro level, a topographic wetness index method is used, as it strongly derives the potential locations for placing catchments based on the existing site contours. Topographic wetness index is a steady state wetness index calculator used to predict the soil and water content for flow over land, based on slope and flow accumulation. Higher values represent drainage depressions while lower values indicate ridges. The calculation mainly depends on two attributes : the cell size and the type of flow accumulation algorithm used. Preprocessing the terrain attributes in this method has been done by using the spot height technique to develop a digitalized evaluation model ( DEM ). The 30m DEM model is used to derive a slope raster using spatial analyst tools from ArcGis. The flow accumulation evaluation 132 | Urban Resilience | Spatial Organization
on the DEM model is used by comparing different algorithms ( Deterministic 8, Deterministic infinity and Triangular multiple flow ). Flow accumulation uses the flow direction map to count the number of cells that naturally drain storm water into another cell. This technique is used to determine the water shed delineation in hydrology study determining stream pathways. In short, the algorithm used for accumulation differ based on the way it considers the flow direction. The single flow(D8 and D-infinity) algorithm transfers the total cell content to the deepest neighbouring cell, while a multiple flow algorithm performs weighted transfer of the cell content. In general all flow accumulation methods are the same. They calculate the number of cell from either single or multiple flow paths. However, the method we used for both the terrain analyses and hydrological study have their limitations.
Low surface run-off accumulation Medium surface run-off accumulation High surface run-off accumulation Diagram [6.3b.3] Flow accumulation using ArcGis
Catchment locations on the Site This method is used to determine the possible locations of Catchment points on site. The points with higher accumulation of surface run-off water will be ideal for locating detention catchments to allow maximum collection of water. It is important to note that the detention catchment location points could have been derived from the hydrological system experiments but since the experiment for spatial organization using Cellular Automata was run parallel to it, DEM was used to obtain possible locations of the catchment points based on the topography and slope of the existing site. The aim is to use these points as open spaces or non-built up at the initial state so as to accommodate the detention catchment basins. These points will be used for the further Cellular Automata experiments to be carried out on the Site.
Architectural Association | Emergent Technologies & Design | 133
6.4 Experiment 1 Set up Cell size : 40m x 40m
Grid : 600m x 600m
Initial state of cells Built up Open Spaces
Selecting open spaces for evaluation Open Spaces selected for calculating average area
Evaluation Criteria: 1: Population Density Person/ hectare
2 : Average open Space Area (sq m)
Generating urban neighbourhood relationships. As discussed previously the site is divided into 3 patches based on the average of the areas of the open spaces. The aggregation of built and open spaces is developed from the neighbourhood relations using Cellular Automata. The aim of this test is to derive rules for developing 3 different types of these neighbourhood relationships corresponding to those existing on site. The cell size is taken as 40m x 40m from the maximum size of the built morphology. The average population of a chawl morphology is approximately 235 people. Thus each cell size of 40x40m is assigned the population of 235 for further calculations. The patch size of 600m x 600m is considered for the initial experiments, which corresponds to the average area of the 3 patches on site. Various rules are applied and CA is allowed to run for 40 iterations each time. Initial percentage of live and dead cells is set to 50-50% and the cell distribution is random. This experiment is repeated 5 times for each rule with variation in the initial position of dead n live cells. This is to derive a rule which gives desired areas even with the change in the initial positions of live and dead cells. 3 open spaces are selected for evaluation of the area of the formed open space. For this, 134 | Urban Resilience | Spatial Organization
open space with maximum area or the one with maximum repetition of that size of open space in the entire grid is selected. The patch is evaluated based on 2 criteria : 1. Population density and 2. Open space areas. Large number of migrants settle down in these informal settlements to manage their livelihood with minimum expenses and end up living in a high dense slum areas. The phenomenon of migration is only going to increase with time. Thus it becomes essential to accommodate the present population density and provide the infrastructure for the possible growth. However, it is also important to preserve the open spaces used by these communities as work yards because their economic growth is completely dependent on it. Also open spaces are required for community ad social gatherings. Thus the experiments are evaluated for the current population density of Dharavi, which is 900p/hectares and are divided into 3 groups based on the range of open space areas.1 from each group is selected based on the open space area required for 3 different patches on site. Thus 3 different rules are derived which can be applied on the 3 different patches on site.
born rule : 3 survive rule 2,3,4,5 initial state : 50-50 average : 0.44
00000 0000 0.40 82 average= 9600
Tests for rule : 3 ve rule 2,3,4,5 state : 50-50
built up : 203200 built up : 200000 green : 156800 green : 160000 = 0.44 average = 0.40 Patchaverage 1 Pop = 0.083 Pop = 0.082 open area average= 8533 open area average= 9600
: 203200 built up : 200000 built up : 203200 built up : 203200 156800 green : 160000 green : 156800 green : 156800 = 0.44 average = 0.40 average = 0.42 average = 0.44 .083 Pop = 0.082 Pop = 0.083 Pop = 0.083 Type: 01.01 ea average= 8533 open area average= 9600 open area average= 8000 open area average= 8533 built up : 200000 built up : 200000 built up : 203200 green : 160000 green : 160000 green : 156800 Avg Open Space Area: 9600 sqm average = 0.40 average = 0.40 average = 0.44 Population Density: 820 (p/Ha) Pop = 0.082 Pop = 0.082 Pop = 0.083 open area average= 9600 open area average= 9600 open area average= 8533
age :born 0.44 rule : 3 survive rule 2,3,4,5 initial state : 50-50
average born rule: 0.44 :4 built up : 203200 green : 156800 survive rule 3,4,5,6 average = 0.44 initial state : 50-50Pop = 0.083
200000 60000 = 0.40 082 a average= 9600
built up : 200000 green : 160000 average = 0.40 Pop = 0.082 open area average= open 8533 area average= 9600
average : 0.34
40000 0000 0.33 98 average= 4266
built up : 230400 built up : 240000 green : 129600 green : 120000 average = 0.45 average = 0.33 Pop = 0.094 Pop = 0.098 open area average= 11200 open area average= 4266
n rule : 4 Type: 02.02 vive rule 3,4,5,6 Avg Open Space Area: 11200 sqm al state built : 50-50 Densitybuilt : 940 230400 upPopulation : 240000 up :(p/Ha) 244800
rage :born 0.34 rule : 4 survive rule 3,4,5,6 initial state : 50-50 born rule : 3,4 survive rule 3,4,7,8 average : 0.34 240000 built up : 230400 initial state : 50-50
built up : 240000 green : 129600 green : 120000 average = 0.45 average = 0.33 Pop = 0.094 Pop = 0.098 open area average= 11200 open area average= 4266
20000 = 0.33 098 a average= 4266
Type: 03.02 average : 0.97
82400 7600 0.73 74 average= 14400
built up : 160000
built up : 182400
Avg Open Spacegreen Area: 16000 sqm green : 177600 : 200000 Population Density : 650 (p/Ha) average = 0.73 average = 0.89
rn rule : 3,4 vive rule 3,4,7,8 ial state : 50-50
Pop = 0.065 Pop = 0.074 open area average= 16000 open area average= 14400
60000 built up : 182400 0000 green : 177600 0.89 average = 0.73 5 Pop = 0.074 average= 16000 open area average= 14400 built up : 182400 green : 177600 average = 0.73 Pop = 0.074 open area average= 14400
built up : 172800 built up : 160000 green : 187200 green : 200000 average = 1.05 average = 0.89 Pop = 0.071 Pop = 0.065 open area average=open 20266 area average= 16000 built up : 182400 built up : 160000 green : 177600 green : 200000 average = 0.73 average = 0.89 Pop = 0.074 Pop = 0.065 open area average= open 14400 area average= 16000
rule : 3,4 born rule : 2,5 erage :born 0.97 rule 3,4,7,8 survivesurvive rule 2,3,5,6,7 initial state initial state : 50-50: 50-50
Type: 04.01 average : 0.97 average : 0.80
182400 77600 = 0.73 074 a average= 14400
0.72 97 average= 13333
built up : 160000
built up : 182400 green : 177600 average = 0.73 Pop = 0.065 Pop = 0.074 open area average= 16000 open area average= 14400
open area average= open 8533 area average= 8000
built up : 203200 built up : 203200 green : 156800 green : 156800 average = 0.42 average = 0.44 Pop = 0.083 Pop = 0.083 open area average= 8000 open area average= 8533
built up : 244800 built up : 230400 green : 115200 green : 129600 average = 0.22 average = 0.45 Pop = 0.099 Pop = 0.094 Type: 02.03 open area average= 3200 open area average= 11200
Density : 990 (p/Ha) built up Population : 240000 built up : 244800 green : 120000 green : 115200 average = 0.31 average = 0.22 Pop = 0.098 Pop = 0.099 open area average=open 5866area average= 3200 built up : 230400 built up : 244800 green : 129600 green : 115200 average = 0.45 average = 0.22 Pop = 0.094 Pop = 0.099 open area average= 11200 open area average= 3200
open area average= 8000 open area average= 5866
built up : 19 green : 164 average = Pop = 0.08 open area built up : 19 green : 164 average = 0 Pop = 0.08 open area average= open 5866 area a
built up : 193600 built up : 203200 green : 166400 green : 156800 average = 0.38 average = 0.42 Pop = 0.079 Pop = 0.083 open area average=open 5866area average= 8000 Test:
built up : 195200 built up : 1 green : 164800 green : 16 average = 0.47 average = Pop = 0.080 Pop = 0.0 2open area average= 9066open area
Dead cell comes to life when it has
built up : 240000 built up : 244800 built up : 236800 built up : 24 exactly 4green living green : 120000 green : 115200 : 123200 green : 120 neighbours. average = 0.31 average = 0.22 average = 0.37 average = Pop = 0.098 Pop = 0.099 Pop =remains 0.097 Pop = 0.09 A living cell open area average= open 5866 area average= 3200 open area average= 5866 open area
alive only when surrounded by 3,4,5 or 6 living neighbours.
built up : 236800 built up : 240000 green : 123200 green : 120000 average = 0.37 average = 0.31 Pop = 0.097 Pop = 0.098 open area average= 5866 open area average= 5866 built up : 244800 built up : 240000 green : 115200 green : 120000 average = 0.22 average = 0.31 Pop = 0.099 Pop = 0.098 Test: open area average= 3200 open area average= 5866
built up : 204800 built up : 238400 Spaces calculating average green : 155200 green : 121600 average = 0.75 average = 0.72 Pop = 0.083 Pop = 0.097 open area average= 15466 open area average= 13333
born 0.80 rule : 2,5 survive rule 2,3,5,6,7 initial state 50-50 4800 built up : :238400
built up : 201600
built up : 204800
3
built up : green : 12 average = Pop = 0.0 open area built up : 240000 built up : 2 green : 120000 green : 12 average = 0.31 average = Pop = 0.098 Pop = 0.0 open area average=open 5866area
Dead cell comes to life when it has built up : 244800 built up : 230400 built up : 240000 built up : 244800 : 236800 built up : 2 exactly 3built or 4upliving green : 115200 green : 129600 green : 120000 green : 115200 green : 123200 green : 12 neighbours. average = 0.22 average = 0.45 average = 0.31 average = 0.22 average = 0.37 average = A living cell Pop = 0.099 Pop = 0.094 Pop = 0.098 Pop = 0.099 Popremains = 0.097 Pop = 0.09 Type: 03.05 open area average= open 3200 area average= 11200 open area average= open 5866 area average= 3200 area average= 5866 open area alive onlyopen when surrounded built up : 172800 built up : 160000 built up : 176000 built up : 172800 builtby up :3,4,7 169600 built up : Avg Open or8 livinggreen neighgreen : 187200 greenSpace : 200000Area: 28800sqm green : 184000 green : 187200 : 190400 green : 18 Population average = 1.05 averageDensity = 0.89 : 700 (p/Ha) average = 1.05 average = 1.05 average = bours. average = 1.12 Pop = 0.071 Pop = 0.065 open area average= open 20266 area average= 16000
built up : 176000 built up : 172800 green : 184000 green : 187200 average = 1.05 average = 1.05 Pop = 0.072 Pop = 0.071 open area average=open 25066 area average= 20266 built up : 160000 built up : 172800 green : 200000 green : 187200 average = 0.89 average = 1.05 Pop = 0.065 Pop = 0.071 open area average= 16000 open area average= 20266
built up : 172800 built up : 160000 Avg Open Area: 15466 sqm green : 187200 greenSpace : 200000 average = Population 1.05 average = 0.89 : 910 (p/Ha) Density Pop = 0.071 Pop = 0.065 open area average= open 20266 area average= 16000
Average Open Space Area > 5800 Open Spaces selected for built up : 201600 built up : 204800 Average Open Space Area < 5800 area green : 158400 green : 155200
Built up Open
Dead cell comes to life when it has built up : 193600 built up : 203200 built up : 195200 built up : 193600 exactly 3 living green : 166400 green : 156800 green : 164800 green : 166400 neighbours. average = 0.38 average = 0.42 average = 0.47 average = 0.38 A living cell remains Pop = 0.079 Pop = 0.083 Pop = 0.080 Pop = 0.079 Type: 01.04 open area average= open 5866 area average= 8000 open area average= 9066open area average= 5866 alive only when surbuilt up : 203200 built up : 203200 built up : 203200 built up : 193600 built up : 193600 rounded by 2,3,4 or green : 156800 : 156800 green : 166400 green : 166400 Avg Open green Space Area: 5866 sqm green : 156800 5 living neighbours. average = 0.44 average = 0.42 average = 0.42 average = 0.38 average = 0.38 Population Density: Pop = 0.083 Pop = 0.083 790 (p/Ha) Pop = 0.083 Pop = 0.079 Pop = 0.079
Pop = 0.072 Pop = 0.071 open area average= open 25066 area average= 20266
A living cell remains alive only when built surrounded byup : 169600 : 190400 2,3,5,6 or 7green living average = 1.12 neighbours. Pop = 0.07
built up : 176000 built up : 172800 green : 184000 green : 187200 average = 1.05 average = 1.05 Pop = 0.072 Pop = 0.071 open area average= open 25066 area average= 20266
built up : 201600
built up : green : 1 average Pop = 0. open area average= 28800 open are
Population Density > 900
up : 201600 builtPopulation up : 212800 built Density < 900 green : 147200 green : 158400 average = 0.86 average = 0.75 average = 0.77 average = 0.86 Pop = 0.083 Pop = 0.082 Pop = 0.082 Pop = 0.087 open area average= 15466 | Emergent open area average= 14400 open area average= open area average= Architectural Association Technologies &15466 Design | 135 14400
built up : 212800
Pop = 0.07 Pop = 0.0 open area average= 28800 open area
built up : 169600 built up : 176000 built up green : 190400 green : 184000 green : average = 1.12 average = 1.05 average Test: 4 Pop = 0.07 Pop = 0.072 Pop = 0 open area average= 28800 open area average= 25066 open ar built up : 172800 built up : 176000 Dead cell comes built up : 176000 built up green : 187200 green : 184000 to life when itgreen has : 184000 green : 1 average = 1.05 average = 1.05 exactly 2 or 5average living = 1.05 average Pop = 0.071 Pop = 0.072 Pop = 0.072 Pop = 0 neighbours. open area average= 20266 open area average= 25066 open area average=open 25066 are
Type: 04.04
Avg Open Space green Area:: 13333 200000 sqm average 0.89 Population Density : 970= (p/Ha)
: 2,5 ule 2,3,5,6,7 38400 e : 50-50 1600
built up : 193600 built up : 203200 built up : 195200 built up : 1 green : 166400 green : 156800 green : 164800 green : 166 average = 0.38 average = 0.42 average = 0.47 average = Pop = 0.079 Pop = 0.083 Pop = 0.080 Pop = 0.07 Test: open 1 area average= 9066open area open area average=open 5866area average= 8000
Avg Open Space Area: 3200 sqm
built up : 230400 green : 115200 green : 129600 average = 0.22 average = 0.45 Pop = 0.099 Pop = 0.094 open area average= 3200 open area average= 11200 built up : 240000 built up : 230400 green : 120000 green : 129600 average = 0.33 average = 0.45 Pop = 0.098 Pop = 0.094 open area average=open 4266area average= 11200
29600 green : 120000 = 0.45 average = 0.33 94 Pop = 0.098 a average= 11200 open area average= 4266 built up : 240000 green : 120000 average = 0.33 Pop = 0.098 open area average= 4266
built up : 203200 built up : 203200 green : 156800 green : 156800 average = 0.42 average = 0.44 Pop = 0.083 Pop = 0.083 open area average= 8000 open area average= 8533
built up : 208000
built up : 212800
built up : 208000 built up : green : 152000 green : 1 average = 0.89 average = Pop = 0.085 Pop = 0.0 open area average= 13333 open are
built up
average : 0.97
built up : 182400 green : 177600 average = 0.73 Pop = 0.074 open area average= 14400 2400 built up : 160000 built up : 160000 00 green : 200000 green : 200000 73 average = 0.89 average = 0.89 Pop = 0.065 Pop = 0.065 verage= 14400 open area average= 16000 open area average= 16000
built up : 160000 built up : 182400 green : 200000 green : 177600 average = 0.89 average = 0.73 Pop = 0.065 Pop = 0.074 open area average= open 16000 area average= 14400 built up : 172800 built up : 172800 green : 187200 green : 187200 average = 1.05 average = 1.05 Pop = 0.071 Pop = 0.071 open area average= 20266 open area average= 20266
built up : 172800 built up : 160000 green : 187200 green : 200000 average = 1.05 average = 0.89 Pop = 0.071 Pop = 0.065 open area average= open area 20266 average= 16000 built up : 176000 built up : 176000 green : 184000 green : 184000 average = 1.05 average = 1.05 Pop = 0.072 Pop = 0.072 open area average= 25066 open area average= 25066
built up : 176000 built up : 172800 green : 184000 green : 187200 average = 1.05 average = 1.05 Pop = 0.072 Pop = 0.071 open area average= open area 25066 average= 20266 built up : 169600 built up : 169600 green : 190400 green : 190400 average = 1.12 average = 1.12 Pop = 0.07 Pop = 0.07 open area average= 28800 open area average= 28800
born rule : 2,5 survive rule 2,3,5,6,7 Tests for Patch 2 Test: 5initial state : 50-50 born rule : 3 born rule : 3 Dead cell comes to life when itsurvive has rule 2,3,4,5,6 survive rule 2,3,4,5,6 : 0.80 exactly average 3 living initial state : 50-50 neighbours. initial state : 50-50
wada TCH PATCH
3 A living cell remains Type: 05.02 alive only when e 2,3,4,5,6 average : 0.39 average : 0.39 surrounded by Avg Open Space Area: 6933 sqm e : 50-50 2,3,4,5 or 6 living Population Density : 890 (p/Ha)
neighbours.
