nutri.net
ALANKRITA AMARNATH IOANNIS BOUSIOS MARGARITA CHASKOPOULOU JUNQIAO LI
THE BARTLETT SCHOOL OF ARCHITECTURE UCL MARCH URBAN DESIGN B-PRO,RC14
MArch URBAN DESIGN RESEARCH CLUSTER 14 TUTORS: ROBERTO BOTTAZZI, TASOS VAROUDIS, EIRINI TSOUKNIDA, VASILEIOS PAPALEXOPOULOS
nutri.net
ALANKRITA AMARNATH IOANNIS BOUSIOS MARGARITA CHASKOPOULOU JUNQIAO LI
CONTENTS INTRODUCTION THEORETICAL BACKGROUND MULTI-SCALAR APPROACH GREAT BRITAIN SCALE FOOD NETWORKS
5 11 13 19
DATA CROSSING ANGULAR SEGMENT ANALYSIS DATA CORRELATIONS
23 25
DATA ANALYTICS AND MACHINE LEARNING MACRO SCALE: DATA DISTRIBUTION MACRO SCALE: PCA AND K-MEANS MESO SCALE: DATA DISTRIBUTION MESO SCALE: PCA AND K-MEANS SITE SELECTION
33 35 39 43 45
SITE ANALYSIS AND SIMULATIONS MICRO SCALE SITE SUNLIGHT ANALYSIS VISIBILITY ANALYSIS SOCIAL MEDIA TRAFFIC VOLUMES AND FLOWS AGENT FLOWS AGENT BASED SIMULATIONS CELLULAR AUTOMATA
49 51 53 55 57 59 61 63
DESIGN STRATEGY INTERVENTION HIERARCHIES GROWTH NETWORK ALGORITHM (GNA) USER PATHS FORM FINDING ITERATIONS AND DESIGN PROGRAM DISTRIBUTION MASTERPLAN SECTION PRODUCTION CANOPIES VERTICAL GROWTH LONDON OVERGROUND GNA APPLICATION AGENT BASED EVALUATION DESIGN VISUALISATIONS
REFERENCES
67 69 85 87 89 91 95 97 101 113 121 125 129 133 139
INTRODUCTION
INTRODUCTION
3
CAN THE RE-EVALUATION OF FOOD FLOWS LEAD TO AUTONOMOUS AND EQUITABLE POST-ANTHROPOCENTRIC URBAN ENVIRONMENTS?
INTRODUCTION
THEORETICAL BACKGROUND The rapid urbanization of the last decades has revealed unsustainable ecosystems, led by consumerism, along with the immeasurable augmentation of production. Now, more than ever, with climate change in the spotlight, cities need to become an integral part of their own solution, re-establishing the balance between the city-consumer and the producing countryside. Food production and distribution is an obscure factor of the environmental balance and in the long run, a major issue for the future city. In the era of the pandemic, the relation between the city-dweller and food consumption has been re-interpreted by the evolution of the food purchases, the minimization of food consumption in public space but also the re-evaluation of food production as part of everyday life. Throughout the pandemic, food remained in the forefront, while the intention of dwellers to cultivate even in the smallest of balconies brought up the necessity of reconsideration of urban space.
5
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STORAGE
OPEN
WH
ES
MARKE
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UC
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S WA
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PROD
SP
PR OC ES SI NG
N IO AT IV LT CU
TR
FRAGMENTED FOOD CIRCLE 6
INTRODUCTION
TIMELINE
1945-49 1898
1934-35
NEW REGIONAL PATTERN L.HILBERSEIMER
GARDEN CITY MOVEMENT EBENEZER HOWARD
BROADACRE CITY FRANK LLOYD WRIGHT
RELATED PROJECTS TECHNOLOGIES PRODUCTION PRACTISES 1914-18 WW1
1929 GREAT DEPRESSION
1908
SMALL HOLDINGS AND ALLOTMENT ACT
1913
HABER-BOSCH PROCESS FERTILIZER PRODUCTION ON INDUSTRIAL SCALE FIRST SUPERMARKET MICHAEL CULLEN
1930 7
1939-45 WW2
2006
FIRST 3D PRINTED FOOD
1999
INTRODUCTION OF VERTICAL FARMING CONCEPT DICKSON DESPOMMIER
2020
1999
PIXEL FARMING LENORA DITZLER
TERRITORY FOR THE NEW ECONOMY ANDREA BRANZI
1947-91 COLD WAR
AGRONICA ANDREA BRANZI SUPERMARKET OF THE FUTURE CARLO RATTI
GREEN REVOLUTION
1993-94 1950-60
1994
2017
GENETICALLY MODIFIED FOOD
1980
INTRODUCTION OF AGRICULTURAL ROBOTS
2013
FIRST CULTURED HAMBURGER MARK POST 8
INTRODUCTION
FOOD FACILITIES SURFACES PRODUCTION AREAS
PROCESSING AREAS
GREENHOUSE 69000m2
MARKET STAND 5m2 KITCHEN 15m2
VERTICAL FARMING 50m2
RESTAURANT 230m
ALLOTMENT 250m2
BUTCHER/FISHMONGER 85m2
POTATOES 20000m2
BAKERY 200m2 SUPERMARKET EXPRESS 280m2
CEREALS 150000m2 PIG HERD 4000m2
SUPERMARKET 5000m2
COWS / BEEF HERD 340000m2
COWS / DAIRY HERD 740500m2
WHOLESALER 12000m2
QUALITY CONTROL LAB 1800m2
SHEEP HERD 1400000m2
PROCESSING FACTORY 5000m2 9
IMPACTS OF FOOD DESERT ALLOTMENTS | FOOD ACQUISITION ALTERNATIVES SERIOUSLY DEGRADED, ALLOTMENTS SCARCE
HEALTH | SPREADING OVER AREAS OF HIGH BAD HEALTH RATES AND INCREASED ANXIETY SYMPTOMS
ETHNICITY | HIGH POPULATION CONCENTRATIONS OF ASIAN ETHNIC BACKGROUND
FOOD DESERT | UNEQUAL ACCESS TO AFFORDABLE AND HEALTHY FOOD OPTIONS
RESTAURANTS | ASSOCIATED WITH EXCLUSIVE AVAILABILITY OF LOW QUALITY READY MADE FOOD
ANNUAL INCOME | LINKED WITH LOW INCOME AREAS AND SIGNIFICANT UNEMPLOYMENT
ETHNICITY | HIGH POPULATION CONCENTRATIONS OF AFRICAN ETHNIC BACKGROUND
10
INTRODUCTION
MULTI-SCALAR APPROACH In order to fully grasp the complexity of food network and its imprint on built space, it is crucial to analyze it in a multiple scale approach. Starting from a national level, food flows have an impact on the transportation systems, the population concentration but also land segregation. At the urban level, food accessibility could affect the segregation of economical classes, the inhabitants’ diversity cluster distribution and by extension the character of a neighborhood. As a multi-scale issue, food flows are strongly related to the environmental concerns since they consist of the generators of additional harmful emissions, while supporting the non-sustainable system of demand instead of the local and seasonal production. In that context, re-evaluating the food flows could have a positive impact on the physical health of the inhabitants while restricting the food transportation to be more local.
