Green Domination DOCUMENTATION Core studio 2 Abhinav Champaneri Anna Kulik Shih-hwa Hung Yuchen Wang
Architectural Association School of Architecture
//
Emergent Technologies and Design 2012-2013
Table of contents
Introduction Abstract Sequence 1 Tissue sampling
4 6 8 8
Selected cities for data analysis Density Analysis of extracted Data Floor space distribution Environmental Data Flow Conclusions
10 12 13 14 16 20 22
Sequence 2 Overall ambition
24 25
Density and density gradient Green Distribution Networks Methodology Site analysis
26 27 28 30 32
Green zones
34
Introduction Distribution Primary green zones // Research Table of open Spaces Circle packing algorithm Critical review
35 36 38 46 48 49
Nodes, Connections, Networks
50
Networks and Plots. Introduction Nodes, Zones and Density Gradient Primary division Secondary subdivision
51 52 53 54
Subdivision Sequence Critical Review
55 58
Blocks
60
Blocks. Introduction Setup for blocks investigation Rules for the Type 1 Plots Rules for the Type 2 Plots Rules for the Type 3 Plots Critical review
61 62 64 66 68 70
Blocks and Building typologies
72
Introduction Initial concept Logic Sequences Research Network Optimization Solar Fan Criteria Building Typology Sequence Fitness Criteria Multiple Fitness Criteria Experiment Multiple Building Experiment Critical review
73 74 75 76 78 79 80 81 82 87 91
Overall conclusions
92
Conclusions
93
Personal reflections
100
Personal Personal Personal Personal
101 104 105 106
reflection reflection reflection reflection
1 2 3 4
3
Introduction
The urban sprawls of today’s cities are under constant demographic pressure and have to cater to an ever increasing demand for more usable space within the city. Localised approaches increase usable space and thereby increase density but are detrimental not only to the existing infrastructure but also the quality of spaces and life within the city. The research and study is into a holistic design approach that aims to accommodate the growing demand for densification as well as generate high quality open spaces which would ultimately become a part of the language of architecture in the locality. The research further looks to increase the efficiency of built forms in terms of their function by being specific in the design approach to cater to specific needs of the locality.
5
Abstract
The experimentation and research is into defining a design approach that attempts to create a high density locality that has a well-connected and well-distributed network of open green spaces. The process would be based on an idea of creating a density gradient that varies according to requirement and existing conditions on the site, but at the same time achieves the required global density. Quality of spaces is another important aspect of the design process where we investigate the possibility of creating open spaces which are rich in terms of aesthetics, environment and accessibility in a dense urban scenario where these aspects are usually lost. The approach would be based on the idea of ‘pedestrian city’ a well-connected pedestrian network would therefore become pre-requisite.
The next part of computation defines the footprints of the buildings and that of local greens. These aspects depend on the size of the plots and distance from the closest node green. The last part defines the built morphology of the buildings. The morphology shows dependencies on factors like solar exposure on greens and building faces, ratio of floor space to green space, location of building from attractors and the desired floor space. The attempt is then to evaluate in terms of the quality of spaces, the type of network that the design is able to generate.
The design approach is divided into two sequences, in the first sequence we study the tissue samples of high density areas in 4 cities. We analyse and map differents aspects of the city like environmental, density and network factors. The second sequence deals with the design of the chosen locality in Isle of Dogs, this sequence has four distinct parts in which the first part generates the nodes by circle packing algorithm and identifies additional nodes existing on the site. This step provides the location and size of the node greens. The second part uses a subdivision algorithm that generates the desired divisions of plots, the size of these plots depend on the location within the site.
7
Sequence 1 Tissue sampling
As the first part of our research, three cities are identified which have high density residential pockets. Tissue samples in these cities are studied in comparison with the selected site at Isle of Dogs. Tissues in New York, Barcelona, Beijing and Isle of Dogs, London would be analysed with parameters like density, height of buildings, green spaces, solar factors, floor-area distribution, pedestrian and vehicular networks. These parameters are selected as some of them would be used to inform the design process and will be used as comparison factors. These would also provide guidelines in regard to design of the locality.
9
Selected cities for data analysis
London, UK
Beijing, China
New York, US
Barcelona, Spain
• •
• • •
• • •
• • •
Site: Isle of Dogs Patch: 500:700m
Site: Patch: 500:700m Why: The typologies of the area seam to be similar to our Site, both have low-rise and high-rise, both are multifunctional, but the density is completely different
Site: Patch: 500:700m Why: High-rises Grid pattern Block city
Site: Patch: 500:700m Why: Grid pattern Block city Blocks with central court-yards forming private spaces Mostly middle rise buildings.
Data for collection
Density
Environmental Data
Functions
Flows and access
• •
• •
•
• •
Footprint of buildings Height of the buildings
Green spaces Shading/Sunlight
Floor area distribution
Public transportation Pedestrian and transportation networks
3 8
5
18
5 7
11
6
3 6 3
17
4
1
5
3 3 6
9
3
1 23
4 5
6
2
3
7 2
42
2
1
2
1 2
2
3
3 2
22
5
2 3
3
3 4
1
11
4
3 1
7
3
4
3
1
1
7
8
6
22 1 8
3
1
2
2 2
22
3
1
10 15 3 5 3
22
5
5
1
8
4
5
3 4
3 3
4 2
5
2
5
11
Density
Patch 500m x 700m Area of the patch: 350,000 m2
London, UK
Beijing, China
New York, US
Barcelona, Spain
3 11
8
5
18
5 7
11
6
3 6 3
11
17
4
1
5
3 3 6
9
3
1 23
4 5
6 11
2
3
7 2
42
2
2
1 2
2
3
3
20 1
2
22
5
3
2 3
3
3
11
3 4
1
5
11
4
3 1
7
3
3
4 11
7
3
3
1
1
8
6
22 1 8
3
11
1
2
2 8
2
22
3
1
3
10 15 3 5
12 & above 6-11 floors
3
3-5 floors 1-2 floors 22
• • • •
5
5
1
8
4
5
3 4
3 3
4 2
5
2
Area of the footprint: 104,697m2 Build/Unbuild: 30% Total floors area: 816,805m2 FAR ratio: 2.3
5
• • • •
Area of the footprint: 125,813m2 Build/Unbuild: 36% Total floors area: 1,538,145m2 FAR ratio: 4,4
• • • •
Area of the footprint: 170,518m2 Build/Unbuild: 49% Total floors area: 2,916,323m2 FAR ratio: 8.33
• • • •
Area of the footprint: 193,557m2 Build/Unbuild: 55% Total floors area: 1,369,078m2 FAR ratio: 4.1
3
Analysis of extracted Data
Patch 500m x 700m Area of the patch: 350,000 m2
London, UK
Beijing, China
New York, US
Barcelona, Spain
Number of buildings
Number of buildings
Number of buildings
Number of buildings
120
145
102
108 90 b
89
1 - 2 floors
86 67 b 45 b
53
48
39 23
5
1
TFA = 816,805 m2
13
10
TFA = 1,538,145 m2
6 - 11 floors
22 b
12 - 29 floors
13
0b
> 30 floors
TFA = 2,916,323 m2
TFA = 1,369,078 m2
28 9
3 - 5 floors
18
22
18
FAR =
FAR ratio comparison
+ +
9.00 6.75
8.3
4.50 4.4
4.1
2.3
0.00 Medium density FAR = [1.5 - 2]
High density FAR = [3 - 8]
Very high density FAR = [9 -
)
8
Low density FAR = [0.55 - 1.2]
2.25
Conclusions • • •
Comparing the patches of the cities, we see that only Beijing and Barcelona are being in the range of High density urban cities (New York patch is in Very High range and London patch is in the Low range). Having the same FAR density ratio, the typologies of the buildings in the cities are absolutely different. Barcelona has the buildings, varying in 5 to 8 amount of floors and providing a Human scale City. FAR is reached by the footprint area; blocked city. Beijing has a dramatic variety of high-rise and low-rise buildings; FAR is reached by having big amount of High-rises, which derives the city being Nonhuman Scaled. For the patch of 350,000m2, using the FAR ratio range from 3 to 8 (High Density City), the Total Floor area of m2 is from 1,050,000 to 2,800,000 (area of the patch multiplied on the FAR number). The priority in typology would be affected by the parameters of creation a Human scale City.
13
Floor space distribution
London, UK
Beijing, China
New York, US
Barcelona, Spain
Av. living area = person per 31.6 m2
Av. living area = 1 person per 32.6 m2
Av. living area = 1 person per 26.4 m2
Av. living area = 1 person per 34 m2
3%
2% 36%
49%
4%
11%
5%
46%
48% 50%
62%
84%
• • •
Residential: 506,419m2 Commercial: 294,050m2 Educational: 16,336m2
• • •
Residential: 738,310m2 Commercial: 753,691m2 Others: 46,144m2
• • •
Residential: 1,350,015m2 Commercial: 1,449,253m2 Educational: 117,055m2
• • •
Residential: 1,150,026m2 Commercial: 150,599m2 Educational: 68,454m2
•
Living population in selected patch: approx 16,025 people
•
Living population in selected patch: approx 22,647 people
•
Living population in selected patch: approx 51,136 people
•
Living population in selected patch: approx 33,824 people
Floor space distribution
London, UK
Beijing, China
New York, US
Barcelona, Spain
Av. living area = person per 31.6 m2
Av. living area = 1 person per 32.6 m2
Av. living area = 1 person per 26.4 m2
Av. living area = 1 person per 34 m2
2,916,323 m2 3,000,000 m2 2,250,000 m2 1,538,145 m
2
1,369,078 m2
1,500,000 m2
816,805 m2
750,000 m2 0m2
Residential
Commercial
Other
Population based on Average living areas Approx 51,136 p.
A.P =
Av. L. area
Approx 33,824 p. Approx 22,647 p. Approx 16,025 p.
Conclusions • •
The amount of residentional zone in Beijing and New York patches occupies around 50% of the total built area while in London and Barcelona the selected patches are more residential and the area occupies from 60% to 80% of the total built area. Having in mind both the character of the exhisting area and the fast development of the Canary Warf buisness district, the area of the mix use will have the percentage of the residentional spaces varying from 50% to 70% of the total built area.
15
Environmental Data
Patch 500m x 700m Area of the patch: 350,000 m2
Green zones. Quality of open space London, UK
Beijing, China
New York, US
Barcelona, Spain
Aprox. pop = 16,025 people
Aprox. pop = 22,647 people
Aprox. pop = 51,136
Aprox. pop = 33,824 people
•
•
•
• • • • •
Areas of public green zones: 46,120m2 Areas of private green zones: 22,642m2 Green zones/patch: 19% Amount of public green zones per person: 2.8m2 Amount of private green zones per person: 1.4m2
• • • •
Areas of public green zones: 42,272m2 Areas of private green zones: 1,552m2 Green zones/patch: 11% Amount of public green zones per person: 1.8m2 Amount of private green zones per person: 0.07m2
• • • •
Areas of public green zones: 12,650m2 Areas of private green zones: 7,035m2 Green zones/patch: 5% Amount of public green zones per person: 0.25m2 Amount of private green zones per person: 0.14m2
• • • •
Areas of public green zones: 29,629m2 Areas of private green zones: 29,087m2 Green zones/patch: 16% Amount of public green zones per person: 1.14m2 Amount of private green zones per person: 1.16m2
Environmental Data Exposure and Shading London, UK
Beijing, China
New York, US
Barcelona, Spain
Conclusions • •
Comparing the solar radiation in the cities it can be observed that in London there is no much of a direct sunlight coming to the streets, which make this data not very relevant comparing to others. The shading map, on contrary, is very important. Not having the direct light on the streets we still have to try to keep the indirect lightening there (like it is observed in Barcelona mostly on the main roads). This bring us to the conclusion of using mostly middle rise building development and bringing to the study of a solar envelope example as a possible decision.
17
Public transportation accessibility // Bus
London
Beijing
5 min 5 min
85% accessibility to the bus stops of the Site
84% accessibility to the bus stops of the Site
New York
Barcelona
5 min
98% accessibility to the bus stops of the Site
5 min
90% accessibility to the bus stops of the Site
Public transportation accessibility // Metro
London
Beijing
5 min
5 min
31% accessibility to the metro stops of the Site
40% accessibility to the metro stops of the Site
New York
Barcelona 5 min
5 min
17% accessibility to the metro stops of the Site
32% accessibility to the metro stops of the Site 19
Flow
London, UK
Beijing, China
New York, US
Barcelona, Spain
Conclusions • •
The patterns of the road transport network and pedestrian network are mapped here. It can be observed that both New York and Barcelona networks are very clear greed systems, while the London and Beijing networks are creating a very random and messy patterns.
Flow
London, UK
Beijing, China
New York, US
Barcelona, Spain
Conclusions •
The planning of street grid is critical in our project in terms of traffic distribution and land-use value. Our aim is to create a well distributed grid of streets which enables smooth traffic and more private residential street use while in certain section of the planning, street choices and sizes will be manipulated to create a higher land value for shops or public transportations.
21
Conclusions Comparing the tissues we understood which parameters can be useful for the further work. Choosing the familiar cities we could really compare the parameters, basing not only on the abstract mapping and counting, but on our personal experiences of living in those cities as well. We found the environmental topic the most interesting for our group for further investigation - the green open spaces and the illumination factors became a quite seductive theme, and there was much more things to explore. What should be the amount of the green space per person? How can we justify its quality? What should be the access range for it? What type of light (direct/indirect) is necessary for such spaces to increase its quality? Combining those questions with the idea of a high density patch we thought it would bring another quality for the city, where high quality open spaces would ultimately become a part of the language of architecture in the locality.
Sequence 2 Overall ambition Methodology Analysis of Site, directions of investigation
Overall ambition “High density city with a maximized pedestrian mobility between the green zones of the area� Defining the architectural ambition, the group decided to mainly focus on the environmental parameter, creating a high density city within a green park, where everyone can find a high quality openspace in a reasonable accesibilyto range. Three major aspects taken in consideration were: Density and density gradient The project seeks to address the aspect of creating a high density locality that can accommodate a residential population of 100000 and a commercial population of 150000. We relate this density to floor area ie. assume 24 sq.m per person for residential use (WHO requirement) and 10 sq.m for commercial use (Commercial Association of London). We implement this on site in the form of Floor Area Ratio (FAR) which is a ratio of Floor Area by Plot Area. As per the case study we establish the required FAR in the site and attempt to create a gradient that varies from FAR 3 to FAR 7. Therefore FAR becomes one of the first parameters that inform the built form. This aspect of the design would create a gradation of density, and add variation in the built environment by reducing monotony of the generated form.
Green Distribution One of the driving aspects of the design is to create good distribution of green spaces, this is important to increase access to these spaces. We use these green pockets as nodes to generate the design. Further, variation in the types of greens to satisfy different functions is another aspect we take into consideration. To make greens spaces more accessible their quality is a vital factor, environmental factors would be used to do this. Therefore we define methods and parameters that enable the above three conditions of distribution, variation and quality to be achieved. Networks The locality is based on the concept of pedestrian networks; therefore there is a very limited vehicular access. Logic that defines hierarchies of these network systems are explored and sectional studies would be the basis of this study. Public greens are seen as nodes that form the primary aspect of the design and therefore need to be well connected.