built up : 228800 : 131200 built up : 204800 green built up : 238400 = 0.40 green : 155200 average green : 121600 = 0.093 average = 0.75 Pop average = 0.72 open average= 5333 Pop = 0.083 Pop area = 0.097 area average= 15466 open average= 13333 builtopen up : 219200 built uparea : 219200 built: up : 201600 green built up : 201600 green 140800 : 140800 green =: 158400 green : 158400 average 0.45 average = 0.45 average Pop = 0.089= 0.86 Pop average = 0.089 = 0.86 Pop = 0.082 0.082 open area average= 6933 openPop area= average= 6933 open area average= 14400 open area average= 14400
built up : 238400 green : 121600 average = 0.72 Pop = 0.097 area average= 13333 built upopen : 228800 built up : 228800 built up 204800 built up : 204800 8400 green :: 131200 green : 131200 green : 155200 green : 155200 00 average = 0.40 average = 0.40 average = 0.75 average = 0.75 72 Pop = 0.093 Pop = 0.093 Pop = 0.083 Pop =area 0.083 Test: open area 6 average= 5333 open average= 5333 open area average= 15466 verage= 13333 open area average= 15466
0.39
a
Dead cell comes to life when it has exactly 3 living neighbours. A living cell remains Type: 06.01 born rule : 3 sur- rule : 3 PATCH alive only whenborn rounded by 3,4,5 or survive rule 3,4,5,6 survive rule 3,4,5,6 6 living neighbours. Avg Open Space Area: 4266 sqm : 930 (p/Ha) initialPopulation state : Density 50-50 initial state : 50-50
:3 ule 3,4,5,6 te : 50-50
: 0.39
,7
200
erage= 2666
average : 0.39 built up : 227200
green : 132800 average = 0.31 Tests for Patch 3 Pop = 0.093 Test: 7 open area average= 4266 built up : 225600 built up : 225600 built up : 227200 built up : 227200 Dead cell comes to green : 134400 green : 134400 green : 132800 green : 132800 life when it has exaverage = 0.48 average = 0.48 average = 0.31 average = 0.31 Pop = 0.092 Pop = 0.092 Pop = 0.093 or 6 living Pop = 0.093 actly1,2,5 open area average= 5333 open area average= 5333 open area average= 4266 open area average= 4266
born rule : 1,2,6,5 survive rule 2,3,4,5,6,7 neighbours. state : 50-50 A living initial cell remains alive only when surrounded by 2,3,4,5,6average or 7 living neighbours. built up : 267200
Dead cell comes to life when it has exactly 2,3 or 4 living neighbours. A living cell remains alive only when surrounded by 3,4,5 or 6 living neighbours.
Avg Open Space Area: 4266 sqm
Population Density : 910 (p/Ha) built up : 219200 built up : 216000 built up : 228800 built up : 219200 built u greenbuilt : 131200 green 140800 green : 140800 green : 144000 green built up :: 204800 up : 201600 built up : 201600 built up : 212800 average = 0.40 average = 0.45 average =green 0.45 : 147200 average = 0.42 averag green : 155200 green : 158400 green : 158400 Pop =average 0.093 = 0.86 Pop = 0.089 Pop = 0.089 Pop = 0.088 Pop = average = 0.75 average = 0.86 average = 0.77 openPop area=average= open 5333 area average= 6933 open area average= open 6933 area average= 6400 open a 0.082 Pop = 0.083 Pop = 0.087 Pop = 0.082 open area open area average= 14400 open average= 14400 area average= built up : 216000 built up :average= 216000 15466 built up open : 224000 builtarea up15466 : 224000 built up : built :up : 208000 green built up : 208000 up : 212800green : built up : 212800 greenbuilt : 144000 144000 green 136000 : 136000 green : 1 green :=152000 green=: 0.35 152000 green : 147200 averagegreen : 147200 average = 0.42 = 0.42 average 0.35 average average average = 0.89 average = 0.77 Pop =average 0.088 = 0.77 Pop = 0.088 Pop = 0.091 Popaverage = 0.091 = 0.89 Pop = 0.0 Poparea = 0.085 Pop = 0.085 0.087 6400 Popaverage= = 0.087 6400 open Pop area=average= open area open average= 4266 open area average= 4266 open are open area average= 13333 open area average= 15466 open area average= 15466 open area average= 13333
Type: 06.02 Avg Open Space Area: 5333 sqm Population Density : 920 (p/Ha) Average Open Space Area > 3700
built up : 225600 built up : 227200 Average green Open: Space 134400 Area < 3700 green : 132800 average = 0.48 average = 0.31 Pop = 0.092 Pop = 0.093 open area average=open 4266area average= 5333 built up : 236800 built up : 236800 green : 123200 green : 123200 average = 0.32 average = 0.32 Pop = 0.097 Pop = 0.097 open area average= 4800 open area average= 4800
Type: 07.03
Population Density : 1100 (p/Ha)
built up : 275200 built up : 267200 green : 84800 green : 92800 average = average = 0 Pop = 0.11 Pop = 0.11 open area average= open 2666 area average= 2666 built up : 283200 built up : 283200 green : 76800 green : 76800 average = average = Pop = 0.11 Pop = 0.11 open area average= 2133 open area average= 2133
Avg Open Space Area: 2666 sqm Population Density : 1100 (p/Ha)
built up : 283200built up : 275200 green : 76800 green : 84800 average = average = Pop = 0.11 Pop = 0.11 open area average= open 2133 area average= 2666 built up : 281600 built up : 281600 green : 78400 green : 78400 average = average = Pop = 0.11 Pop = 0.11 open area average= 2133 open area average= 2133
Type: 08.01
Type: 08.04
Avg Open Space Area: 3733 sqm
Avg Open Space Area: 2666 sqm Population Density : 1000 (p/Ha)
born rule :Population 2,3,4 Density : 980 (p/Ha) survive rule 3,4,5,6 Built up initial state : 50-50 Open Spaces
built up : 241600 green : 92800 average = 0 Pop = 0.098 136 | Urban open area average= 3733 built up : 248000 built up : 248000 green : 112000 green : 112000 average = average =
Population Density > 900
built up : 225600 built up : 236800 Population Density < 900 green : 134400 green : 123200 average = 0.48 average = 0.32 Pop = 0.092 Pop = 0.097 open area average= open 5333 area average= 4800 built up : 228800 built up : 228800 green : 131200 green : 131200 average = 0.39 average = 0.39 Pop = 0.093 Pop = 0.093 open area average= 5866 open area average= 5866
built green avera Pop open built up green : 1 average Pop = 0 open are
Type: 07.05
Open Space Area: 2133 sqm : Avg 0.61
green : 92800 average = 0 Pop = 0.11 open area average= 2666 built up : 275200 built up : 275200 green : 84800 green : 84800 average = average = Pop = 0.11 Pop = 0.11 Test: 8 open area average= 2666 open area average= 2666
ATHER
600
average : 0.39
Type: 05.04
Open Spaces selected for calculating average area
built up : 248000 built up : 241600 green : 112000 green : 92800 average = average = 0 Pop| Spatial = 0.101 Organization Pop = 0.098 Resilience open area average= open 2666 area average= 3733 built up : 249600 built up : 249600 green : 110400 green : 110400 average = average =
average : 0.61 born rule :3 born rule : 3 survive rule 2,3,4,5 survive rule 2,3,4,5 initial state : 50-50 initial state : 50-50
built up : 281600 built up : 283200 green : 78400 green : 76800 average = average = Pop = 0.11 Pop = 0.11 open area average= open area 2133 average= 2133 built up : 278400 built up : 278400 green : 81600 green : 81600 average = average = Pop = 0.11 Pop = 0.11 open area average= 2666 open area average= 2666
Average Open Space Area > 500
Population Density > 900
Average Open Space Area < 500
Population Density < 900
built up : 249600built up : 248000 green : 110400 green : 112000 average = average = Pop = 0.101 Pop = 0.101 open area average= open 2600 area average= 2666 built up : 246400 built up : 246400 green : 113600 green : 113600 average = average =
built up : 246400 built up : 249600 green : 113600 green : 110400 average = average = Pop = 0.10 Pop = 0.101 open area average= open area 2666 average= 2600 built up : 249600 built up : 249600 green : 110400 green : 110400 average = average =
bu gr av P op
bu gr av P op
Evaluations The experiments were segregated based on the average areas of the selected open spaces into 3 groups corresponding to the required areas of the 3 patches. Every rule had 5 iteration based on the variation in the position of initial state of cells. It is observed that sometimes the same rule can give a distinctive variation in Population Density and Average open space area. This is because the outputs are derived after 40 iterations each time and are not based on stable states of each test. So, usually there is a scope for further changes in the aggregation of built and open spaces. So, only those rules are selected which provide consistent values of population density and open space areas. Test 4 is selected for Patch 1, where Dead cell comes to life when it has exactly 2 or 5 living neighbours while a living cell remains alive only when surrounded by 2,3,5,6 or 7 living neighbours. Here the survival rate of the cells is very high which allows for higher aggregation of built neighbourhood, but the rate of transformation from “dead” to “alive” is low, thus aiding the growth of open space simultaneously. This
rule gives us required neighbourhood relationship for Patch 1. Test 6 is selected for Patch 2 and Test 8 is selected for Patch 3. However it is important to note that there is only 1 minor difference in the “alive” rules of these 2 tests but they give considerably different outcomes. It was also observed that for Patch 3 the open areas obtained are higher than minimum required on the site even when the population density requirement is satisfactorily fulfilled. This is owing to the high density configuration of chawl morphology which facilitates the provision of higher open space area coupled with high density aggregation. These 3 rules would be carried forward and applied to the 3 patches on Dharavi site.
Architectural Association | Emergent Technologies & Design | 137
born rule : 3,4 survive rule 3,4,7,8 initial state : 50-50
LEATHER PATCH born rule : 4 survive rule 3,4,5,6 built up : 172800 initial state : 50-50 green : 187200
average : 0.97 built up : 182400 green : 177600 average = 0.73 Pop = 0.074 open area average= 14400
rn rule : 2,5 rvive rule 2,3,5,6,7 tial state : 50-50
built up : 160000 green : 200000 average = 0.89 Pop = 0.065 open area average= 16000
average = 1.05 Pop = 0.071 open area average= 20266
average : 0.34
6.5 Experiment 2
LEATHER
built up : 240000 green : 120000 average = 0.33 Pop = 0.098 open area average= 4266
built up : 23 green : 129 average = 0 Pop = 0.09 open area
built up : 212800
bu gr av Po op built up : 160 green : 2000 average = 0 Pop = 0.065 open area a
Catchment locations on existing site derived from ArcGis results. .
koliwada PATCH
born rule : 3,4 survive rule 3,4,7,8 initial state : 50-50
built up : 204800 green : 155200 average = 0.75 Pop = 0.083 open area average= 15466
built up : 238400 green : 121600 average = 0.72 Pop = 0.097 open area average= 13333
built up : 201600 green : 158400 average = 0.86 Pop = 0.082 open area average= 14400
average : 0.97
50% Live (Built) and 50% Dead (open) cells as initial state.
born rule : 3
Rules extracted to test on site survive rule 2,3,4,5,6
initial state : 50-50
may b leatheraverage : 0.39
Patch 1 built up : 267200 green : 92800 average = 0 Patch 2 Pop = 0.11 open area average= 2666 Patch 3
born rule : 3 built up : 275200 green : 84800rule 3,4,5,6 survive average = Pop = 0.11 initial state : 50-50 open area average= 2666
average : 0.39
surrounded by 2,3,5,6 or 7 living neighbours.
built up : 228800 average : 0.80 green : 131200
Dead cell comes to life when it has exactly 3 living neighbours. A living cell remains alive only when surrounded by 3,4,5 or 6 living neighbours. built up : 283200
green06.02 : 76800 Type: = Avgaverage Open Space Area: 5333 sqm Pop = 0.11 Population Density : 0.092 open area average= 2133
built up : 227200 For Patch 3 green : 132800
average Dead cell= 0.31 comes to life when it has Pop = 0.093 exactly 2,3 or 4 living open area average= 4266 neighbours.
Experiments on existing site patch
A living cell remains alive only when surrounded by 3,4,5 or 6 living neighbours.
Three different rules are selected from the initial tests and Type: 08.04 are applied to 3 identified patches on site. An initial state of Avg Open Space Area: 2666 sqm 50% live and 50% dead cells is considered for all iterations, Population Density : 0.10 each of them have random configurations. However theborn rule : 1,2,6,5 cells for detention basins, derived from ArcGis are alwayssurvive rule 2,3,4,5,6,7 assigned the initial state of dead cells for every iteration. 5initial state built up : 241600 built up : 248000 built up : :249600 50-50 green : 92800 are run on the site with green : 112000rules assigned to green : 110400 experiments same average = 0 average = average = each Pop patch = 0.098 but with random configuration Pop = 0.101 of initial state of Pop = 0.101 open area average= 3733 open area average= 2666 open: area average= 2600 average 0.61 cells. These experiments are further evaluated based on 4 different evaluation criteria.
POTTERY
138 | Urban Resilience | Spatial Organization
average = 0.77 Pop = 0.087 Open Spaces area average= 15466 built up : open 182400 green : 177600 average = 0.73 Pop = 0.074 open area average= 14400
For Patch 1
For Patch 2
erage : 0.61 koliwada PATCH
Built green :up 147200
born Dead rulecell : 2,5 comes to life when it has survive rule exactly 2 or2,3,5,6,7 5 living neighbours. living cell remains alive only when initialAstate : 50-50 Type: 04.01 average = 0.40 Avg PopOpen = 0.093Space Area: 13333 sqm open area average= 5333 Population Density : 0.097
rn rule : 1,2,6,5 rvive rule 2,3,4,5,6,7 tial state : 50-50
erage : 0.61
bu gr av Po op
Set up
erage : 0.80
rn rule : 2,3,4 rvive rule 3,4,5,6 tial state : 50-50
built up : 176000 green : 184000 average = 1.05 Pop = 0.072 open area average= 25066
built up : 219200 green : 140800 average = 0.45 Pop = 0.089 open area average= 6933
built up : 238400 green : 121600 average = 0.72 Pop = 0.097 open area average= 13333
built up : 281600 green : 78400 average = Pop = 0.11 open area average= 2133
built up : 225600 green : 134400 average = 0.48 Pop = 0.092 open area average= 5333
built up : 246400 green : 113600 average = Pop = 0.10 open area average= 2666 built up : 267200 green : 92800 average = 0 Pop = 0.11 open area average= 2666
built up : green : 1 average Pop = 0.0 open are
built up : 204 green : 15520 average = 0.7 Pop = 0.083 open area av
built gree aver Pop open
built up green : average Pop = 0 open ar
built gree aver Pop open
built up : 2752 green : 84800 average = Pop = 0.11 open area ave
Evaluation Criteria
1: Population Density (PD) Evaluating the number of people per sq m of built up space can occupy.
2 : Open space ratio (OSR) Calculating the area of open space available per person.
3 : Distance of open spaces from built spaces (ABO) Evaluating the distance between building morphologies and the nearest open space and determining the minimum distance.
4 : Distance between open spaces (AO) Evaluating the distance between open spaces to achieve an even distribution of these spaces on site.
Experiment evaluation The first aim of the design experiments is to achieve a greater population density for the existing site patch. The experiment results are evaluated on this criteria for the built spaces configured from the automation. Along with this, a spatial configuration of open spaces is also derived from the tests and this is evaluated in terms of the open space available per person (sq m/person). As per the site analysis, the public spaces within the existing patch are distributed unevenly. Hence, further evaluation of the cellular automata results is based on the calculating the distance between these open spaces from one another. Greater the distance, more is the chance for them to be evenly distributed on site. Also a walking distance from the
surrounding built forms to an open space is evaluated with priority given to the open spaces which are in close range to the built units. In principle, the chief aim of running the test for cellular automata is achieving a spatial configuration that gives a higher population density than the existing site with a greater open space ratio. The configuration of these open spaces is intended to be in a closer walking range to the neighbouring building blocks and to be evenly distributed all over the urban patch.