11
BRITAIN
LONDON
MACRO
12
MESO
MICRO
INTRODUCTION
GREAT BRITAIN SCALE: TRANSPORTATION NETWORK
LEED 1.9
The road network depicts the dependence of cities to their surroundings, with urban territories resembling to nodes of converging roads, the modern-day paths of supplies. Line lengths indicate the traffic congestion.
GLASGOW 1.26mil
LONDON
MANCHESTER
TRAFFIC FERRY MOTORWAY PRIMARY ROAD SECONDARY ROAD RAILWAY 13
MANCHESTER 2.7mil
BRITAIN
LONDON
MACRO
MESO
MICRO
DS mil
BIRMINGHAM 2.6mil LONDON 10.9mil
ABERDEEN 547MILES
NORWICH 116MILES BIRMINGHAM 126MILES SHREWSBURY 168 MILES BRISTOL 119MILES
BRIGHTON 53.3MILES
0
250km
PRODUCT TRAVEL DISTANCE
100
14
200
300km
INTRODUCTION
GREAT BRITAIN SCALE: VISIBLE AND INVISIBLE NETWORKS The outdated techniques of dispersed mass food production attempting to cover the increased demand, are dependent on the existing networks, setting a burden on the national scale. Food flows consist one of the invisible network on that level, with others being the power, the communication and the water network. Even though not directly related, the infrastructure of food systems is based upon a network allowing the rapid exchange of information, while closely monitoring the needs and changing of food habits. Additionally, the provision of accessibility to power systems is strongly related to the size and the location of food infrastructure. In the same scope, the natural network of surface and underground water streams highlights the needs of this system along with the danger posed by fertilizers and chemicals used.
15
BRITAIN
LONDON
MACRO
GLASGOW 1.26mil
MESO
MICRO
LEEDS 1.9mil
MANCHESTER 2.7mil
BIRMINGHAN 2.6mil
LONDON 10.9mil
POWER GRID GAS PIPE WINDPOWER GENERATOR
POWER NETWORKS
MOTORWAY RAILWAY TV,RADIO MAST ANTENNA
INVISIBLE NETWORKS
WATER NETWORKS
MAIN RIVER SECONDARY RIVER HIGH RISK AREA 16
INTRODUCTION
GREAT BRITAIN SCALE: FOOD DESERT
LEED 1.9
The main urban hubs sustain low food desert score in addition to high numbers of retail establishments providing their residents with the adequate goods. Distance to the urban centers is inversely proportional to the food desert score.
GLASGOW 1.26mil
LONDON
MANCHESTER
RETAIL SIZE RETAIL FOOD DESERT LOW
HIGH 17
MANCHESTER 2.7mil
BRITAIN
LONDON
MACRO
MESO
MICRO
DS mil
BIRMINGHAM 2.6mil LONDON 10.9mil
0
250km
FOOD PROCESSING PLANTS AS URBAN SATELITES
100
18
200
300km
INTRODUCTION
FOOD NETWORKS Integral parts of the food flows are the various in-between steps following the raw production, setting a complex, multi-centered network of interconnected nodes, with the aim of collecting, processing and distributing food. This system commences from the various categories of processing plants, passes through packaging centers and distribution hubs and ends on the numerous retail locations. The main urban hubs retain most of the retail activity, leading to the lowest food desert score, however they showcase the lowest concentration of production areas, perfectly displaying the requirement to correct the environmental imbalance.
COLD STORAGE DISTRIBUTION CENTRE WHOLESALE MARKET
100
200
300km
19
BRITAIN
LONDON
MACRO
20
MESO
MICRO
DATA CROSSING
DATA CROSSING
ANGULAR SEGMENT ANALYSIS Primary part of the data analytics pursued throughout this section, is the Angular Segment maps. Approaching the issue of centrality of specific urban areas, Choice and Integration maps were produced, in order to evaluate the location of different phenomena. A complete structure of primary and secondary roads is revealed, underlying both the overall London scale circulation hierarchy, along with the various specific neighborhood street organization.
INTEGRATION R500 METRIC
CHOICE R500 METRIC
INTEGRATION R1000 METRIC
CHOICE R1000 METRIC
INTEGRATION R2000 METRIC
CHOICE R2000 METRIC
23
BRITAIN
LONDON
MACRO
INTEGRATION R1000 METRIC 3-48 48-78 78-114 114-167 167-309
MESO
MICRO
1.5
24
3
4.5km
DATA CROSSING
DATA CORRELATIONS | FOOD PATTERNS The basis for this first set of London scale data association maps is the “Food Desert Rate”, describing the access to affordable and healthy food options. Apparently the presence of restaurants, as well as retail areas, isn’t always related with low levels of food desert rate. As it appears, the offered services and products of quality are not equally distributed throughout the city. Alarming is also the fact that the extended levels of food desert are accompanied by increased “Bad Health Rates”, while “Income Levels” are also seriously lowered over the same areas, in a sense highlighting territories of degradation.