25
Density and density gradient High density city with a maximized pedestrian mobility between the green zones of the area
• •
Working with the density gradient within the High Density City range. Gradient is based on the existing Site conditions FAR = [ 3 - 7 ]
FAR =
Zone 1
9.00 Zone 2
6.75 4.50
Zone 3
2.25 0.00 Medium density FAR = [1.5 - 2]
High density FAR = [3 - 8]
Very high density FAR = [9 - )
8
Low density FAR = [0.55 - 1.2]
+ +
Green Distribution High density city with a maximized pedestrian mobility between the green zones of the area
• •
Maximizing the green areas, public zones and increasing the quality of these spaces Distribution of first main zones is based on the existing Site conditions
Circle packing of park of surtain size + accesibility distance
XXS
XS
S
M
+ 2-3 min
+ 4-6 min
+ 6-8 min
+ 8-10 min
27
Networks High density city with a maximized pedestrian mobility between the green zones of the area
• •
Maximizing the pedestrian mobility Creation of the primary network through the relation of density gradient and green zones
29
Methodology
Critical review
Data catalog
For the methodology diagram was invented a specific Data catalog, which contained the constant data - the population number - and variable data. (Figure 1) The relationship between variable and constant data built a parametric model, which was dependent on the changes of the variable data. As starting point the group set the parameters, which were already higher than the original law-based rules for development in UK. All the variables had the possibility of increment, that, as we supposed, was going to bring a higher quality for the further design of space.
Looking on the results we’ve achieved with created system, we believe that the idea of the catalog was quite successful. It gave to the group the starting point for the algorithm, at the same time it wasn’t creating a very fixed model. All of the parameters were used in its time, and the constant search of the better solution through the variables was continuing through the work.
Logic diagram
Site Attractor type 2
Attractor type 1 The methodology can be described as a simple logic diagram on the right. It was based on the existing Site conditions, which we set as the attractors, and fed with Data Catalog. We were trying to somehow make the system very dependent on its parameters an each of the results. In a sertain sequence, the system was getting more and more complex. With the change of one variable the whole system had to change accordingly.
Data Catalog Attractor type 3
Density Gradient
Circle packing Nodes
Greens
Connection Network
Primary plots
Subdivision Network
Critical review
As a diagram it is clear and logical, but now we find it too linear. As it was observed by Claudia Pasquero on the final presentation, it might have been a good idea to introduce a loop system for the whole diagram. As we developed her observation more, we started thinking that it might have been useful to introduce both the loop systems for each part, and the loop system for the whole. That would have brought the feedback , and based on our architectural ambition we could have introduced fitness criteria for the algorithm to compare with the feedback and to improve the results. In the same time, we understand that computationally that would have been a very difficult thing to construct in a given period of time.
Blocks
Block Typology
Area distribution
Solar Fan Analysis
Building Block
Elevated network 31
Site analysis Main attractors
•
Canary Wharf business district
•
DLR and Ferry stations
•
River front and the Millwall dock front
Attractors, Density gradient
5 min
10 min As it was explained with the logic diagram scheme, the whole sequence was based on the existing Site conditions and on the Data catalog. Having the Site absolutely clean there were just 3 left attractors to start to work with. (Figure 1) We used those attractors mainly for the transportation-flow-accessibility reasons, creating a density gradient based on the closeness to the points.
Density gradient is based on the attractor points and 5 - 10 min accessibility to these points
Critical review
In our case density gradient worked as a tool. Even though from the very beginning we were talking about the 3 attractors for the system, for the density gradient we used just 2 (Canary wharf business district closeness and the transportation nodes). It might be questionable as we were creating a system for a mixed use neighborhood, and even from Developer point of view the most dense area for the residential buildings would be the one closer to the water edges. We didn’t really consider it in that step, but we had the reflection on that attractor for the typologies situated in the area.
•
Zone 1
•
Zone 2
•
Zone 3
33
High density city with a maximized pedestrian mobility between the green zones of the area
Green domination. Sequence 2 Green zones
Introduction As our overall ambition is “High density city with a maximized pedestrian mobility between the green zones of the area�, the green zones is a dominant factor in our work. Basing our system on the introduces catalog, it was decided to use minimum 25% of our Site as the green areas.
25% were taken as a minimum amount, but as a parameter for our system - changing it to a bigger value will change the whole system accordingly.
35
Distribution
Types of green space
Ambition
• • •
25% of the whole Site to be occupied by green, equally distributed on Site. (TOTAL GREEN AREA)
G1 - Primary - Main green zones and parks G2 - Secondary - additional green zones in blocks G3 - additional in the building typologies
Total Green Area G1
G3 G2
50%
12.5% of Site
Set of activities and the range of access
35%
8.75% of Site
Set2 of activities. No range of access
15%
3.75% of Site
Set3 of activities. No range of access
Final descision and personal reflection
There are many different ways for distribution the green zones. It could be put in all as one big park as well as allocated in small parcels along the Site.
To understand the minimum and maximum size of parks to work with, there was made a research of 4 cities green zones (similar to the starting one of city fabrics), including the analysis of activities, users comparing to the size of each.
In our particular case, it was decided to use the equal distribution of green areas on Site and aggregation of it into some kind of a network chain, that has visual connections, where you can see from one green to the other.
VS
37
Primary green zones // Research G1
To understand which parks of which sizes can be used for our 25% of Site-area allocation there was done a research of the green zones in London, Moscow, New York and Barcelona.
What we call open space? • • • • • •
Big parks Squares Small green spaces Boulevards Alleys Organized big spaces with pavement and urban furniture
Sizes • • • • • •
XL L M S XS XXS
To define: • • •
Proportions - scale to Site Activities Users
Why is it important? Analyzing different types of space the necessary activities for our Site can be define. Moreover, some of the sizes of parks, as can be seen on the next page, are simply out of our working range an have to be eliminated for the further definition.
2.8 km2 Area of Site
XL open spaces 5 - 1 km2
London, UK
Moscow, Russia
New York, US
Barcelona, Spain
Hyde park = 2.3km2 XL
MGU park= 5km2 XL
Central Park = 4km2 XL
Montjuic = 3km2 XL
+
Occupation = 82% of entire Site
+
Occupation = 214% of entire Site
+
Occupation = 142% of entire Site
Activities provided • • • • • • • •
Biking Central Park Zoo Film Festivals Concert and music Children Activities Dating places Educational places Exercising
+
Occupation = 107% of entire Site
Users • • • • • • • •
Sports Museums Horse drawn carriages Pets Picnics and restaurants Marriage in the park Family activities Olympic stadiums
•
Ice skating rink in winter
• • • •
Residents Working in the area users Tourists Passerby
39
L open spaces 1 - 0.5 km2 2 - 5 parks of XL Size occupying all the area (100% of the Site)
London, UK
Moscow, Russia
New York, US
Barcelona, Spain
Battersea park = 0.8km2 L
Gorkii park = 0.75km2 L
0 L
Guell = 0.5km2 L
Occupation = 29% of entire Site Max on Site = 3 parks of L size
Occupation = 27% of entire Site Max on Site = 3 parks of L size
Occupation = 0 Max on Site = 0
Occupation = 18% of entire Site Max on Site = 5 parks of L size
Activities provided • • • • • • • •
Biking Film Festivals Concert and music Children Activities Dating places Educational places Exercising Sports
Users • • • • • •
Museums Pets Picnics and restaurants Marriage in the park Family activities Ice skating rink in winter
• • • •
Residents Working in the area users Tourists Passerby
M open spaces 0.5 - 0.1 km2 5 - 28 parks of M Size occupying all the area (100% of the Site)
London, UK
Moscow, Russia
New York, US
Barcelona, Spain
Southwark park = 0.3km2 M
Danilovskii park = 0.35km2 M
0 M
Ciutadella = 0.35km2 M
Occupation = 11% of entire Site Max on Site = 9 parks of M size
Occupation = 12,5% of entire Site Max on Site = 8 parks of M size
Occupation = 0 Max on Site = 0
Occupation = 12.5% of entire Site Max on Site = 8 parks of M size
Activities provided • • • • • • • •
Biking Film Festivals Concert and music Children Activities Dating places Educational places Exercising Sports
Users • • • • •
Museums Pets Picnics and restaurants Marriage in the park Family activities
• • • •
Residents Working in the area users Tourists Passerby
41
S open spaces 0.1 - 0.05 km2 28 - 56 parks of S Size occupying all the area (100% of the Site)
London, UK
Moscow, Russia
New York, US
Barcelona, Spain
Archbishop’s park = 0.1km2 S
Bolotnaya Square = 0.05km2 S
Washington Square park = 0.05km2 S
Plaza Catalunia = 0.06km2 S
Occupation = 3.5% of entire Site Max on Site = 28 units of S size
Occupation = 0.7% of entire Site Max on Site = 56 units of S size
Occupation = 0.7% Max on Site = 56 units of S size
Occupation = 2.14% of entire Site Max on Site = 46 units of S size
Activities provided • • • • • • • •
Biking Concert and music Children Activities Dating places Exercising Pets Jogging Sport, not requiring specific facilities
Users •
Coffee drinking
• • • •
Residents Working in the area users Tourists Passerby
XS open spaces 0.05 - 0.005 km2 56 - 588 open spaces of S Size occupying all the area (100% of the Site)
London, UK
Moscow, Russia
New York, US
Barcelona, Spain
Bedford Square = 0.01km2 XS
Manyejnaya Square = 0.03km2 XS
Maddison Square park = 0.028km2 XS
Plaza Univercitat = 0.005km2 XS
Occupation = 0.4% of entire Site Max on Site = 250 units of XS size
Occupation = 1.07% of entire Site Max on Site = 93 units of XS size
Occupation = 1% Max on Site = 100 units of XS size
Occupation = 0.17% of entire Site Max on Site = 588 units of XS size
Activities provided • • • • • • • •
Biking Concert and music Children Activities Dating places Exercising Pets Jogging Sport, not requiring specific facilities
Users • •
Coffee drinking Small public events
• • •
Working in the area users Tourists Passerby
43
XXS open spaces 0.005 - 0.001 km2 588 - 2857 open spaces of S Size occupying all the area (100% of the Site)
London, UK
Moscow, Russia
New York, US
Barcelona, Spain
Hanover Square = 0.004km2 XXS
Stoleshnikov Square = 0.002km2 XS
Cooper Union Square = 0.0024km2 XS
Plaza Urquinaona = 0.0036km2 XXS
Occupation = 0.14% of entire Site Max on Site = 714 units of XXS size
Occupation = 0.07% of entire Site Max on Site = 1400 units of XXS size
Occupation = 0.08% Max on Site = 1250 units of XXS size
Occupation = 0.12% of entire Site Max on Site = 833 units of XXS size
Activities provided
Users
• •
• • •
Dating places Coffee drinking
Working in the area users Tourists Passerby
Table of open Spaces Parks - squares
Size mark
Size range in km2
Amount of OS on 100% of Site
Type
Example in London
100%
XL
5km2 - 1km2
0-2
Park
L
1km2 - 0.5km2
2-5
Park
M
0.5km2 - 0.1km2
5 - 23
Park
S
0.1km2 - 0.05km2
23 - 46
Park, Square
XS
0.05km2 - 0.005km2
46 - 457
Square
XXS
0.005km2 - 0.001km2
457 - 2283
Square
45
Table of open Spaces Variables: % of Site to be occupied S to XS to XXS parks % Accessibility Ratio
Analyzing the sizes of green spaces the suitable for the Site were defined. What is more, the Access relationship was found. The bigger the area of the park is the more sense it makes to walk more to reach it, while the little square won’t have that much of attraction. That was reflected on the attraction of the parks area. For further algorithms the parks of XXS, XS, S, M sizes were used.
M
0.5km2 - 0.1km2
5 - 23
Park
S
0.1km2 - 0.05km2
23 - 46
Park, Square
XS
0.05km2 - 0.005km2
46 - 457
Square
XXS
0.005km2 - 0.001km2
457 - 2283
Square
XXS
XS
S
M
+ 2-3 min
+ 4-6 min
+ 6-8 min
+ 8-10 min
47
Circle packing algorithm
To allocate the green areas was used the circle packing algorithm. As sizes for circles were used the XXS, XS, S, M ranges plus the ranges of accessibility (from 1 to 10 minutes accordingly). All of these green zones are within the group of primary, main greens. Secondary and tertiary green areas don’t require the accessibility ranges.
was decided to redistribute the mass of the green area along the river edge. In places, where there appear an empty spot in between circles, are allocated secondary and tertiary greens. In this case it is somehow always a green way between the parks, but the activities are provided only in the primary green zones.
The amount of parks was determined as well - otherwise there is absolutely no limits for the possibilities for the algorithm. In our case the amount of parks was no less than 20 parks, as we wanted to have an equal distribution and interesting pedestrian chain-connection possibility. As a result of running the algorithm were produced 23 parks, 4 of size XS and 19 of size S. In places,where parks were overlapping the edge of the river and the water surface it
23 PARKS 4 XS 19 S In the places where park is intersecting with water surface the park will be redistributed along the edge.
Critical review The research part for the main park zones went quite successful. The accessibility range seems to be logical, but the results already give us very similar pattern. Even though for the GA were used 4 different sizes of spaces, in the end to achieve enough amount of parks (which was already determined by the group for the network nodes necessity reasons) only the XS and S parks were produced. The amanities for these parks look very similar and dramaticly change from, for instance, parks of the M-size.
Trying to solve it in a very rapid way a lack of analytical review of the generated results was admited and now it have led us to discussion in our group about if it was not better to allocate the green space in a different way, with a possibility of a different strategy for the networks and all the following sequence (More detailed in a Personal reflection 1).
Speaking about the circle packing in particulary, in the end it became very questionable if there was a worthy reason to use this algorythm. It worked as a tool to provide the group enough new nodes to continue with developing of a system for further networks and blocks generation, but at the same time created ivisible at that moment base for a very faintly differenciated morphology. It also resulted in some parts in a quite an area without any primary green zone. It was also quite difficult to control the algorythm because of the landscape in GA. The genes were the sizes of Circles, amount of pars, the fitness was a desired amount of the total green area (25%). There was no really the best solution for it and it took a lot of time and effort to get to the final results through the whole junk data.
49
High density city with a maximized pedestrian mobility between the green zones of the area
Green domination. Sequence 2 Nodes, Connections, Networks
Site
Data Catalog
Attractor type 2
Attractor type 1
Attractor type 3
Density Gradient
Circle packing Nodes
Connection
Networks and Plots. Introduction This part of the process attempts to develop a logic system that provides
Greens
Network
Primary plots
us with pedestrian and vehicular networks and in the process divide the site area into reasonable block sizes to accommodate the built forms. One
Subdivision
of the chief ambitions in the process is to not only to have a direct physical connection between the node greens but also maintain visual contact between them. So from one node green one can always see other node greens. The global ambition to achieve a density gradient is initiated
Network
Blocks
in this part. A density gradient map is generated and the distribution of plot sizes in a gradient is also discussed. Sectional studies that reference street – built form relationship are carried out. This study is aimed at
Block Typology
understanding the hierarchy of routes, their dimensions and the kind of activities they can accommodate. On basis of this study pedestrian and vehicular routes would be generated.
Area distribution
Solar Fan Analysis
Building Block
Elevated network 51
Nodes, Zones and Density Gradient
Nodes: The circle packing algorithm in the previous step, describes the sizes of the parks and the extent of their influence in the locality. The centres of these circles form the first set of nodes. Additional nodes are considered are the existing DLR stations and Ferry Station. These nodes become the reference points and the connection points for the impending network logic that would be applied. Density Gradient: A density gradient is mapped on the site depending on the proximity from the attractors such as the DLR and Ferry stations existing on the site. The weight on these attractors also influences the gradient which is derived from the proximity to the commercial area of Canary Wharf. So closer the attractor is to the Canary Wharf higher is its weight. This is one of the aspects that define the density in that area. Depending on the weight of the attractor a zone of influence is drawn, for example the South Quay station which is closer to the canary wharf would have a larger area under high density (FAR 7 +) as compared to Mudchute Station which is further away. Zones: The site is divided into 3 zones depending on the density gradient. This would define the global FAR that each zone would need to achieve. The zone would also affect the sizes of plots occurring in it. For example, Zone I will have plots of Type I & II with higher percentage of Type I plots, Zone II will have plots of all Types: I,II & III but with higher percentage of Type II and Zone III will have Type II & III with higher percentage of Type III plots. This increases differentiation in the plot sizes and therefore adds variation in the block morphology.