Architectural Association | Emergent Technologies & Design | 139
Tests for the Site
Differential Weighing of Evaluation Criteria
PD : 40%
OSR : 20% ABO : 20% AO : 20%
SITE TEST_01
Population Density (PD) :
960 p/Ha
Open Space Ratio (OSR) :
3.60
Built up
Avg distance from Built to nearby open space (ABO) : 42.95 m
Open Spaces
Avg distance between open spaces (AO) :
79.16 m
Built up : 742400Open area: 392000Population: 109040Pop Density : 0.096OSR: 3.60 PD : 20%
OSR : 20% ABO : 20%
AO : 40%
SITE TEST_09
Built up : 681600Open area: 452800Population: 10011
Population Density (PD) :
890 p/Ha
Open Space Ratio (OSR) :
4.36
Built up
Avg distance from Built to nearby open space (ABO) : 42.12 m
Open Spaces
Avg distance between open spaces (AO) :
140 | Urban Resilience | Spatial Organization
86.04 m
Selection Based on Maximum Open Space Ratio (OSR)
SITE TEST_06
Population Density (PD) :
870 p/Ha
Open Space Ratio (OSR) :
4.66
Avg distance from Built to nearby open space (ABO) : 41.78 m
Built up
Avg distance between open spaces (AO) :
Open Spaces
74.72 m
up : 673600Open area: 460800Population: 98935Pop Density : 0.087OSR: 4.66Avg B-O : Avg O-O :
Built up : 688
Conclusion The experiment was run 10 times with the same set of rules applied to the 3 patches forming the entire site. A variation can be seen in terms of distribution of built and open space. These variations were achieved because of the 50-50% initial state assigned in automation rules where the location of live and dead cells vary randomly for each iteration. The existing open space ratio of Dharavi is 1.16 sqm/person. On an average, the open space ratio obtained from these experiments has increased thrice the existing value. This will enhance the availability of the open space to carry out their occupational works and also to cater to their social needs. The distance of each built up to nearby open space is also reduced to half compared to the existing scenario thus improving the proximity of an open work yard or public space from individual units. 3 patches were selected from this experiment based on differential weighing of the evaluation criteria. Site test_01
was selected based on higher weightage given to the Population Density whereas Site test _09 was selected based higher weightage to average distance between Builtonup : 675200Open area: 459200Population: open spaces which is to ensure an even distribution of these open spaces across the site. Moreover an additional experiment Site test_06 is selected because it ranked highest in terms of open space ratio. This experiment was selected mainly to include an option for low population density and a high open space ratio for the further experiments of system integration of the spatial organization obtained from CA and the hydrological networks explained in the later chapters. It should be noted that though CA was based on neighbourhood relations, an evaluation of average distance between open spaces shows prominent distribution of open spaces at the urban level. Thus these 3 experiments will be carried forward to be used for system integration experiments. Architectural Association | Emergent Technologies & Design | 141
99170Pop Density : 0
6.6 Aggregation Selection of Patch for aggregation
MEDIUM SIZED PATCH
SMALL SIZED PATCH
LARGE SIZED PATCH
142 | Urban Resilience | Spatial Organization
Experiment Set up
High Density
Built
Medium Density
Open Selecting Patch for aggregation
Low Density Assigning High, Medium and Rotation angles : Rotation of all chawl rotation rotation 90, 180 and 90, rotation 180 270 and 90,270 180 90, and 180 270 and 270 Morphologies at varying Low density Chawl Morpholo90° / rotation 180° / 270° gy in varying percentage degrees as a parameter
Set up 3 different sized patches are selected from one of the CA alternatives to study aggregations of the chawl morphology. A spatial organization of built-open is derived at an urban scale from CA, where chawl morphology form the “built” units. Chawl morphology has a provision of a courtyard which facilitates the “live-work” life style of these informal settlements. Thus, it becomes important to study the aggregations of these chawl morphologies around open spaces and the relationship of the chawl - courtyard to its neighbouring open spaces. Chawl morphology of 3 different densities for 3 different courtyard sizes have been designed. They are selected in varying percentages to obtain a variation in the overall density of the selected patch. These morphologies are then subjected to rotation in the range of 0° to 360°, with an interval of 90°. The aim is to derive various orientations of the chawl morphology to study the possibilities of connectivity between surrounding chawl morphologies and between a
chawl morphology and its neighbouring open space. This experiment is run at small patch level to understand local scale connectivity with just one open space in the centre and then run on larger patch to study connectivity between multiple open spaces in the vicinity.
Architectural Association | Emergent Technologies & Design | 143
Evaluation :
Pop: 2200 Sunlight hrs: 19419
Pop: 2443 Sunlight hrs: 17899
Most Connected
1: Population Density Evaluating the number of people per sq m
Pop: 2507 Sunlight hrs: 18574
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
Hrs 10.00
2: Solar Exposure Evaluating number of hours of sunlight on the ground surface <= 0.00
144 | Urban Resilience | Spatial Organization
Pop: 2884 Sunlight hrs: 15398
Pop: 2443 Least Sunlight hrs: 17899 Connected
3: Space Syntax analysis Evaluating connectivity based on agent movement
ation 90, 180 and 270
Derivation based on Connectivity Evaluation:
Local Scale
Neighbourhood Scale
Social Yards at local scale rotation 90, 180 and 270
Work Yards at local scale
Social Yards at Neighbourhood scale
Work Yards at Neighbourhood scale
Evaluation Criteria The aggregation experiment is evaluated based on the derived population density to obtain variation for the same patch. It is further evaluated based on Solar exposure to analyse the environmental conditions resulting from various orientation and clustering of chawl morphology. The usual occupational activities include pottery, fishing leather tanning and laundry. The open spaces used to carry out these activities need to have adequate solar exposure for drying either pots, fishes, leather or clothes respectively/ It is important to have higher solar exposure in these work yards as well as in courtyards within the chawl morphology. However the open spaces to be used as social yards need to have comfortable outdoor environment with comparatively low solar exposure. Solar exposure is calculated using Ladybug plugin for Grasshopper which requires EnergyPlus Weather files (.EPW) of the city to evaluate different environmental conditions.
Syntactical analysis is used to determine the formation of various connectivity. DepthMap is used generate this analysis with series of agents released on the patch with timestep of 1000 and the agents are restricted to a maximum of three steps before change in their direction. The use of the open space is primarily governed by its connectivity to other open spaces or chawl morphologies. At a local scale the social yard requires less interaction with the adjacent chawl morphologies thus segregating it from the chawl-courtyard which is usually used a place of work. A work yard, on the other hand, works well when it is overlooked by the courtyards of surrounding chawls, making it one large space for occupational activities. On a larger scale these work yards need to be connected to other work yards formed by aggregation of various other chawl morphologies. While a social space needs to be connected to other social spaces to enhance the connectivity from one social space to another. Architectural Association | Emergent Technologies & Design | 145
6.6a Aggregation : small scale patch
S_01
S_03
S_05
146 | Urban Resilience | Spatial Organization
Pop: 2919 Pop: 2 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419Pop: 2200 Sunligh Pop: 2919
Total sunlight hours : 18655
Hours
Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected <= 0.00 High
Medium
Low
Morphology density
0.42 0
1
Built-footprint
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
Pop Density : 1394 p/hectares
Total sunlight hours : 19419
Hours
Pop: 2443 Sunlight hrs: 17899 Pop: 2919 Pop: 2200
Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected
<= 0.00 High
Medium
Low
Pop: Pop 2200 Density : 1120 p/hectares 5 Sunlight hrs: 19419 Morphology density
0.40 0
1
Pop: 2443 Sunlight hrs: 17899
Built-footprint
Total sunlight hours : 17900
Hours
Pop: 2507 Sunlight hrs: 18574
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
<= 0.00 High
Medium
Low
Morphology density
0.40 0
1
Built-footprint
Pop Density : 1168 p/hectares
Architectural Association | Emergent Technologies & Design | 147
Most Connected
Least Connected
S_07
S_08
148 | Urban Resilience | Spatial Organization
p: 2443 ight hrs: 17899
Pop: 2507 Sunlight hrs: 18574 Total sunlight hours : 18574
Hours
Pop: 2884 Sunlight hrs: 15398
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected <= 0.00 High
Medium
Low
0.40 0
1
Pop: 2507 Pop Density : 1302 p/hectares Sunlight hrs: 18574 Morphology density
Pop: 2884 Sunlight hrs: 15398
Built-footprint
Total sunlight hours : 15398
Hours
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected <= 0.00 High
Medium
Low
Morphology density
0.44 0
1
Built-footprint
Pop Density : 1558 p/hectares
Experiment evaluation: This experiment was run to establish a variation in population around a specific open space chosen from the site. It is observed that high population can be achieved when the chawls of higher density are used more than 40%. Medium population density can be achieved by maintaining a balance between high and medium density chawls in the range of 30-40% each. Whereas, the low population is achieved by having low and medium density chawls in the range of 40-50% each. Also there is a variation observed in solar exposure on the ground surface for different aggregation. It is observed that higher population aggregation usually gain less solar exposure, for e.g. aggregation S_08, while the lower population aggregation has comparatively high solar exposure, for e.g. S_03. Central open space is invariably well connected in all the
aggregations. Various levels of connectivity are observed between central open space and surrounding Chawl Morphologies in different aggregation. For e.g.. S_01, S_03 and S_05 have higher connectivity between the courtyards of the chawls and the central open space. These central open spaces will be highly functional when used as work yards for different occupational requirements. However, S_07 has higher connectivity of the peripheral chawls towards exterior which establishes the possibilities of further connections with the neighbouring clusters. This type of open space can be used as Public open space which is well connected to chawls within larger radius even outside this aggregation. Architectural Association | Emergent Technologies & Design | 149
6.6b Aggregation : medium scale patch
M_03
M_07
150 | Urban Resilience | Spatial Organization
Pop: 3698 Sunlight hrs : 32595 Total sunlight hours : 32595
Pop: Sunli Hours
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected
<= 0.00
High
Medium
Low
0.40 0
Pop: 3698 Built-footprint Sunlight : 32595 Pop Density : 1129 hrs p/hectares
Pop: 4183 Sunlight hrs : 35888
1
Morphology density
Pop: 4183 Sunlight hrs : 35888 Total sunlight hours : 35888
Hours
Pop: Sunl Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected
<= 0.00 High
Medium
Low
0.38
0 Pop: 4183 Built-footprint Sunlight hrs : 35888 Pop Density : 1236 p/hectares Morphology density
1
Pop: 4086 Sunlight hrs : 31970
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M_08
M_09
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Pop: 4086 Sunlight hrs : 31970 Total sunlight hours : 32595
Hours
Pop: 360 Sunlight Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected
<= 0.00
High
Medium
Low
0.41
0 Pop: 4086 Built-footprint Sunlight hrs : 31970
Pop: 3608 Sunlight hrs : 33019
1
Morphology density
Pop Density : 1256 p/hectares
Pop: 3608 Sunlight hrs : 33019 Total sunlight hours : 33019
Pop: Sunli Hours
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
<= 0.00
High
Medium
Low
0.39
0 Pop: 3608 Built-footprint Sunlight hrs : 33019 Pop Density : 1118 p/hectares
1
Morphology density
Experiment evaluation: As the patch size for the experiment increased, the number of chawls also increases and we could study the outcomes of more chawls aggregating together. Even when the over all solar exposure was comparatively high in almost all the experiments, the solar exposure of the networks formed when 3 or more built-edges of chawls come together is very low, as in the case of M_08. Also, the connectivity of an open space to the chawls increases if 2 or more courtyardâ&#x20AC;&#x2122;s of the chawl are facing the open space, for example M_09. Also
diversity in the population density is observed with different combination of low, medium and high density chawls. M_03 has a good balance of population density and solar exposure on the ground surface. Also, it has higher connectivity between the central open space and the surrounding built chawl morphologies in the aggregation. Moreover some peripheral chawls also open up the outer connectivity to the next level of aggregation. Architectural Association | Emergent Technologies & Design | 153
Most Connected
Least Connected
6.6c Aggregation : large scale patch
L_06
L_09
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Pop: 6934 Sunlight hrs : 74896
Pop: 7636 Sunlight hrs
Total sunlight hours : 74896
Hours
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
Most Connected
Least Connected
<= 0.00
High
Medium
Low
Morphology density
0.34 0
1
Built-footprint
Pop Density : 1006 p/hectares
Pop: 8384 Sunlight hrs : 74021
Pop: 707 Sunlight h
Total sunlight hours : 74021
Hours
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
<= 0.00
High
Medium
Low
Morphology density
0.36 0
1
Built-footprint
Pop Density : 1202 p/hectares
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Most Connected
Least Connected
L_10
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Pop: 7076 Sunlight hrs : 70904 Total sunlight hours : 70904
Hours
Pop: 2919 Pop: 2200 Sunlight hrs: 18655 Sunlight hrs: 19419
10.00
<= 0.00
High
Medium
Low
Morphology density
0.34 0
1
Built-footprint
Pop Density : 1066 p/hectares
Experiment evaluation: This experiment was devised for a larger patch area with higher area of open spaces and large number of chawls. It is observed as in the case of L_06 and L_09, that similar solar exposure can be obtained even with difference in population densities of the aggregation. In terms of connectivity, it is evident that the central open space in L_06 is very well integrated, but the connectivity between the chawls on the right-hand side of the central open space is not particularly enhanced. On the other hand, experiment L_09 shows relatively higher integration of the open spaces formed between these chawls. L_09, has high connectivity between the central open space and the surrounding chawls as well as amongst these chawls, and can function well as a work yard. Moreover, a work yard would require higher solar exposure
through the day to carry out various occupational activities and L_09 demonstrates the fairly high solar exposure in the chawl courtyards and the work yard. L_10 displays that the central open space is well integrated but its connectivity does not reach the surrounding chawls. However the peripheral chawls display the existence of possibility of further connectivity towards the exterior of the aggregation, thus the central space can be used as social yard for the surrounding communities. L_10 displays decrease in the solar exposure which is beneficial for social activities and gatherings. Thus various configurations of chawl morphologies can be used to accommodate different functions of the central open space. Architectural Association | Emergent Technologies & Design | 157
Most Connected
Least Connected
M_03
L_09
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Most Connected
Least Connected
Social Yards formed by connection between central open spaces of neighbourhoods
Built up
Most Connected
High Density Medium Density Low Density Open spaces Work yards formed between chawls
Least Connected
Work Yards formed by connection of the central open space to neighbouring chawl morphologies
Conclusion: The experiment of aggregating various chawls around the different size of open space areas gives a wide range of outcomes with different population densities and solar exposure. It is interesting to note that the lowest population density achieved in all the aggregation is still higher than the population density determined during the CA experiments. This provides the liberty to eliminate certain chawl units during design proposal if required to be replaced by an open area or a water catchment basin. Also it is observed that the built-footprint does not change much across samples of any particular experiment, thus the open area per person would be inversely proportional to the population density achieved. However, in the case of high population densities the requirements for higher open space areas would then be fulfilled by the open areas provided on the upper floor levels
within the chawl morphologies. Also, the open spaces can be categorized into social space or work space based on difference in solar radiation, connectivity and population density around it. M_03 forms a social yard as its central open space is not overlooked by surrounding chawl morphologies but is well connected with the neighbouring open spaces. L_09 demonstrates large number of work yards formed between the peripheral chawl morphologies because of their orientation. These smaller work yards are well connected to the central open space which forms the larger work yard. On the whole, function of the open space is dependent mainly on the orientation of the chawl morphologies around it which also affects the solar exposure on the ground surface. Architectural Association | Emergent Technologies & Design | 159
7. System Integration Experiment Setup and Evaluation criteria System Integration Experiment
7.1 Experiment Setup and Evaluation criteria
Detention catchments as social spaces
Underground storm water storage tank
Primary network
Built & Open space configuration derived from neighbourhood relationship experiments
Open spaces Built spaces Furthest
Placement of detention catchments within open spaces away from primary networks
Closest
Relationship of catchments to open spaces On a global level, the hydrological system adapts a top down approach to configure the location of detention catchments to store and delay the discharge of storm water. The potential location of these catchments is initially based on the lower topographic points on site. However, when the two systems ( neighbourhood spatial configuration & hydrological networks ) are integrated, it is essential to set rules of setting the exact location of these catchments with their context on site. Essentially, they are intended to be designed as social spaces within the urban fabric of Dharavi. Hence, the primary aim of configuring these catchments is to place them within the open spaces derived from the neighbourhood experiments. The catchments are intended to be of two types; primary catchments and secondary catchments. The initial hydrological experiments indicate an area where the primary catchments can be located to cater to each watershed region. But considering the networks and built spaces on site, the primary catchments are designed as interconnected fragments to integrate the built spaces and primary networks for vehicular circulation. 162 | Urban Resilience | System Integration
The secondary catchments are smaller fragments which are intermediate storage units in the hydrological system. Rules for experiment setup & evaluation : Each primary catchment identifies the volume of water necessary to be discharged for the corresponding watershed and determines its required storage capacity. This catchment is then fragmented and placed away from the primary networks to be feasible for vehicular road access. All the primary and secondary catchments fall on the secondary and tertiary hydrological channels within the global network. The evaluation process is based on the percentage of catchments falling within open spaces. The built spaces overlapping the catchments are converted into open areas, although the optimum results are considered for the highest percentage of catchments falling on existing open spaces.
Open spaces
Social yards
Catchments
Work yards
Ratio of area of a catchment to social yards around it is considered as 1:5
Open spaces around catchments as Social Yards and the remaining configured as Work Yards
Relationship between social spaces and work yards It has been established that open spaces play a a crucial role in the urban fabric of Dharavi to cater to the social and economic needs of the residents. The neighbourhood relationship from the bottom up system approach determines the distribution of these open spaces in context with the built up areas on site. Essentially, the open spaces in the existing scenario for Dharavi are used for two functions. The industrial and livework sector utilize the open areas as work yards for the economic activities while the remaining open spaces are communal areas for social gatherings. While integrating the neighbourhood system with the hydrological networks and catchments, it has been discussed that the catchments are configured within the open spaces on the site patch. However, it is essential to maintain a balance between the social spaces and work yards, which become two aspects of the open spaces designed on site. Hence, certain rules are established to enable the segregation of work yards from social spaces and to identify that the catchments are located only in the â&#x20AC;&#x2DC;social spacesâ&#x20AC;&#x2122; within these open areas.