RESTAURANTS
BAD HEALTH
FOOD DESERT
ANNUAL INCOME RATE
COMMERCIAL BUILDINGS
RETAIL BUILDINGS
BUILDINGS BASEMAP 25
BRITAIN
LONDON
MACRO
MESO
MICRO
1.5
26
3
4.5km
DATA CROSSING
DATA CORRELATIONS | FOOD PATTERNS
NOx NO x EMISSIONS
SUPERMARKETS RED MEAT P.PURCHASE | ASIAN ETHNICITY
ALLOTMENTS
UNEMPLOYMENT RATE
BUILDINGS BASEMAP RED MEAT P.PURCHASE | BLACK ETHNICITY
27
BRITAIN
LONDON
MACRO
MESO
MICRO
1.5
28
3
4.5km
DATA CROSSING
DATA CORRELATIONS | FOOD PATTERNS
DEPRIVATION SCORE
RED MEAT P.PURCHASE
FRUIT | VEGETABLE P.PURCHASE
RED MEAT P.PURCHASE | MIXED ETHNICITY
ETHNICITY MIXED
ETHNICITY BLACK
ETHNICITY ASIAN
VEGETABLES P.PURCHASE | WHITE ETHNICITY
ETHNICITY WHITE
29
BRITAIN
LONDON
MACRO
MESO
MICRO
1.5
30
3
4.5km
DATA ANALYTICS AND MACHINE LEARNING
DATA ANALYTICS AND MACHINE LEARNING
MACRO SCALE: DATA DISTRIBUTION
INCOME
TESCO TRANSACTIONS
RESTAURANTS
UNEMPLOYED PERSONS
RED MEAT
SUPERMARKETS
ETHNICITY
RESTAURANTS
DEPRIVATION
33
LONDON
MACRO
MESO
MICRO
FRUIT & VEG
BRITAIN
INCOME
AGE 20 - 29 RESTAURANTS
NOX EMMISIONS
CO2 EMMISSIONS MOOD & ANXIETY
EMISSIONS
MOOD & ANXIETY
FRUIT & VEG
PM10
RED MEAT
ETHNIC BLACK
INCOME 34
DATA ANALYTICS AND MACHINE LEARNING
MACRO SCALE: PRINCIPAL COMPONENT ANALYSIS
Dimensionality reduction algorithms such as Principal Component Analysis reduce the data into set components in order to get them into a more manageable form for further analysis.
35
BRITAIN
LONDON
MACRO
36
MESO
MICRO
DATA ANALYTICS AND MACHINE LEARNING
MACRO SCALE: K-MEANS The K-Means cluster heatmap is indicative of how different datasets are represented within various clusters. Picking the most influential components to further visualize them on the map, it was found that clusters 1 and 3 best represented the problematic areas, in need for intervention.
ITERATION 1
ITERATION 2
KMEANS CLUSTERS
ITERATION 3
PCA
ITERATION 4
ITERATION 5 37
37
BRITAIN
LONDON
CLUSTER 1
MACRO
CLUSTER 2
MESO
MICRO
CLUSTER 3
MICRO AREA
PRINCIPAL COMPONENT 0
1
38
3km
DATA ANALYTICS AND MACHINE LEARNING
Fruit & Veg
MESO SCALE: DATA DISTRIBUTION
Unemployed Persons
With the results from the PCA and through detailed studies of the various datasets, a ‘Meso’ region was picked within the macro. Resampling the data to the network at this scale allows for all the data to be perceived in a uniform manner. Following the resampling process the data was visualized in a 3D space to use the ‘peaks’ and ‘valleys’ to study inter-relationships. Plotting some of these inter-relationships on a 2D scatterplot, unique relationships were found between demographic data like unemployed persons and the probability of purchase of fruits and vegetables.
39
BRITAIN
LONDON
MACRO
MESO
MICRO
D RE
D BA
AT ME
H LT A HE
TS N A UR A ST RE
D YE O L MP E UN
40
S ON S R PE
DATA ANALYTICS AND MACHINE LEARNING
MESO SCALE: DATA DISTRIBUTION
X NO
N IO S IS EM
N IO T A GR E T IN
T UI R F
Y IT S R VE I D
41
D AN
G VE
RE O SC
BRITAIN
LONDON
MACRO
MESO
MICRO
PAIRPLOT SCATTERPLOT 42
DATA ANALYTICS AND MACHINE LEARNING
PC
ZE RO
PC
ON E
KM EA NS
MESO SCALE: PCA AND K-MEANS
43
BRITAIN
LONDON
MACRO
MESO
MICRO
T-SNE method gives the gradual development of interactions between data until they are distributed in clusters with common levels of interaction. 44
DATA ANALYTICS AND MACHINE LEARNING
SITE SELECTION
PRINCIPAL COMPONENT 0
1
HIGH
FOOD DESERT SCORE
PRINCIPAL COMPONENT 0
AREA SELECTION
LOW
45
2
3km
O ICR SCA LE
M
MACRO
46 Peckham 51.475715,-0.056324 Queens Road Peckham
51.493114,-0.062226 South Bermondsey
Camberwell 51.474172,-0.093281 Denmark Hill Bermondsey
Walworth Rd 51.488284,-0.095781 Elephant & Castle
Vauxhall 51.486292,-0.122756 Vauxhall station
LONDON
Brixton 51.464173,-0.114318 Brixton station
Nine Elms 51.477910,-0.142164 Battersea Park
Churchill Gardens 51.486549,-0.144255 Victoria station
BRITAIN MESO MICRO
INTERVENTION
CONSUMPTION
RETAIL
K-MEANS CLUSTERS
URBAN FABRIC
SITE ANALYSIS AND SIMULATIONS
SITE ANALYSIS AND SIMULATIONS
MICRO SCALE SITE
The selected location of intervention is the area around the Vauxhall subway and bus stations. As indicated by the data analytics, this consists one of the eight highlighted points and it will be perceived as an example solution for all the intervention points. Located near the river Thames, in between the large public green spaces of Vauxhall Park and Vauxhall Pleasure Gardens, the location consists of a major connection node of transportation in an area collecting high food desert and deprivation scores. In order to determine in detail, the spatial characteristics of the urban fabric, further analysis is required.
