EMERGENT TECHNOLOGIES & DESIGN 2015 | 2016 CORE STUDIO II | CITY SYSTEMS Yorgos Berdos . Marcella Carone . Francesco Massetti . Molly Minot
COURSE DIRECTOR MICHAEL WEINSTOCK GEORGE JERONIMIDIS STUDIO MASTER EVAN GREENBERG TUTORS ELIF ERDINE MANJA VAN DE WORP 2
CONTENTS ABSTRACT INTRODUCTION
05 06
1.0 MANCHESTER 1.1 DATA MINING AND ANALYSIS
07 08
2.0 PATCH SELECTION 3.0 STRATEGY 4.0 NETWORK OPTIMIZATION
11 12 14
5.0 EVENTS INTENSITY 5.1 DENSITY MAPS 5.2 BLOCKS DESIGN
17 18 19
6.0 ZONING AND USES DISTRIBUTION 7.0 MORPHOLOGIES 8.0 EVOLUTION AND GENETIC ALGORITHMS 9.0 SPACE SYNTAX ANALYSIS 10.0 PROJECT
21 22 23 28 29
CONCLUSIONS
32
APPENDICES CA EXPERIMENT SOCIAL PROVISIONS COMPARISON
33 33 36
BIBLIOGRAPHY
41
3
4
ABSTRACT This project focuses on high-density urban configurations while combined with large people’s flows and how an urban design can be adaptive to different density scenarios. Moreover, the cohabitation parameters of resident and floating populations are under investigation as well as the combination of fixed programmatic uses with nomadic and unstable uses, triggered by various events. The events placement is treated as the main tool for an urban development in a specific area of East Manchester, when existent flows between the main Piccadilly station and the Etihad stadium are being emphasize to provoke significant density fluctuation and, consequently, attract new residents to the area. Computational tools have been used to translate the behaviour of the stable and changing inhabitants into open and built areas, urban blocks, pedestrian routes, plazas, residential, commercial and recreational uses.
5
INTRODUCTION “Architecture has always been as much about the event that takes place in a space as about the space itself.” (Bernard Tschumi) The build urban environment has been repeatedly considered as the background, the “theatrical stage” of various events by different architectural, societal and political movements. Most of the conceptual affinities to this approach can be traced back in the theoretical work and the suggested utopian projects developed during the 60’s, the 70’s and on some architectural efforts to overcome the deterministic rationality of the built environment during the 80’s. Undoubtedly, the insertion of the terms “event” and “movement” to the architectural discourse was influenced by the Situationist movement and by the ’68 era in general.
in the broader area. The Manchester Piccadilly Station provides the area with a constant influx of people while the Etihad Stadium generates large periodical changes of flows, through the events that take place there. The presented approach aspires to move away from normative urban design methods, while focusing on the way that organizational principles can be extracted from computational design processes and evolutionary simulations. How can these two “flows providers” be combined with other event/flows generators and high permanent population figures? What is the affect that the juxtaposition of heterogeneous events with unprecedented combinations of programs and spaces can have on the urban space?
Big cities and especially cities with high density, diverse urban fabric and frequently fluctuating population constitute the ideal terrain where multiple events can take place and ultimately affect the way that the urban space is being produced and experienced. Big, crowded cities host dense networks of flows. Flows of people, resources and information that are generating a complex intertwined complex of activities, which affects the built and the unbuilt environment. These flows are increasingly perceived from the viewpoint of the events that give them rhythm or disrupt that rhythm. 1 The space type under investigation should be adaptable enough to facilitate a mosaic of different events and flows in a densely populated urban environment. The proposed plot is situated in a low-density area of East Manchester and its borders are defined by Manchester Piccadilly Train Station and Etihad Stadium, two of the most visited spaces 6
ANTOINE, Picon. Smart Cities, A Spatialised Intelligence. AD Primers. John Wiley & Sons Ltd, 2015. p.51 1
1.0 | MANCHESTER INTRODUCTION Manchester is located in the northwest of England and is characterized by a rich industrial heritage that influences its actual dynamics and growing pattern. For instance, its population grew steadily throughout the middle ages and rose dramatically during Industrial Revolution, reaching the lowest level of 416.400 inhabitants in 1999. After a severe decline from 1950 onward, the current population is approximately 510.000.