Rules for experiment setup & evaluation : The first rule is to identify the catchments configured on site and to find the adjacent open spaces around those catchments. From these identified open space cells, a ratio of the catchment area to its adjacent cells which are termed as social spaces is maintained. This ratio is taken as 1:5 for catchments to social spaces based on case study. The remaining open spaces are designed as work yards. The evaluation of this experiment is conducted by calculating the percentage of area of social spaces to the area of work yards.
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Hydrological system as a whole
Surface runoff into channels
Hierarchy of discharge outlets & catchment connectivity 4
4 4
4
3
4
2 2
Secondary detention catchments connecting to primary catchments
3
3
3 1
2 2 3
1 2
Closest catchment but not in the hierarchy
Primary catchments of each water shed connected to final outfalls
1st order catchment connecting to its closest catchment in hierarchy with the lowest topographical location
Shortest feasible connection Discarded longer connection
Order 1 Order 2 Order 3 Order 4
Discharge channel hierarchy & connectivity Since the integrated system is intended to be flood resilient, an adequate storm water discharge capacity needs to be derived for the site. Primary and secondary catchments constitute the storage outlets in the hydrological system. The secondary catchments are detention units that help store the storm water and discharge it through channels to the primary catchment outlets. These discharge channels are the secondary and tertiary networks in the hydrological system. The primary catchments provided for each water shed are the first source of discharge outlets for the storm water in that corresponding territory. The primary catchments are connected to each other in a hierarchical order by an underground channelling system. The 1st order discharge outlets are configured in the northern boundary of Dharavi close to Mithi river, to drain the storm water into the river through a channelling system. The depth of any primary catchment outlet is determined by the length of the discharge channel and slope required between its previous catchment outlet. 164 | Urban Resilience | System Integration
Rules for experiment setup & evaluation : Every primary catchment finds its closest and lowest catchment point in the immediate hierarchy, which has a better slope in terms of topography. Every catchment outlet determines its depth based on the slope and discharge channel length of its previous outlet. The optimum slopes are considered to be in the range of 1:80 to 1:110. This system is evaluated based on the efficiency of its discharge capacity to the final source of outlets in the hierarchy and the optimum result is recorded.
Non accessible network points
- Identifying access nodes on site boundary - Identify designed hydrological networks as secondary and tertiary pathways and the non accessible network points
- Connect the non accessible network points to establish primary network to access nodes with shortest and least angular path.
- Evaluate the centrality of the primary networks designed for vehicular access.
Primary network - betweenness & centrality A syntactical analysis on the existing network in Dharavi states that the site has a higher integration of its primary networks that connect the eastern side to the western part of Dharavi. The south eastern part lies along a railway line. The ambition for the designed patch is to achieve a higher centrality to connect the major access nodes on site. These access nodes are identified to be on the southern part connecting the business district of the city, the northern part connecting the bazaars and civic centers and the eastern parts connecting the industrial area and transport routes. The primary networks are to be designed as the only vehicular access routes on site while the secondary and tertiary networks are pedestrian pathways ( All networks carry storm water discharge channels ). Hence, achieving a greater centrality factor becomes necessary for the socio economic activities in Dharavi.
Rules for experiment setup & evaluation : The first step is to identify the non accessible points within the hydrological network and to find possible connections to its existing neighbours. The next step is to find the shortest and least angular path connecting to the access points on site. The evaluation criteria is based on the betweenness factor and centrality of the entire network connecting the siteâ&#x20AC;&#x2122;s access points.
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7.2 System Integration Experiments Differential weighing Syntactical factor 10%
70% 10% 10% Economic factor
10% 10%10%
70%
Outlet 1
Outlet 4 Outlet 3
Outlet 2
Variation 6 (a) Outlet connectivity
Depth : 2.0 to 5.5 m
8079
235
Betweenness Centrality
Discharge capacity Catchment volume : 274412 m3
27%
38%
180mm/hr Required 25 mm/hr 250mm/hr Existing Provided
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Catchments on open spaces : Open spaces Primary catchments Secondary catchments
46%
Work yards :
70%
Social yards : 158317 m2 Work yards : 313600 m2
Experiment : variation 6(a) The experiments were run with a combination of 6 variations of hydrological and urban networks and 3 variations from the spatial organization tests. These experiments were evaluated on the basis of a differential weighing of criteria. In the variation 6 showing above, a higher weightage was given to the economic factor and the centrality of the networks with the siteâ&#x20AC;&#x2122;s access points. The higher economic factor lead to a greater percentage in the area of work yards ( 313,600 sqm ) as compared to social spaces ( 158,317 sqm )
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Differential weighing Hydrological factor 70%
10%10%10% Socio-Economic factor
10%10%
40%
40%
Variation 3 (c)
Outlet 1
Outlet 2
Outlet connectivity
Outlet 3
Outlet 4
Depth : 3.5 to 6.0 m
6111
208
Betweenness Centrality
Discharge capacity 27% Catchment volume : 279567 m3
38%
180mm/hr Required 25 mm/hr 250mm/hr Existing Provided
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Catchments on open spaces : Open spaces Primary catchments Secondary catchments
51%
Work yards :
67%
Social yards : 165552 m2 Work yards : 348800 m2
Experiment : variation 3(c) The variation 3 (c) from the set of experiments was evaluated by giving a higher weightage to the criteria for the systemâ&#x20AC;&#x2122;s discharge capacity for per hour rainfall, since the priority of the system was to make it flood resilient. From the previous iteration, the percentage of social spaces and work yards was also balanced to achieve a better socio-economic factor. This variation was considered for the design development stage to resolve the architectural details of the system by adding the layers of density gradient and program distribution for the built morphologies.
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8. Design Proposal Overview Density Gradient and Program Distribution Design Development Water management Design Details
8.1 Overview
Approach to Design Development After integrating the top down and bottom up systems, an urban configuration of built spaces with their relationship to open spaces ( work yards and social yards ) and the components of the hydrological system, is established. For the design development, a density gradient rule along with the program distribution is set up for the site patch. A catalogue of chawl morphologies having three density types and courtyard sizes integrated with their corresponding retention catchments is laid out. These morphologies are assigned to the built up cells after running the tests on a density gradient for the whole site. A detail description of the program distribution and density gradient is explained further. The design proposal is set out by selecting a specific area of the whole site patch and detailed in the following chapter.
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High Density
Medium Density
Low Density
C_01A_06 Population Density : 2369 pop/ha Fresh water reserve : 2.0 LPCPD
C_01B_05 Population Density : 1884 p/hec Fresh water reserve : 2.2 LPCPD
C_01C_03 Population Density : 1510 pop/ha Fresh water reserve : 5.0 LPCPD
C _02A_04 Population Density : 2875 pop/ha Fresh water reserve : 0.70 LPCPD
C_02B_03 Population Density : 1703 pop/ha Fresh water reserve : 0.70 LPCPD
C_02C_03 Population Density : 1398 pop/ha Fresh water reserve : 2.0 LPCPD
C _03A_01 Population Density : 2414 pop/ha Fresh water reserve : 2.0 LPCPD
C _03B_01 Population Density : 1754 pop/ha Fresh water reserve : 3.2 LPCPD
C _03C_04 Population Density : 1488 pop/ha Fresh water reserve : 4.0 LPCPD
Average Population Density (High): 2414 p/ha
Average Population Density (Medium) : 1754 p/ha
Average Population Density (Low) : 1488 p/ha
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4
741 647
8.2 Density Gradient and Program Distribution
G5_06
G1_03
G1_03
Population :
G3_05
G5_06
133575 persons
Population :
146597 persons
Open Space Ratio (OSR) : 3.32 sqm / person Population : 133575.1248 OSR : 3.317983 High density radii : 0.985185 Low density radii : 2.117647
Open Space Ratio (OSR) : 3.02 sqm / person Population : 146597.8944 OSR : 3.023236 High density radii : 1.007407 Low density radii : 1.705882
G3_05
G7_05
Population :
G7_05
141947 persons
Open Space Ratio (OSR) : 3.12 sqm / person Population : 141946.7776 OSR : 3.122297 High density radii : 0.966667 Low density radii : 1.705882 High Density 2414 p/hec Medium Density 1754 p/ Hec Low Density 1488 p/ Hec
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Population :
124987 persons
Open Space Ratio (OSR) : 3.55 sqm / person Population : 124986.5152 OSR : 3.545983 High density radii : 0.981481 Low density radii : 2.117647
Residential Commercial
Mixed use
Residential
Mixed use :
53.24%
Residential :
40.51%
Low impact zone :
6.25%
Residential
Detention catchment Community built up Low impact zone
Density gradient and Program distribution A poly-centric distribution method is adopted for density gradient across the site. The catalogue of chawl morphology is used to obtain the average values of high, medium and low population densities to be used for calculating the resultant population density. The primary network is form based on high CentralityBetweenness with the site exit points. Thus the primary network is used as an attractor for high density and mixed use built-up to maximize the population having direct access to the primary network and provision of commercial activities along the primary network as observed from the existing site conditions. The low lying areas on the site are usually the first to get flooded and are identified for providing detention catchment basins to collect surface runoff water. The locations of detention catchment basins are used as attractors for the low density built-up to minimize the population in the flood risk zone. These areas usually accommodate “low impact zones”. Low impact zones include community and religious spaces which can be allowed to flood without major loss to the urban fabric. The rest of the built units form residential chawls which invariably includes
“live-work” infrastructure. The test for density gradient was run for 10 Generations and 10 Population to derive a balance between the population and the Open Space per person ratio. The aim is to obtain a balance between high population Density and high open space per person ratio. This balance is essential because with increase in population, more open space will be required for their economic and social needs. Thus this experiment is evaluated based on these 2 experiments. The selected samples demonstrate a variation in the values of population density and open space per person ratio. The sample with maximum population is selected for further tests because the least value of open space per person ratio is 3.02 sqm/ person , which is approximately 3 times higher than the existing open space per person of Dharavi which is 1.16 sq m/ person. Also it will be interesting to develop the design based on high population density to accommodate urban growth. The program distribution is conducted on this selected iteration to evaluate the percentage provision of mixed use , residential and low impact zone. Architectural Association | Emergent Technologies & Design | 175
8.3 Design Development
B A
A
B High Density Medium Density Low Density Detention catchment Social Yard
N
Design proposal- Plan The design proposal is comprised of 3 water shed regions selected from the site patch of Dharavi. The configuration of built and open spaces, the placement of primary-secondary catchments along with their discharge channels and urban networks are derived from the experiments shown in system integration- chapter 6 Once this spatial and network order is established within the patch, the built morphologies are to be allocated as per the density gradient. While doing so, a test is run to select the blocks from the morphology catalogue for each density type and to assign them to the built up block cells. This is compared with iterations to evaluate which combination of block aggregations give the highest population density to achieve the target population. The orientation of these blocks is allowed to be modified at a maximum 270 degrees in steps of 90 degrees.
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The orientation of the blocks is tested with an agent based syntactical analysis in 3 iterations. The Diag[8.3.1] shows the 1st iteration with evaluation of spatial connectivity. However, it does not show a connectivity between most of the open spaces on the patch. Along with block orientation, the major reason for this is the resolution of urban networks. Once the subsequent iterations have the urban networks resolved by adjusting the block placement, the final iteration (Diag[8.3.2]) shows a fluent connectivity between the public spaces and a connectivity from the semi public to the public spaces. For achieving this connectivity, it was essential to adjust the blocks as per the configuration of urban networks as well as eliminating a minor proportion of blocks for permeable access.
1 Diag [8.3.1]
2
Iteration 1
3
Keyplan
Designed
Existing Most Connected
Population Density Open Space Ratio Discharge Capacity
915 (p/ha)
884 (p/ha)
2.36 sqm/per
1.16 sqm/per
250 mm/hr
25 mm/hr
Diag [8.3.2] Final iteration
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Least Connected
8.4 Water management using retention catchments
Courtyard inlet connection to retention Terrace inlet connection to retention Slope Retention catchment Detention catchment Social vicinity(dia-60m) with terrace inlet
Diag [8.4.1] 2D Plan for retention inlet points.
Distribution of fresh water inlet points on site Reflecting back on the Msc phase the building catalog is used for the proliferation of retention catchments. The fresh water supplied in the courtyards doesnâ&#x20AC;&#x2122;t differ from the average 2.5 of the required 3 liter fresh water storage per capita per day (LPCPD) during dry season. As the water management strategy is an important part of the system the capacity of retention catchment were maintained in terms of its storage capacity. However, the distribution of inlets for the retention catchment vary as per spatial organization. Diag[8.4.1] The aggregation with a collective open space as work yards are provided inlets inside courtyards. While the aggregation with social yards are provided with inlets through terrace into the underground catchment.[Diag[8.4.2] and Diag[8.4.3] The change inlet was strategic as storm water storage in the detention catchment along with the extruded inlets of retention catchment used for seating
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decreases the need for adequate collective open space. Vicinity radius of Chawls around social yard is 30m. The radius was kept to a minimum to remove courtyard inlets only from neighboring Chawls. Diag[8.4.1]The aggregation formed around a yard would be connected in a loop to maintain distribution of water. Diag[1.1.4] and Diag[8.4.5]
D
D
Diag[8.4.2] Typical work yard with retention inlets
C
C
Diag[8.4.3] Typical social yard with inlets.
Diag [8.4.4] Section showing connection between terrace and underground storage.
Section CC
Section DD
Interconnected loop
Interconnected loop
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Diagram [8.4.5] Section with extruded inlet inside courtyard.
8.5 Design Details
Section AA
Lvl : 13m
Lvl : 0.3m Lvl : -1m walk way channel
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vehicular road
walk way channel
Lvl : 13 m
Primary Network
Secondary Network
Chawl courtyard
Sloped Landscape
Perforated channel
Sloped Walk way
Perforated channel
Social yard
Sloped Landscape
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Lvl : 0.3m Lvl : -1 m
Detention catchment
Chawl courtyard with retention catchment
Section AA continued
Lvl : 13m
Lvl : 0.3m Lvl : -2m Steps on open mouth Detention catchment
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Lvl : 13 m
Lvl : 0.3m Social Yard
Detention catchment
View showing internal courtyard in Chawl Morphology
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Lvl : -2 m
Chawl courtyard with retention catchment
Secondary Network
Chawl courtyard with retention catchment
Primary Network
Section BB
Detention Catchment
Section BB continued
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Chawl courtyard with retention catchment (Terrace inlet)
Lvl : 13 m
Lvl : 0.3m Chawl courtyard with retention catchment
Courtyard with retention cacthment (terrace inlet)
Lvl : -2 m
Lvl : 13 m
Lvl : 0.3m Secondary Network
Work Yard
Chawl courtyard with retention catchment
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Lvl : -2 m
Aerial View of the designed Site
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Detention catchment in Social Yards ( Dry season)
Detention catchment in Social Yards ( Wet season)
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8. Conclusion Overview System Evaluation Further Developments
8. Conclusions
Overview : The dissertation deals with designing an urban resilient system for the informal settlement of Dharavi in Mumbai. The chief aim of this system was to cope with the urban flooding scenario, while improving the socio-economic lifestyle of the residents within the informal community. The initial system for a local scale comprised of three components : Chawl morphologies catering to a live and work lifestyle, branched foundation system to stabilize these morphologies on the low soil bearing capacity of reclaimed land and the retention water catchments to address the issues of drinking water shortage. These components formed a basis to extend the resilient system from a local to a global scale. On a global level, the resilient system comprises of a two way approach to the design. A top down approach is adopted for designing a hydrological network system in order to cope with urban flooding for the informal settlement of Dharavi (Mumbai). The bottom up approach is considered for designing the spatial organization to redevelop the existing urban fabric of the informal settlement.
System Evaluation : Hydrological system : The existing storm water discharge system in Mumbai can cater to a discharge of 25 mm per hour of rainfall. However, to cope with urban flooding, considering the average annual rainfall in Mumbai, the required discharge capacity is 180 mm per hour. The hydrological system comprising of detention water catchments and the discharge channel networks in their hierarchy was able to achieve a discharge capacity of 250 mm rainfall. The main aim of this system was to optimize
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the networks flanked by water channels based on the standard slope ( 1:80 to 1:110 ) required to drain storm water. These networks were designed by considering the existing slopes of the site topography. By using an agent movement vector field method, it was possible to derive network configurations which fulfilled the slope requirement up to 50%. By using a genetic algorithm ( GA ) to optimize the networks derived by the agent movement method, it was possible to attain a better control of the drainage slopes on a macro level. This would not have been possible to achieve if a flat landscape was considered for the networks. However, by using an agent movement method, the design of the hydrological network channels was dictated by an orthogonal and a 45 degree angular formation rule. Although the method appeared to resolve the design for channel networks and their slopes, it reflected as a technocratic approach and restricted the organic formation of channel systems. An increase in the step range for the vector movement could have helped in breaking free from an orthogonal or angular design of networks. Neighborhood relationship for spatial organization : An analysis of the existing spatial organization in Dharavi made it possible to extract parameters and retain the positive aspects of the site. The method of cellular automata was used to derive a spatial relationship between the built up and open spaces, targeting a high density population as well as a better distribution of open spaces, as compared to the existing scenario. By adhering to the idea of designing adaptive functions of open spaces for social and occupational purposes, it was possible to generate variations in the aggregations for the chawl morphologies based on their orientations. This helped in evolving private, semi public and public spaces. The design of each chawl cluster as well as their aggregations allowed to achieve a population density (915 persons/
hectare) higher than the existing scenario in Dharavi (884 persons/hectare). The aggregation tests were based on the orientation of chawl morphologies and the connectivity between the private and public spaces. The type of open spaces ( social yards or work yards ) defined the orientation rule for the morphologies. Although the orientation rules of the chawls were clear, these initial tests were on local scale aggregations which were independent of their context. While evolving the global patch and configuring aggregated blocks, it became essential for the contextual open spaces to have physical connectivity between them. This resulted in a series of iterations since a clear configuration could not be derived directly from the local aggregation tests. System Integration : After deriving a top down and a bottom up approach for the resilient system, it became essential for these two systems to evolve by being informed by one another. In this process, it became necessary to give priority to one of the approaches based on the ambition set out in this dissertation. Hence, as per the intention of tackling urban floods, the spatial organization derived from he neighborhood relationship experiments was allowed to be modified, to enable its integration with the hydrological system. The detention catchments on the global level, were allowed to overlap a built up cell and transform it into a social space to cater to the hydrological system. The urban networks were derived from the channel networks of this system and tested on the basis of their centrality to the siteâ&#x20AC;&#x2122;s access points to the surrounding commercial and industrial sectors. The system integration evaluated the two approaches based on a differential weighing of criteria. However, it could have been beneficial, if a feed back loop between both the systems was established. This would have helped optimize the two systems by running them parallel to one another.