49
BRITAIN
LONDON
MACRO
BUILDING HEIGHT LOW
MESO
MICRO
500
HIGH
50
1000
1500m
SITE ANALYSIS AND SIMULATIONS
SUNLIGHT ANALYSIS
AVERAGE ANNUAL SUNLIGHT
MINIMUM SUNLIGHT (JANUARY)
Sunlight is essential for any form of cultivation activity, thus data is collected about the sunlight hours on the micro scale site throughout the year in order to use the dataset for the calculation of the optimal surfaces of intervention. LOW
HIGH 51
BRITAIN
LONDON
MACRO
MESO
MICRO
JANUARY
FEBRUARY
MARCH
APRIL
MAY
JUNE
JULY
AUGUST
SEPTEMBER
OCTOBER
NOVEMBER
DECEMBER
52
SITE ANALYSIS AND SIMULATIONS
VISIBILITY ANALYSIS
ISOVIST
VGA
OVERLAY
Visual connectivity is a spatial characteristic that is restricted by the railway platforms. In order to determine the visual segregation around the site and further adjust the approach, visibility graph analysis and isovist studies were conducted. LOW
HIGH 53
BRITAIN
LONDON
MACRO
54 54
MESO
MICRO
SITE ANALYSIS AND SIMULATIONS
SOCIAL MEDIA In order to re-interpret the urban space through its users, data were collected from social media such as Twitter and Flickr. The geolocated data show the most attractive locations from the users as well as possible interests connected to food related topics. Further analysis of those data could determine the program of the intervention.
55
BRITAIN
LONDON
MACRO
MESO
BIG BEN
OVAL
MICRO
LAMBETH
LAMBETH BRIDGE
VAUXHALL BRIDGE BATTERSEA POWER STATION
LONDON EYE
NINE ELMS
56
DATA ANALYTICS AND MACHINE LEARNING
TRAFFIC VOLUME AND FLOWS
GPS DATA POINTS
DIFFERENT TRACING ID
VEHICULAR MOVEMENT 57
BRITAIN
LONDON
MACRO
MESO
MICRO
00:00-02:00
02:00-04:00
04:00-06:00
06:00-08:00
08:00-10:00
10:00-12:00
12:00-14:00
14:00-16:00
16:00-18:00
18:00-20:00
20:00-22:00
22:00-00:00
58
SITE ANALYSIS AND SIMULATIONS
AGENT FLOWS
FOOD DESERT
SUN ANALYSIS
DIVERSITY
Initial attempts to reveal existing motion patterns in the local scale, include simulations carried out through grasshopper, with agents following various paths, according to values from image files depicting different datasets. 59
BRITAIN
LONDON
MACRO
MESO
INITIAL SIMULATIONS MAX SPEED 3.5KM/H ANGLE OF VIEW 130° INITIAL POPULATION 50 SEARCHING RADIUS 500M 60
MICRO
SITE ANALYSIS AND SIMULATIONS
AGENT BASED SIMULATIONS Inserting the design iterations into the custom programmed agent based simulation developed allows to visualize how people movement has shifted as well as how the built environment is being used. Two scenarios are visualised:a pre and a post-covid scenario.
AGENTS GENERATED FROM RESIDENTIAL BUILDINGS IN THE REGION.
DETAIL A
AGENTS ATTRACTED BY THE NEAREST RESTAURANTS AND LARGEST, NEAREST SUPERMARKETS.
DETAIL B
AGENTS ATTRACTED TO ALLOTMENTS AND ATTRACTED TO OPEN SPACES TO SHOW MOVEMENT DURING BETTER WEATHER.
TO SUPERMARKETS TO ALLOTMENTS
DETAIL C
TO RESTAURANTS DELIVERY AGENT 61
BRITAIN
LONDON
MACRO
PRE-COVID | COVID SCENARIO
POST-COVID SCENARIO 62
MESO
MICRO
SITE ANALYSIS AND SIMULATIONS
CELLULAR AUTOMATA As part of our experimentation process, following the overall analysis of VGA, sunlight and GPS data, the points with highest values were selected and used as the alive points that would initiate the growth of the cellular automata based on the rules of game of life. The algorithm was given a bound of 10 iterations to avoid uncontrollable growth. The outcomes of each final iteration were combined and after the selection of intersecting voxels, an agent simulation was used to filter them. The final outcome was an experimental approach of a new urban setting growing based on the data analysis.
FILTER CELLS
SIMULATION
UNION CELLS
EXPANSION RULES
63
BRITAIN
LONDON
MACRO
64
MESO
MICRO
DESIGN STRATEGY
DESIGN STRATEGY
INTERVENTION HIERARCHIES
67
68
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA)
RAILWAY LINES ROAD NETWORK GROWTH NETWORK RETAIL RESTAURANTS 250
500
750m
69
DATA INPUT
SUNLIGHT PURCHASES AREA POPULARITY
DIVERSITY SCORE FOOD DESERT SCORE
REAL-TIME DATA
CENSUS DATA
ALLOTMENTS STREET MARKETS SUPERMARKETS RESTAURANTS PRODUCTIVE TREES INTEGRATION R500
70
SPATIAL DATA
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA) A custom Growth Network Algorithm was implemented that can predict the optimum scenarios of expansion based on data collection. The data input is both spatial constant data such as locations of various attractors and network integration and transient census data that are collected on discrete time-points, as well as real-time data such as weather data, purchase preferences and area popularity. GNA works on a grid of 10x10 meters by taking into consideration the spatial characteristics such as railway lines and buildings, in addition to a calculated weight of each grid cell deriving from the input data. The data weights present variations according to the chronological framework, as in every season differences occur in residents’ habits.