Nodes:: Green zones, DLR stations
Density gradient overlap
Primary division
The layers of nodes and density gradient are overlapped and the nodes are sorted into the 3 zones. These nodes are to be connected with a logic that can maintain not only physical but also direct visual contact between the node greens. Therefore, the nodes of DLR stations connect to the nodes in Zone I, Zone I nodes connect to Zone II nodes, Zone II nodes connect to Zone III nodes and Zone III nodes connect to the waterfronts. Each node connects to at least two other nodes to increase connectivity at these locations. Further nodes in each zone connect to each other to complete the polygons. We use evolutionary engine Galapagos to optimize these connections in a way that each node has between two to six connections and has maximum number of divisions with area greater than 35000 sq.m, so as to effectively use subdivision algorithm to get the desired sizes of plots. This division of site gives the Primary Network that connects all the nodes.
80%
20%
Zone I
20%
Zone II
Type I plots Type II plots
20%
60%
Type III plots 20%
80%
Zone III
53
Secondary subdivision
A subdivision algorithm is used to divide the site further into manageable plot sizes. To bring in variation in typology of buildings we attempt to derive plot sizes also in a gradient that vary according to the zone, i.e. the plots closer to high density zone are of Type I and as we move away from this zone plots of Type II come in. The plots furthest away would be generally Type III. To maintain a gradual gradient each zone has a certain percentage of two or more plot sizes. The case study done in various high density cities gives us a range of block sizes to work with. The Type I plots are of the smallest size with area between 3600-6400 sq.m, these would accommodate 1 or 2 buildings. The Type II plots are medium size plots with area between 6400-100000 sq.m, with capacity to contain 3 to 5 buildings. The Type III plots are the largest plots with area in the range 100000-144000 sq.m and would accommodate 6 or more buildings. These sizes need to be distributed such that Zone I has more of Type I plots with few of Type II, Zone II has all the three types but more of Type II and Zone III has more of Type III and few of Type II.
Subdivision Sequence
A subdivision algorithm is employed with the logic that divides the plot by connecting the midpoints of two of the longest edges of the plot. The algorithm in the first step subdivides all the plots which are greater than 144000 sq.m (the largest size of plot used). The subdivision continues till all the plots are smaller than the threshold size. Further, each plot size is checked for its area and its location with respect to its zone. If the desired number of that size of plot is reached it is further divided. For example, if a plot is in Zone II and its area is 120000 sq.m, the desired percentage of this size of plots is 20%, if this number is already reached than its further divided else it continues to survive. The process is looped till the desired condition is reached. The exact percentage cannot be reached as in the process the plots which are already of the smallest sizes cannot be further divided.
1
2
3
85 85
12254
58.5
6809
65 101
52.5 8160 80 68.5
9095 65
81
58.5
68.5 6809
101
101
5
4094
65
137 9095
52.5
85 85
58.5
65
81
As a result of the process a number of subdivided plots are smaller than the minimum desired size of 3600 sq.m. These plots combine with one of the neighbouring plots with which it shares the longest edge.
4
105
58.5
101
6 2502
5658
2502 4094
5658
4094
863 6822 3372
3437
7685 4782
3437
1410
55
Secondary subdivision
To understand the network hierarchy we do a sectional study of streets to understand proportions and street widths. The primary connections between the nodes form the first level pedestrian routes with street widths of 12 Metres that would be plazas with their own green spaces intertwined with walking and cycling lanes. The offsets from these routes which connect the various blocks would form the secondary level pedestrian routes and would be 9 Metres wide. The widths have been decided keeping in mind accessibility to all parts of the site for emergency services. A tertiary level of pedestrian connection would be generated through the process that derives the block morphology within each plot.
Subdivision sequence As the design aims to create a predominantly pedestrian locality the road network for vehicular access is minimized. Therefore the road would be designed as a single closed loop that would cover maximum site area with minimum length. A further criterion would be that the chosen route would be along those paths which have angles greater than 120 degrees between them. This vehicular route would be a 15 Metre wide road which would cater to road public transport in the site. As this is the only road through the site, the location of functions would be affected and influenced by this road.
57
Critical Review The basic objective of this part of the experimentation was to derive a system to generate optimized plot divisions and networks for the site. The adopted logic is linear and generates the nodes, division and networks in a step by step sequence. However, the experimented logic revealed a number of deficiencies which need to be addressed to improve computational thinking: A large number of rules and methods were employed to generate a division pattern. The only objective achieved is that the node greens are well connected; the other divisions appear randomized due to the subdivision logic used. So, the process uses a lot of effort to generate a more or less randomized solution. The linear step by step method employed is systematic but in certain respects deterministic as the feedback loops are small. For example, the subdivision algorithm applied to primary divisions to generate secondary divisions resulted in a number of plots being smaller than the required size, to solve this, another step was added. Alternatively, if there was feedback loop informing the logic of primary division, the process could be better optimized (Figure 1). Therefore, more comprehensive feedback loops that not only work within each step but also between steps would be more effective. Another aspect about the subdivision algorithm is that it generated a number of triangular and sometimes odd shaped plots which are not the most ideal conditions for buildings. Strategies to control the number of edges in the plots and define minimum ratio between length and width could be an evaluation criterion which could further improve the results of the output.
The sectional studies influenced the decision of street widths but were deterministic; a more effective method would be to increase dependencies between the street widths, their connective functions and the desired sectional configuration which would not only inform proportions but also functions within the built form.
Conclusion Methods such as subdivision algorithms involve certain kind of randomization to generate a result. In such randomized results sometimes the objective is lost and the output may not be logical. But, these processes involving randomization are capable of generating a number of different iterations with small manipulations. An evaluation process needs to be applied to rate each of these options to select the best. This evolutionary method of decision making and designing would not only be more effective but would yield an optimized solution to the problem. Another aspect to be understood is that a comprehensive feedback loop that improves the system is an effective way to better optimize results. Adopted Method PRIMARY DIVISIONS
SECONDARY DIVISIONS
PLOTS
DESIRED PLOTS UNDESIRED PLOTS
Alternative method that loops till desired condition is reached PRIMARY DIVISIONS
SECONDARY DIVISIONS
PLOTS
DESIRED PLOTS
CORRECTION
High density city with a maximized pedestrian mobility between the green zones of the area
Green domination. Sequence 2 Blocks
Blocks. Introduction Our working sequence is to have generated blocks (gradient according to three zones) further subdivide into proper building plots and local greens based on conceived logic and algorithm. The aim is to generate urban morphology which provide layers of urban greens with pedestrian mobility.
61
Setup for blocks investigation
The subdividing sequence creates gradiences of blocks in terms of block sizes. We developed a logic to further subdivide the blocks into building plots and local greens and paths. The logic is to apply specific strategy in respond to three different ranges of block sizes and the distance from a block to the nearrest primary green node to create greens within a single block and leisure paths. In a global scale, the local greens from each block will be added to the area of primary greens and both serve as parameters to achieve 25 percent of site as green area where greens on buildings will be counted in the later phase.
Circle packing algorithm creates the zones of influence which serve as a guidline on how we create local greens and paths according to the proximity to primary green nodes. Rules applied depend on the size of blocks and the influence zone they are located in. Centroids of different geometry shape are taken as measuring references as we define distance and locations.
Green distribution Green distribution
G1
80%
20%
Zone I
Type I plots Type II plots
40%
10% of Site
20%
60%
20%
Zone II
Type III plots 20%
80%
Zone III
40%
10% of Site
G1
Total Green Area (25% of Site) G2 Total Green Area (25% of Site) G2 40%
10% of Site
40%
10% of Site
G3 G3
20%
5% of Site
20%
5% of Site
Green distribution
Blocks typologies Total Green Area (25% of Site)
G1
40%
10% of Site
G2
40%
10% of Site
Relationship to the zone of inĂ&#x;uence G3
20%
5% of Site
63
Rules for the Type 1 Plots Small sizes of blocks ranging from 3600 square meter to 6400 square meters derive Type I building plots. The strategy is firstly defining the influencing zone in which the centroid of the block geometry falls. The followinig algorithm will determine the furtherest point of block geometry and create a local green where the area is in proportion (10% to 20% of block area) to the proximity to primary green. The strategy enables accessible green pockets on type I blocks to neighborhood through the site.
Type I Plot Area of the block
3600-6400 m Green %
20%
Green distribution
Blocks typologies Total Green Area (25% of Site)
G1
G2
Type I Plot G3 Area of the block
40%
10% of Site
40%
10% of Site
20%
5% of Site
3600-6400 m Green %
10%
Left: Distribution of type I plots. According the distribution rules we set up for three ranges zones, type I plots occupy densely within zone I towards the center of the site in response to the urban typology of commercial based and high flow population expected to grow from the adjacent Canary Wharf business district. Bottom: the example of generated type I plots in related to the proximity to primary green. Bottom Left: Total green distribution has a target value among primary greens, local greens and on-building greens. We have two approaches, one is having the algorithm target the fixed value. The other way is through floating value to achieve optimized distribution among the three.
Green distribution Total Green Area (25% of Site) G1
40%
10% of Site
G2
40%
10% of Site
G3
20%
5% of Site
65
Rules for the Type 2 Plots Plots on Type II block Medium sizes of blocks ranging from 6,400 square meter to 10,000 square meters derive Type II building plots. The strategy starts by defining the influencing zone in which the centroid of the block geometry falls. The followinig algorithm will determine two paths from center points of two sides from furtherest point of the block geometry that lead to the local courtyard green where the area is in proportion to the proximity to primary greens ranging from 15% to 25% of type II blocks. This strategy further subdivide the block size into reasonable building plots and enables more straightforward pedestrian networks.
Type II Plot Area of the block
6400-10000 m Green %
25% Green distribution Total Green Area (25% of Site) G1
G2
Type II Plot G3 Area of the block
40%
10% of Site
40%
10% of Site
20%
5% of Site
6400-10000 m Green %
15%
Left: Distribution of type I and type II plots
Below: darkest area represent the detailed distribution of type II plots around three primary greens. The more these plots being developed, the more we can define networks from various scales. Even the very local paths can be combined with paths or streets in different scales to strategized desired networks.
Green distribution Total Green Area (25% of Site) G1
40%
10% of Site
G2
40%
10% of Site
G3
20%
5% of Site
67
Rules for the Type 3 Plots Plots on Type III block The largest sizes of blocks ranging from 10,000 square meter to 14,400 square meters derive Type III building plots and prominent green pockets from each block. The strategy starts by defining the influencing zone in which the centroid of the block geometry falls. The followinig algorithm will determine paths from center points of each side of the block geometry that lead to the courtyard green where the area is in proportion to the proximity to primary greens. The furtherest path from primary green widen according to the algorithm as well to create the pocket along with courtyard. This strategy further subdivide the block size into reasonable building plots and enables more straightforward pedestrian networks. As we see here the local green percentage ranges up to 30% of a block area, the bigger the block, the more local green we have.
Type III Plot Area of the block
10000 - 14400m Green %
30%
Green distribution
Blocks typologies Total Green Area (25% of Site)
G1
G2
Type III Plot G3 Area of the block
40%
10% of Site
40%
10% of Site
20%
5% of Site
10000 - 14400m Green %
20%
Left: completed distribution of three types of plots
Below: darkest area demonstrates plot distribution from type III blocks.
Green distribution Total Green Area (25% of Site) G1
40%
10% of Site
G2
40%
10% of Site
G3
20%
5% of Site
69
Critical review As soon as the blocks and primary network have been developed, we examined the block area and its mobility in terms of pedestrian scale. The blocks then need a subdivision strategy to sustain proper plot area for buildings and increase in pedestrian mobility where shortest path is always available under various travel intentions. We sucessfully applied algorithms to work with three types of blocks and achieve the goal as a green and high pedestrian mobility typology on site. However, the block typologies were too identical through the whole site. footprint and local greens. We developed the algorithm with fitness criteria concerningsize of blocks, distances to green nodes and pedestrian mobility but still, from the result, the model is still oversimplified due to lacking of examination from different scsales and performance of such typology. The green nodes need rerdistribution in terms of allocation and sizes to create distinct urban morphology. Different hierarchy of networks need to sort out with clear orientation and purposes. To take advantage of Canary wharf and water front( inner or outer) as criteria for differential weighting which will add variety to the whole planning. The “ green”and “network” have been our primary concern while planning in various scales. Our fitness criteria were, however, limited its own scope. We created a city filled with green spaces and prevailing networks, although based on clear intention and algorithm, still lack of further examination on how people actually utilize the
space and the opportunity to take advantage of green and network as the opportunity for functional green infrastructure. Emergent technology is a more profound engine that we should be able to generate an urban tissue that self sustain, orient and grow towards more than simply urban morphology. The extented concern should lay eyes on how the morphology perform to lower urban energy consumption and strategize “functional greens and species”instead of public spaces prevailed on site with only rendered vegetation. Public spaces should be planned more wisely as human population is still on its stage of exploration, traditional protectionist or conservation approach often has not been working fully effectively and is now becoming less feasible as space becomes limiting. Architectural approach has lots of potential for its spatial planning logic and physical presentation of surface area and materiality. The walking city with green domination is feasible and has great potential, if well studied, to examplify sustainable urban planning and its integrated contribution to the whole ecological system.
High density city with a maximized pedestrian mobility between the green zones of the area
Green domination. Sequence 2 Blocks and Building typologies
Introduction This part of the exercise experiments with built typology. The process aims at establishing dependencies between various variables and parameters that would result in an optimised built form. We use parameters that help to achieve the desired density and still maintain quality of open spaces. Environmental factors are a major consideration in this regard. A large part of the experimentation is based around multi-parameterization to achieve a built form which performs well in more than one aspect. Experiments with differential weighting are carried out to calibrate parameters to achieve desired results and to study the effects of different parameters on the built form.
73
Initial concept When we start to look into the building typology we would like to give each building more differentiation than just extruded footprint. The initial concept of the building typology is to introduce the block and slab to each building of the growth strategy. There are a few benefits for using this particular typology in our design process, such as: - Increase the special quality when set back at the higher level which decrease the visual domination by tall building blocks. - Provide more sunlight for the street level and more natural light into buildings behind. - Potentially Have a different programmatic approach to the base and top level. - When the building set back on the higher level it would provide more open spaces for semi-public and private uses above the ground level. This process can be accommodate with the remaining green areas that are not able to be put into the ground level.
Office/ Residential
Office/ Residential
Retail/ Restaurant
Retail/ Restaurant
Office/ Residential
- The elevated open spaces can be connected in a cluster of buildings to provide less vertical travel distances from one building to another. Retail/ Restaurant
Green distribution Total Green Area (25% of Site) G1 Office/ Residential
40%
10% of Site
G2
46%
11.8% of Site
G3
14%
3.2% of Site
Retail/ Restaurant
Office/ Residential
Office/ Residential
Logic Sequences The logic of the process continues after the block typology with each of the block and the foot print of the buildings. This sequence can be separated into the following steps: - The total area of the site been calculated from the data catalog and separated with the according zoning to each of the blocks.
Site
Data Catalog
Attractor type 2
Attractor type 1
Attractor type 3
Density Gradient
- The solar fan is calculated for each primary green parks to insure it will allow a minimum of certain hours of direct sun light in the daily average through out the year.
Circle packing Nodes
Greens
Connection
- From the solar fan of hight limitations with the area distribution of each block the building block is extruded. At the same time the sky garden spaces are calculated with in the process.
Network
Primary plots
Subdivision
- The sky gardens of a cluster of buildings can be connect with an elevated network to provide a better connecting network above the ground level.
Network
Sometimes during the second step when the block is near a park on the south side and the area of the block are not reached, the remaining area will be distributed to the nabouring blocks.