City Area Population Urban Density GDP GDP per capita
Manchester City 115.65 km2 Urban 630.30 km2 City 520,215 Urban 2,553,379 3,468km2 US$ 20 billion US$ 35,029
City: Area:
Populatio
Urban De GDP: GDP per c
2.75 Km2 pop. approx 9537
City Area
The shrinking phenomenon have to be concern but Manchester has the potential to attract both resident and floating population. The East Manchester area, when the main train station and the Etihad Stadium are located, represents a good example of considerable density fluctuation in a short time. This particular characteristic can be used to design a new urban planning to attract people and increase local density.
Population
Comparing it to others post-Industrial cities as Barcelona and Hamburg which had been passed through urban renovations, it is clear that Manchester population and density are significant lower, however, the GDP is similar to the Spanish city. In other hand, the low-dense Hamburg has the highest GDP per capita.
Population
Urban Density GDP GDP per capita
Barcelona City 101.4 km2 Urban 636.0 km2 City 1,604,555 Urban 4,693,000 5,060/km2 US$ 60 billion US$ 34,821
City: Area:
Populatio
Urban De GDP: GDP per c
2.75 Km2 pop. approx 13915
City Area
Urban Density GDP GDP per capita
Hamburg 240 km2 755 km2 1,774,242 4,300,000 2,300/km2 US$ 112 billion US$ 61,142
City: Area:
City Urban City Urban
2.75 Km2 pop. approx 6325
Populatio
Urban De GDP: GDP per c
map source: Open Street Map + Elk
7
1.1 | DATA MINING AND ANALYSIS CLIMATE Manchester is located in the latitude 53°28’ N and has the temperate oceanic climate. Known as one of the most humid cities in the UK, its humidity figures vary between 80% and 90%, with a yearly average rain at 861.1mm (1981-2010). The city is crossed by the important Medlock River and Ashton and Rochlade Canals. Blended with the urban tissue, the fluvial system impacts nearby areas and flooding are the principal problems. The Map 1 shows the most affected areas and its probability of flood risk.
However, the studied area has the density fluctuation singularity because of the Etihad Station and its weekly events, while the density can vary between 3468 and 9265 people per sq.km. 0.1%-1% annual probability of flood risk >1% annual probability of flood risk
River Medlock
MAP 1
Etihad Campus
New Islington
TRANSPORTATION Easily accessible, the city is composed by a significant train, tram and bus networks. Manchester Picadilly and Victoria are the two main train stations and responsible for the connection outer city. A modern light rail tram cross the urban tissue and ensures the connectivity. Buses represents one of the most extensive network outside London and transports 11% of the population. DENSITIES
8
East Manchester is characterised for a low density (18 people/ha) when compared to the city centre (74-250 people/ha).
tram stations tram route train route
Picadilly station
MAP 2
18 people/ha 40 people/ha 55 people/ha 74 people/ha 250 people/ha
MAP 3
Map Source: Open Street Map + Elk
Velopark
EDUCATION schools universities kindergarten other
4
12 12
7
7
1
1
5
5
1 school per 1900 habitants
0 universities HEALTHCARE 1 kindergarten per 3180 habitants hospitals 1 “other” per 2380 habitants health centres practices/ clinics other
voronoi | proximity
number of social provisions per city
4
number of social provisions per city
MAP 4
44
CULTURAL museums libraries other
55
LEISURE 1 school per 1900 habitants playgrounds 0 universities sports 1 kindergarten per 3180 habitants parks 1 “other” per 2380 habitants other
voronoi | proximity
33
MAP 6
Map Source: Open Street Map + Elk
Social Provisions Source: Google Maps
voronoi | proximity
number of social provisions per city
MAP 5
15 m
in
SERVICES
2 2
3
3
0 hospitals 0 health centre per 4760 habitants 1 clinic per 3180 habitants 0 “other”
9
number of social provisions per city
1
voronoi | proximity
0 museum 1 library per 9537 habitants 0 “other”
number of social provisions per city
MAP 7
2
2
4 4
1
MAP 8
Map Source: Open Street Map + Elk
10
11 min
voronoi | proximity
1
0 playgrounds 1 “sport” per 2380 habitants 1 park per 9537 habitants 1 “other” per 4770 habitants
Social Provisions Source: Google Maps
PUBLIC SPACES/ GREEN SPACES
SOCIAL PROVISIONS
Manchester has a high level of green spaces when compared to others cities. Evaluating it per inhabitants, the actual figures are 6.78 m2 pp for green spaces and 7.73 m2 pp for public spaces. However, it is clear that the city has a lack of public spaces that should be consider in a future urban intervention.