Optimal_Path(x,y,t) Optimal Path (x, y, t) = = argmax(f(x,y,t)), arg max(f (x, y, t)), (x,y) ∈ Ω
Optimal Path (x, y, t) = arg max(f (x, y, t)), 11 where (x,y) ∈ Ω w i (t)g i (x, y, t), f (x, y) = i=1
f (x, y) =
11
w i (t)g i (x, y, t),
i=1
g1(x,y,t)= Sunlight (x,y,t), g2(x,y,t)= Purchases (x,y,t), g3(x,y,t)= Area Popularity (x,y,t), g4(x,y,t)= Diversity Score (x,y,t), g5(x,y,t)= Food Desert Score (x,y,t), g6(x,y,t)= Allotments (x,y,t), g7(x,y,t)= Street Markets(x,y,t), g8(x,y,t)= Supermarkets (x,y,t), g9(x,y,t)= Restaurants (x,y,t), g10(x,y,t)= Productive Trees (x,y,t), g11(x,y,t)= Integration R500 (x,y,t) wi(t) 1 ≤ i ≤ 11
1 1 71
ITERATIONS WITH MONTHLY WEIGHT DIFFERENTIATION
JANUARY WEIGHTS
FEBRUARY WEIGHTS
MARCH WEIGHTS
APRIL WEIGHTS
MAY WEIGHTS
JUNE WEIGHTS
JULY WEIGHTS
AUGUST WEIGHTS
OCTOBER WEIGHTS
NOVEMBER WEIGHTS
DECEMBER WEIGHTS
SEPTEMBER WEIGHTS
72
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA)
ITERATION 01
ITERATION 02
ITERATION 03
ITERATION 04
ITERATION
ITERATION 10
ITERATION 11
ITERATION 12
ITERATION 13
ITERATION
ITERATION 19
ITERATION 20
ITERATION 21
ITERATION 22
ITERATION
73
ITERATIONS WITH STARTING POINT DIFFERENTIATION
N 05
ITERATION 06
ITERATION 07
ITERATION 08
ITERATION 09
N 14
ITERATION 15
ITERATION 16
ITERATION 17
ITERATION 18
N 23
ITERATION 24
ITERATION 25
ITERATION 26
ITERATION 27
74
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA) TYPE A
TYPE B
ALL YEAR
SUMMER ONLY
1 STEP PER WEEK PERMANENT
4 STEPS PER WEEK SEASONAL
COMBINATION PROGRAM
CULTIVATION ONLY
ALL DATA
SUNLIGHT TRAFFIC
DETERMINISTIC ALGORITHM
EVOLUTIONARY ALGORITHM
75
CELL TYPOLOGY
TYPE A GROWTH TYPE B GROWTH RAILWAY LINES RETAIL RESTAURANTS
250
76
500
750m
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA)
The Growth Algorithm progresses by four steps per month, or approximately one per week, with the exception being the Type B behavior, i.e. the summer expansion. Its growth is organic, driven by the temporal differentiation of the input data and in extent, the respective weights. This feature is not limited to the expansion, but the algorithm can also choose to contract from certain extremities should there no longer exist a need for it in the area, or should the inhabitants recommend it.
START FROM 3 POINTS START FROM RAILWAY IF BLOCKED RESTART FROM DIFFERENT ORIGIN IF NO FURTHER NEED AT A POINT, CONTRACTION
STEP 01
STEP 02
77
TEMPORAL GROWTH
STEP 03
STEP 04
STEP 05
78
DESIGN STRATEGY
HIGH > 8H
HIGH 5CM
PERIOD
ALL YEAR
DEPTH
PRODUCTION
SWALLOW 30/40CM
SMALL <1kg/m2
0.35kg/m2
0.75kg/m2
MEDIUM 6-8H
0.25kg/m2
11.1£
WATER
IMPORT|EXPORT (BILLION)
SUNLIGHT
1.3£
PRODUCTIVE LANDSCAPES
MEDIUM 2.5CM
WARM MONTHS
2.5kg/m2
2kg/m2
5kg/m2
4kg/m2
66% OF POPULATION
MEDIUM 45/60CM
CONSUMPTION OF PORTION/DAY
MEDIUM 1<5kg/m2
LOW 4-6H 2kg/m2
DEEP >100CM
SPRING
LOW 2CM
10kg/m2
15kg/m2
79
56.7% FARMLAND
LARGE >5kg/m2
SURFACE OF LAND UK
1.5kg/m2
CULTIVATION SURFACE R500 | 58931 INHABITANTS R1000 | 199453 INHABITANTS
ITERATION 01
R500
7 POINTS MIN DISTANCE FROM RAILWAY MAX AREA NOT OVER RAILWAY
R1000
AREA= 29839m2 PRODUCTION VEGETABLES 0.83% R500/YEAR 0.24% R1000/YEAR
ITERATION 02
7 POINTS MIN DISTANCE FROM RAILWAY/5 DIAMETRICAL OPPOSITES MAX AREA NOT OVER RAILWAY AREA= 87307m2 PRODUCTION VEGETABLES 2.5% R500/YEAR 0.7% R1000/YEAR
ITERATION 03
6 POINTS MIN DISTANCE FROM RAILWAY DIAMETRICAL OPPOSITES MAX AREA NOT OVER RAILWAY AREA= 38681m2 PRODUCTION VEGETABLES 1.08% R500/YEAR 0.3% R1000/YEAR
RAILWAY LINES ROAD NETWORK GROWTH NETWORK RETAIL RESTAURANTS
250
80
750m
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA) As an extension of the Growth Algorithm an additional function allows it to evaluate the optimal program/use distribution for each cell following the general characteristics of G.N.A. It never handles a point individually, but rather it analyses all its surrounding elements. Decision making regarding food uses two main assumptions, i.e. that each individual should have access to a market within approximately 250 metres and that there should exist a restaurant within a 100-metre radius. What is more, in order to avoid congestion and achieve a more balanced distribution, a 50-metre radius empty zone is considered when allocating each use.