Blocks
Block Typology
Area distribution
Solar Fan Analysis
Building Block
Logic Diagram
Elevated network 75
10m
Research At first we started a research based on the different type of the sky garden in consideration idea that based on the uses and method of achieving it computationally. Activity can be used with different sizes of the open spaces. Such as the area, width, liner or square like spaces. Within the same size of scaling, the positioning of the scaling can affect the usage of the 6m sky garden as well. 6m
11m 5m
We started using an area of 2000m2 with a 70% scale to be the sky garden area. From type 1-3 we have used the uniform scale but the scaling 10mposition are different. We can see that in type 1 and 3, the sky garden will always have a very thin strip of area with a very narrow width. This can be only limited to a private uses such as balcony of the residential etc. And it is very difficult to increase the area as we are trying to avoid the top part of the building block to be too narrow. In type 2 the sizes are very generous with two side of the building to be open and scaled to the corner of the building.
SimpliÞed extracted logic
11m
5m
These methods above are very limited and we would like to explore more using a non uniformed scale and see how the spaces can be used. We have only tested with two sized and we can clearly see the benefits of this methods. The spaces can have different size variation and possible to have different separated spaces that can’t be achieved with the previous methods. These spaces can be used as semi public or private sky gardens that are used by that particular building users. From these experiments we understands the uses of having different sky gardens sizes with the different activities and we have also trying to understand the possibilities to do it computationally.
6m ¥ Set 6mof offsets for a sky-garden
¥ Orientation of10ma sky-garden as a
factor
6m
6m
5m
11m
50m
Grow Strategy
10m Structural Environmental Public/Private Green Zone
Scale 70% of Floor Area Green Space: 600m2 40m
Testing Area: 2000 m2 Scale of open space: 70% Open space area: 600 m2
Type 1.
5m
Sim Type 2.
¥S
50m
19m
6m
10m
6m
Scale 70% of Floor Area Green Space: 600m2
10m 40m
19m
SimpliÞed extracted logic
10m
¥ Set of offsets for a sky-garden Grow Strategy
10m5m Scale 70% of Floor Area 10m
19m
Structural Environmental Public/Private Green Zone
Green Space: 600m
2
10m
10m
¥ Orientation of a sky-garden as a
factor
SimpliÞed extracted logic
¥ Set10mof offsets for a sky-garden 10m ¥ Orientation of a sky-garden as a factor
Type 3.
Type 4.
Type 5.
77
Network Optimization When we start to evaluate the results from the block typology process, we have realized some of the block sizes are still very large in length and this would give a big block with not very good connecting network. Therefore we have decided to break the block in to two or three smaller blocks if it has any side larger than 150m. We still took in consideration of the neighboring blocks and try to connect with the new path created. From the diagram on the right it shows part of site to give an example of this process. We can see that after the optimization process the network have increase with the number of connection. The problem with this process is that sometimes the connection does not connecting from the inner courtyard to the primary green parks, and it should be much efficient specially and environmentally beneficial to do so. We have also try to avoid any sharp angles of any corner with a minimum of 40o. Looking at the diagram below, the sharp conner with be sliced and leaving it to be part of the ground pedestrian network that can be used as a public space.
6 Hours Direct Sunlight
Solar Fan Criteria
60-100% of total area
6 Hours Direct Sunlight 60-100% of total area
Using the solar fan tool of the diva component in the grasshopper ensure the quality of the primary green parks. Similar to the inverse of building solar envelope, It works out the volume of the spaces that cannot be obstructed with the other buildings. By looking at the environmental date in London we can see that the maximum of direct sunlight receive daily average is 7 hours in June and with three month of 6 hours. This would give an average of 4 hours of direct sunlight daily average per year. When we are using the date to the primary green park, we took the whole site to be receiving 4 hours of sunlight daily. At the same time we took 60 % of the area of the site from center to ensure this space to receive 6 hours of direct sunlight. The idea is that when we are doing the testing we have realized that the building in the south of each primary park were affected greatly if we use 6 hours and the height of the buildings are limited to only 3-5 meters high. At the same time it might not be necessary to apply to the whole site and still want to keep the quality of the space for people to enjoy so we have ensure 6 hours of direct sunlight in the center of every primary parks.
4 Hours Direct Sunlight 100% of total area
Footprint
Park Area
Park Area
Footprint
N
Block Size
Block Size
Max Angle: 48.1 Min Angle: 27.4
Max Angle: 47.6 Min Angle: 32.1
Month Month
Jan Jan Feb Feb March March April April May May June June July July Aug Aug Sept Oct Sept Nov Oct Dec Nov Dec
1 1 22 44 55 66 7 7 6 66 56 35 23 1 2 1
8 8 10 10 12 12 14 14 16 16 17 17 16 15 16 13 15 11 13 9 11 8 9 8
(hours)
(hours)
Month Jan Feb March April
Temperature Relative Temperature humidity Relative Average Record Average Min Max RecordMin humidity Max am pm Min Max Min Max am pm 2 6 -10 14 86 77 2 6 -10 14 86 77 2 7 7 -9 -9 85 72 2 16 85 1672 3 10 10-8 -8 81 64 3 21 81 2164 6 26 71 2656 6 13 13-2 -2 71 56 8 30 70 3057 8 17 17 70 57 12 20 5 33 70 58 12 20 5 33 70 58 14 22 7 34 71 59 226 7 71 59 13 14 21 38 76 3462 213 6 76 62 11 13 19 30 80 3865 8 11 14 26 85 3070 19-4 3 80 65 5 19 85 2678 8 10 14-5 -4 85 70 4 7 -7 15 87 81 5 10 -5 19 85 78 Average Average 4 Average 7 -7 Temperature 15 Temperature 87 81Relative Sunlight Month (hours) 1Jan 2Feb March 4 April 5
Sunlight Daylight (hours) 18 10 2 12 4 14 5
humidity Average Average Record Record Daylight (hours) MaxMin MinMax MaxMin am Max Min pm 28 6 2 -10 6 14 -10 86 14 77 210 7 2 -9 7 16 -9 85 16 72 312 10 3 -8 10 21 -8 81 21 64 614 13 6 -2 13 26 -2 71 26 56
Max Angle: 60.7 Min Angle: 42.1 Footprint
Footprint
W
E
W
Park Area
Park Area
Max Angle: 60.1 Min Angle: 48.3
4 Hours4Direct HoursSunlight Direct Sunlight S Narea 100% of total 100% of total area W
E
Park Area
N
Block Size
Max Angle: 48.1 Min Angle: 27.4
Max Angle: 60.7 Min Angle: 42.1
Footprint
Footprint
Block Size
Block Size
6 Hours6Direct HoursSunlight Direct Sunlight 60-100% 60-100% ofNtotal area of total area S
W
E
Block Size
Max Angle: 47 Min Angle: 32
N
S
E Park Area
Footprint Block Size
Park Area
Footprint
Footprint
Block Size
Block Size
Max Angle: 34.8
Footprint
Park Area
Block Size Park Area Footprint Max Angle: 34.8 Block Size
Park Area
N
Park Area
S
Park Area
Footprint Block Size
Max Angle: 47.6 Max Angle: 47.6 Min Angle: 32.1 Min Angle: 32.1
N
Average Daylight Average Daylight (hours)
S
Park Area
Block Size
Max Angle: 47.6 Min Angle: 32.1
Max Angle: 48.1 Max Angle: 48.1 Min Angle: 27.4 Min Angle: 27.4
Average Sunlight Average Sunlight (hours)
Footprint
Block Size
Max Angle: 48.1 Min Angle: 27.4 Footprint
E
Footprint
Block Size
E
W
W
E
W
S
Footprint
Footprint
Block Size
Block Size
Temperature Average Record Min Max Min Max 8 2 6 -10 14 10 2 7 -9 16 12 3 10 -8 21 14 6 13 -2 26 16 8 17 30 17 12 20 5 33 16 14 22 7 34 15 13 21 6 38 Jul Aug Sep Oct Nov Dec 13 11 19 3 30 Relative humidityAverage Sunlight(Hours) Oct Feb 3 Mar Apr 11 May 8 Jun 14 Jul -4 26 Jan Aug Sep am pm Nov 2 9 5 10 -5 19 86 77 Average Sunlight(Hours) Dec 1 8 4 7 -7 15 85 72 8 8 81 64 7 7 71 56 Average
8 Month Sunlight (hours) 7 Jan 1 6 Feb 2 4 5 March 5 4 April May 6 3 June 7 2 July 6 6 1 Aug Jan Feb Mar Apr May Jun Sept 5
Average Daylight (hours)
Average
Average
(hours)
(hours)
1 2 4 5 6 7 6 6 5 3 2 1
8 10 12 14 16 17 16 15 13 11 9 8
Footprint Month Footprint Sunlight Park Area Daylight Park Area Block Size
Block Size
Jan Max Angle: 60.7 Max Angle: Feb 60.7 Min Angle: 42.1 Min Angle:March 42.1 April May June July Aug Sept Oct Nov Dec
N
Park Area
Max Angle: 34.8 Max Angle: 34.8
8 7 6 5 4 3 2 1
Footprint
N
Park Area
Park Area
Block SizeTemperature Footprint FootprintRecord Average Max 35.5 MinAngle:Max Min Max Block Size Block Size 2 6 -10 14 2 -9 Angle: 60.1 16 Max 7Angle: 60.1 Max 3 -8 Angle: 21 Min10 Angle: 48.3 Min 48.3 6 13 -2 26 8 17 30 S 20 S 5 12 33 14 22 7 34 13 21 6 38 11 19 3 30 8 14 -4 26 5 10 -5 19 4 7 -7 15 Footprint
Footprint
Block Size
Block Size
Footprint
Relative humidity Block Size
am
pm
86 Angle: 77 34 Max 85 81 71 70 70 71 76 80 85 85 87
72 64 56 57 58 59 62 65 70 78 81
Max Angle: 35.5 Max Angle: 35.5
Relative humidity 18 am 16 pm 86 77 14 85 72 12 81 64 10 71 56 8 70 57 6 70 58 4 71 59 2 76 62 80 Jan 65 Feb Mar Apr May 85 Average 70NovDaylight(Hours) Oct Dec 85 78 87 81 18 16
18 16
18 16 14 8 12 7 10 68 56 44 32 Jul Aug Sep Oct Nov Dec Jun 2 Jan Feb Mar Apr May 1
Jun Jul Aug S Average Daylight(Hours) Jan Feb Mar Apr May Jun 79 Jul Aug S Average Sunlight(Hours)
Building Typology Sequence When we were looking at the research part, there are a few different part been studied and we started to look at it in the computation methods. Galapagos checks for the fitness criteria and evaluate it for the optimal solution. However, these steps doesn’t run in the sequence, so the following steps explains in a logical order. - Use the footprint of each building as input, it would be extruded to form the first base levels. - The upper level footprint formed with scale of offset. The open areas checks with the 5 % of the total area to be the sky garden.
Scale
- The footprint extruded again until the total floor area reached for each building in the block. - The upper volume moves position according to the sky garden solar exposure until the optimal solution calculated.
Plot Base Extrusion
The process of galapagos randomly changes the input and improve the result in each generation. For example, when it checks with the solar exposure on the sky gardens it might changes the extrusion of the base volume and the footprint of the upper part. Therefore it is very difficult to change any part without affecting others. The diagram on the right shows two different computational methods with scale and offset. Some of the sequence would be similar between the two. For scaling, it scales the footprint to a certain percentage and then extrude. This would be a easy process to achieve but it has limitations to the variety of the sky garden areas. For the offset would be more difficult because the offset process moves each edge and extend to join with the neighboring two edges. Although it take much more time to achieve, it makes the sky garden level more interesting as it opens up all sides and creating variety of spaces.
offset
Scale Check Skygarden Area
Second Part Building Extrusion
Check Skygarden Solar Exposure
Best Position for Solar Exposure Exposure
Fitness Criteria Using the Galapagos as the genetic algorithm for computational process, there are a few different ness criteria and limitations in the process. As we testing and calibrating with the Galapagos, there more fitness criteria introduced.
the fitare are
There are two limitation which are the footprint from the block typology process and the height limitations from the solar fan. At the same time four of the fitness criteria have been put into the generating process. 1. Total floor area to each blocks from the density gradient. As the hight of each building been limited, some of the area been distributed to the neighboring blocks. 2. The area of the sky gardens have been set to 5 % of the total floor area of the buildings. As there are more floor area in the building, the total sky garden area should also increase in order to accommodate the occupiers.
KCOLB GNIDLIUB
3. The building facade solar exposure ensure that the building on the south of the block will not cover the others. 4. The secondary green garden of the courtyard solar exposure.
BUILDING BLOCK
MFLOOR ORF AEAREA RA ROFROM OLF TN DENSITY EIDARG GRADIENT YTISNED
TN FOOTPRINT IRPTOOF
FO SOLAR NAF RFAN ALOOF S NPRIMARY EERG YRAGREEN MIRP
BUILDING BLOCK
TATERTIARY SNEERG GREENS YRAITRETAT LEELEVATED VEL DETAVLEVEL ELE
NEXPOSURE O ERUSOPXON E ECBUILDING AFRUS GNSURFACE IDLIUB
81
Multiple Fitness Criteria Experiment When we started with testing with the Galapagos, we have realized that sometimes the results become very unpredictable and the time and accuracy changed dramatically. The problem is the number of fitness criteria that put in to Galapagos. As the number of fitness Generation 3010increase.60 90 120 criteria increases the time would Therefore Generation 20 30 44 we have started with two sets of experiments for unTotal Area Require 40000 40000 40000 40000 Total Area Require 40000 40000 Generation 40000 derstanding and calibrating the system. 40000
II - Area +I -Garden Area Area
Total Area Remain -14668 -3131 Total Remain is to -18989 -10228 -6585 The firstArea experiment use four buildings with a -430 I Area Total Garden Require 2733 total area of 40,000 m2 in 2949 the block and test with 2156 Total Garden Require 2511 2329 2021 the increasing number of fitness criteria and add the
10
Total Garden Remain
489
280
20 30 44 cess runs, the total garden area does not always give a GardenTotal Exposure 0 0 Total Area Require 40000 40000 40000 40000 GardenTotal Exposure GardenTotal Exposure good optimized result. Value Therefore, we have 0 added the 0 0 0 Total Area Remain -10228 -6585 sky garden area as the second fitness -430 criteria. 1.15 We can Value Value GardenTotal Exposure see the result that the total garden2021 area 0remaining Total Garden Require 2511 2329 2000 0 Value sky has decreased by 12%489 but the 280 total area remaining Total Garden 0.81 19.16 has I -Remain Area
40000
40000
-10228
-6585
Total Garden Require 30Total Garden 44 Remain 8.35
2511
2329
1.15 2028 2000
10
00
GardenTotal Exposure 40000 40000 Value a Remain -10228 -6585 GardenTotal Exposure den Require 2511 2329 Value den Remain 489 280
a Require
40000 0 -430
40000 0 1.15
0
2021 0 0.81
2000 0 19.16
0
Total Floor Area
0 Total Garden Require
High30Exposure Value 60 GardenTotal Exposure Total0 Area Require 40000 40000 Total ValueFloor Area Total Area Remain -18989 -14668 Total Sky Garden Area Exposure Total0 Garden Require GardenTotal 2949 2733 Value Total Garden Remain Building 265 Solar Exposure 174
0
0 0
2156 2028 GardenTotal Exposure Value 1.3 8.35
0
0
0
0
0
0
0
0
Total Garden Require
0
0
Total Garden Remain
0
120 40000 -558
0
2028 8.35 0
0 90
0 120
0
0
40000 0 -3131
40000 0 -558
0
0
0
0
0
0
Sky Garden Area 0 Total 0 0 1
0
00
IV 10
V 10
Total Sky Garden Area 0 1
1
1
1
Building Solar Exposure 0 0
1
00 1
Garden Solar Exposure 0 0
0
1 V 10
0
0
0
0
0
I Total Floor1 Area
1
0
00
5 I 1
II 1
III 1
IV 1
V 10
VI 20
Total Sky Garden Area 1 0
1
1
1
10
V Solar VI 0 Building Exposure 0 10 20 Garden Solar 0 Exposure 0 1 10
I 1 1
II 1 1
III 1 1
IV 1
5
V 10
1 1
1
5
1
50
1
III 11
1 1
1
1
VI 20
0
II 1
1
III 1
0
0
GardenTotal Exposure 0 0 Value
IV 1
1
Garden Solar Exposure 0 0
0
0
III 1
I II Total Floor Area 1 1
IV 1
0
0 0
0
2028 0 8.35 II Total Floor Area 1 0 1 0
174
0
1.3
2156 0 1.3 I
2733
0
0
0
III 1
0
265
174
GardenTotal Exposure Value
Total Sky Garden Area
2949
0
0
I II Total Floor Area 1 1
0
-18989 -14668 -3131 GardenTotal Exposure 2949Value 2733 2156
0
Area
8.35
0
GardenTotal Exposure Value
Garden Solar Exposure
1.3
0
0Building Solar0 Exposure
Garden Solar Exposure Garden Solar Exposure Building Solar Exposure
174 0
II Solar Exposure 0 Building 0 10 0 1
Total Sky Garden Area
265 0
High Exposure Value
Total Floor Area Building Solar Exposure Building Solar Exposure
2028
0
0Total Sky Garden 0 Area
0
-558
2156
-3131 -558Value High Exposure
0
Garden Solar Exposure
-3131
2733
Total Exposure Value 40000 40000
0
0 Sky 0 Area Total Garden Area0 Total Sky Garden
-14668
2949
0-14668
osure Value
otal Exposure
-18989
040000
0
0 Floor 0 Total Area Total Floor Area
120 40000
0-18989
0
0 0 Garden Solar Exposure 0 0
90 40000
0 40000
0
Total Exposure Value
60 40000
0Total Area Remain 0
Garden Remain 265 IITotal - Area + Garden Area Total Exposure Value
Total Remain 90 Garden120
19.1660
30 40000
0Total Area Require 0
osure Value
otal Exposure
0
0.81 30
II - Area Garden Area 00 Total+Exposure Value 0Exposure GardenTotal 0High Exposure 0 Value 0 0 Value 0 Generation 30 60 90 GardenTotal Exposure 0 0 Total Area Require 40000Value 40000 40000 0
Total Area Require
40000 Total Area Remain -430 1.15 II - Area + Garden Area Total Garden Require 2021 2000
0
00 Total Area Remain
Generation
44
40000
Generation 280 489
High Exposure Value 2000 00 2021GardenTotal 00Exposure 0.81Value 19.16
increased. At the same0 time when we look at the0overTotal Exposure Value 0 0 all generations, even just adding one more criteria the High Exposure Value 030 044 0 0 20 Generation time 10 have increase greatly.
