Nutri.net - Bartlett B-Pro , Urban Design, UCL

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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

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MARKE

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UC

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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/

139

140

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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