For a precise analysis, social provisions were divided in Education, HealthCare, Cultural, Leisure and Services. Well-known as a student city (12, 5% population), Manchester presents a significant number of universities and schools. However, the lack of healthcare and cultural services are
evident in Map 6 and 7. In the Leisure field, sports related provisions are dominant and higher when compared with parks in the East. The Voronoi diagrams and the Delaunay triangulation have been used as tools that illustrate the distance and the topological relation of the social provisions examined in each category. Through these diagrams we can examine the areas that are not served by
enough provisional services (bigger Voronoi cells) and associate the varying density of nodes (services) to different programmatic uses of the specific area or lack of proper regional urban design.
in 3m
2.0 | PATCH SELECTION
3m
in
in 3m in
Map Source: Open Street Map + Elk 3m
In one hand, the new urban plan considered the connections with the actual city and its history by taking in consideration important road junctions and existent pedestrian routes. On the other hand, in order to propose an innovative intervention, just permanent elements were preserved, as the river, railways, the station and the stadium.
3m in
The selected patch represents the area that will be first affect by the station-stadium complex and the upcoming minor events. With an area of 1.2km2, the urban tissue covers a variety of uses, morphologies and densities. The concept is to reinforce the connection between Piccadilly and Etihad, provoking the densification with new events placement, a sub-utilized urban potential.
2Km
11
3.0 | STRATEGY
ATHLETIC COMPLEX
MAINLY PERIODICAL EVENTS GREAT CHANGES IN DENSITY
BUSIEST STATION IN MANCHESTER
PICCADILLY STATION
MINIMUM ATTRACTOR POINTS IN THE EAST = NO MAJOR INCOMING FLOWS
LOW INCOME FAMILIES RELATIVELY HIGH UNEMPLOYMENT COMMUTING TO THE CITY CENTER
RESIDENTIAL
FLOODINGS UNTAPPED RESOURCES
RIVERS AND CANALS 12
As mentioned above, the main aspiration of this project is to investigate the cohabitation parameters of large numbers of “resident” (the amount of people that permanently reside inside the selected urban patch) and “floating” populations (the amount of people visit the plot because of an event, commute there or just pass through it). To achieve that, we proposed that the only elements from the existing situation that will be preserved are the rivers and canals, the Manchester Piccadilly Train Station and the athletic complex including the Etihad Stadium (home stadium of Manchester City FC, a prominent British football club). The main mechanism that would trigger the development of the plot consists of what we call “temporal event capacitors”. These capacitors consist of an infrastructural network that attracts and is able to support various events, in relation or not with the periodical athletic or musical events that take place in Etihad Stadium already.
The proposal is relevant to the different combinations of floating and resident populations, as can be seen by the following diagram. Bigger numbers of floating and residential populations will result different permanent and temporal densities, different needs and consequently variable spatial configurations. As the density in our plot increases, more permanent inhabitants would result the need for additional housing with viable GSI (Ground Space Index), additional transportation nodes, extra social provisions and commercial activities. There is not one finite and static solution but variable proposals instead, which relate to the changing densities and flows inside our plot. In this particular project, three different density scenarios have been examined (F: 80,000 - R: 50,000 | F: 60,000 - R: 100,000 | F: 120,000 - R: 150,000) and the last, most extreme one has been selected to be further developed.