n
30
Total Area Remain
fitness criteriaExposure lookingI -atArea the results of total area GardenTotal GardenTotaland Exposure Total Exposure Value 0 0 0 area 00 0 remaining and total sky garden remaining has 00 Value Value High Exposure Value 0 0 been very good. However, sometimes when the proGeneration
20
-558 Total Area Require
Generation 10 Total Garden Remain 265 weight-age to each accordingly. This would174 allow to 1.3 Total Garden Remain 489 280 us 20 0.81 19.16 Total Area Require 40000 40000 40000 40000 Value see how the processing time and accuracy of GalapaTotal Exposure Total Exposure Value 0Remain 0accuracy. 00 -430 0 ValueTotal 0 0 gosTotal and Exposure how to improve the efficiency and Area -10228 -6585 1.150 2329 High Exposure Value 0 0Require 2511 00 High Exposure ValueTotal Garden To start with, we have just added the total area as the
II - Area + Garden Area
I - Area
1
VI 20
5
5
50
I 1
II 1
0
1
1
10 5
000
0
50
0
0
II 1
III 1
0
VV 0 10 10
0
0
11 11 11
IV 1
0 0
IV IV 11
VIVI 1 20 20 0
11 III 1
10 10
IV 1
V 10
1
1
1
1
515
1
0
1
5
11
V 10
VI 20
55
VI 20 10
50 50
5 50
1
1
1
5
FLOOR AREA FROM DENSITY GRADIENT
Multiple Fitness Criteria Experiment
III - Area + Garden Area + Facade Solar
When we are looking at the result from the previous two tests, we have realized the building height for any of the four buildings are generated randomly. Therefore it might face a problem of the building on the south will overshadowing the north side. So the evaluation criteria would be the35relationship70of the Generation four building height. According to this evaluation criTotal 40000 40000 Generation teria we Area have Require added the facade solar exposure value to the fitness criteria. Using the solar exposure tool in Remain -11815 -2792 Require theTotal gecoArea plug-in in grasshopper it Total wouldArea work out the solar exposure value and apply it into the Galapagos Total Garden Require 2590 Total Area2140 Remain generating results.
III - Area + Garden Area + Facade Solar FOOTPRINT III - Area + Garden Area + Facade SolarGeneration
Total Garden Remain
105
140
40000 35
40000 70
105
288 31 Require 20 2590 Total Garden
11 2140 1693 31
Value Generation
35
70 Exposure 105 GardenTotal TotalGardenTotal Area Require 40000 Exposure 40000 Value40000 0 0 0 TotalValue Area Remain -11815 GardenTotal -2792 Exposure -2657 Total Garden Require 2590 Value 2140 2132
Total Garden Remain a + Garden Area + Facade288Solar
140
0 40000
2132 20
1691
1693
35High Exposure 70 Value 105 915 140 874 GardenTotal Exposure 40000 40000 40000 0 40000 0 Value -11815 -2792 -2657 -2647 GardenTotal 2590 2140Exposure 2132 0 2132 0 Total Floor Area Value 288 31 20 11
900
901
0
0
0
0
0
0 0 Garden Solar Exposure 0
0
40000
Total Area Remain
-11815
-2792
-2657
-2647
Total Garden Require
2590
2140
2132
2132
Total Garden Remain
288 BUILDING 31 BLOCK 20
Total Floor Area
1734
1695
1691
1693
2132 High Exposure Value
915
874
900
901
0
0
0
0
0
0
0
0
Value 1693
GardenTotal Exposure 901 Value
Total Sky Garden Area
I 1
Building II Solar Exposure III
1
1
Garden Solar Exposure I
1693
0
1
1
Total 901 Sky Garden Area
0
0
1
Building Solar Exposure 0
0
0
0
0
11
-2647 Total Exposure Value
Total Floor Area
Garden Solar Exposure
0
40000
0
1695
915 Building 874 900 Solar Exposure
40000
0 -2647 EXPOSURE ON 0 0 0 2132 BUILDING SURFACE 11
Garden Area 1734 Total Sky 1695 1691
40000
0
20
1734
Total Area Require
0
31
Total Exposure Value
140
11GardenTotal Exposure
ELEVATED LEVEL 874 900
0
105
40000
III - Area + Garden Area + Facade Solar 901TERTIARY 1695 GREENS1691 AT 0
70
140
-265740000 -264740000 40000 SOLAR FAN OF 2132-11815 2132PRIMARY -2792 GREEN -2657
With the third fitness criteria added the generations Exposure Remain 1691 288 hasTotal increased more.Value Looking at 1734 theTotal totalGarden area1695 remaining, the result has increased to 5% of error comparing Exposure 915 874at Value 900 1734 Total Exposure to High the last result. Value However, when we looking the sky garden area remaining, the result has improved High Exposure Value 915 GardenTotal towards the end Exposure with only 11m2. 0 0 0
35
1 0 0 0
IV 1 1 1 1
II 1 1 0 0
V 10 III 1 1 1 5
1
VI 20 IV 1 10 5
1
1
1 50
0
1 83
Multiple Fitness Criteria Experiment
III - AreaIII+ -Garden Area + Area Garden + Facade Area +Solar Facade Solar When evaluating the result Area of the last IV -weArea + Garden + experiments, Facade Solar + Garden Solar we have found again the result has been sometimes
eration inconsistent. Generation
70 when we 105 70 use the 105 140 140 This 35 is because 35 that building solar facade exposure value to measure and 35necessary 105 40000 140 al Area Require TotalGeneration Area Require 40000 it does 40000 40000 40000 4000070 40000 generate each building, not means 40000 the taller on the north will get higher value. -2657 Total Area Require 40000 40000 40000 -2647 40000 al Area Remain Total Areabuilding Remain -11815 -11815 -2792 -2792 -2657 -2647 This is because when the building on the south side Generation 35 70 have aGarden larger facade always2140 get a high 2132 Total AreaRequire Remain -13286 -11305 -10750 2132 -10152 al Garden Total Require 2590 area would 2590 2140 2132 2132 value, thus would extrude higher. Total Therefore, we have Area Require 40000 40000 Total Require 2664 is the31 2565ex- 20 al Garden Total Remain Garden Remain 288 288 31which 20solar 112537 112507 added one Garden more fitness criteria -13286 -11305 posure value of the courtyard for Total each Area block.Remain
Total Exposure Value
1732
1735
1745
1752
High 35 Exposure 70Value
105892
140908
915
914
40000 40000 GardenTotal Exposure 40000 40 Value -13286 -11305 -10750
40000 40 -10152
40
40
Floor Area Total Floor Area re 2664 2565 GardenTotal Exposure 2537 10 Total Floor Area Value in 464 421 400
2507 12 395
12
Sky Garden Total Area Sky Garden Area Garden Area1745 1732Total Sky1735 ding Solar Building Exposure Solar Exposure e 892 Building 908 915 Solar Exposure en SolarGarden Exposure Solar Exposure ure 40 Garden Solar 40 Exposure40
e
10
Generation
35
70
105
140
40000
40000
40000
40000
-13286
-11305
-10750
-10152
2664
2565
2537
2507
464
421
400
395
1732
1735
1745
1752
892
908
915
914
40
40
40
40
10
12
12
14
IV - Area + Garden Area + Facade Solar + Garden Solar Total Area Require
Total Garden Remain 1734 al Exposure Total Value Exposure Value 1734 1695464 1695 1691421 1691 1693400 1693395 Total Garden Require 2664 2565 The experiments ran Value again with all four of the1735 fitness Total Exposure 1732 1745 1752 h Exposure High Value Exposure Value 915 915 874 874 900 900 901 901 criteria. The result of error has increase more with the Total Garden Remain 464 421 amount of time it took. Looking at the total area re- + Garden High Exposure Value 892 908 915 914 IV Area + Garden Area + Facade Solar Solar rdenTotalGardenTotal Exposure Exposure Total 1732 0 1735 00 to 00 theValue maining, the error 0has increased 25%Exposure with sky 00 ue ValueGardenTotal Exposure garden area remaining increased to 13%. 40 Exposure 40Value 40892 40908 High Value Generation 35 70 105 140 rdenTotalGardenTotal Exposure Exposure 00 GardenTotal 00 Exposure 00 0 TotalGardenTotal Area Require0Exposure40000 40000 40000 4040000 40 ue Value Value 10 12 12 14 TotalValue Area Remain -13286 -11305 -10750 -10152 Total Garden Require 2664 GardenTotal 2565 Exposure 2537 10 2507 12 Value Total+Garden Remain 421 400 395 den Area Facade Solar + 464 Garden Solar
ure
IV - Area + Garden Area + Facade Solar + Garden Solar
12
12
Total1752 Floor Area
14
-10750 400 1745 915 40 12
40
914 GardenTotal Exposure Value 40 14
I II II III III IV 1 1I 1 1II 1 1III Total Sky Garden Area 1 1 1 0 1 Building Solar 1 1 Exposure 1 1I 0 1 11 0 0 Garden Solar 0 1 Exposure 1 1 0 0 10 00 00 01 0 0 00
0
0
Total Area Remain 140 Total Garden Require 40000 Total Garden Remain -10152 Total Exposure Value 2507 High Exposure Value 395 GardenTotal Exposure 1752 Value
Total Floor Area
I 1
0
Building Solar Exposure
40000 2537
914
Total Sky Garden Area
105
Garden 14Solar Exposure
0 I
II
III
IV
IV V IV 1 10 1 1 1II 11 11 11 15 10
V VI 10V20 10 1 III 10 11 15 11 5 50 51
VI 20VI 20 10IV 10 1 5 51 50 50 1
V 10
0
0
1
5
V
VI
1 1
Multiple Fitness Criteria Experiment
VVI- All - Allwith withWeight Weight
We can see from the previous four experiments that as we increase the number of fitness criteria, the result not only took longer to generate, the accuracy has decrease a lot. As all the criteria has been treated Generation 25 to be 1, we 50would equally given all of the weighting Generation 20 40 start to change the weightings for each criteria deTotal Area Require 40000 40000 pendsTotal on the desired results. Area Require 40000 40000
VI - All with Weight
V - All with Weight 75 100 60 80 25 50 75 100 40000 Generation 40000 40000Total Area40000 RequireVI - All40000 40000 40000 40000 with Weight Total Area Remain -17722 8447 -938 Total Area-427 Remain -17722 8447 -938 -427 As you canArea see from the chart below, Total Remain -14911the experiment 161 179 205 Total Garden Require 20 2885 40 2422 60 2046 80 2021 number V given Require 10 to the total floor area 5 toWeight V 2422 -and All with Generation Total Garden 2885 2046 2021 Totalexposure. Garden Require 2744 1989Total Garden 1988 the solar The result has improved 1990 by a lot Total Area RequireRemain 40000171 40000313 40000 117 40000 47 Total Garden Remain 171 313 47 even with a small changes.Generation Within 25 50 117 100 1427 1429 1401 1405 Total Garden Remain 717100 generations, 290 49375Total Exposure 402Value Total Area Remain
-14911
161
179
High25 Exposure Value 50 GardenTotal Exposure otal Area Require 40000 40000 Value otal Area Remain -17722 8447 Exposure otal Garden Require GardenTotal 2885 2422 Value otal Garden Remain 171 313
1181 75
1192 100
1160 Generation 1163
40000 40 -938
40000 40 -427
Total 40 Area Require 40
40000 Total Floor40000 Area
Total Area Remain
-14911
161
2046 18 117
2021 18 47
Total 18 Garden18Require
2744
1990
Total Garden Remain
1405
Building Solar 717 290Exposure493 Total Floor Area
Total Exposure Value
1611 1555 Garden Solar Exposure 1517
Total Floor Area
otal Exposure Value
1427
1429
1401
High Exposure Value
1181
1192
Total Sky Garden 1160 1163 Area
GardenTotal Exposure alue
40
40
GardenTotal Exposure alue
18
Total Floor Area Total Floor Area 18
Total SkySky Garden Area Total Garden Area
20
Total Sky Garden Area
High Exposure Value
1377 68
GardenTotal Exposure Value
31
40000
40000
161
179
205
2744
1990
1989
1988
Total Garden Remain
717
290
493
402
Total Exposure Value
1611
1555
1517
1566
High Exposure Value
1377
1310
1276
1335
68
68
68
62
31
31
31
31
GardenTotal Exposure Value GardenTotal Exposure 80 Value 40000 40000 60
179
205
1989
1988
493
402
1517
1566
1276
1335
31
31 Total Sky 31Garden 0 Area
I 1 0
402 1566 1335
0
68
0
31
31
Total Sky1Garden Area1
Building Solar Exposure
0
0
1 Building1Solar Exposure
Garden Solar Exposure
0
0
0
II 1
III 1
IV 1
1
1
1
I0I 101
62
1
Total Floor Area 0
40000
31
0
I 1
40000 -14911
179 205 GardenTotal Exposure Value 1989 1988
III 1 Area Total Floor
Garden Solar Exposure Garden Solar Exposure
Total Area Require Total Area Remain
68
II 1
Building Solar Exposure Building Solar Exposure Total Sky Garden Area
tal Sky Garden Area
31
I 1
80
68
Garden Solar Exposure
Total Floor Area
tal Floor Area
68
60
68
1276
Building Solar Exposure
Value Garden Solar Exposure 18
1310
40
60 80 GardenTotal Exposure Value 40000 40000
Total Sky Garden Area
40Building Solar 40 ExposureGardenTotal Exposure 18
40
20
Total Garden Require
205
the total floor area has improved 1 % of error Total Area to Require 40000 and 40000 40000 Exposure 40000 Value 1181 19901192 1989 1160 1988 1163 Total Exposure 1427 1429 TotalHigh Garden 1405 Require leaving only 47m2 Value ofValue area for the sky garden remain- 1401 Total Exposure 1611 1555 1566 2744 Total Area Remain -17722 8447 1517 -938 -427 TotalGardenTotal Garden Remain 290 493 402 Exposure 717 ing. Then, with the last experiment, each of the fitness 40 40VI - All with 40 Weight 40 High Exposure Value 1181 1163 Total Garden Require 1192 2885 2046 2021 High Exposure Valueweighting 1377 1310This24221160 1276 criteria has been given accordingly. TotalValue Exposure 1335 Value 1611 1555 1517 1566 Totalresult Garden Remain 171 has313 117 47 hasGardenTotal added someExposure error to the but the time HighGardenTotal Exposure Value 1310 1276 1335 Exposure1377 V - AllTotal with Weight Exposure Value 1427 1429 40 1401 1405 GardenTotal 18 18 40 40 40 increase notably inExposure comparing to the previous experiGeneration 20 18 40 18 Value 68 68 68 62 Value GardenTotal Exposure Value 1181 1192 1160 1163 ments. This is because whenHigh weExposure added the weight-age Value 68Area Require 68 68 62 Total 40000 40000 Value 50 75 100 to eachGeneration criteria , since it25doesn’t have that much of GardenTotal Exposure Total Area Remain -14911 161 40 40 40 40 Total Areaas Require 40000 40000 40000 Value V,40000 GardenTotal Exposure a difference in experiment the weight-age difGardenTotal Exposure GardenTotal Exposure 18 18 18 18 31 31 31 31 Total Garden Require 2744 1990 31Exposure 31-427 31 31 Total Area Remainthem -17722 8447 -938 Value ferences between hasGardenTotal decreased. Therefore, we Value Value 18 18 18 18 Total Garden Remain 717 290 Total Garden Require 2885 2046 between 2021 have learned that the differential weighting Value 2422 each ofTotal theGarden criteria more differentiation Remainshould 171have 313 117 47 1611 1555 VI - All with Weight Total Exposure Value V - All with Weight for theTotal generating process to work efficiently. Exposure Value 1427 1429 1401 1405 High Exposure Value 1377 1310
eneration
Generation
IV 1
1
00 00 00
Garden Solar Exposure
II 1
III 1
IV 1
V 10
VI 20
1
1
1
1
10
1 0 V 10
IIII 11
62 I Total Floor1Area
1
0 0 Garden 1 0 Solar Exposure 10 1
1
20
1 5 01
IV 1
5 0 50 0
11VI
11
1
0010
11
1
00
1
20
1 1
00
5
I 1
5 50
II 1
III 1
IV 1
IV 1
1 III 1
0III 1 III
1
III 1
I II V VI 0 Exposure 0 Building 1 Solar 1 10
1
II 1
V 10
1
1
IV 1
0
1
1
1
V 10
1
V1 10 1
1 0
II 1 1
III 1
0
1
1
1
0
05
1
0
0
0
V 10
VI 20
10
1
VI 20
5
1
5
10
50
1
VI VI 20 20
5
5
I 1
5
VI 20
10 10 55 50 50
50 IV 1
V 10
1
1
1
1
1
5
85
Multiple Fitness Criteria Experiment
I
II
III
IV
V
VI
Total Floor Area
I 1
II 1
III 1
IV 1
V 10
VI 20
Total Sky Garden Area
0
1
1
1
1
10
Building Solar Exposure
0
0
1
1
1
5
Garden Solar Exposure
0
0
0
1
5
50
Multiple Building Experiment Generation
10
20
30
Two buildings without weighting
The flowing experiments test with the number of buildings with in each block and how would the weighting be applied to each criteria.