Overall, the integrated systems were evaluated based on the discharge capacity of storm water, the ratio of work yards to open spaces and the number of detention catchments configured within open spaces. The system was developed on a steady state scenario with a target discharge capacity. However, it needed to be tested by a digital simulation in Computational Fluid Dynamics (CDF) to understand the dynamics of the system.
Further Developments : Considering Dharavi as a dense urban fabric, the redevelopment of the existing patch can be based on a phase wise sequential approach depending on the hierarchy of primary detention catchments. The sub water shed divisions can be used as a guide line to select the initial patch with the catchments located near the final discharge outlets on site. There on, the water sheds with catchments in the immediate hierarchy would be developed. This would help the existing urban fabric to adapt to the redevelopment process. Since the catchments are intended to store water on a year round basis, a system indicating the purification process for both- the catchments in social spaces as well as the ones used to store drinking water, needs to be detailed out. The aggregated chawl morphologies were resolved only on a basis of spatial organization of the private, semi public and public spaces and population density. However, there is a potential for these morphologies to connect physically and develop interesting variations in terms of connectivity on the upper levels integrating terraces and corridor spaces. The dissertation has touched upon different techniques to resolve existing issues to form a resilient system. Its further potentials trigger the possibilities of making this system dynamic from a local architectural scale to an urban scale.
Architectural Association | Emergent Technologies & Design | 191
Appendix
Experiments : 1 Fixed direction varying length Iteration : 1 Order
No.
3rd
190
800 m
1 : 56 to 1 : 184
2nd
151
400 m
1 : 48 to 1 : 184
1st
217
200 m
1 : 37 to 1 : 91
Max Length Slope
Parameters : Number of direction : 3 Step length : 50.0m Criteria : Segments with adequate slope : 34%
Iteration : 2 Max Length Slope
Order
No.
rd
3
133
935 m
1 : 82 to 1 : 172
2nd
105
275 m
1 : 45 to 1 : 106
1st
187
220 m
1 : 71 to 1 : 86
Parameters : Number of direction : 3 Step length : 55.0m Criteria : Segments with adequate slope : 35%
Iteration : 3 Order
No.
3
106
804 m
1 : 112 to 1 : 138
2nd
84
180 m
1 : 82 to 1 : 112
1st
141
180 m
1 : 62 to 1 : 66
rd
Max Length Slope
Parameters : Number of direction : 3.0 Step length : 60m Criteria : Segments with adequate slope : 34% 194 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 195
Experiments : 1 Fixed direction varying length Iteration : 4 Order
No.
3rd
88
936 m
1 : 129 to 1 : 200
2nd
67
325 m
1 : 53 to 1 : 90
1st
121
325 m
1 : 60 to 1 : 281
Max Length Slope
Parameters : Number of direction : 3 Step length : 65.0m Criteria : Segments with adequate slope : 32%
Iteration : 5 Order
No.
rd
3
50
490 m
1 : 110 to 1 : 298
2nd
71
420 m
1 : 53 to 1 : 82
1st
115
337 m
1 : 73 to 1 : 252
Max Length Slope
Parameters : Number of direction : 3 Step length : 70.0m Criteria : Segments with adequate slope : 38%
Iteration : 6 Max Length Slope
Order
No.
rd
3
33
750 m
1 : 92 to 1 : 191
2nd
70
331 m
1 : 60 to 1 : 185
1st
99
225 m
1 : 57 to 1 : 748
Parameters : Number of direction : 3.0 Step length : 75m Criteria : Segments with adequate slope : 30% 196 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 197
Experiments : 1 Fixed direction varying length Iteration : 7 Max Length Slope
Order
No.
3rd
84
560 m
1 : 48 to 1 : 472
2nd
48
273 m
1 : 48 to 1 : 472
1st
88
240 m
1 : 45 to 1 : 1289
Parameters : Number of direction : 3 Step length : 80.0m Criteria : Segments with adequate slope : 29%
Iteration : 8 Max Length Slope
Order
No.
rd
3
26
595 m
1 : 242 to 1 : 2504
2nd
44
340 m
1 : 53 to 1 : 920
1st
77
290 m
1 : 47 to 1 : 93
Parameters : Number of direction : 3 Step length : 85.0m Criteria : Segments with adequate slope : 31%
Iteration : 9 Max Length Slope
Order
No.
rd
3
22
897 m
1 : 237 to 1 : 577
2nd
34
180 m
1 : 56 to 1 : 325
1st
71
540 m
1 : 48 to 1 : 68
Parameters : Number of direction : 3.0 Step length : 90.0m Criteria : Segments with adequate slope : 34% 198 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 199
Experiments : 1 Fixed direction varying length Iteration : 10 Max Length Slope
Order
No.
3rd
18
475 m
1 : 127 to 1 : 187
2nd
37
380 m
1 : 59 to 1 : 170
1st
57
285 m
1 : 51 to 1 : 360
Parameters : Number of direction : 3 Step length : 95.0m Criteria : Segments with adequate slope : 34%
Iteration : 11 Max Length Slope
Order
No.
rd
3
25
741 m
1 : 99 to 1 : 104
2nd
27
300 m
1 : 49 to 1 : 72
1st
56
400 m
1 : 51 to 1 : 345
Parameters : Number of direction : 3 Step length : 100.0m Criteria : Segments with adequate slope : 30%
200 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 201
Experiments : 2 Fixed length varying direction Iteration : 1 Max Length Slope
Order
No.
3rd
88
360 m
1 : 104 to 1 : 288
2nd
164
180 m
1 : 67 to 1 : 529
1st
219
180 m
1 : 108 to 1 : 125
Parameters : Number of direction : 2 Step length : 45.0m Criteria : Segments with adequate slope : 22%
Iteration : 2 Max Length Slope
Order
No.
rd
3
152
585 m
1 : 44 to 1 : 17927
2nd
180
180 m
1 : 55 to 1 : 288
1st
264
315 m
1 : 96 to 1 : 108
Parameters : Number of direction : 3 Step length : 45.0m Criteria : Segments with adequate slope : 35%
Iteration : 3 Max Length Slope
Order
No.
rd
3
180
630 m
1 : 67 to 1 : 74
2nd
255
315 m
1 : 43 to 1 : 43
1st
218
243 m
1 : 44 to 1 : 124
Parameters : Number of direction : 4 Step length : 45.0m Criteria : Segments with adequate slope : 48% 202 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 203
Experiments : 2 Fixed length varying direction Iteration : 4 Max Length Slope
Order
No.
3rd
178
585 m
1 : 74 to 1 : 311
2nd
267
468 m
1 : 43 to 1 : 43
1st
231
243 m
1 : 39 to 1 : 44
Parameters : Number of direction : 5.0 Step length : 45m Criteria : Segments with adequate slope : 46%
Iteration : 5 Max Length Slope
Order
No.
rd
3
186
693 m
1 : 74 to 1 : 311
2nd
256
397 m
1 : 43 to 1 : 43
1st
295
370 m
1 : 44 to 1 : 616
Parameters : Number of direction : 6 Step length : 45.0m Criteria : Segments with adequate slope : 44%
Iteration : 6 Max Length Slope
Order
No.
rd
3
159
651 m
1 : 51 to 1 : 101
2nd
271
513 m
1 : 44 to 1 : 1016
1st
361
397 m
1 : 32 to 1 : 43
Parameters : Number of direction : 7 Step length : 45.0m Criteria : Segments with adequate slope : 45% 204 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 205
Experiments : 2 Fixed length varying direction Iteration : 7 Max Length Slope
Order
No.
3rd
156
588 m
1 : 101 to 1 : 113
2nd
238
505 m
1 : 20 to 1 : 1016
1st
267
352 m
1 : 32 to 1 : 74
Parameters : Number of direction : 8 Step length : 45.0m Criteria : Segments with adequate slope : 45%
206 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 207
Experiments : 3 Random combination Iteration : 1 Max Length Slope
Order
No.
3rd
32
706 m
1 : 185 to 1 : 945
2nd
70
600 m
1 : 40 to 1 : 48
1st
126
362 m
1 : 30 to 1 : 50
Parameters : Number of direction : 8 Step length : 75.0m Criteria : Segments with adequate slope : 46%
Iteration : 2 Max Length Slope
Order
No.
3rd
32
565 m
1 : 97 to 1 : 150
2nd
30
482 m
1 : 44 to 1 : 482
1st
67
341 m
1 : 40 to 1 : 70
Parameters : Number of direction : 6.0 Step length : 100.0m Criteria : Segments with adequate slope : 44%
Iteration : 3 Max Length Slope
Order
No.
rd
3
120
627 m
1 : 47 to 1 : 106
2nd
150
385 m
1 : 48 to 1 : 4409
1st
179
517 m
1 : 43 to 1 : 164
Parameters : Number of direction : 6.0 Step length : 55m Criteria : Segments with adequate slope : 45% 208 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 209
Experiments : 3 Random combination Iteration : 4
Max Length Slope
Order
No.
rd
3
35
520 m
1 : 78 to 1 : 94
2nd
76
90 m
1 : 89 to 1 : 90
1st
119
195 m
1 : 82 to 1 : 310
Parameters : Number of direction : 3 Step length : 65.0m Criteria : Segments with adequate slope : 33%
Iteration : 5 Order
No.
3rd
96
517 m
1 : 47 to 1 : 106
2nd
140
220 m
1 : 48 to 1 : 1005
1st
166
275 m
1 : 43 to 1 : 275
Max Length Slope
Parameters : Number of direction : 4 Step length : 55.0m Criteria : Segments with adequate slope : 49%
Iteration : 6 Max Length Slope
Order
No.
3rd
35
406 m
1 : 185 to 1 : 382
2nd
68
481 m
1 : 90 to 1 : 748
1st
101
362 m
1 : 43 to 1 : 50
Parameters : Number of direction : 4.0 Step length : 75m Criteria : Segments with adequate slope : 49% 210 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 211
Experiments : 3 Random combination Iteration : 5 Max Length Slope
Order
No.
3rd
10
341 m
1 : 104 to 1 : 154
2nd
26
465 m
1 : 52 to 1 : 150
1st
69
524 m
1 : 25 to 1 : 99
Parameters : Number of direction : 8 Step length : 100.0m Criteria : Segments with adequate slope : 45%
212 | Urban Resilience | Appendix
Primary catchment Flow accumulation > 1:80 and < 1:110 1:80 to 1:110
Sub - Watershed
Architectural Association | Emergent Technologies & Design | 213
HER THER
HER THER
HER HER
HER HER
bornborn rulerule :3 :3 survive survive rulerule 2,3,4,5 2,3,4,5 initial initial state state : 50-50 : 50-50 average average : 0.44 : 0.44 born born rulerule :3:3 survive survive rulerule 2,3,4,5 Tests for Patch 12,3,4,5 initial initial state state : 50-50 : 50-50 born born rule rule : 3: 3 survive survive rule rule 2,3,4,5 average : 0.44 :2,3,4,5 0.44 Test: 1 average initial initial state state : 50-50 : 50-50 Dead cell comes to life when it has exactly 3
living neighbours. A living cell remains alive only when surroundaverage average : :0.44 ed by 2,3,4 or 5 living neighbours. born born rule rule :3: 30.44
survive surviverule rule2,3,4,5 2,3,4,5 initial initialstate state: :50-50 50-50 bornborn rulerule :4 :4 survive survive rule rule 3,4,5,6 3,4,5,6 average average : :0.44 0.44 initial initial state state : 50-50 : 50-50
R ATCH PATCH
ATCH PATCH
ATCH PATCH
PATCH ATCH
HER THER
HER THER
HER HER
HER HER
her eather
eather her
eather her
average average : 0.34 : 0.34 born born rule rule : 4 :4 Dead cell comes to life when it has exactly 4 living neighbours. survive survive rulerule 3,4,5,6 3,4,5,6 A living cell remains alive only when surroundinitial initial state state : 50-50 : 50-50 ed by 3,4,5 or 6 living neighbours. born born rule rule : 4: 4 survive survive rule rule 3,4,5,6 average average : 0.34 :3,4,5,6 0.34 initial initial state state : 50-50 : 50-50
Test: 2
average average : :0.34 born born rule rule :4: 40.34 born born rule : 3,4 : 3,4 survive survive rule rule 3,4,5,6 3,4,5,6 survive survive rule:rule 3,4,7,8 3,4,7,8 Test: 3 initial initial state state :50-50 50-50 Dead cell comes to lifestate when it:has exactly 3 initial initial state 50-50 : 50-50 or 4 living neighbours. average :0.34 0.34 A living cell average remains alive :only when surrounded by 3,4,7 or8 living neighbours. average average : 0.97 : 0.97 born born rulerule : 3,4 : 3,4 survive survive rulerule 3,4,7,8 3,4,7,8 initial initial state state : 50-50 : 50-50 born born rule rule : 3,4 : 3,4 survive survive rule rule 3,4,7,8 average average : 0.97 :3,4,7,8 0.97 initial initial state state : 50-50 : 50-50 born rulerule : 2,5: 2,5 Test: 4born survive survive rule rule 2,3,5,6,7 average average : :2,3,5,6,7 0.97 0.97 Dead cell comes to life when it :has exactly 2 or 5 born born rule rule :3,4 3,4 living neighbours. initial initial state state : 50-50 : 50-50 A living cell remains alive rule only when surrounded by survive survive rule 3,4,7,8 3,4,7,8 2,3,5,6 or 7 living neighbours. initial initialstate state: :50-50 50-50 average average : 0.80 : 0.80 born born rulerule : 2,5 : 2,5 average average :2,3,5,6,7 :0.97 0.97 survive survive rule rule 2,3,5,6,7 initial initial state state : 50-50 : 50-50 born born rule rule : 2,5 : 2,5 survive survive rule rule 2,3,5,6,7 average average : 0.80 : 2,3,5,6,7 0.80 initial initial state state : 50-50 : 50-50 214 | Urban Resilience | Appendix
average average : :0.80 : 2,5 0.80 born born rule rule :2,5
built upbuilt : 200000 up : 200000 green :green 160000 : 160000 average average = 0.40 = 0.40 Pop = 0.082 Pop = 0.082 open area openaverage= area average= 9600 9600
built upbuilt : 203200 up : 203200 green :green 156800 : 156800 average average = 0.44 = 0.44 Pop = 0.083 Pop = 0.083 open area openaverage= area average= 8533 8533
built upbuilt : 203200 up : 2 green :green 156800 : 15 average average = 0.42 = Pop = 0.083 Pop = 0.0 open area openaverag area
built up built : 200000 up : 200000 green green : 160000 : 160000 average average = 0.40= 0.40 Pop = Pop 0.082 = 0.082 open area openaverage= area average= 9600 9600
built up built : 203200 up : 203200 green green : 156800 : 156800 average average = 0.44= 0.44 Pop = Pop 0.083 = 0.083 open area openaverage= area average= 8533 8533
built up built : 203200 up : 20 green green : 156800 : 156 average average = 0.42= Pop = Pop 0.083 = 0.08 open area openaverag area
built up built : 200000 up : 200000 greengreen : 160000 : 160000 average average = 0.40 = 0.40 Pop =Pop 0.082 = 0.082 openopen area area average= average= 96009600
built up built : 203200 up : 203200 greengreen : 156800 : 156800 average average = 0.44 = 0.44 Pop =Pop 0.083 = 0.083 openopen area area average= average= 85338533
built up built : 203200 up : 20 greengreen : 156800 : 156 average average = 0.42 =0 Pop =Pop 0.083 = 0.083 openopen area area averaa
Type: 01.01 Avg Open Space Area: 9600 sqm Population Density: 820 (p/Hec) built built upup : 200000 : 200000
Type: 01.02 Avg Open Space Area: 8533 sqm (p/Hec)
green green : 160000 : 160000 average average = 0.40 = 0.40 Pop Pop = 0.082 = 0.082 open open area area average= average= 9600 9600 built upbuilt : 240000 up : 240000 green :green 120000 : 120000 average average = 0.33 = 0.33 Pop = 0.098 Pop = 0.098 open area openaverage= area average= 4266 4266
Population Density: 830 built built upup : 203200 : 203200 green green : 156800 : 156800 average average = 0.44 = 0.44 Pop Pop = 0.083 = 0.083 open open area area average= average= 8533 8533 built upbuilt : 230400 up : 230400 green :green 129600 : 129600 average average = 0.45 = 0.45 Pop = 0.094 Pop = 0.094 open area openaverage= area average= 11200 11200
built built upup : 203200 : 2032 green green : 156800 : 15680 average average = 0.42 = 0.4 Pop Pop = 0.083 = 0.083 open open area area avera ave built upbuilt : 244800 up : 24 green :green 115200 : 115 average average = 0.22 = Pop = 0.099 Pop = 0.09 open area openaverag area