RESTAURANT 100m RADIUS
MARKET 250m RADIUS
HUMAN CROSSING <10 & > 90
GNA OUTPUT 81
PROGRAM ALLOCATION
82
DESIGN STRATEGY
GROWTH NETWORK ALGORITHM (GNA) In addition to the allocation of food related functions, the G.N.A. takes into consideration the social character of the intervention by suggesting locations of social spaces. The decision making regarding these spaces considers the optimal way of establishing a physical communication/ interaction network. That is a two-part process, firstly it adds social spaces in areas with high human crossing metrics, encouraging interaction, and secondly, it creates social spaces in areas with low human crossing metrics, aiming to provide a social point of reference.
SOCIAL SPACES
SOCIAL SPACES SOCIAL SPACES
SOCIAL SPACES CULTIVATION CELL
CULTIVATION CELL
83
PROGRAM ALLOCATION
CULTIVATION CELL
SOCIAL SPACES SOCIAL SPACES
SOCIAL SPACES
CULTIVATION CELL
SOCIAL SPACES
CULTIVATION CELL
SOCIAL SPACES
84
DESIGN DESIGN STRATEGY STRATEGY
USERS PATHS The success of such an interconnected system will derive from the involvement of every kind of user, human or machinic. In this scenario, paths designed for the participating entities acquire a primary role in the organization of spaces, defining places of exclusive use or lands of convergence.
85
EXISTING SUPERMARKETS EXISTING RESTAURANTS
ROBOT PATHS
ALGORITHM LOCATIONS
HUMAN PATHS
86
DESIGN STRATEGY DESIGN STRATEGY
FORM FINDING The output of the Growth Network Algorithm in combination with the user paths create a complex system of connection possibilities for the machinic actors of the intervention. This system along with the GNA depicted locations provide the framework where the organic architectural forms are developed.
GROWTH ALGORITHM
ROBOT CONNECTIONS
ORGANIC FORMS
ITERATIONS THIRD YEAR
ITERATIONS SECOND YEAR 87
B
A
ITERATIONS FIRST YEAR
A 88
B
DESIGN STRATEGY
ITERATIONS AND DESIGN The final form is the result of the combination of user paths and GNA-generated positions. It is noted that the latter derive from an organic algorithm, dependent on spatial and temporal characteristics. Therefore the forms can vary based on the algorithmic output. Depending on the locations and the existing context the forms adjust both in shape, orientation and height. Thus there is a fluidity and versatility in the design components that allows for the adjustment to different context with ease. This lines up well with the general aim of the intervention in terms of adaptation to the context without overshadowing the existing, following the concept of an urban parasite that benefits its host.
HUB
DESIGN OUTCOME
CONNECTIONS
GNA OUTPUT
ITERATION 06
89
HUB
HUB
ITERATION 07
ITERATION 03
90
DESIGN STRATEGY
PLATFORM
HUB GROWTH NETWORK
The program is divided based on functionality: Closed spaces such as labs and storage spaces are to be relocated within the walls of the railway. The street opens itself onto what is envisioned as productive landscape, where growth of produce, markets and the public realm converge. This notion carries itself above the railway lines as well using the barrier infrastructure as a backbone and bridge, bringing together people of different ethnic groups and economic backgrounds through food. A similar level of distribution is seen even at the street - level using the GNA to guide appropriate funtionality, be it production, retail or public activity.Overall integrating to form a productive, sustainable and public landscape.
CANOPY
PROGRAM DISTRIBUTION
PUBLIC SPACES/MARKET PRODUCTIVE SPACES VISUAL LINKAGES 91
Productive Canopy that house markets at the hub
Platform floating above railway line, integrating the network and providing surface for public activity
92 92
DESIGN DESIGN STRATEGY EVALUATION
VERTICAL GROWTH ELEMENT
GROWTH ALGORITHM
DAYTIME VIEW VAUXHALL PARK 93
CULTIVATION MODULES
ROBOT PATHS
MARKET
COMMUNICATION INTERFACE
94
DESIGN STRATEGY
MASTERPLAN In order to address the physical barriers created by the railway platform, a dynamic platform is created that sits above the railways. The structure itself is envisioned as a public realm, housing various activities from markets to plazas for people to gather all while integrating the existing restaurants and supermarkets in the region. This platform allows people to move across the barrier and bridges people and communities together.
RIVERSIDE INTERVENTION
RESTAURANTS SUPERMARKETS 95
PEDESTRIAN PATHS
HUB
PRODUCTION CANOPIES
96
DESIGN STRATEGY
PROGRAMATIC SECTION The section cuts through the ‘hub’ highlighting the multi-layered nature of the design. Distributing the program right from the uses on the underside of the railway platform, to the activated public realm above, all of which is covered by productive landscapes that valley into the city streets.
Using the underside of the railway for productive meat and food labs.
PUBLIC SPACES PRODUCTIVE SPACES 97
Platform floating above railway line, integrating the network and providing surface for public activity.
Productive Canopies that house markets at the hub.
98
DESIGN DESIGN STRATEGY STRATEGY
INTERVENTION GENERAL VIEW
99
100
DESIGN STRATEGY
PRODUCTION CANOPIES Principal elements of the intervention are the production canopies scattered across the area over the GNA algorithm defined locations. Through the lift of cultivation areas above the ground, these elements provide for new public spaces while maintaining the production of food in multiple stories of truss bearing the vegetables modules. With the ground floor accessible to the people, the rest of the construction is mainly used by the cultivation robots, constantly reassessing the food process according to data.