40
Generation
40000
40000
40000
40000
Total Area Remain 7087 Generation 10 Total Garden 1645 Require Require 40000 30 Total Area 40 Generation
6991 20
6991 30
7032 40
Total Area Require
10
20
30
Two buildings with weighting
Total Area Require
40000
40000
40000
40 40000
Total Area Remain -1275 91 91 -36 Generation 10 20 30 40 Total Garden 1650 1650 1645 2063 1995 1995 2001 40000 10 40000 20 40000 30 Require Total Area 40000 40000 40000 40000 10 20 40Require Starting with two buildings in Generation experiment I, although Garden Total Garden Remain 7087 Remain -1275 91 91 -36 the number of buildings is veryTotal miminum, it took over40000 40000Total Area -13Require6991 9 400006991 9 400007032 1 40000 Area 142 232 232 222 Area Require 40000 40000 Total Area 40000 Remain Remain 100 generations for the more optimaised results. Total Garden Total Garden 1645 2063 1995 1995 2001 Total Area Remain 7087 6991Total 7032 Total Area Remain1650 -12751650 91 1645 91 Total -36 Exposure Exposure When we have added to the weightage to each crite- 6991 Require Require Generation 813 829 829 820 780 10 809 20 809 30 807 40 Value Value Generation 10 20 30 40 Generation 10 20 30 40 ria, we can see starting from generation 20, the result Total Garden Total Garden 1650Total Garden 1645 2001Area Require 142 40000232 40000232 40000222 40000 -13 9 2063 9 1995 1 1995Total Garden already has improved. In comparason to the 1645 experi- 1650 Total Require Require High Exposure 40000 High Exposure 40000 Remain Remain Total Area Require Require 70840000 73040000 73040000 718Total Area 65940000 69240000 69240000 690 ment I, within 40 genrations the numders of the total Value Value Total Area Remain 7087 6991 6991 7032 Total Garden Total Garden TotalRemain Exposure TotalRemain Exposure area remaining has increase, but looking at the -13 sky 9 1 142 8297032 232 820Total 232 222 Total9Area 7087 8136991 8296991 Area -1275 780 91 809 91 809 -36 807 Remain Remain GardenTotal GardenTotal Value Value 81 81 81 81 81 81 81 81 Total Garden garden area remaining has deraesed. This is becasue 1645 1650 1650 1645 Exposure Value Exposure Value Total Garden Total Garden Require 1645 1650 1650 1645 2063 1995 1995 2001 in the weightage we have put heigher 813 to the 829 Require Totalmuch Exposure Total Exposure High Exposure High Exposure 829GardenTotal 820 780 730 809 718Require 809GardenTotal 807 708 730 659 692 692 690 Value Value 62 total floor area whan the sky garden area. The garden Value Value Total Garden 62 62 62 62 62 62 62 Exposure Value Exposure Value -13 9 9 1 Total Garden Total Garden exposure has remained the same as both buildings Remain -13 Exposure 9 9 1 142 232 232 222 High Exposure High GardenTotal GardenTotal 708 730 730 718 659 692 692 690 Remain Remain 81 81 81 81 81 81 81 81 does not obstructed gardens. Value Value Exposure Value Exposure Generation 10 20 30 40 Generation Generation 10 20 30 10 20 30 40 Generation 10 TotalValue Exposure 40 813 829 829 820 Total Exposure Total Exposure Value 813 829 829 820 780 809 809 807 GardenTotal GardenTotal GardenTotal GardenTotal 62 62 81 62 81 62Value 62 62 62 62 81 81 81 81 81 81 Total Area Require 40000 Value 40000 40000 Value Total Area Require 40000 40000 40000 Total Area Require 40000 Total Area Require 40000 Exposure40000 Value Exposure Exposure40000 Value Exposure Value High Exposure 708 730 730 718 High Exposure High Exposure GardenTotal Total Area Remain Value 7087 6991 6991 GardenTotal 7032 730 Total Area730 Remain -1275 91 91 -36 Total Remain 7087 6991 6991 7032 692 Total Area690 Remain -1275 708 659 692 62 62 62 62 62 Area718 62 62 62 Value Value Exposure Value Exposure Value GardenTotal Total Garden Total Garden Total Garden 2063 Total 81 81 Garden81 81 1645 GardenTotal 1650 1650 1645 1995 1995 2001 1645 1650 1650 1645 2063 GardenTotal Exposure Value Require Require 81 Require 81 Require 81 81 81 81 81 81 Exposure Value Exposure Value GardenTotal 10 Total Garden Total Garden Total Garden 142 GardenTotal Total 62 Garden62 5 62142 -13 GardenTotal 9 232 232 -13 Exposure 9 Value 9 1 62621 19 1 62 1 62 162 62 11 62 62222 Remain Remain Remain Remain 62 Exposure Value Exposure Value 10Total Exposure 20 Value
10Total Exposure 20Total Exposure 30 780 10 20 30 Value Value
Generation Total Area Require
40000
Total Area Remain
-1275
Total Garden Require
2063
Total Garden Remain
142
Total20 Exposure 30 Value 40000 40000 High Exposure Value91 91
40 780
30 813
Building Solar Exposure Garden Solar Exposure
40 829 Generation 829 820 Generation
40 809 813 40
Building Solar Exposure Garden Solar Exposure
809 807 829 829 Generation 10 1 Total 692 690 730 Area Require 730
1 1
1 1
820 10
1 1
1 5
40000 659 -36
GardenTotal 1995 1995 Exposure Value
81 2001
GardenTotal 232 Value232 Exposure
62 222
Total Exposure 780 20 30 40 Value 5 Total Area Require 40000 40000 40000 40000 Total Area 40000 140000 40000 1 Require 1 40000 1Exposure High Exposure High High Exposure Exposure Total 40000 40000 40000 40000 40000 1High 40000 40000 40000 708 730 730 Area Require 718 659 692 708 718 659 Value Value Value Value Total Area Remain 7087 6991 6991 7032 Total Area Remain 10-1275 91 91 -36 Total Area Remain 7087 6991 6991 7032 5 TotalI Area Remain -1275 91 III 91 -36 IV II 1 GardenTotal 1 1 1 1 1 GardenTotalGardenTotal GardenTotal 81 81 Total Garden 81 81 81 81 81 1 81 Total Garden 81 81 Total Floor Area 1 1 10 1645 1650 1645 2063 1995 1995 2001 Total Garden Total Garden Exposure1650 Value Exposure Exposure 1645 Value 1650 Value 1650 1645 2063 Exposure 1995 Value 1995 2001 Require Require Require Require GardenTotal GardenTotalGardenTotal 62 GardenTotal 62 Total Garden 62 62 62 62 1 62 62 62 1 Total Sky Garden Area62 1 1 Total Garden Exposure Value Exposure232 Value Exposure232 Value Exposure -13 9 9 1 142 222 10 Total Garden TotalI Garden III1 232 1 1 II142 1 232 5Value -13 9 9 1 222IV Remain Remain Remain1 Remain 1 1 1 1 1 Total Floor AreaExposure 1 1 1 10 Building Solar 1 1 1 1 Total Exposure Total Exposure I 813 809 829 II809 829 807 820 III IV V VI 813 829 829 820 780 Total Exposure Total Exposure 780 809 809 807 Value Value Value Value1 Total Floor Area 1 1 1 10 20 50 Total SkySolar Garden Area 1 1 1 Garden Exposure 1 1 1 5 High Exposure High Exposure 708 730 730 718 659 692 692 690 High Exposure High Exposure 708 730 730 718 659 692 692 690 Value Value Value Value1 Total Sky Garden Area 1 1 1 1 10 20 Building Solar Exposure 1 1 1 I II III IV GardenTotal GardenTotal 81 81 81 81 81 81 81 81 Total Floor GardenTotal GardenTotal 81 Area 81 81 Exposure Value Total Garden Exposure Value Floor Area Building Solar Exposure 1 81 1 1 1 Exposure 1 1 Solar Exposure 1 Value 1 81 5 181 1 815 10 81 5 Exposure Value GardenTotal GardenTotal 1062 10 62 62 62 62 62 Total 62 Area62 GardenTotal GardenTotal5 Sky62Garden 62 62 Exposure Value Total Sky Garden 1 Area 1 1 Exposure 1 ValueValue 1 1 621 1 1 Exposure 1 1 Garden Solar Exposure 1 62 1 1 62 51 1 50 162 100 1 62 Exposure Value Generation
10
809
809
807
692
692
690
81
V 81 20
81
62 10
62 10
62 1
V 20 5 10 50
I 5 V 1 20 50 110
5 50
1 1 187
VI 50 5
20 5
10
Multiple Building Experiment For this experiment, we have increased to four buildings in to the generating sequence. The experiment was taken from the previous tests of four buildings with adding fitness criteria. In comparason with the two buildings, the generationGeneration time has increase 35 by a 70 large amount before or after adding the weighting.