COMMERCIAL ACTIVITIES
ATHLETIC COMPLEX
PERIODICAL EVENTS
TEMPORAL EVENTS CAPACITOR
MAINLY PERIODICAL EVENTS GREAT CHANGES IN DENSITY
ATHLETIC CULTURAL MUSICAL EDUCATIONAL ENVIRONMENTAL COMMERCIAL POLITICAL ADDTIONAL TRANSPORT NODE
INFRASTRUCTURE FOR DIVERSE EVENTS
PICCADILLY STATION
BUSIEST STATION IN MANCHESTER
ENCOURAGE INCOMING FLOWS
ADDITIONAL HOUSING CREATE JOB POSITIONS IN PLACE
HOUSING
RESIDENTIAL
SOCIAL PROVISIONS
LOW INCOME FAMILIES RELATIVELY HIGH UNEMPLOYMENT COMMUTING TO THE CITY CENTER
RIVERS AND CANALS
WATER COLLECTION POROUS SURFACES URBAN FARMING
HARNESSING THE NATURAL RESOURCES TRANSPORTATION ENERGY
INCREASE DENSITY MAINTAINING A VIABLE GSI
FLOODINGS UNTAPPED RESOURCES
3,468/sq km
residents 50,000/ sq km
10,000/sq km
EDUCATION HEALTHCARE LEISURE SERVICES
60,000/ sq km
100,000/ sq km 80,000/ sq km
150,000/ sq km 120,000/ sq km
+150,000/sq km
13
floating
4.0 | NETWORK OPTIMIZATION EVENTS DISTRIBUTION
NEW NODES Primary Secondary Open Spaces
Central Distribution
EXISTENT NODES Primary Secondary Spread Events
Floating Population
DENSITY SCENARIOS
CONNECTIONS
1
150.000
100.000
2
50.000
14
1. 80.000 F 50.000 R
the whole network. The Picadilly Station, the Etihad Stadium and the proposed main events capacitors are represented by the primary nodes. Secondary nodes are either existent roads junctions or small events. In addition, open spaces that can be used as events spaces were placed in flood risk areas. It was decided that similar nodes could have dissimilar intensity and affect the whole network differently. The concept is to evaluate the relation between the nodes, in different time lapses, that direct affect the density migration. To optimize this process, genetic algorithms were used to generate better spatial combinations. The densification urban plan appeared as a result of the network analysis and the possibilities to related it to a massive density fluctuation strategy.
3. 120.000 F 150.000 R
80.000 60.000
The distribution of the events’ capacitors was based on a combination of various spatial – temporal criteria regarding density fluctuation, zoning and climate conditions. Firstly, in order to evaluate a range of possibilities related to events’ intensity, three density scenarios were set regarding floating (F) and resident (R) populations.
2. 60.000 F 100.000 R
3
120.000
Open Spaces
“While the networked city that progressively emerged with the industrial era accorded absolute priority to flow management, the latter often tends to fade into the background behind the perception of the dense web of events that take place in cities and the plan to control their evolution in order to construct ideal development scenarios” 2
Resident Population
As far as the creation of the events’ network is concerned, Delaunay triangulation was used to connect fixed existent and dynamic changing nodes, allowing an evaluation of the distances between the nodes and the topological refinement of
ANTOINE, Picon. Smart Cities, A Spatialised Intelligence. AD Primers. John Wiley & Sons Ltd, 2015. p.51 2
OPTIMIZATION CRITERIA
flooding area road junction
station
flooding area
stadium road junction
1 identify existent - fixed nodes RULES main events nodes should be in a minimal distance of 500m (6min) from other main event node.
road junction
2 place dynamic - changeable nodes
the flooded risk areas can be just occupied by fixed open spaces nodes. No residential sone is permitted in this nearby area. Fixed secondary nodes represent existent road junctions. The network gets denser close to the main nodes
3 evaluate distances between fixed and
mobile nodes. Optimize network based on given distances
15
PHENOTYPES Strategy 0.20 0.50 0.50 0.80 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size
G10.1
G20.1
Generation 1 Mean Fitness Value Standard Deviation factor
0.266 0.016
Generation 10 Mean Fitness Value Standard Deviation factor
0.250 0.020
Generation 20 G10.2
G20.2
Mean Fitness Value Standard Deviation factor
0.205 0.020
25
NORMAL DISTRIBUTION
20
15
10
5
0
0
0.05
0.1
0.15
0.2
0.25
FITNESS VALUE
16
G10.10
G20.10
Generation 1
Generation 10
Generation 20
0.3
0.35
5.0 | EVENTS INTENSITY NODES INTENSITY 2
80.000 F
3
50.000 R
60.000 F
1
100.000 R
120.000+ F
2
3
150.000+ R
1
Both Debord and Tschumi demanded the construction of situations (through dérive, détournement or the combination of those). Tschumi uses Michel Foucault’s work to define an event as not simply a logical sequence of words or actions, but rather “the moment of erosion, collapse, questioning, or problematization of the very assumptions of the setting within which a drama may take place - occasioning the chance or possibility of another, different setting.
long duration, like exhibitions or intense but short, like the people commuting through Manchester Piccadilly Station.