built up built : 240000 up : 240000 green green : 120000 : 120000 average average = 0.33= 0.33 Type: 02.01 Pop = Pop 0.098 = 0.098 open Open area openaverage= area average= 4266 Avg Space Area:4266 4266
built up built : 230400 up : 230400 green green : 129600 : 129600 average average = 0.45 = 0.45 Type: 02.02 Pop = Pop 0.094 = 0.094 open area open average= area average= 11200 1120011200 Avg Open Space Area:
built up built : 244800 up : 24 green green : 115200 : 1152 average average = 0.22= 0 Pop = Pop 0.099 = 0.099 open area openaverag area a
sqm Population built up built : 240000 up : Density 240000 : 980 (p/Hec) greengreen : 120000 : 120000 average average = 0.33 = 0.33 Pop =Pop 0.098 = 0.098 openopen area area average= average= 42664266
built built upup : 240000 : 240000 green green : 120000 : 120000 average average = 0.33 = 0.33 built upbuilt : 182400 up : 182400 Pop Pop = 0.098 = 0.098 green :green 177600 : 177600 open open area area average= average= 4266 4266 average average = 0.73 = 0.73 Pop = 0.074 Pop = 0.074 open area openaverage= area average= 14400 14400
Type: 03.01
Avg Open Space Area: 14400 sqm Population Density : 740 (p/Hec)
Density : 940 builtPopulation up built : 230400 up : 230400 greengreen : 129600 : 129600 average average = 0.45 = 0.45 Pop =Pop 0.094 = 0.094 openopen area area average= average= 1120011200
sqm (p/Hec)
built built upup : 230400 : 230400 green green : 129600 : 129600 average average = 0.45 = 0.45 built upbuilt : 160000 up : 160000 Pop Pop = 0.094 = 0.094 green :green 200000 : 200000 open open area area average= average= 11200 11200 average average = 0.89 = 0.89 Pop = 0.065 Pop = 0.065 open area openaverage= area average= 16000 16000
Type: 03.02
built up built : 244800 up : 244 greengreen : 115200 : 1152 average average = 0.22 = 0. Pop =Pop 0.099 = 0.099 openopen area area averag av
built built upup : 244800 : 24480 green green : 115200 : 115200 average average = 0.22 = 0.22 built upbuilt : 172800 up : 1 Pop Pop = 0.099 = 0.099 green :green 187200 : 18 open open area area averag ave average average = 1.05 = Pop = 0.071 Pop = 0.07 open area openaverag area
Avg Open Space Area: 16000 sqm Population Density : 650 (p/Hec)
built up built : 182400 up : 182400 green green : 177600 : 177600 average average = 0.73= 0.73 Pop = Pop 0.074 = 0.074 open area openaverage= area average= 1440014400
built up built : 160000 up : 160000 green green : 200000 : 200000 average average = 0.89= 0.89 Pop = Pop 0.065 = 0.065 open area openaverage= area average= 1600016000
built up built : 172800 up : 17 green green : 187200 : 187 average average = 1.05= Pop = Pop 0.071 = 0.07 open area openaverag area
built up built : 182400 up : 182400 greengreen : 177600 : 177600 average average = 0.73 = 0.73 Pop =Pop 0.074 = 0.074 openopen area area average= average= 14400 14400
built up built : 160000 up : 160000 greengreen : 200000 : 200000 average average = 0.89 = 0.89 Pop =Pop 0.065 = 0.065 openopen area area average= average= 16000 16000
built up built : 172800 up : 17 greengreen : 187200 : 187 average average = 1.05 =1 Pop =Pop 0.071 = 0.071 openopen area area averaa
built built upup : 182400 : 182400 Type: 04.01
green green : 177600 : 177600 average average = 0.73 =Space 0.73 Area: 13333 sqm Avg Open Pop Pop = 0.074 = 0.074 Population Density : 970 (p/Hec) built uparea built : area 238400 upaverage= : 238400 open open average= 14400 14400 green :green 121600 : 121600 average average = 0.72 = 0.72 Pop = 0.097 Pop = 0.097 open area openaverage= area average= 13333 13333
built built upup : 160000 : 160000 Type: 04.02 green green : 200000 : 200000 average average = 0.89 = 0.89Space Area: 15466 sqm Avg Open Pop Pop = 0.065 = 0.065 Population Density built up built : 204800 up :average= 204800 open open area area average= 16000 16000: 830 (p/Hec) green :green 155200 : 155200 average average = 0.75 = 0.75 Pop = 0.083 Pop = 0.083 open area openaverage= area average= 15466 15466
built built upup : 172800 : 1728 green green : 187200 : 18720 average average = 1.05 = 1.0 Pop Pop = 0.071 = 0.071 built uparea built : area 201600 upave :2 open open avera green :green 158400 : 15 average average = 0.86 = Pop = 0.082 Pop = 0.08 open area openaverag area
ge= 8533 8533
0
= age= 8533 8533 8533
00 0 4
rage= 8533 8533
builtbuilt up :up 203200 : 203200 green green : 156800 : 156800 average average = 0.42 = 0.42 PopPop = 0.083 = 0.083 open open areaarea average= average= 8000 8000
builtbuilt up :up 193600 : 193600 builtbuilt up :up 195200 : 195200 green green : 166400 : 166400 green green : 164800 : 164800 Average Population Density > 900 average average = 0.38 = 0.38 Open Space Area > 5800average average = 0.47 = 0.47 PopPop = 0.079 =Average 0.079 = 0.080 = 0.080 Open Space Area < 5800PopPop Population Density < 900 open open areaarea average= average= 5866 5866 open open areaarea average= average= 9066 9066
built builtbuilt up up: :203200 up 203200 : 203200 green green green : :156800 156800 : 156800 average average average ==0.42 0.42 = 0.42 Pop Pop= Pop =0.083 0.083 = 0.083 open open open area areaarea average= average= average= 8000 8000 8000
built builtbuilt up up: :193600 up 193600 : 193600 green green green : :166400 166400 : 166400 average average average ==0.38 0.38 = 0.38 Pop Pop= Pop =0.079 0.079 = 0.079 open open open area areaarea average= average= average= 5866 5866 5866
built builtbuilt up up: :195200 up 195200 : 195200 green green green : :164800 164800 : 164800 average average average ==0.47 0.47 = 0.47 Pop Pop= Pop =0.080 0.080 = 0.080 open open open area areaarea average= average= average= 9066 9066 9066
built up built : 203200 up : 203200 greengreen : 156800 : 156800 average average = 0.42= 0.42 Pop =Pop 0.083 = 0.083 open area openaverage= area average= 8000 8000
built up built : 193600 up : 193600 greengreen : 166400 : 166400 average average = 0.38= 0.38 Pop =Pop 0.079 = 0.079 open area openaverage= area average= 5866 5866
built up built : 195200 up : 195200 greengreen : 164800 : 164800 average average = 0.47= 0.47 Pop =Pop 0.080 = 0.080 open area openaverage= area average= 9066 9066
Type: 01.03
Type: 01.04
Avg Open Space Area: 8000 sqm Population Density: built built upup : built 203200 : 203200 up : 203200830 (p/Hec)
e= 1200 11200
green green : 156800 :green 156800 : 156800 average average average == 0.42 0.42 = 0.42 Pop Pop == 0.083 0.083 Pop = 0.083 open open area area open average= average= area average= 8000 8000 8000 builtbuilt up :up 244800 : 244800 green green : 115200 : 115200 average average = 0.22 = 0.22 PopPop = 0.099 = 0.099 open open areaarea average= average= 3200 3200
Avg Open Space Area: 5866 sqm Population built built upup : built 193600 : 193600 upDensity: : 193600 790 (p/Hec)
11200 ge= 11200 11200
built builtbuilt up up: :244800 up 244800 : 244800 green green green : :115200 115200 : 115200 average average average ==0.22 0.22 = 0.22 Type: 02.03 Pop Pop= Pop =0.099 0.099 = 0.099 Avg Open Space Area: 3200 open open open area areaarea average= average= average= 3200 3200 3200
built builtbuilt up up: :240000 up 240000 : 240000 green green green : :120000 120000 : 120000 average average average ==0.31 0.31 = 0.31 Type: 02.04 Pop Pop= Pop =0.098 0.098 = 0.098 Avg Open Space Area: 5866 open open open area areaarea average= average= average= 5866 5866 5866
3200 00 44
verage= = 8533 8533 8533
0
11200 age= 11200
00 0 5
1200 11200 rage= 11200
6000 = 16000
sqm Population Density : 990 (p/Hec) built up built : 244800 up : 244800
green green : 166400 :green 166400 : 166400 average average average == 0.38 0.38 = 0.38 Pop Pop == 0.079 0.079 Pop = 0.079 open open area area open average= average= area average= 5866 5866 5866 builtbuilt up :up 240000 : 240000 green green : 120000 : 120000 average average = 0.31 = 0.31 PopPop = 0.098 = 0.098 open open areaarea average= average= 5866 5866
sqm Population Density : 980 (p/Hec) built up built : 240000 up : 240000
Type: 01.05 Avg Open Space Area: 9066 sqm (p/Hec)
Density: 800 built built upPopulation up : built 195200 : 195200 up : 195200 green green : 164800 :green 164800 : 164800 average average average == 0.47 0.47 = 0.47 Pop Pop == 0.080 0.080 Pop = 0.080 open open area area open average= average= area average= 9066 9066 9066 builtbuilt up :up 236800 : 236800 green green : 123200 : 123200 average average = 0.37 = 0.37 PopPop = 0.097 = 0.097 open open areaarea average= average= 5866 5866
built builtbuilt up up: :236800 up 236800 : 236800 green green green : :123200 123200 : 123200 average average average ==0.37 0.37 = 0.37 Type: 02.05 Pop Pop= Pop =0.097 0.097 = 0.097 Avg Open Space Area: open open open area area area average= average= average= 5866 5866 5866
5866 sqm Population Density : 970 (p/Hec)
greengreen : 120000 : 120000 average average = 0.31= 0.31 Pop =Pop 0.098 = 0.098 open area openaverage= area average= 5866 5866
built up built : 236800 up : 236800 greengreen : 123200 : 123200 average average = 0.37= 0.37 Pop =Pop 0.097 = 0.097 open area openaverage= area average= 5866 5866
built built upup : built 244800 : 244800 up : 244800 green green : 115200 :green 115200 : 115200 average average average == 0.22 0.22 = 0.22 builtbuilt up :up 172800 : 172800 Pop Pop == 0.099 0.099 Pop = 0.099 green green : 187200 : 187200 open open area area open average= average= area average= 3200 3200 3200 average average = 1.05 = 1.05 PopPop = 0.071 = 0.071 open open areaarea average= average= 20266 20266
built built upup : built 240000 : 240000 up : 240000 green green : 120000 :green 120000 : 120000 average average average == 0.31 0.31 = 0.31 builtbuilt up :up 176000 : 176000 Pop Pop == 0.098 0.098 Pop = 0.098 green green : 184000 : 184000 open open area area open average= average= area average= 5866 5866 5866 average average = 1.05 = 1.05 PopPop = 0.072 = 0.072 open open areaarea average= average= 25066 25066
built built upup : built 236800 : 236800 up : 236800 green green : 123200 :green 123200 : 123200 average average average == 0.37 0.37 = 0.37 builtbuilt up :up 169600 : 169600 Pop Pop == 0.097 0.097 Pop = 0.097 green green : 190400 : 190400 open open area area open average= average= area average= 5866 5866 5866 average average = 1.12 = 1.12 PopPop = 0.07 = 0.07 open open areaarea average= average= 28800 28800
Avg Open Space Area: 20266 sqm Population Density : 710 (p/Hec)
Avg Open Space Area: 25066 sqm Population Density : 720 (p/Hec)
greengreen : 115200 : 115200 average average = 0.22= 0.22 Pop =Pop 0.099 = 0.099 open area openaverage= area average= 3200 3200
Type: 03.03
Type: 03.04
Type: 03.05
Avg Open Space Area: 28800 sqm Population Density : 700 (p/Hec)
6000 e= 6000 16000
built builtbuilt up up: :172800 up 172800 : 172800 green green green : :187200 187200 : 187200 average average average ==1.05 1.05 = 1.05 Pop Pop= Pop =0.071 0.071 = 0.071 open open open area areaarea average= average= average= 20266 20266 20266
built builtbuilt up up: :176000 up 176000 : 176000 green green green : :184000 184000 : 184000 average average average ==1.05 1.05 = 1.05 Pop Pop= Pop =0.072 0.072 = 0.072 open open open area areaarea average= average= average= 25066 25066 25066
built builtbuilt up up: :169600 up 169600 : 169600 green green green : :190400 190400 : 190400 average average average ==1.12 1.12 = 1.12 Pop Pop= Pop =0.07 0.07 = 0.07 open open open area areaarea average= average= average= 28800 28800 28800
6000 ge= 16000
built up built : 172800 up : 172800 greengreen : 187200 : 187200 average average = 1.05= 1.05 Pop =Pop 0.071 = 0.071 open area openaverage= area average= 2026620266
built up built : 176000 up : 176000 greengreen : 184000 : 184000 average average = 1.05= 1.05 Pop =Pop 0.072 = 0.072 open area openaverage= area average= 2506625066
built up built : 169600 up : 169600 greengreen : 190400 : 190400 average average = 1.12= 1.12 Pop =Pop 0.07= 0.07 open area openaverage= area average= 2880028800
0
000 age= 6000 16000
466 15466
built built upup : built 172800 : 172800 up : 172800 Type: 04.03
green green : 187200 :green 187200 : 187200 average average average == 1.05 1.05 = 1.05 Avg Open Space Area: 14400 sqm Pop Pop == 0.071 0.071 Pop = 0.071 Population Density : 920 (p/Hec) built built up :open up 201600 :average= 201600 open open area area average= area average= 20266 20266 20266 green green : 158400 : 158400 average average = 0.86 = 0.86 PopPop = 0.082 = 0.082 open open areaarea average= average= 14400 14400
built built upup : built 176000 : 176000 up : 176000 Type: 04.04
built built upup : built 169600 : 169600 up : 169600 Type: 04.05 green green : 184000 :green 184000 : 184000 green green : 190400 :green 190400 : 190400 average average average = = 1.05 1.05 = 1.05 average average average = = 1.12 1.12 = 1.12 Area: 13333 sqm Avg Open Space Area: 15466 sqm Avg Open Space Pop Pop == 0.072 0.072 Pop = 0.072 Pop Pop == 0.07 0.07 Pop = 0.07 Population Density : 910 (p/Hec) Population Density : 900 (p/Hec) built built up :open up 208000 :average= 208000 built built uparea :up 212800 :average= 212800 open open area open average= area average= 25066 25066 25066 open open area area average= area average= 28800 28800 28800 green green : 152000 : 152000 green green : 147200 : 147200 average average = 0.89 = 0.89 average average = 0.77 = 0.77 PopPop = 0.085 = 0.085 PopPop = 0.087 = 0.087 Architectural Association | Emergent Technologies open open areaarea average= average= 13333 13333 & Design | 215 open open areaarea average= average= 15466 15466
HER THER
da daPATCH PATCH
ther leather
da daPATCH PATCH ther eather
born born rulerule : 3,4 : 3,4 average average : 0.97 : 0.97 survive survive rulerule 3,4,7,8 3,4,7,8 initial initial state state : 50-50 : 50-50 average average : 0.97 : 0.97
born born rulerule : 2,5 : 2,5 survive survive rule 2,3,5,6,7 2,3,5,6,7 Tests forrule Patch 2 initial initial state state : 50-50 : 50-50 born bornrule rule: :33 Test: 5 born born rulerule : 2,5 :rule 2,52,3,4,5,6 survive survive rule 2,3,4,5,6 Deadaverage cellaverage comes to: life when it has exactly 3 0.80 : 0.80 survive survive rule rule 2,3,5,6,7 2,3,5,6,7 living neighbours. initial initialstate state: :50-50 50-50 A living cell remains alive only when surinitial initial state state : 50-50 : 50-50 rounded by 2,3,4,5 orrule 6 living born bornrule : 3: 3neighbours. average average: :0.39 0.39 survive survive rule 2,3,4,5,6 2,3,4,5,6 average average : 0.80 : rule 0.80 initial initialstate state: 50-50 : 50-50 average average: 0.39 : 0.39 Test: 6 Dead cell comes to life when it has exactly 3 living neighbours. A living cell remains alive only when surrounded by 3,4,5 or 6 living neighbours.