Generation 70 weighting 105 Four buildings35without
140
40000 35 -13286
40000 140 -10152
Total Area Require Generation Total Area Remain
40000 70 -11305
40000 105 -10750
Generation 40 60 Four buildings20with weighting 40000 20 -14911
Total Area Require Generation Total Area Remain
40000 40 161
80
40000 60 179
40000 80 205
105Total 140RequireGeneration 80 Require Total Area 40000 40000 20 40000 40 40000 60 Total Total Area 40000 40000 40000 40000 Garden Garden 2664 2565 2537 2507 2744 1990 1989 1988 Require Require RemainTotal Area -13286 -1075040000 -1015240000Total Area Remain -14911 161 179 205 Total Area Require 40000 40000 40000Total Area 40000 Require-1130540000 40000 Total Garden Total Garden When we have added more building, the weighting of 464Remain 421 -14911400 161 395 179 Total Garden 717 290 493 402 Total -10152 Garden Total Area Remain -13286 -11305 -10750 Total Area 2664 2565 2537 2507 2744 1990 1989 1988 Remain Remain205 should also increased with a larger differnication beRequire Require Garden result. HowevTotal Garden tween them depending on theTotal desired Exposure Exposure 2664 2565 2537Total 2507 1988 1735 27441745 19901752 1989Total 1611 1555 1517 1566 Total Garden Total Garden Require Require1732 464 421 400 395 717 290 493 402 Value Value er, as we have explained earlier, the problem with the Remain Remain Generation 35 70 105 140 Generation 20 40 60 80 Generation 35 70 105 140 Generation 20 experiment is that although the weighting value beTotal Garden Total Garden Exposure High Exposure 464 421 400 High 395 402 892 908 717 915 290 914 493 Total Exposure 1377 1310 1276 1335 Total Exposure tween some of the criteria hasRemain increased, the differenRemain 1732 1735 1745 1752 1611 1555 1517 1566 Value Value Total Area Require 40000 40000 40000 40000 Total Area Require Total Area Require4000040000400004000040000400004000040000 Total Area Require 40000 Value Value ciation between them is very small. In result, the part Total Exposure Total Exposure Total Area Remain -13286 -11305 -10750 -10152 Total Area Remain -14911 161 179 205 Total Area Remain -13286 -11305 -10750 -10152 Total Area Remain -14911 1732 of1735 1745GardenTotal 1752 1566 with the weighting doesnt improved much in terms 40 40 1611 40 1555 40 1517GardenTotal 68 68 68 62 High Exposure High Exposure Value 908 915 914Total Garden 1377 1310 1276 1335 Exposure Value Value 892 Exposure Value Total Garden Value Value the generation time overall. Total Garden Total Garden 2664 2565 2537 2507 2744 2664 1990 2565 1989 2537 1988 2507 2744 Require Require Require Require High Exposure High Exposure 10 12 1377 12 1310 14 1276GardenTotal 31 31 31 31 892 908 915 GardenTotal 914 1335 GardenTotal GardenTotal Value Value Exposure Value Exposure Value 40 40 40 40 Total Total 68 68 68 62 Garden Total Garden Garden Garden Value 717 464 290 Total 464 421 400 395 Exposure Value Exposure 421 493 400 402 395 717 Remain Remain Generation 35 Remain 70 105 140 Generation 20 Remain 40 60 80 GardenTotal GardenTotal 40 40 40 GardenTotal 40 68 68 68 62 GardenTotal 3514 Total70 105 140 Generation 31 20 40 60 80 12 12 31 31 31 Exposure Total Exposure Exposure10Value Generation Exposure Exposure Value Total Exposure Total Total Area Require 40000 40000 40000 1735 Total Area1745 Require 1752 40000 Value 40000 40000 40000 Exposure Value 40000 1732 Exposure Value1611 1732 1555 1735 1517 1745 1566 1752 1611 Value Value Value GardenTotal GardenTotal Total Area Require 40000 40000 40000 40000 Total Area Require 40000 40000 40000 40000 10Total Area Remain 12 12 High-11305 14 31 31 31 31 -13286 -10750 -10152 Total Area Remain -14911 161 179 205 Exposure Exposure Value High High Exposure Exposure Value Exposure 1377 892 1310 908 1276 915 1335 914 High Exposure 892 908 915 914 1377
Generation
35
Total Area Require
40000
Total Area Remain
-13286
Total Garden Require
1
Total Garden Remain
Total Garden Require
2664
Total Garden Remain
464
Total Exposure Value 70 105 High Exposure 40000 40000 Value
-11305 -10750 GardenTotal 1 Exposure Value 1 2664 2565 GardenTotal2537 Exposure Value
Total Floor Area 464
421
1732 140 892 40000 -10152 40
1
2507 10
400
395
Total Exposure Value
Total1732 Sky Garden Area 1735 1745 Total Floor Area
Total Floor Area
High Exposure Value
Building Solar 908Exposure 915 Total892 Sky Garden Area
914
Total Sky Garden Area
GardenTotal Exposure Value
Garden Solar Exposure 40 40 Total Floor Area 40 Building Solar Exposure
40
Building Solar Exposure
GardenTotal Exposure Value
1752
1 10 12Garden12Area Total Sky
Garden Solar Exposure
Garden Solar Exposure
1 14
Value Total Remain -13286 Value -11305 Value -10750 -10152 TotalArea Garden 2565 2537 2507 2744 1990 1989 1988 Require GardenTotal GardenTotal Total GardenTotal 40 40 Garden 40 402664 68 2565 2537 250740 Exposure Value Exposure Value Require Exposure Value Total Garden 421 400 395 717 290 493 402 Remain GardenTotal GardenTotal Total 10 12 Garden 12 14464 GardenTotal 31 20 421 Value400 395 10 Exposure Value Exposure Exposure Value Remain Total Exposure 17351 1745 1 1752 1611 1555 1517 1566 1 1 Value Total Generation 20 40 Exposure 60 80 1732 1735 1745 1752 Generation 35 70 105 140 Generation 20 Value 20 High Exposure 908 915 914 1377 1310 501276 1335 Total Area Require 40000 40000 40000 40000 Value 1 Require 1 40000 High 1 Exposure 1 Total Area 40000 40000 40000 Total 40000 892 908 Area Require 915 914 20 Value Total40Area Remain -14911 161 10 179 20568 GardenTotal 40 40 68 68 62 5 Total Area Remain -13286 Exposure -11305 Value -10750 -10152 Total Area Remain -14911 GardenTotal Total Garden 40 40 40 40 2744 1990 1989 1988 GardenTotal Total Total Exposure Require 12 Garden 12 14 31 31 Garden 31 I 31 2664 2565 Value 2537 2507 2744 Exposure Value Require Require GardenTotal 10 12 20 12 1 1014 Total Garden 717 290 493 402 Value Total Garden Total Garden 1 1 464 1Exposure 1 400 Remain 421 395 1 1717 Remain Remain I 1 Total Exposure 1611 1555 1517 1566 Total Exposure Total Exposure 1 1611 Value 1732I 1735 1745II 1752 IV Value ValueIII High Exposure 1 1 1310 1 1335 1 10 1377 1276 High Exposure High Exposure 1 1377 Value 892 908 915 914 Value Value I II GardenTotal 1 50 1 1 68 1 68 68 1 62 1 1 20 GardenTotal GardenTotal Exposure Value Floor Area 1 68 40 Total 40 40 40 10 5 Value Exposure Value1 Exposure 1 GardenTotal 31 31 31 31 1 1 14 1 1 1 11 GardenTotal GardenTotal Exposure Value Sky Garden 10 1Total 12 31 1 12 Area 1 Exposure Value Exposure Value 1
Building Solar Exposure
1Building Solar Exposure 1
Garden Solar Exposure
Garden SolarIExposure 1
Total Floor Area Total Sky Garden Area
Total Floor Area
1
50
1 II 1 1
1 1
5 III 1 1
Total Area Remain
Total Garden 68 40 68 Require Total 12 Garden 31 31
10 Remain
622744 40
12
3171714
5
161 Value
1555
1 1 1310 68
1517 V
20
1276
III 110
1 1 20
31
1
5 31
1
1
50
IV 10 1
1
I 1
V 20 10
1989
290
493
1555
1517
1310
1276
402 31
31
31
1555
1517
1566
1310
1276
1335
68
68
31
20 31
IV 10
62 31
V 20
10
5
IV 1 10
V 10 20
1 1
5 10
IV
I V 5 112050
II 1
VI 50 50 5
I 1
1 55
1510
1 20 50
1
1
1 5
15
1
1 50
1 100
1
III 1
IV 10
5
II 1
1990
GardenTotal 290 493 Exposure Value
VI 50
1
179
68
1 1020 62 1 10 31
40000
161
68
1566
1 1 1335
40000
198868
III 1 1
68
205
60
GardenTotal 1990 1989
50
1611 80
5
II 1 1
179
50Exposure Value
High Exposure 40000 40000 40000 1377 Value 161 179 205 GardenTotal 68 Exposure Value 1990II 1989 50 1988III GardenTotal 1 31 1 5 Exposure Value 290 4931 402 1
1 1
40
Total Exposure 40 60 Value
10
-14911
40
100 VI 50 20
Multiple Building Experiment Six buildings without weighting Learning from the previous experuiments, we have tried to put all the result into the last experiment, which we increase to six buildings to generate. Looking at the result before added the weighting, Generation 30 the total area remain is more that 50% of the area 40000 required in 120 generations, andTotal it Area tookRequire more than half an hour to reach this stage. Then when we added Total Area Remain -31931 the weighting to each of the criteria accordingly. The Total experiments Garden result would be best out of all the we 3597 have done. Each of the areas haveRequire improved, left with only 1 % of error. Total Garden
Generation Total Area Require Generation
30
60
40000
30
40000
60
Six buildings with weighting
90
120
40000
40000
90
120
Generation
15
Total Area Require Generation
30
40000
40000
15
30
45 45
60
40000
60
40000
Area Remain -31931 -30151 -2960615 -2413230 Total Area Remain60 -17081 -6419 -2791 -156 60 Total90 45 Require Total Area120 Require Generation 40000 40000 40000 40000 Total Area 40000 40000 40000 40000 Total Garden Total Garden Total Area Remain 3597 -31931 -30151 3480 -29606 -24132 Total40000 Area Remain40000 -17081 2847 -6419 2316 -27912136-156 2004 40000 Require 40000 40000 Total Area3508 Require 40000 3206 40000 Require Total Garden
Total Garden
3597 3508 3480 2316 2136 2004 -30151 Total -29606 Total Area Remain -17081 3206 -6419 Total -2791 Garden Garden -1562847 Require-24132 1158 Require 1028 950 703 899 447 286 240 Remain Remain Total Garden 3508 3480Total Garden 3206 2847 7032316 Total Garden 2136 2004 899 1158 1028 950 447 286 240 Remain Remain Require Total Exposure Total Exposure 1755 1755 1760 1760 1831 1955 1961 1944 Value Total Value Generation 30 60 90 120 Generation 15 30 45 60 Exposure Total Exposure Total Garden 1755 1755 1760 1760 1831 1955 1961 1944 Generation 30 60 90 120 Generation 1158 1028 950Value 703 899 447 Value 286 240 Remain Remain High Exposure High Exposure 1462 1558 40000161740000 1622 Total Area Require 149540000 1500 40000 1492 40000 40000 Total Area Require 40000 40000 1609 Value High Exposure Value Total Area Require 40000 40000 40000 40000 Total Area Require High Exposure We can also see the difference by looking at the build1495 1500 1492 1462 1558 1617 1622 1609 Total Exposure Exposure ValueArea Total Area Remain Total -31931 -30151 -29606 Remain 1944 -17081 -6419 -2791 -156 1755 1755 1760Value 1760 1831 -24132 1955 Total 1961 ing massing between two part. Overall courtyard reTotal Area Remain -31931 -30151 -29606 -24132 Total Area Remain Value Value GardenTotal GardenTotal 95 98 107 107 68 131 129 126 cieved much more sunlight from the lower building GardenTotal GardenTotal Total Garden Total Garden Exposure Value Exposure Value 95 98 107 107 68 131 129 126 3597 3508 3480 3206 2847 2316 2136 2004 Total Garden Total Garden Exposure Exposure Value Require Value Require High Exposure High Exposure 3508 3480 3206 on the south and overall the building massing has 1495 de1500 GardenTotal 1492 1462 1558 1617 GardenTotal 1622 1609 3597 Require Require Value Value 40 45 55 55 23 74 92 89 GardenTotal GardenTotal creased in height. Total Garden Total Garden 40 45 55 55 23 74 92 89 Exposure Value Exposure Value 1158 1028 950 703 899 447 286 240 Total Garden Total Garden Exposure Exposure Value Remain Value Remain 1158 1028 950 703 GardenTotal GardenTotal Remain 95 98 107 107 68 131 Remain 129 126 Generation 30 120 Value Generation 15 30 45 60 Exposure Value Total60Exposure 90 Exposure Generation 30 Total Exposure 60 90 120 Generation 15 30 45 1755 1755 1760 1760 1831 1955 1961 1944 Total Exposure Value Value 1755 1755 1760 1760 Value 4000055 40000 55 40000 GardenTotal 40000 Total Area Require 40000 40000 40000 89 40000 23 74 92 Total Area Require 40000 40000 40000 40000 Total Area Require 40000 High Exposure High Exposure Exposure 1495 Value 1500 1492 1462 1558 1617 1622 1609 High Exposure Total Area Remain -31931 Value -30151 -29606 -24132 Total Area Remain -17081 Value -6419 -2791 -156 1495 1500Area Remain 1492 1462 Total Area Remain -31931 Value -30151 -29606 -24132 Total -17081 100 100 Total Garden Total Garden 3597 GardenTotal 3508 3480 3206 2847 GardenTotal 2316 2136 2004 95 98 107 107 68 131 129 126 Total Garden Total Garden GardenTotal Require Require 3597 Exposure 3508Value 3480 3206 2847 Exposure Value 95 98 107 107 50 Value Require Require Exposure 50 GardenTotal GardenTotal 20 Total Garden Total Garden 40 45 55 55 23 74 92 89 1158 Exposure 1028 Value950 703 899 447 Value286 240 GardenTotal Total Garden Total Garden 20 40 45 55 55 Remain Remain 1158 Exposure 1028 Value 950 703 899 5 1 1 1 Remain 1 Exposure Remain5 1 1 1 1 100 Exposure Generation 30 60Total Exposure 90 120 1755 Generation 45 60 1831 1755 1760 151760 30Total 1955 1961 1944 Total Exposure Total Exposure Value Value 1755 Generation 1755 1760 1760 Generation 30 60 90 120 15 30 45 601831 Value Value 50 Total Area Require 40000 40000 40000 40000 Total Area Require 40000 40000 40000 40000 High Exposure High Exposure 1495 1500 1492 1462 1558 1617 1622 1609 Total Area Require 40000 40000 40000 40000 Total Area Require 40000 40000 40000 40000 High Exposure High Exposure 20 Value Value 1495 1500 1492 1462 1558 Total Area Remain -31931 -30151 -29606 -24132 Total Area Remain -17081 -6419Value-2791 -156 Value 5 100 Total Area Remain -31931 -30151 -29606 -24132 Total Area Remain -17081 -6419 -2791 -156 1 1 1 1 I II III GardenTotal GardenTotal 95 Total Garden 98 107 107 68 131 129 126 Total Garden GardenTotal Exposure Value 3206 Exposure Value 2004 95 Total 98 107 107 1 GardenTotal 1 3597 Total 3508 3480 2847 2316 2136 Floor Area Total Garden Garden 50 1I Require Require IIExposure 2136 III 68 Exposure 3480 Value Value 3597 3508 3206 2847 2316 2004 Require Require GardenTotal GardenTotal 20 40 45 55 55 23 74 92 89 Total Floor Area 1 1 23 GardenTotal Total Garden Total Garden Exposure ValueArea Exposure Value 55 1 1GardenTotal 5 Sky Garden 1158 Total 1028 950 703 899 447 286 240 40 Total 45 1 55 1 1 1 Exposure 1 Value Exposure Value1 Total Garden Garden Remain Remain 1158 1028 950 703 899 447 2861 240 1 1 1 Remain Remain SkyBuilding Garden Area Total Exposure Total1755 Total Exposure Exposure 11 1 1 1 1 1755 Solar 1760 1760 1831 1955 1961 1944 Total Exposure Exposure Value Value 1755 1760 1831 1961 I1755 II1760 Total III IV1955 V1944 Value Value High Exposure Building High Exposure Solar Exposure 1 1 Garden Solar Exposure 1 1 1 Total Floor Area 1 1 1 10 201 1495 1500 1492 1462 1558 1617 1622 1609 High Exposure High Exposure Value Value 1495 1500 1492 1462 1558 1617 1622 1609 100 Value Value I II III GardenTotal Garden Exposure 1 1 1 95 Solar 107 107 68 1311 129 1261 Total Sky Garden Area GardenTotal 1 1 10 Total98 Floor Area 1 1 1 50 Area GardenTotal GardenTotal Exposure Value Exposure Value Floor 95 Total98 107 107 68 131 126 50 129 Exposure Value Exposure Value 20 GardenTotal GardenTotal 20 40 Total45Sky Garden 55 Area 55 23 74 92 89 1 1 1 5 GardenTotal GardenTotal1 Building Solar Exposure 145 1 1 74 5 89 1 1 1 Value 1 Exposure Value Exposure Sky Garden 40 Total 23 92 155 Area55 1 1 Value 1 Exposure Value Exposure
GardenTotal Exposure Value
Garden Solar Exposure
Total 40Area Require 45
Building Solar Exposure
Building 1 Solar Exposure 1
Garden Solar Exposure Total Floor Area
Total50 Floor Area
Garden Solar Exposure I 100 1
1
1
1
1
1 II 1
III 1
1
5
50
1 IV 10
I 1
V
30
45
40000
40000
40000
-17081
-6419
-2791
2847
2316
2136
899
447
286
60 1831
1955
1961
Total Exposure Value 40000 40000 High Exposure -6419 -2791 Value
40000 1558 -156
1617
1622
GardenTotal 2316 2136 Exposure Value
2004 68
131
129
GardenTotal 447 Value 286 Exposure
23 240
74
92
1955
1961
1944
1617
1622
1609
IV 131 10
V 129 20 126 IV V
50
10 92
1 74
89 10 20
5
1 VI 1 50
IV 10
5 I 20
V 100 20
5 1
10
1
1 1
1
5
1001
5 100 20
15
II 1
VI 50
1
VI 50
20 5
20
5
10
5
50
5
100
5 50 III 1
VI
II 50 50 1 1 1 1
20 5 100
89IV
10
Critical review With the undertaken experimentation we were able to methodically understand the effects of each parameter on the built form. Using this as a reference and evaluating the built form at each step we were able to calibrate the differential weighting of parameters. With each experiment we were able to improve the performance of the built form; however, a number of other aspects could have been considered to provide better results: · The built form predominantly looked at pedestal-building kind of typology which gave uniformity to the design. Studying and employing other kinds of typologies would have added variation and differentiation in the built form. · The process of network optimization looked at pedestrian links that were created as a result of the chosen built typology. These small connections did not materialize into networks as these were small and broken, however, modifying certain aspects of the process and taking into consideration these links as another level of pedestrian network would not only add an interesting character but also optimise pedestrian connections.