After generating the nodes’ intensity map, we created the floating and resident population density maps. These maps are affected by the local and regional density fluctuations and illustrate the density patterns over time. Initially the Floating population map was created, harnessing the data available from the events intensity map. Then, after taking into consideration “The event here is seen as a turning point the areas with dense floating population … I would like to propose that the future of and the three different resident population architecture lies in the construction of such scenarios (50,000, 100,000, 150,000 people), clouds of points were placed in events”. 3 the areas with smaller floating population In this particular proposal, the nodes density values. Subsequently, the that belong to the network, which was resident population density maps were optimised as described above, have generated, which illustrate the growing been assigned with different intensities, density around the previously generated corresponding to the three different floating points. The parameters that affect the population scenarios, as shown on the resident population density levels and its small diagrams. Each node has a different distribution along the plot are the proximity charge-intensity, which depends on between the generated points and the parameters like the number of people that vicinity with the dense “floating populated” each event contains, the duration of the areas. event, its frequency and the detour of the visitors when going or leaving from each event. The events/flows maybe periodical, like weekly football matches, unexpected 3 like various open space activities TSCHUMI, Bernard. Architecture and Disjunction. depending on the weather conditions, with MIT Press, 1996. p.256
17
5.1 | DENSITY MAPS
80.000 F
Floating Population
50.000 R
60.000 F
1
100.000 R
120.000+ F
2
18 3
150.000+ R
Resident Population
5.2 | BLOCKS DESIGN Resident Population
Furthermore, a similar computational approach was used in order to define the predominant grid system that would generate the urban blocks, in the three density scenarios under examination. Using scripting tools (Python), each curve was divided every 70m. This length was chosen as a typical block size length appropriate for the particular study that would later vary according to the uses
Python code was used to divide the density lines in each 70m and connect the closest points. Blocks division follows the resident population density.
distribution inside each block. In addition to that, all the division points of the curves were connected to the closest division points of the following resident population density iso-curve, generating the guidelines for the final block division. Finally the geometry of those guidelines was rationalized to produce the outline of the urban blocks, pedestrian roads and open plazas.
19
CNC MODEL
20
6.0 | ZONING AND USES DISTRIBUTION
26%
26%
EVENTS
24%
24%
GREEN SPACES
35% 35%
13%
RESIDENTIAL
13% COMMERCIAL OFFICES
EVENTS The first approach to distribute the uses in the complex high-density urban tissue was the use of a Cellular Automata (full exploration in the appendices). Cells to represent events, residential and green spaces were set and some percentages could be extract. GREEN SPACES However, a proper relation between uses and density changing could not be achieve. For this reason, as a second experiment, after the blocks design within the residential density diagrams, the uses were manually set into different zones following not only the existent conditions as river and railway but also the nodes RESIDENTIAL categories. As the floating population increases, the coverage decreases and the residential building get higher. COMMERCIAL OFFICES
3
SOCIAL 2% PROVISIONS HEALTH SPORTS EDUCATION
120.000+ F
120.000+ F
2%
3
150.000+ R
150.000+ R
SOCIAL PROVISIONS HEALTH different uses in the same building SPORTS EDUCATION
21
7.0 | MORPHOLOGIES
HIGH DENSITY RESIDENTIAL COMMERCIAL OFFICES
MEDIUM DENSITY
For the masterplan design and scale analysis only generic blocks were represented. However a deep investigation on urban morphologies have been done and a proper detail was predict for each use and density. In the evolutionary algorithms, mixed used buildings were taken in consideration and, in further developments, these morphologies can be incorporated to the computational design process.
RESIDENTIAL COMMERCIAL OFFICES
LOW DENSITY RESIDENTIAL COMMERCIAL OFFICES
LOW DENSITY
SOCIAL PROVISIONS HEALTH SPORTS EDUCATION
22
3d printed model
8.0 | EVOLUTION AND GENETIC ALGORITHMS “Cities are in constant evolution, developing the ability to perceive and process vast amounts of information. This sentience is guided by a series of decisionmaking processes capable of managing the growth and transformation of the city.� 4
taller buildings closer to the nodes
maximize pedestrian routes
increase ground level exposure
trimmed buildings
maximize density
to be further analysed.