101
MULTISTOREY DESIGN 102
DESIGN STRATEGY
CANOPIES FORMATION The canopies scattered across the intervention acquire their form primarily by the facility they respond to, ranging from markets, restaurants and social related closed spaces. At the same time following a specifically designed algorithm, the production volumes receive their temporary positions over the structures. INTERVENTION TYPOLOGIES
RESTAURANT
SOCIAL SPACE
MARKET 103
PRODUCTION MODULES SIZING
PRODUCTION MODULES POSITIONING
104
DESIGN STRATEGY
TRIPLE LAYER STRUCTURE More specifically over the construction of the canopies, they follow a triple layered structure. Deriving from the GNA algorithm and the indication of their position, the canopies are primarily formed as a set of robotic paths that respond to the capacity of the robotic arms. Following are the wooden structural frames, assembled altogether to the canopy forms, that are created in order to receive the production modules on top.
FORMATION 1
FORMATION 2
FORMATION 3
FORMATION 4
FORMATION 5 105
ROBOT PATHS
PRODUCTION MODULES
STRUCTURAL FRAME
106
DESIGN STRATEGY
ROBOTIC ENTITIES Integral part of the suggested intervention are the robotic entities that are present from the very first stages of the project all the way to its function and maintenance. More specifically the use of robotic arms allows the rapid assemblage of the canopies in various positions. Apart from the construction, the robots are responsible for the continuous repositioning of the production modules according to the GNA algorithm. Also, through the complete monitoring of production they maintain the crops while also collecting those modules that are ready to be consumed.
STEP 1
STEP 2
FINAL STATE ROBOTIC ASSEMBLAGE OF CANOPIES 107
PRODUCTION MODULES REPOSITIONING
BRINGING PRODUCTION TO PEOPLE
108
PRODUCTION MODULES MAINTAINING
DESIGN STRATEGY
PIXEL CULTIVATION
PRODUCE
SUNLIGHT
ROOT DEPTH
109
PIXEL ALGORITHM
25
110
50m
DESIGN DESIGN STRATEGY EVALUATION
CULTIVATION MODULES
AQUA CULTIVATION MODULES
DAYTIME VIEW WATERFRONT 111
ROBOT PATHS
COMMUNICATION INTERFACE
GROWTH ALGORITHM
112
DESIGN STRATEGY
VERTICAL GROWTH The need to achieve a bigger percentage of the annual food consumption of the inhabitants led to the design of small vertical elements that increase the production capacity while maintaining a small footprint on the urban fabric. These elements emerge near high mobility circulation axes, taking advantage of certain pollutants in the atmosphere that have been proven beneficial for the vegetable’s growth speed. The machinic actors of the intervention contribute to the element’s construction and allow the vertical movement of modules.
113
30m
s
ule
mod 200
es du l mo
mo
500m2
114
00
0 40
20
es
l du
14m
20m 2
DESIGN STRATEGY
OPTIMAL LOCATION RS
E ST
U
S
CL
N EA
KM
ON
I AT
GR
AL
E NT
I
SU
VI
ST
I OV
IS
T
H IG
NL
L
SU
A NU
AN
LE
RO
A SC
C MI
CLUSTER CLUSTER CLUSTER CLUSTER
0 1 2 3
An evolutionary algorithm has been implemented in the area of cluster 1, in order to define the optimal positions for the vertical elements. 115
250
116
500m
DESIGN STRATEGY
OPTIMAL LOCATION
ITERATION 01
ITERATION 02
ITERATION 03
ITERATION 04
ITERATION 01
ITERATION 02
ITERATION 03
ITERATION 04
ITERATION 01
ITERATION 02
ITERATION 03
ITERATION 04
ITERATION 01
ITERATION 02
ITERATION 03
ITERATION 04
117
CORRELATION
GNA CULTIVATION LOCATIONS
OPTIMAL LOCATIONS
CLUSTER 1
118
DESIGN DESIGN STRATEGY EVALUATION
GROWTH ALGORITHM
VAUXHALL STATION RECREATIONAL SPACE
AFTERNOON VIEW VAUXHALL STATION 119
ROBOT PATHS
CULTIVATION MODULES
USER PATHS
TO PLATFORMS
120
DESIGN STRATEGY
LONDON OVERGROUND The elevated railway infrastructure is participating actively to the intervention’s complexity. It facilitates empty spaces under the tracks that provide a great opportunity to house additional functions that would benefit the organization and completeness of the intervention. These spaces are transformed to meat laboratories, food processing facilities, as well as storage units, even small shops, where the participation of inhabitants is encouraged. This ties up the wholistic experience of the intervention and weaves socio-economic and ecological solutions into one organic process.
121
122 122
DESIGN STRATEGY
HYBRID MACHINE - HUMAN The intervention’s aim is the cooperation of human and non-human actors in the creation of the new food network. The function of the project relies on this cooperation thus the human actors are responsible for the tending of the plants while the mechanic actors transport the modules and maintain the structure.
GROWTH ALGORITHM
MARKET
MODULE
COMMUNICATION THROUGH INTERFACE
123
COMMUNICATION THROUGH INTERFACE
ROBOT PATHS
TRANSPORTATION ROBOT
124
DESIGN STRATEGY
GNA.CO.APPLICATION The symbiosis of the participating actors of the intervention is possible through an organizing and communicating interface on the mobile phone. This interface permits the inhabitants to access information, provide input on the growth algorithm and participate in the cultivation process in a social context. The interface depicts the state of the plant, its location and needs while informing the robots of its transportation should the user decide it, in a two-way communication flow. It provides the user with information about its own module and allows the reservation of additional available units for planting.