ATCH TCH
born bornrule rule: :33 survive surviverule rule3,4,5,6 3,4,5,6 initial initialstate state: :50-50 50-50
TCH ATCH
born bornrule rule: 3: 3 average average: :0.39 0.39 survive surviverule rule3,4,5,6 3,4,5,6 initial initialstate state: 50-50 : 50-50
ERY TERY
ERY TERY
HER HER
HER HER
Tests for Patch 3
average average 0.39 : 0.39 Test: 7 born born rule rule : 1,2,6,5 :: 1,2,6,5
Dead cell comes to life when it has exactsurvive survive rulerule 2,3,4,5,6,7 2,3,4,5,6,7 ly1,2,5 or 6 living neighbours. A living cell remains alive only when surinitial initial state state : 50-50 : 50-50 rounded by 2,3,4,5,6 or 7 living neighbours.
born born rulerule : 1,2,6,5 : 1,2,6,5 average average : 0.61 :2,3,4,5,6,7 0.61 survive survive rule rule 2,3,4,5,6,7 initial initial state state : 50-50 : 50-50 average average : 0.61 : 0.61
Test: 8 Dead cell comes to life when it has exactly 2,3 or 4 living neighbours. A living cell remains alive only when surrounded by 3,4,5 or 6 living neighbours.
born born rulerule : 2,3,4 : 2,3,4 survive survive rulerule 3,4,5,6 3,4,5,6 initial initial state state : 50-50 : 50-50 born born rulerule : 2,3,4 : 2,3,4 born born rule :0.61 :33 average average : rule 0.61 :|3,4,5,6 216survive | Urban Resilience Appendix survive rule rule 3,4,5,6 survive survive rule rule 2,3,4,5 2,3,4,5 initial initial state state : 50-50 : 50-50 initial initialstate state: :50-50 50-50
built upbuilt : 182400 up : 182400 green green : 177600 : 177600 average average = 0.73= 0.73 Pop = Pop 0.074 = 0.074 open area openaverage= area average= 14400 14400 built up built : 182400 up : 182400 green green : 177600 : 177600 average average = 0.73= 0.73 Pop =Pop 0.074 = 0.074 open area openaverage= area average= 1440014400
built built upup : 228800 : 228800 green green : built 131200 :: 131200 built up 238400 up : 238400 average average = 0.40 = 0.40 green green : 121600 : 121600 Pop Pop = 05.01 0.093 =average 0.093 Type: average = 0.72= 0.72 open open area area average= average= 5333 Pop = Pop 0.097 = 0.097 5333
Avg Open Area: open area openSpace average= area average= 133335333 13333 sqm Population Density : 930 (p/Hec) builtbuilt up :up 228800 : 228800 built up : 238400 up : 238400 green green :built 131200 : 131200 green green : 121600 121600 average average = 0.40 =: 0.40 average average 0.72= 0.72 Pop Pop = 0.093 ==0.093 Pop =area Pop 0.097 = 0.097 open open area average= average= 5333 5333 open area openaverage= area average= 1333313333
Type: 06.01 Avg Open Space Area: 4266 sqm Population Density : 930 (p/Hec) built built upup : 227200 : 227200
built upbuilt : 160000 up : 160000 green green : 200000 : 200000 average average = 0.89= 0.89 Pop = Pop 0.065 = 0.065 open area openaverage= area average= 16000 16000
built upbuilt : 17280 up : green green : 187200 :1 average average = 1.05 Pop = Pop 0.071 = 0. open area openaver are
built up built : 160000 up : 160000 green green : 200000 : 200000 average average = 0.89= 0.89 Pop =Pop 0.065 = 0.065 open area openaverage= area average= 1600016000
built up built : 172800 up : 1 green green : 187200 : 18 average average = 1.05= Pop =Pop 0.071 = 0.0 open area openavera area
built built upup : 219200 : 219200 green green 140800 :up 140800 built upbuilt :: 204800 : 204800 average average = 0.45 0.45 green green : 155200 :=155200 Pop Pop = 0.089 = 0.75 0.089 Type: 05.02 average average = = 0.75 open open area area average= average= 6933 6933 Pop = Pop 0.083 = 0.083
built built upup : 216000 : 2160 green green :built 144000 14400 built up :: 20160 up : average average = 0.42 = 0.4 green green : 158400 :1 Pop Pop = 0.088 = 0.088 average average = 0.86 open open area area avera av Pop = Pop 0.082 = 0.
Open Space openAvg area open average= area average= 15466Area: 154666933
sqm Population Density : 890 (p/Hec)
builtbuilt up :up 219200 : 219200 built up built ::204800 up : 204800 green green 140800 : 140800 green green : 155200 155200 average average = :0.45 = 0.45 average average = =0.75 = 0.75 PopPop = 0.089 0.089 Pop =open Pop 0.083 =area 0.083 open area average= average= 6933 6933 open area openaverage= area average= 1546615466
open area openaver are
builtbuilt up :up 216000 : 216 builtgreen up built : 201600 :2 green : 144000 : up 1440 green green : 158400 average average = 0.42 =: 15 0. average 0.86= Pop Pop = average 0.088 ==0.088 Popopen =area Pop 0.082 = 0.0 open area avera av open area openavera area
Type: 06.02 Avg Open Space Area: 5333 sqm (p/Hec)
green green : 132800 : 132800 average average = 0.31 = 0.31 Pop Pop = 0.093 = 0.093 open open area area average= average= 4266 4266
Population Density : 920 built built upup : 225600 : 225600 green green : 134400 : 134400 average average = 0.48 = 0.48 Pop Pop = 0.092 = 0.092 open open area area average= average= 5333 5333
built built upup : 23680 : 236 green green : 123200 : 1232 average average = 0.32 =0 Pop Pop = 0.097 = 0.097 open open area area ave a
builtbuilt up :up 227200 : 227200 green green : 132800 : 132800 average average = 0.31 = 0.31 PopPop = 0.093 = 0.093 open open areaarea average= average= 4266 4266
builtbuilt up :up 225600 : 225600 green green : 134400 : 134400 average average = 0.48 = 0.48 PopPop = 0.092 = 0.092 open open areaarea average= average= 5333 5333
builtbuilt up :up 23680 : 23 green green : 123200 : 123 average average = 0.32 =0 PopPop = 0.097 = 0.097 open open areaarea avera
built upbuilt : 275200 up : 275200 green green : 84800 : 84800 average average = = Type: 07.02 Pop = Pop 0.11 = 0.11 open area openaverage= area average= 2666 2666
built upbuilt : 283200 up : 283 green green : 76800 : 7680 average average = = Pop = Pop 0.11 = 0.11 open area openaverage area a
built upbuilt : 267200 up : 267200 green green : 92800 : 92800 average average =0 =0 Type: 07.01 Pop = Pop 0.11 = 0.11 open area openaverage= area average= 2666 2666
Avg Open Space Area: 2666 sqm Population Density : 1090 (p/Hec) built up built : 267200 up : 267200 green green : 92800 : 92800 average average =0 =0 Pop =Pop 0.11= 0.11 open area openaverage= area average= 2666 2666
Avg Open Space Area: 2666 sqm Population Density : 1090 (p/Hec)
built up built : 275200 up : 275200 green green : 84800 : 84800 average average = = Pop =Pop 0.11= 0.11 open area openaverage= area average= 2666 2666
Type: 08.01
Type: 08.02
Avg Open Space Area: 3733 sqm Population Density : 980 (p/Hec)
Avg Open Space Area: 2666 sqm Population Density : 1010 (p/Hec)
built upbuilt : 241600 up : 241600 green green : 92800 : 92800 average average =0 =0 Pop = Pop 0.098 = 0.098 open area openaverage= area average= 3733 3733
built upbuilt : 248000 up : 248000 green green : 112000 : 112000 average average = = Pop = Pop 0.101 = 0.101 open area openaverage= area average= 2666 2666
built up built : 283200 up : 2832 green green : 76800 : 76800 average average = = Pop =Pop 0.11= 0.11 open area openaverage= area av
built upbuilt : 249600 up : 249 green green : 110400 : 1104 average average = = Pop = Pop 0.101 = 0.101 open area openaverage area a
000 0 9
age= e= rage= 16000 16000 16000
ge= =16000 16000 16000
00 00 5
=933 rage= 6933 6933
ge= = age= 15466 15466 15466
00 0 5
rage= 933 6933 6933 15466 e= 15466 15466
600 00 48
e= 5333 erage= 5333 5333
600 00 48
=erage= 5333 5333 5333
0
= age= e= 2666 2666 2666
2666 e= 2666 2666
0
= age= e= 2666 2666 2666
built built built upup : up 172800 : 172800 : 172800 green green green : 187200 : 187200 : 187200 average average average = 1.05 = =1.05 1.05 Pop Pop Pop = 0.071 = =0.071 0.071 open open open area area area average= average= average= 20266 20266 20266 built built built up up:up :172800 172800 : 172800 green green green : :187200 187200 : 187200 average average average ==1.05 1.05 = 1.05 Pop Pop Pop ==0.071 0.071 = 0.071 open open open area area area average= average= average= 20266 20266 20266
built built up up :built 216000 : 216000 up : 216000 green green : 144000 green :built 144000 :201600 144000 built built up up : up : 201600 : 201600 average average average =green 0.42 =: 158400 0.42 0.42 green green : 158400 : =158400 Pop Pop =average 0.088 =average Pop 0.088 = =0.088 Type: 05.03 average 0.86 = =0.86 0.86 open open area open area average= average= area average= 6400 6400 6400 Pop Pop Pop = 0.082 = =0.082 0.082
Avg Open Space Area: 6400 open open open area area area average= average= average= 14400 14400 14400sqm Population Density : 880 (p/Hec) built builtupup built : 216000 : 216000 up : 216000 built built built up :up :201600 201600 201600 green green : green 144000 :up 144000 :: 144000 green green green 158400 158400 : 158400 average average average =:=:0.42 0.42 = 0.42 average average ==0.86 0.86 = 0.86 Pop Pop = =average 0.088 Pop 0.088 0.088 Pop Pop Pop ==0.082 0.082 =average= 0.082 open open area area open average= area average= 6400 6400 6400 open open open area area area average= average= average= 14400 14400 14400
Type: 06.03 Avg Open Space Area: 4800 sqm Population Density : 970 (p/Hec) built built up up :built 236800 : 236800 up : 236800 green green : 123200 green : 123200 : 123200 average average average = 0.32 = 0.32= 0.32 Pop Pop = 0.097 =Pop 0.097 = 0.097 open open area open area average= average= area average= 4800 4800 4800 built builtupup built : 236800 : 236800 up : 236800 green green: green 123200 : 123200 : 123200 average average average = =0.32 0.32= 0.32 Pop Pop= =0.097 Pop 0.097 = 0.097 open openarea area open average= average= area average= 4800 4800 4800
built built built upup : up 283200 : 283200 : 283200 green green green : 76800 : 76800 : 76800 average average average = == Type: Pop Pop Pop =07.03 0.11 = =0.11 0.11 open open open area area area average= average= average= 2133 2133 2133
Avg Open Space Area: 2133 sqm Population Density : 1100 (p/Hec) built built built up up:up :283200 283200 : 283200 green green green : :76800 76800 : 76800 average average average == = Pop Pop Pop ==0.11 0.11 = 0.11 open open open area area area average= average= average= 2133 2133 2133
built built built upup : up 176000 : 176000 : 176000 green green green : 184000 : 184000 : 184000 average average average = 1.05 = =1.05 1.05 Pop Pop Pop = 0.072 = =0.072 0.072 open open open area area area average= average= average= 25066 25066 25066
built built built upup : up 169600 : 169600 : 169600 green green green : 190400 : 190400 : 190400 average average average = 1.12 = =1.12 1.12 Pop Pop Pop = 0.07 = =0.07 0.07 open open open area area area average= average= average= 28800 28800 28800
built built built up up:up :176000 176000 : 176000 green green green : :184000 184000 : 184000 average average average ==1.05 1.05 = 1.05 Pop Pop Pop ==0.072 0.072 = 0.072 open open open area area area average= average= average= 25066 25066 25066
built built built up up:up :169600 169600 : 169600 green green green : :190400 190400 : 190400 average average average ==1.12 1.12 = 1.12 Pop Pop Pop ==0.07 0.07 = 0.07 open open open area area area average= average= average= 28800 28800 28800
Average Open Space Area > 3700
Population Density > 900
Average Open Space Area < 3700
Population Density < 900
built built up up :built 224000 : 224000 up : 224000 green green : built 136000 green :built 136000 :: up 136000 built upup 212800 : 212800 : 212800 average average average =green 0.35 = :0.35 0.35 green green 147200 : =147200 : 147200 Pop Pop = 0.091 =05.04 Pop 0.091 = 0.091 Type: average average average = 0.77 = =0.77 0.77 open open area open area average= area average= 4266 4266 4266 Pop Pop Pop = average= 0.087 = =0.087 0.087
built built up up :built 214400 : 214400 up : 214400 green green : 145600 green : 145600 : 145600 built built built up up : up 208000 : 208000 : 208000 average average average =green 0.39 = green 0.39 =: 0.39 green : 152000 152000 : 152000 Pop Pop = 0.087 =Pop 0.087 = 0.087 Type: 05.05 average average average = 0.89 = =0.89 0.89 open open area open area average= average= area average= 5866 5866 5866 Pop Pop Pop = 0.085 = =0.085 0.085
Avg Open Space Area:15466 4266 sqm open open open area area area average= average= average= 15466 15466 Population Density : 910 (p/Hec)
Avg Open Space Area: 5866 sqm open open open area area area average= average= average= 13333 13333 13333 Population Density : 870 (p/Hec)
built builtupup built : 224000 : 224000 up : 224000 built built up up:up :212800 : 212800 green green : green 136000 :built 136000 : 212800 136000 green green green : :0.35 147200 147200 : 147200 average average average = =0.35 = 0.35 average =0.77 0.77 = 0.77 Pop Popaverage =average =0.091 Pop 0.091 ==0.091 Pop Pop Pop ==average= 0.087 0.087 =area 0.087 open open area area open average= average= 4266 4266 4266 open open open area area area average= average= average= 15466 15466 15466
built builtupup built : 214400 : 214400 up : 214400 built built up up::up :208000 208000 : 208000 green green:built green 145600 : 145600 145600 green green : :152000 152000 :=152000 average average average =green =0.39 0.39 0.39 average average ==0.89 0.89 = 0.89 Pop Pop= =average 0.087 Pop 0.087 = 0.087 Pop Pop Pop =average= =0.085 0.085 = 0.085 open openarea area open average= area average= 5866 5866 5866 open open open area area area average= average= average= 13333 13333 13333
Type: 06.04
Type: 06.05
Avg Open Space Area: 5866 sqm
built built up up :built 228800 : 228800 up Density : 228800 : 930 (p/Hec) Population green green : 131200 green : 131200 : 131200 average average average = 0.39 = 0.39= 0.39 Average Open Space Area Pop Pop = 0.093 =Pop 0.093 = 0.093 open open area open area average= average= area average= 5866 5866 Space 5866 Average Open Area
Avg Open Space Area: 5866 sqm
> 500 < 500
built builtupup built : 228800 : 228800 up : 228800 green green: green 131200 : 131200 : 131200 average average average = =0.39 0.39= 0.39 Pop Pop= =0.093 Pop 0.093 = 0.093 open openarea area open average= average= area average= 5866 5866 5866
Population Density : 920 (p/Hec) built built up up :built 225600 : 225600 up : 225600 green green : 134400 green : 134400 : 134400 average average average = 0.41 = 0.41= 0.41 Population Density > 900 Pop Pop = 0.092 =Pop 0.092 = 0.092 Population Density < 900 open open area open area average= average= area average= 5866 5866 5866 built builtupup built : 225600 : 225600 up : 225600 green green: green 134400 : 134400 : 134400 average average average = =0.41 0.41= 0.41 Pop Pop= =0.092 Pop 0.092 = 0.092 open openarea area open average= average= area average= 5866 5866 5866
built built built upup : up 281600 : 281600 : 281600 green green green : 78400 : 78400 : 78400 average average average = == Type: 07.04 Pop Pop Pop = 0.11 = =0.11 0.11 open open open area area area average= average= average= 2133 2133 2133
Avg Open Space Area: 2133 sqm Population Density : 1100 (p/Hec) built built built up up:up :281600 281600 : 281600 green green green : :78400 78400 : 78400 average average average == = Pop Pop Pop ==0.11 0.11 = 0.11 open open open area area area average= average= average= 2133 2133 2133
built built built upup : up 278400 : 278400 : 278400 green green green : 81600 : 81600 : 81600 average average average = == Type: Pop Pop Pop = 0.11 =07.05 =0.11 0.11 open open open area area area average= average= average= 2666 2666 2666
Avg Open Space Area: 2666 sqm Population Density : 1100 (p/Hec)
built built built up up:up :278400 278400 : 278400 green green green : :81600 81600 : 81600 average average average == = Pop Pop Pop ==0.11 0.11 = 0.11 open open open area area area average= average= average= 2666 2666 2666
Type: 08.03
Type: 08.04
Type: 08.05
Avg Open Space Area: 2600 sqm Population Density : 1010 (p/Hec)
Avg Open Space Area: 2666 sqm Population Density : 1000 (p/Hec)
Avg Open Space Area: 2666 sqm Population Density : 1020 (p/Hec)
built built built upup : up 249600 : 249600 : 249600 green green green : 110400 : 110400 : 110400 average average average = == Pop Pop Pop = 0.101 = =0.101 0.101 open open open area area area average= average= average= 2600 2600 2600