In the following pages, all the experiments are presented and a clear evolution can be seen through generations. Between the first and the twenty generations, the mean fitness value is decreasing, in other words, it is converging to an optimal solution. Manchester post-industrial condition is Beside the thirty generation shows a clearly related to its population shrinkage, higher mean value, the fittest individual delineating a perfect scenario for an urban still presents better results than the past exploration with high densities and the generations. 120.000 F | 150.000 R scenario were considered for the urban morphologies consideration. Massive densification in a restrict space implies the decrease of solar level exposure in the ground. However, the project aim is to generate a dense urban tissue, allowing the maximum ground solar exposure and comfortable pedestrian routes to connect the proposed nodes, enabling rapid density variation. The criteria is to have the highest building close to the design open spaces and trim them to permit the solar light passage.
35m2/pp
In addition, some initial gathered data were imputed in the analysis. The specification of 35m2/pp and expansion of green and public spaces are examples. 30 generation, with 10 individual each were 4 SAGRAVES, Daniel. AD System City. Data City. ran and the fittest individual were selected John Wiley & Sons Ltd, 2013. p.121
23
1ST GENERATION Strategy G1.1
G1.2
0.392371585
0.30 0.10 0.30 0.80 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size
G1.3
0.424305372
0.42607147
Fitness Criteria
1
3
1
2
G1.8
3
1
2
G1.9
0.439032115
F1 maximize pedestrian routes F2 maximize density F3 increase ground level exposure
3
2
G1.10
0.447678795
Comparison Fitness Criteria
0.457485075
30
25
20
15
10
5
0
1
1
0
0.1
0.2
0.3
0.4
1
Mean Fitness Value Standard Deviation factor 3
24
2
3
2
3
2
0.5
0.6
0.431 0.016
10th GENERATION Strategy G10.1
G10.2
0.37910192
0.30 0.20 0.50 0.80 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size
G10.3
0.392462913
0.391837075
Fitness Criteria
1
F1 maximize pedestrian routes F2 maximize density F3 increase ground level exposure
1
1
Convergence Graph 3
2
G10.8
3
G10.9
0.427825236
3
2
2
G10.10
0.430575144
F1 F2 F3 Comparison Fitness Criteria
0.431237449
30
25
20
15
10
5
0
1
1
0
0.1
0.2
0.3
0.4
1
Mean Fitness Value Standard Deviation factor 3
2
3
2
3
0.5
0.6
0.413 0.018
2
25
20th GENERATION Strategy G20.1
G20.2
0.350259646
0.40 0.30 0.50 0.80 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size
G20.3
0.356187688
0.359307064
Fitness Criteria
1
1
F1 maximize pedestrian routes F2 maximize density F3 increase ground level exposure
1
Convergence Graph 3
3
2
G20.8
2
G20.9
0.37309842
3
2
G20.10
0.380184733
F1 F2 F3
Comparison Fitness Criteria
0.418999997
30
25
20
15
10
5
0
0.1
0.2
0.3
0.4
1
1
1
0
Mean Fitness Value Standard Deviation factor 3
26
2
3
2
3
2
0.5
0.6
0.369 0.019
30th GENERATION Strategy G30.1
G30.2
0.348384568
0.50 0.40 0.50 0.80 10
Elitism Mutation Probability Mutation Rate Crossover Rate Population Size
G30.3
0.371649785
0.373909904
Fitness Criteria
1
1
F1 maximize pedestrian routes F2 maximize density F3 increase ground level exposure
1
Convergence Graph 3
3
2
G30.8
2
G30.9
0.390743922
3
2
G30.10
0.393914311
F1 F2 F3 Comparison Fitness Criteria
0.398777362
35
30
25
20
15
10
5
0
0.1
0.2
0.3
0.4
1
1
1
0
Mean Fitness Value Standard Deviation factor 3
2
3
2
3
0.5
0.6
0.379 0.014
2
27
9.0 | SPACE SYNTAX ANALYSIS integration
betweeness 350m radius (5 min walk)
1000m
28
The integration measure typically shows the cognitive complexity of reaching a street. In other words it illustrates the number of turns that are needed to reach a specific street from all the other streets of the network. The integration value is relevant to the whole network and shows its “topological depth�.