PEOPLE-MACHINE
PEOPLE-PRODUCE
GNA APP
PEOPLE-COMMUNITY
125
GNA LOCATION
ADD AND REMOVE
MAINTENANCE
PRODUCE READINESS AND QUANTITY
SHOP AT MARKET
RESTAURANT ORDERS
IDENTIFY NEARBY USES
EMPTY MODULES
COMMUNITY ACTIVITIES
126
PLANTED MODULES
PROVIDE JOB OPPORTUNITIES
DESIGN STRATEGY
GNA.CO.APPLICATION Additionally to the communication of user and algorithm, since the outmost goal of the project is the advancement of social relations, the user interface promotes the human interaction. It allows the access on information regarding community events and availability of common spaces aiming especially in the convergence of groups with various ethnic and economic backgrounds, under the notion of nutrition. Equally, it allows accessibility to job opportunities related to the food processing facilities in the area, supporting in that notion, low income communities.
USER ID | INTERRFACE
DETAILS: NAME: PROFESSION: AGE: STATUS: LOCATION:
RUTH MCFERRY INSURANCE COMPANY 35 MARRIED BATTERSEA
GOALS: HEALTHIER DIET PARTICIPATE IN ACTIVITIES SOCIALIZE WITH NEW PEOPLE PERSONAL PROJECT
CHARACTERISTICS:
MEAT CONSUMPTION: VEGETABLE CONSUMPTION: ETHNIC FOOD CONSUMPTION: COOKING SKILLS: CULTIVATION SKILLS:
127
MAINTENANCE INFORMATION
COMMUNICATION PRODUCE-HUMAN-MACHINE
128
DESIGN STRATEGY
AGENT BASED EVALUATION AGENTS POV
Through these preliminary design ideations it is clear, using the platform as a bridge, the agents can break through the barriers created by the high railway lines. The movement, as previously seen is not merely outwardly - away from the railway but converges inward feeding the food deserts and enlivening open spaces.
AGENTS GENERATED FROM RESIDENTIAL BUILDINGS IN THE REGION.
AGENTS ATTRACTED BY THE NEAREST RESTAURANTS AND LARGEST, NEAREST SUPERMARKETS
AGENTS ATTRACTED TO ALLOTMENTS AND ATTRACTED TO OPEN SPACES TO SHOW MOVEMENT DURING BETTER WEATHER.
129
VAUXHALL BRIDGE
VAUXHALL TUBE STATION
VAUXHALL PARK
130
DESIGN STRATEGY
AGENT BASED EVALUATION
VAUXHALL PARK
TO RESTAURANTS DELIVERY AGENT TO SUPERMARKETS TO ALLOTMENTS 131
VAUXHALL BRIDGE
VAUXHALL TUBE STATION
PLEASURE GARDENS
132
DESIGN STRATEGY
BLUE - WATER DEFFICIENCY PINK - RIPENESS YELLOW - TENDING NEED PURPLE - REPLACEMENT
COLOR INDICATOR
LABORATORY
COMMUNICATION INTERFACE
NIGHTTIME VIEW VAUXHALL PLEASURE GARDENS 133
R
COMMUNICATION INTERFACE
USER PATHS
134
DESIGN STRATEGY
ROBOT PATHS CULTIVATION MODULES
COMMUNICATION INTERFACE
AQUA CULTIVATION MODULES
AFTERNOON VIEW WATERFRONT 135
BLUE - WATER DEFFICIENCY PINK - RIPENESS YELLOW - TENDING NEED PURPLE - REPLACEMENT
COLOR INDICATOR
136
DESIGN STRATEGY
CU
ROBOT PATHS
GROWTH ALGORITHM COMMUNICATION INTERFACE
AQUA CULTIVATION MODULES
NIGHTTIME VIEW WATERFRONT 137
ULTIVATION MODULES BLUE - WATER DEFFICIENCY PINK - RIPENESS YELLOW - TENDING NEED PURPLE - REPLACEMENT
COLOR INDICATOR
138
REFERENCES|SOURCES UK traffic data | https://roadtraffic.dft.gov.uk/ UK roads shapefile | https://osdatahub.os.uk/ UK powergrid, gas pipe, river, antennas location | https:// www.nationalgrid.com/uk/ Food Desert data | https://data.cdrc.ac.uk/ UK retail | https://geolytix.com/ England Crop Distribution england-crome-2019
|
https://data.gov.uk/dataset/
London Roads, Railway, Buildings gov.uk/dataset/openstreetmap
|
https://data.london.
Restaurants | https://digimap.edina.ac.uk/ (poi) Flickr, Twitter | Data scrapping London Trees, Diversity Score, Allotments, Street Markets | https://data.london.gov.uk/ Food Data | Tesco Dataset (https://figshare.com/collections/Tesco_Grocery_1_0/4769354) Health, Financial, Emissions, data.london.gov.uk/
Ethnicity
Data
|
https://
Collage | https://photostockeditor.com/ GPS Data | www.openstreetmap.org Building uses data | London Data Store (https://data.london.gov.uk/) Images used in app | https://thenounproject.com/
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REFERENCES|SOURCES Histogram | https://cittaconquistatrice.it/wp-content/uploads/2016/06/ https://architizer.com/blog/practice/details/modernist-utopian-architecture/ https://www.archpaper.com/2017/05/charles-waldheim-urban-agriculture/ https://www.metalocus.es/es/noticias/ https://knowledgecenter.ubt-uni.net/cgi/viewcontent.cgi?article=1556&context=etd https://thereaderwiki.com/en/Pesticide https://carloratti.com/project/supermarket-of-the-future/
Food Facilities Surfaces | https://www.pinterest.se/pin/8444318026557426/ https://www.feedstrategy.com/dairy-cattle-nutrition/examining-the-components-of-optimal-dairy-cow-nutrition/ https://theregister.co.nz/2015/08/17/dairy-downturn-time-bomb-rural-retailers/2527-5dee537076c43/ https://www.donaldson.com/en-us/industrial-dust-fume-mist/technical-articles/ https://recruiterflow.com/db_0de4fddbf8bce344972d9745a58946f2/jobs/2 https://www.examinerlive.co.uk/news/business/business-profiles/fascinating-video-showing-co-operatives-14521883 https://expansion.mx/empresas/2016/11/18/trump-causara-un-alza-en-los-precios-de-los-alimentos-en-eu
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