built built built upup : up 246400 : 246400 : 246400 green green green : 113600 : 113600 : 113600 average average average = == Architectural Pop Pop Pop = 0.10 = =0.10 0.10 open open open area area area average= average= average= 2666 2666 2666
Association |
built built built upup : up 249600 : 249600 : 249600 green green green : 110400 : 110400 : 110400 average average average = == Emergent Technologies & Pop Pop Pop = 0.102 = =0.102 0.102 open open open area area area average= average= average= 2666 2666 2666
Design | 217
Tests for the Site
SITE TEST_01
SITE TEST_02
Built up : 742400Open Built up : area: 742400Open 392000Population: area: 392000Population: 109040Pop Density 109040Pop : 0.096OSR: Density3.60 : 0.096OSR: 3.60 SITE TEST_03
SITE TEST_04
Built up : 681600Open Built up : area: 681600Open 452800Population: area: 452800Population: 100110Pop Density 100110:
up : 715200Open Built up area: : 715200Open 419200Population: area: 419200Population: 105045Pop Density 105045Pop : 0.093OSR: Density 3.99 : 0.093OSR: 3.99
y00110Pop : 0.088OSR: Density 4.5 : 0.088OSR: 4.5 218 | Urban Resilience | Appendix
Built up
Built up : 702400Open Built up area :
Built up : 694400Open Built up area: : 694400Open 440000Population: area: 440000Population: 101990Pop Densit1
SITE TEST_05
SITE TEST_06
ea: 432000Population: 103165Pop Density : 0.091OSR: 4.19 SITE TEST_07
Built up : 673600Open area: 460800Population: 98935Pop Density : 0.087OSR: 4.66Av SITE TEST_08
ity : 0.090OSR: 4.31
Built up : 675200Open
g087OSR: B-O : Avg 4.66Avg O-O : B-O : Avg O-O : Built up : 688000Open Built up area: : 688000Open 446400Population: area: 446400Population: 101050Pop Density 101050Pop : 0.089OSR: Density 4.42Avg : 0.089OS B-O Architectural Association | Emergent Technologies & Design | 219
SITE TEST_09
SITE TEST_10
Avg sity :B-O 0.089OSR: : Avg O-O 4.42Avg : B-O : Avg O-O : Built up : 684800OpenBuilt area: up449600Population: : 684800Open area: 100580Pop 449600Population: Density : 0.088OSR: 100580Pop4.47Avg Density :B-O 0.0
Avg distance from OpenBuilt area: up443200Population: : 691200Open area: 101520Pop 443200Population: Density : 0.089OSR: 101520PopOpen 4.36Avg Density :B-O 0.089OSR: : Built Avg O-O 4.36Avg : B-O : Avgbetween O-O : Avg distance Population Density Space Ratio to nearby open TESTS
open spaces (AO)
(PD)
(OSR)
Site Test_01
960
3.6
space (ABO) 42.95
Site Test_02
880
4.5
41.79
83.24
Site Test_03
930
3.99
41.82
80.23
Site Test_04
900
4.31
41.54
81.91
Site Test_05
910
4.19
42.3
81.1
Site Test_06
870
4.66
41.78
74.72
Site Test_07
870
4.63
41.83
80.4
Site Test_08
890
4.42
42.32
82.38
Site Test_09
890
4.36
42.12
86.04
Site Test_10
880
4.47
42.72
78.26
220 | Urban Resilience | Appendix
79.16
Architectural Association | Emergent Technologies & Design | 221
S_01
Population : 2919 persons
Total sunlight hours : 18655
Population Density : 1394 per/hec
S_02
Hours 10.00
Pop: 2919 Pop: 2443 Pop: 2200 Population : 2368 persons Total sunlight hours : 17989 Sunlight hrs: 17899 Sunlight hrs: 18655 Sunlight hrs: 19419 Population Density : 1146 per/hec
S_03 <= 0.00
Most Connected
Least Connected
Population : 2200 persons Population Density : 1120 per/hec
222 | Urban Resilience | Appendix
Total sunlight hours : 19419
Pop: 2507 Sunlight hrs:
S_04
Population : 2652 persons
Total sunlight hours : 18783
Population Density : 1308 per/hec
S_05
Population : 2443 persons
Total sunlight hours : 17899
Population Density : 1168 per/hec
Hours 10.00
S_06 <= 0.00
Most Connected
Population : 2479 persons
Total sunlight hours : 17495
Population Density : 1283 per/hec
Architectural Association | Emergent Technologies & Design | 223
Least Connected
Pop: 2919 Sunlight h
S_07
Hours 10.00
Pop: 2919 Pop: 2443 Pop: 2200 Population : 2507 persons Total sunlight hours : 18574 Sunlight hrs: 17899 Sunlight hrs: 18655 Sunlight hrs: 19419 Population Density : 1301per/hec
S_08 <= 0.00
Most Connected
Population : 2884 persons Least Connected
Population Density : 1557 per/hec
224 | Urban Resilience | Appendix
Total sunlight hours : 15398
Pop: 2507 Sunlight hrs:
S_09
Population : 2328 persons
Total sunlight hours : 17401
Population Density : 1217 per/hec
Hours 10.00
S_10 <= 0.00
Most Connected
Population : 2328 persons
Total sunlight hours : 16164 Least Connected
Population Density : 1235 per/hec
Architectural Association | Emergent Technologies & Design | 225
Pop: 2919 Sunlight h
Pop: 4071 Sunlight hrs : 35283
P S
M_01
Population : 4071 persons
Pop: 4071 Sunlight hrs : 35283
Population Density : 1168 per/hec
Total sunlight hours : 35283
Pop: 3831 Sunlight hrs : 34073
M_02
Hours
10.00 1 hrs : 34073
Pop: 2919 Pop: 2443 Pop: 2200 Population : 3831 persons Total sunlight hours : 34073 Sunlight hrs: 17899 Sunlight hrs: 18655 Sunlight hrs: 19419 Population Density : 1163 per/hec
Pop: 3698 Sunlight hrs : 32595
M_03
<= 0.00
Most Connected
Least Connected
Population : 3698 persons Population Density : 1129 per/hec
226 | Urban Resilience | Appendix
Total sunlight hours : 32595
Pop: 2507 Sunlight hrs:
Pop: 4095 Sunlight hrs : 34138 M_04
Population : 4095 persons Population Density : 1219 per/hec
Total sunlight hours : 34138
Pop: 4070 Sunlight hrs : 35315
Pop: 3 Sunligh
M_05
Population : 4070 persons Population Density : 1224 per/hec
Total sunlight hours : 35315
Pop: 3837 Sunlight hrs : 33665
Hours 10.00
M_06
<= 0.00
Most Connected
Population : 3837 persons
Total sunlight hours : 33665
Population Density : 1175 per/hec
Architectural Association | Emergent Technologies & Design | 227
Least Connected
Pop: 2919 Sunlight h
P Su
Pop: 4183 Sunlight hrs : 35888
s : 33665 M_07
Hours
10.00 3 hrs : 35888
Pop: 2919 Pop: 2443 Pop: 2200 Population : 4183 persons Total sunlight hours : 35888 Sunlight hrs: 17899 Sunlight hrs: 18655 Sunlight hrs: 19419 Population Density : 1236 per/hec
Pop: 4086 Sunlight hrs : 31970
M_08
<= 0.00
Most Connected
Least Connected
Population : 4086 persons Population Density : 1256 per/hec
228 | Urban Resilience | Appendix
Total sunlight hours : 31970
Pop: 2507 Sunlight hrs:
Pop: 3608 Sunlight hrs : 33019
0
Pop Su
M_09
Population : 3608 persons Population Density : 1118 per/hec
Total sunlight hours : 33019
Pop: 3971 Sunlight hrs : 32259
Hours 10.00
M_10
<= 0.00
Most Connected
Population : 3971 persons
Total sunlight hours : 32259
Population Density : 1222 per/hec
Architectural Association | Emergent Technologies & Design | 229
Least Connected
Pop: 2919 Sunlight h
Pop: 7323 Sunlight hrs : 76528
L_01
Pop: 7323 Sunlight hrs : 76528
Population : 7323 persons Population Density : 1045 per/hec
Pop: 7743 Sunlight hrs : 71994
Total sunlight hours : 76528
L_02
Pop: 7743 Sunlight hrs : 71994
Population : 7743 persons Population Density : 1130 per/hec
Pop: 7631 Sunlight hrs : 73082
Total sunlight hours : 71994
L_02
Population : 7631 persons Population Density : 1125 per/hec
230 | Urban Resilience | Appendix
Total sunlight hours : 73082
Pop: 7469 Sunlight hrs : 75141
73082
72932
L_04
Population : 7469 persons Population Density : 1070 per/hec
Pop: 7609 Sunlight hrs : 72932
Pop: 693 Sunlight
Pop: 6934 Sunlight hrs : 74896
Pop: 7636 Sunlight hrs
Total sunlight hours : 75141
L_05
Population : 7609 persons Population Density : 1102 per/hec
Total sunlight hours : 72932
L_06
Population : 6934 persons
Total sunlight hours : 74896
Population Density : 1006 per/hec
Architectural Association | Emergent Technologies & Design | 231
Pop: 6934 Sunlight hrs : 74896
Pop: 7636 Sunlight hrs : 76754
Pop: 7636 Sunlight hrs : 76754
L_07
Population : 7636 persons Population Density : 1100 per/hec
Pop: 7854 Sunlight hrs : 74514
Total sunlight hours : 76754
L_08
Population : 7854 persons Population Density : 1146 per/hec
232 | Urban Resilience | Appendix
Total sunlight hours : 74514
74021
Pop: 8384 Sunlight hrs : 74021
L_09
Population : 8384 persons Population Density : 1202 per/hec
Pop: 7076 Sunlight hrs : 70904
Total sunlight hours : 74021
L_10
Population : 7076 persons
Total sunlight hours : 70904
Population Density : 1065 per/hec
Architectural Association | Emergent Technologies & Design | 233
Pop: 7076 Sunlight h
System Integration : Variation 1
Variation 1 (a)
8079
235
Catchments on open spaces
Betweenness Centrality
Outlet connectivity
Depth : 8.8, 5.1 , 3.9, to 2.2 m Outlet 2
Outlet 1
453m
Discharge capacity (Rainfall per hour)
234 | Urban Resilience | Appendix
Outlet 3 Outlet 4
323m
246m
Work yards and social yards
Variation 1 (b)
Variation 1 (c)
Open spaces Primary catchments Secondary catchments
Social yards Work yards
Architectural Association | Emergent Technologies & Design | 235
System Integration : Variation 2
Variation 2 (a)
8079
235
Catchments on open spaces
Betweenness Centrality
Outlet connectivity
Depth : 8.8, 6.14, 6.26, to 2.2 m Outlet 2 Outlet 3 Outlet 4
Outlet 1
461m
Discharge capacity (Rainfall per hour)
236 | Urban Resilience | Appendix
347m
343m
Work yards and social yards
Variation 2 (b)
Variation 2 (c)
Open spaces Primary catchments Secondary catchments
Social yards Work yards
Architectural Association | Emergent Technologies & Design | 237
System Integration : Variation 3
Variation 3 (a)
8079
235
Catchments on open spaces
Betweenness Centrality
Outlet connectivity Outlet 1
260m
Depth : 3.9, 8.8, 4.9, to 2.2 m
Outlet 2
Outlet 3
480m
Discharge capacity (Rainfall per hour)
238 | Urban Resilience | Appendix
Outlet 4
344m
Work yards and social yards
Variation 3 (b)
Variation 3 (c)
Open spaces Primary catchments Secondary catchments
Social yards Work yards
Architectural Association | Emergent Technologies & Design | 239
System Integration : Variation 4
Variation 4 (a)
8079
235
Catchments on open spaces
Betweenness Centrality
Outlet connectivity
Depth : 8.8, 5.5, 5.6, to 2.2 m
Outlet 1
Outlet 2
458m
Discharge capacity (Rainfall per hour)
240 | Urban Resilience | Appendix
Outlet 3 Outlet 4
364m
321m
Work yards and social yards
Variation 4 (b)
Variation 4 (c)
Open spaces Primary catchments Secondary catchments
Social yards Work yards
Architectural Association | Emergent Technologies & Design | 241
System Integration : Variation 5
Variation 5 (a)
8079
235
Catchments on open spaces
Betweenness Centrality
Outlet connectivity
Depth : 8.0, 5.31, 2.84, 2.2 m
Outlet 1
Outlet 3 Outlet 4
Outlet 2
444m
Discharge capacity (Rainfall per hour)
242 | Urban Resilience | Appendix
373m
205m
Work yards and social yards
Variation 5 (b)
Variation 5 (c)
Open spaces Primary catchments Secondary catchments
Social yards Work yards
Architectural Association | Emergent Technologies & Design | 243
System Integration : Variation 6
Variation 6 (a)
8079
235
Catchments on open spaces
Betweenness Centrality
Outlet connectivity
Depth : 8.0, 5.0, 2.55, 2.2 m
Outlet 1
Outlet 3 Outlet 4
Outlet 2
444m
Discharge capacity (Rainfall per hour)
244 | Urban Resilience | Appendix
346m
194m
Work yards and social yards
Variation 6 (b)
Variation 6 (c)
Open spaces Primary catchments Secondary catchments
Social yards Work yards
Architectural Association | Emergent Technologies & Design | 245
System Integration Statistics
Rainfall Intensity (mm/hr)
BetweennessCentrality
Work yards area (sqm)
Social yards area (sqm)
Variation 1a
0.232
223775
41
34
188800
371888
Variation 1b
0.232
223775
Variation 1c
0.232
223775
44
41
252800
370688
45
39
235200
368688
Variation 2a
0.227
65588
41
65
302400
163278
Variation 2b Variation 2c
0.227
65588
41
69
371200
163278
0.227
65588
42
68
352000
163278
Variation 3a
0.247
253257
34
65
308800
165552
Variation 3b
0.247
253257
41
69
369600
165552
Variation 3c
0.247
253257
51
67
340800
165552
Variation 4a
0.231
177495
24
66
320000
168320
Variation 4b
0.231
177495
38
69
372800
168320
Variation 4c
0.231
177495
49
67
342400
168320
Variation 5a
0.238
199077
34
66
312000
159191
Variation 5b
0.238
199077
47
69
364800
162391
Variation 5c
0.238
199077
47
68
347200
162391
Variation 6a
0.242
288313
34
66
313600
158317
Variation 6b
0.242
288313
40
70
376000
158317
Variation 6c
0.242
288313
46
70
353600
155117
TESTS Variation 1
% of Catchments Work yards % on Open Spaces
Variation 2
Variation 3
Variation 4
Variation 5
Variation 6
246 | Urban Resilience | Appendix
Architectural Association | Emergent Technologies & Design | 247
_06
133575.1248 983 adii : 0.985185 adii : 2.117647
Density Gradient G1_03
G1_06 G1_06
G1_03
Population : 125789.6128 OSR : 3.523343 High density radii : 1.011111 Low density radii : 1.882353
G2_01 G2_01
Population : 133575.1248 OSR : 3.317983 High density radii : 0.985185 Low density radii : 2.117647
G3_05 G3_05
4_05
G4_05
G2_9 G2_09
Population : 146386.48 OSR : 3.027602 High density radii : 0.959259 Low density radii : 1.705882
G3_07
G4_08 Population : 141946.7776 OSR : 3.122297 High density radii : 0.966667 Low density radii : 1.705882
G4_08
G1_06
G1_08
G3_04 Population : 125789.6128 OSR : 3.523343 High density radii : 1.011111 Low density radii : 1.882353
G3_07
G4_05
41946.7776 97 dii : 0.966667 dii : 1.705882
G1_08
G2_9
G3_04
G4_02 Population : 125366.784 OSR : 3.535227 High density radii : 1.018519 Low density radii : 1.823529
G4_02
Population : 136534.9264 Population : 125789.6128 OSR : 3.246056 OSR : 3.523343 High density radii : 1.048148 High density radii : 1.011111 Low density radii : 1.823529 Low density radii : 1.882353
G3_07 Population : 145583.3824 OSR : 3.044303 High density radii : 0.951852 Low density radii : 2.0
G4_08 G5_02 Population : 125472.4912 OSR : 3.532248 High density radii : 1.022222 Low density radii : 1.764706
G5_02
Population : 125472.4912 Population : 136429.2192 OSR : 3.532248 OSR : 3.248571 High density radii : 1.022222 High density radii : 1.048148 Low density radii : 1.764706 Low density radii : 1.823529
High Density 2414 p/hec Med Density 1754 p/ Hec Low Density 1488 p/ Hec
Population : 134103.6608 OSR : 3.304906 High density radii : 0.985185 Low density radii : 2.176471
Population : 134103.6608 OSR : 3.304906 High density radii : 0.985185 Low density radii : 2.176471
248 | Urban Resilience | Appendix
Population : 141523.9488 OSR : 3.131625 High density radii : 0.962963 Low density radii : 2.058824
Population : 126149.2944 Population : 141523.9488 OSR : 3.131625OSR : 3.513297 High density radii : 1.040741 High density radii : 0.962963 density radii : 2.117647 Low density radiiLow : 2.058824
01
G5_06
G6_01
G5_06
G6_08
Population : 135372.1472 OSR : 3.273938 High density radii : 1.011111 Low density radii : 1.941176
G6_08
G9_07
G9_07
G9_08
G9_08
G6_03
G6_01
Population : 146597.8944 OSR : 3.023236 High density radii : 1.007407 Low density radii : 1.705882
Population : 136429.2192 OSR : 3.248571 High density radii : 1.048148 Low density radii : 1.764706
Population : 129981.08 OSR : 3.409727 High density radii : 0.959259 Low density radii : 1.764706
Population : 131355.2736 OSR : 3.374056 High density radii : 0.955556 Low density radii : 2.058824
G7_05G7_05
G6_03
G6_03
Population : 135372.1472 OSR : 3.273938 High density radii : 1.011111 Low density radii : 1.941176
G8_06
G8_06
G9_08 Population
Population : 141312.5344 OSR : 3.136311 High density radii : 0.962963 Low density radii : 2.0
G1_03
133575
Open Space Ratio Population : 145266.2608 OSR : 3.050949 (OSR) High density radii : 0.937037 Low density radii : 1.882353 3.32
G1_06
125790
3.52
G1_08
136535
3.25
G2_01
146386
3.03
G2_09
125367
3.54
G3_04
145583
3.04
G3_05
141947
3.12
G3_07
125472
3.53
G4_02
136430
3.25
G4_05
134103
G4_08
141524
G5_02
126150
G5_06
146597
3.02
G6_01
135372
3.27
G6_03
141312
3.14
G6_08
136430
3.25
G7_05
124987
3.55
G8_06
145266
3.05
G9_07
129981
3.41
G9_08
131355
3.37
Population : 124986.5152 TESTS OSR : 3.545983 High density radii : 0.981481 Low density radii : 2.117647
3.3
Population : 131355.2736 OSR : 3.374056 3.13 High density radii : 0.955556 Low density radii : 2.058824
3.51
Architectural Association | Emergent Technologies & Design | 249
G
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Architectural Association | Emergent Technologies & Design | 253
Architectural Association School of Architecture Emergent Technologies and Design