In our particular case we can see that the central long axes that connects the Piccadilly Train Station with Etihad Stadium is well integrated (red colour) and we can also distinguish the pedestrian roads that are expected to be used more often than the rest of the roads inside the denser areas of the network.
10.0 | PROJECT
plan
section 29
FOLIES A system of dispersed elements, which have been spread as the vertices of a square grid of 250m*250m, is supporting the various events that are taking place on the open spaces. They constitute an infrastructural network that provides energy, fresh water, information and everything that is needed for the production of event spaces and the support of large flows. This points - “folies� compose a superimposed system of elements around which the main events are taking place and various activities are being articulated.
30
31
CONCLUSIONS The main idea that is being articulated and confronted in the present project consists in how urban growth and planning can be triggered from and co-exist with large flows of people caused by various events with different intensity through time. The ambition was to design a spatial urban configuration able to accommodate high-density populations (resident and floating), on a relatively small urban patch. The challenge faced was to combine diverse types of data to generate possible density scenarios and design an urban plan that could not only adapt to this population fluctuation but also attract resident inhabitants, ensuring a proper urbanity experience. The use of genetic algorithms to generate the built and unbuilt spaces gives the promise of great adaptability and adjustment by the accurate data that could be imputed. However, the main question remains almost the same as in the 60’s: Does the space need to psychically adapt and alter to host different uses, events, flows, visitors, etc. or the adaptability should be product of an inherent design intelligence that allows the space to be physically stable but yet adaptable?
32
APPENDICES
3.0 CELLULAR AUTOMATA EXPERIMENTS 1 There should be no initial pattern for which there is a simple proof that the population can grow without limit
Born rules_a new cell is born if surrounded by n ‘alive’ neighbors Survive rules_a cell survives if surrounded by n ‘alive’ neighbors
2 There should be no initial pattern that apparently do grow without limit 3 There should be simple initial patterns that grow and change for a considerable period of time becoming to end in 3 possibles ways A Fading away completely (from overcrowding or becoming too sparse) B Settling into a stable configuration that remains unchanged thereafter C Entering an oscillation phase in which they repeat an endless cycle of 2 or more periods
PROGRAM
LAND USE
BORN
SURVIVE
GENERATION TYPOLOGY
A_Events
50%
2
2 1
45
B_Residential
30%
2
6
45
C_Green areas
20%
1
1 2
45
5 designed typologies for each category 33
3.1 DIFFERENTIAL GROWTH OVER TIME
1
3
STEPS
DENSITY
PROGRAM
GENERATION
BORN
SURVIVE
1
50.000
A_Events B_Residential C_Green areas
20 0 10
2
1
A_Events B_Residential C_Green areas
25 5 15
2
3 1
A_Events B_Residential C_Green areas
30 10 20
2
2
3 34
2
100.000
150.000 +
3 1 3
INDIVIDUALS
5 random same type same position
3.2 URBAN MORPHOGENESIS
d
d
d
PROGRAM
BORN
SURVIVE
GENERATION
A_Events
0 To 5
2 0 To 5
0 To 50
B_Residential
C_Green areas
0 To 5
0 To 5
2 0 To 5
0 To 50
2 0 To 5
0 To 50
INDIVIDUALS
5 random same type same position
FITNESS CRITERIA MAXIMISE DISTANCE d (to avoid overlapping) MAXIMISE CELLS TOTAL AREA (to occupy the entire grid) MAXIMISE PROBABILITY FOR A SPECIFIC TARGET PERCENTAGE OF COVERAGE (for each category) 35
SOCIAL PROVISIONS COMPARISON 44
1 school per 1900 habitants 0 universities 12 1 kindergarten per 3180 habitants 1 “other” per 2380 habitants 12
77 11
BARCELONA
HEALTHCARE hospitals health centres practices/ clinics other
number of social provisions per city
22
1 school per 2320 habitants 1 university per 1390 habitants 28 0 kindergarten 1 “other” per 1160 habitants
1515
28
88
voronoi | proximity Map Source: Open Street Map + Elk