· The process did not take into account distribution of functions. This aspect could have been added to the evolutionary process, where the built typology informs the kind of function the building or part of the building has and at the same time calculates the desired distribution of functions. For example, in a certain situation of pedestal-building it is decided that the pedestal part would always be commercial and that this part would always be a certain percentage of residential part. The evolutionary could be used to optimize this in addition to other parameters. · There was no feedback loop between the process that generated the footprint and the process that generated the built typology. It was a linear process that generated the footprint in the first step and built typology in the next. Therefore in situations where a combined footprint would have been better optimized could not be achieved. · The process that dealt with solar exposure did not present a true scenario as buildings in each block were generated independent of their surrounding buildings in neighbouring blocks. So, the buildings on the south side which shadow the other building were not considered. The ideal way would be to generate buildings of the entire site together, but limitation in computational power did not allow us to do this.v
91
High density city with a maximized pedestrian mobility between the green zones of the area
Green domination Overall conclusions
Conclusions The six week studio task provided opportunities to learn tools, techniques and introduced various aspects of urban design thinking. The time span did not allow a complete and comprehensive process of design but brought into acquaintance aspects that are needed to understand complexities of city design. The sequential and systematic method was adopted to establish a process driven design approach. This presented scope to adopt various different computational tools like circle packing algorithm, subdivision algorithm and generative algorithms to produce results in different parts of the system. The critical review of each part covers the shortcomings in that part but the analysis of the whole design process needs further assessment. The process is a top down approach and at certain points becomes extremely deterministic. It would be interesting to analyse the same process in a bottom up approach where the built morphology informs the network systems. The system lacked well-defined feedback logic; the small feedback circles were localized to each part and did not inform the system as a whole. The process would be better placed if the built morphology was able to inform other aspects like networks and green spaces to reach a more optimized condition. Further, we adopted an evaluation criterion that analysed only the built morphology, the other processes and the final result lacked an evaluation system which would be able to tell that the designed system is better than the existing conditions. The approach also did not convincingly consider the existing conditions like the dock on site. The process educated us on a lot of aspects to be considered while designing an urban scenario. It helped in understanding inter parameter dependencies and multi parameter integration and differentiation.
93
streets greens
Green park chain within the high density city
6m passage
Secondary green
9m street
9m street
4m passage
Primary green
Visual connections of the primary green zones
95
PAVEMENT 1.35
BICYCLE ROUTE
PAVEMENT
2.40
1.35
12.00
12 meter road with building setback
Pedestrian pass from one park to another
PAVEMENT 1.30
BICYCLE ROUTE
PAVEMENT
2.40
1.30
6.40 9.00
9 meter road next to green pocket
Secondary green zones
97
PAVEMENT
GREEN BELT
PAVEMENT 1.50
1.50 6.00 9.00
9 meter road with elevated greens
Tertiary green, terraces and roofs
99
High density city with a maximized pedestrian mobility between the green zones of the area
Green domination Personal reflections
Personal reflection 1 Student :: Anna Kulik Word count :: 795 words Signature ::
Having the absolutely cleaned Site – as it was determined by the brief – brought us (and almost all other groups) to a very open position. Apart of the water edges and transportation nodes there were not really other attractors or things to start with. Constructing a computationally driven design system, we needed a theme, a topic, an architectural ambition that could possibly lead us to the first steps of our patch-design, with all its following complexity of networks, clusters, blocks. Having a much opened position I started with the research of green zones, parks, squares in different cities, coming up with the “Green domination” topic. It was a successfull study as whithin a group we wanted to create a patch of a high density city, very well interconnected by the green chain of parks, with visual connections from one park to another, where everyone had an easy access to a high quality open area or a private green zone. That became our architectural ambition, where the green zones were playing a dominant role.
“High density city with a maximized pedestrian mobility between the green zones of the area” Having a lot of tasks to complete in this project, many areas were not explored to its maximum. Doing the analysis of the green zones in different cities, I was finding it as a theme that could have been investigated much more than in the end it was. Even
though the green space became the leading theme of our project, the main question of justification of quality of the space still wasn’t answered and personally I believe that if there was enough time and effort to do so, it would bring the project on a completely different level. Even though we’ve learned a lot about the activities that could be accommodated in the green zones, users of those spaces, in scales from a huge park up to a small terrace, we didn’t really manage to incorporate that knowledge to the chain of the green spaces we created, or maybe were not successful enough in showing that information. Talking about the way of constructing this system, we were trying to be experimental and ambitious, attempting to create a very flexible algorithm with variable parameters (Figure 1). The total green area parameter was a dominant one, as for us it was the starting point. The green zones were supposed to give us nodes, nodes were bringing us to networks and step by step the system was getting more and more complex. The question, which surprisingly didn’t appear that time but is very critical now, is what if we could actually connect those parameters as well? That would make the whole system interconnected and dependant where change in only one specific parameter would affect the whole outcome dramatically, providing variable possibilities in all parts of sequence.
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Anyhow, for our system it was decided to take 25% of the area to be occupied by green as a starting point, and then it was supposed to be a flexible parameter with a possibility of increment. All of the 25% were decided to be equally distributed along the area, but what I might begin to question now is if it has been better to put them all in one area? And could it be aggregated in a different way?
This is a result of a very experimental system, process we created, and we can’t really use the process to justify the end. Which is why we should be quite neutral in assessing it, I suppose. For such a short time that studio was more like an exercise, we were learning techniques, algorithms, but not really critically analysing what and why we were doing. Writing this critical reflection now, I think it was a great introduction before the final dissertation project.
In many urban scenarios all the green would be allocated somewhere on Site as a one big peace (Figure 2, 2a). For an ecological urbanist it could be possible to position the main green mass along the water edges, transforming them into an eco-park land, and then having some fingers connecting it (Figure 3, 3a). There is no right or wrong about it, and our group took the decision based on specific reasons - computationally we were searching for the nodes for our pedestrian network, and at the same time conceptually there was an idea of the green network chain with visual connections, where from one park could be seen another (Figure 4, 4a). But looking on what we’ve achieved, we see the outcome of computational system that we designed. And almost as any other group, we have got a relatively homogeneous distribution through the area, which probably would be quite different with a different primary strategy for the allocation of the green zones.
Figure 1:: Data Catalog; Figure 2:: 25% of green zone allocated as one big peace; Figure 2a:: New York, Central Park; Figure 3:: Eco park land along the edges; Figure 3a:: Sochi Olympic Park project; Figure 4:: Green network chain, final result; Figure 4a:: Render Green Domination project.
F1 F2a F3a F4a F2 F3 F4
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Personal reflection 2 Student :: Shih-Hwa Hung Word count :: 450 words Signature ::
Through the Core2 studio, my contribution started with advocating the “green pedestrian network� idea derived from the understanding of High Line project in New York City which though laid more emphasis on reusing industrial overpass as elevated public green belt, the concept derived is focused on the urban typology within its influencing area. Urban green spaces in the future urban scenario should be deployed efficiently which efficiently embedded in urban tissue and at the same time benefit people who work and live in by offering leisure public open space. The group studied and defined urban green typologies and network connection while at the same time, I researched the street scale in sections for possible implementation of integrated greens and passage along buildings . These section studies include street with traffic, pedestrian only paths (promenade), elevated passage, primary ground green area, local courtyard green, green pockets and elevated green belt. Public spaces, retail spaces and leisure spaces are all integrated with network to provide mobility for people while enjoying green spaces which supports our argument to construct a feasible walking and cycling city. After the blocks and primary network have been developed, I experimented with various algorithm to compute the further subdivision of blocks into proper plot area and layout local green area within blocks. The result was prominent with distribution of three types of plots and local greens met the target ratio in three defined zones.
Prevailing greens and networks of different hierarchy were successfully planned out. However, the working sequence failed to differentiate urban morphology and led to less distinct orientation and over identical planning. I worked with the group to further study differential weighting and concluded the distribution of primary green and blocks should consider the influence of water front and Canary Wharf district as reference while writing more specific algorithm targeting more than just three roughly assumed zoning border. My personal interest derived from this project is to fully explore the potential of emergent technology to push the urban green infrastructure which emphasizes on the performance of green and network with architectural design as a mean of measuring and performing to lower urban energy consumption and self-sustain the system with introduction of biodiversity. While setting up this ambitious goal to utilize green infrastructure as an urban engine, architectural programs and planning logic will simultaneously calibrate the direction of the project to ensure the feasibility in existing urban context.
Personal reflection 3 Student :: Yuchen Wang Word count :: 780 words Signature ::
Public Spaces - Design the quality
The core 2 project ‘Green Domination’ is focused primarily on the open spaces that contained primary and secondary green parks, pedestrian only networks, and elevated public/ private spaces. At the same time, I have also investigated on the relationship between the green parks, the pedestrian network with the building typology; and how it was generated by the influence of other factors. The differncial weighting experiments with multipul criteria for the building typology was very successful in order to have a bettering understanding of the GA generating process. However, I think one of the crucial part that was not been focused on, which is the quality of the public spaces. Although, one of the fitness criteria was the solar fan that ensure the building doesn’t not cover the public green park for it to receive 4 hours direct sunlight, the design of the public parks and the pedestrian network were not considered.
as a large influence with the planning process. The characteristics of public space vary in the change of culture, environment and the design of the space. These factors ensure the quality of the public space. At the same time, the change of climate conditions around the world or through day to night in the same location can have a huge effect on the public space. Such as the humidity, air flow, sunlight and rain fall. For each project, these factors have to be though to ensure the quality of the public spaces. It would be different on the activity and social behaviour of how would people react and interact to public spaces in different culture or environment. At last, I would research on the design of the public space and how it could be combined with the urban infrastructure.
Public space is a crucial factor in each city, town and village. The uses of public space can be though as space for people to meet or social, for people to travel and for people to trade or exchange. Traditionally, these activates could be all balanced and occurred at the same place. With the fast growth of traffic and economical trading, street has taken over by cars and shops have moved indoor to a multilevel shopping mall. The sense of public space has disappeared.
There are many different factors for designing a public space. The initial separations of size differences, they can be categorised into five different types. That is the public green, plaza, courtyard, pocket space and promenade. Each of the type would have slight variation on the activity and purpose of the designed public space. For example, plaza would consider serving a district or the city with different and generally are well connected with high visibility. For all these different types, they can all be measured with the physical qualities.
Especially in developing countries, with the fast generation of populations, the building and streets are the primary focus, and the open space would not be considered
The physical qualities could be compared with different types of public space and they can be measured in the flowing factors.
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Street frontage: It would be the connection between the public space and the adjoining street. The frontage sized would be as large as possible to ensure a good visibility and popularity.
Long street frontage
Frontage on both sides
Width to Length ratio: The ratio can be defined the visibility and activity of the public space. The elongated space would more be used as a circulation purpose rather than the well-proportioned space can be used as a multiple functions for the users.
Well- proportioned space
Elongated space maily for circulation purposes
Minor space is visible and connected to major space
Minor Space of appropriate proportion and having street frontage
Major / Minor space: This would be similar to the width to length ratio that also can be defined as the activity usage. A major space is generally the large area and attached with the minor spaces. The different shape and how they can be combined together will have an effect on the activities.
Visibility and openness: The direct visible contact without blockage for the people welcomes them into the public spaces. At the same time, the integration of canopy and trees within could create more confortable micro climate.
Length of stay/ Amenities arrangement: The public space can be attached with the urban infrastructure to create a combined space. The integration between the building and open spaces determines the different type of space circulation such as passing by or attraction.
Flexible central space for a variety of activities
Flexible and variety in arrangements
To conclude, people has come to more awareness on the design of the public space and its quality can be measured in many different ways. Looking into the idea of Hutong in China, a social community is formed with in the public spaces. The private and public spaces are well integrated to form the social infrastructure. Could this social interaction be recreated in the dense urban cities?
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Personal reflection 4 Student :: Abhinav Champaneri Word count :: 665 words Signature ::
The Un-Built Vitality
The ‘Green Domination’ research and experimentation process was based around quality and connectivity of green spaces. The exercise inspired an idea that considers open spaces not only as a requirement and characteristic but as a driver and an aspect that shapes built forms and networks of an urban scenario. As an afterthought, the undertaken design exercise to some extent uses parts of this idea in addressing the quality of green spaces and in its design of elevated greens. However, design of open spaces can influence a number of other parameters that can inform built environment and the subsequent networks of vehicular and pedestrian access. In this notion, open spaces become the ruling factor and the main conceptual aspect on which the rest of the parameters shaping the city or locality depend. The perceptible image of a city or a locality can generally be described by their configuration of open spaces. These spaces can vary from streets, garden and plazas in public domain to terraces and balconies in private domains. Further, these spaces are an embodiment of social, cultural and in most cases environmental aspects of the place. These not only indicate how individuals use the space but also how the built environment of an urban system accommodates these spaces. These factors are localized therefore the response in terms of built and un-built spaces is also localized. So, open space configuration becomes an important aspect that gives a place a unique identity
differentiating it from other places. To work with open spaces one of the important aspects to be considered is their quality. Quality of open space has no hard and fast definition, one of the interpretation of this was used in the design exercise where the quality was defined by the connectivity and solar exposure obtained from sun fan factor. Apart from this a number of other factors can contribute to quality: accessibility and proximities to and from strategic location is one of the obvious parameter. Environmental response is another measure of quality: one of the ways to interpret this is to study how open spaces respond to environmental factors and how effective are they in creating a micro-climate suitable for use. These qualities shape open spaces which in turn shape the built environment around them. Experiential qualities that a space is able to provide becomes a different kind of qualitative judge of that space: it can be analysed on the basis of the experiences it can provide in terms of activities, views etc. Quality of open spaces should be a measure that not only defines the usability but also the appropriate application of open spaces. There are other factors that cannot be generalized but are essential to be considered in designing and studying open spaces like cities and localities that have a unique natural
setting. For example, in edge cities where they have a sea or a river in their proximity, the open spaces along these banks naturally attract people as they provide a unique experience. Another aspect that can prominently affect open space configuration and built morphology is hierarchy that it follows which can be with accessibility or visibility. Designing with open spaces as the core driver provides a lot of scope to design environmentally responsive and aesthetically rich urban systems. The dependencies of parameters on open spaces would influence and inform variables affecting size, shape, orientation, proportion, location and arrangement of not only the open spaces but also of the built forms. Some of the parameters can be read in a direct way such as open space – built form ratio or environmental factors like solar exposure, however, other parameters such as hierarchy of spaces and those that define experiential qualities which are the softer aspects of the place need to be addressed with a different perspective. Innovative methods need to be researched and experimented to read and measure this kind of information and implement in the design. This could add depth to an otherwise very technical design.
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ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES COVERSHEET FOR SUBMISSION 2012-13
Programme::
Emergent Technologies & DesignA
Term::
2013 - Term 2
Course title::
Core Studio II
Course tutor::
Michael Weinstock, George Jeronimidis Evan Greenberg, Mehran Gharleghi
Submission date::
01 May 2013
Student name(s)::
Abhinav Champaneri Anna Kulik Shih-hwa Hung Yuchen Wang
Submission title::
Green Domination
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):
Date::
01 May 2013
Thank you. 111
Green Domination project documentation. EMTECH Studio 2013. Archiitectural Association School of Architecture (AA)