SERVICES
number of social provisions per city
22
Social Provisions Source: Google Maps
voronoi | proximity
36
CULTURAL museums libraries other LEISURE playgrounds sports parks other
17 17
HAMBURG
EDUCATION schools universities kindergarten other
55
voronoi | proximity
number of social provisions per city
MANCHESTER
88
77
66
77
1 school per 6325 habitants 1 university per 6325 habitants 1 kindergarten per 6325 habitants 1 “other” per 1265 habitants
number of social provisions per city
MANCHESTER
44
5 5
1 school per 1900 habitants 0 universities 1 kindergarten per 3180 habitants 1 “other” per 2380 habitants
15 m
in
number of social provisions per city
3m
in
HAMBURG
1 school per 2320 habitants 1 university per 1390 habitants 0 kindergarten 1 “other” per 1160 habitants
66 12 12
10 10
11 min
voronoi | proximity delaunay | distance
3m
in
BARCELONA
number of social provisions per city
voronoi | proximity delaunay | distance
3m
in
33
EDUCATION schools universities kindergarten other
11 11
1 school per 6325 habitants 1 university per 6325 habitants 1 kindergarten per 6325 habitants 1 “other” per 1265 habitants
11
55
Map Source: Open Street Map + Elk
Social Provisions Source: Google Maps
voronoi | proximity delaunay | distance
12 min
37
0 hospitals 0 health centre per 4760 habitants 1 clinic per 3180 habitants 0 “other”
22 33
HEALTHCARE hospitals health centres practices/ clinics other 22
22
99
in
12 m
22
21
2
2
22
38
Map Source: Open Street Map + Elk
Social Provisions Source: Google Maps
voronoi | proximity delaunay | distance
3m
in
number of social provisions per city
HAMBURG
1 hospital per 6950 habitants 1 health centre per 3480 habitants 1 clinic per 1546 habitants 1 “other”per 6950 habitants
44
voronoi | proximity delaunay | distance
3m
in
BARCELONA
number of social provisions per city
voronoi | proximity delaunay | distance
3m
in
number of social provisions per city
MANCHESTER
25 min
1 hospital per 3160 habitants 1 health centre per 3160 habitants 1 clinic per 6325 habitants 1 “other”per 3160 habitants
MANCHESTER
number of social provisions per city
0 museum 1 library per 9537 habitants 0 “other”
voronoi | proximity delaunay | distance
3m
in
1
11
3m
in
number of social provisions per city
BARCELONA
1 museum per 13915 habitants 1 library per 1990 habitants 0 “other”
CULTURAL museums libraries other
voronoi | proximity delaunay | distance
77
Map Source: Open Street Map + Elk
Social Provisions Source: Google Maps
voronoi | proximity delaunay | distance
3m in
number of social provisions per city
HAMBURG
9m
in
22 33
1 museum per 2110 habitants 1 library per 6325 habitants 0 “other” per 3160 habitants
11
20 m
in
39
0 playgrounds 1 “sport” per 2380 habitants 1 park per 9537 habitants 1 “other” per 4770 habitants
22
44 11
0 playgrounds 1 “sport” per 2320 habitants 1 park per 1550 habitants 0 “other”
66 99
LEISURE playgrounds sports parks other
3m in
HAMBURG
number of social provisions per city
voronoi | proximity delaunay | distance
3m
in
number of social provisions per city
BARCELONA
11 min
voronoi | proximity delaunay | distance
3m in
number of social provisions per city
MANCHESTER
SERVICES
25 min
11
1 playgrounds per 6325 habitants 1 “sport” per 2110 habitants 1 park per 2110 habitants 0 “other”
33
33
40
Map Source: Open Street Map + Elk
Social Provisions Source: Google Maps
voronoi | proximity delaunay | distance
23 min
BIBLIOGRAPHY
MENGES, Achim. AHLQUIST, Sean. Computational Design Thinking. AD Reader. John Wiley & Sons Ltd, 2011 WEINSTOCK, Michael. The Architecture of Emergence. John Wiley & Sons Ltd, 2010 CARROLL, Sean B. Endless Forms Most Besutiful. Weidenfeld & Nicolson, 2006 ANTOINE, Picon. Smart Cities, A Spatialised Intelligence. AD Primers. John Wiley & Sons Ltd, 2015 SAGRAVES, Daniel. AD System City. Data City. John Wiley & Sons Ltd, 2013 TSCHUMI, Bernard. Architecture and Disjunction. MIT Press, 1996 PONT, Meta Berghauser. HAUPT, Per. Spacematrix. Nai Publishers, 2010 MVRDV. Farmax: excursions on density. Nai Publishers, 1998
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42