Aashi Mathur Mingze Chen Nermeen Abbas Zaidi Shivani Sathiyamurthy Yanqi Zeng RC 14: Machine Learning Urbanism MArch Urban Design, B-Pro The Bartlett School of Architecture, UCL AGRO-MATRIX
DESIGN TUTORS: ROBERTO BOTTAZZI, TASOS VAROUDIS, EIRINI TSOUKNIDA, VASILEIOS PAPALEXOPOULOS, MARGA RITA CHASKOPOULOU, PROVIDES NG BARTLETT SCHOOL OF ARCHITECTURE B-PRO, MARCH URBAN DESIGN RESEARCH CLUSTER 14 Authors: Aashi Mathur 20160705 Mingze Chen 20082329 Nermeen Abbas Zaidi 20060825 Yanqi Zeng 21093408 Shivani Sathiyamurthy 21116059
AGRO -MATRIX
CONTENT BACKGROUND AND CONCEPT FOOD AND ENVIRONMENT [PART ONE] DATA ANALYTIC AND MACHINE LEARNING SITE SELECTION [PART TWO] 1 2 3 4 5 5 5 57 57 21 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT [PART FIVE] DATA CROSSING ANALYSIS [PART THREE] DESIGN STRATEGY ROOF, ELEVATED, VERTICAL, ON-GROUND FARMING [PART FOUR] 2 BACKGROUND AND CONCEPT
How can we promote a food-centric lifestyle at a neighbourhood level?
Source: @_foodStorieS Instagram 3UCL BARTLETT B-PRO RC14
4 BACKGROUND AND CONCEPT
BACKGROUND AND CONCEPT FOOD AND ENVIRONMENT [PART ONE]
This section explains food transportation at different scales, historic timeline of agriculture and concept generation.
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WORLDWIDE ANALYTICS | Food Security of 113 Cities by the United Nations Low High
The Global Food Security Index consists of a set of indices from 113 countries. It measures food security across most of the countries of the world. The score includes Affordability, Availability, Quality & Safety.
6 BACKGROUND AND CONCEPT
UK: 79.1 Ranking: 17th 7UCL BARTLETT B-PRO RC14
WORLDWIDE ANALYTICS | WORLDWIDE FOOD TRANSPORTATION
The food transport network depicts the import and export of food on a global scale. As can be seen from this map, Europe has a significant share. According to the relevant data, the UK relies heavily on food imports from the EU, which undoubtedly increases food costs and environmental pollution.
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OVERALL SCORE UK: 79.1, Ranking: 17th LEGEND SCORE 30-40 40-70 70-80 80-85 >85 UK: 83.6, Ranking: 19th UK: 74.4, Ranking: 17th UK: 80.9, Ranking: 18th AVAILABILITY AFFORDABILITY QUALITY & SAFETY The following parameters are considered for giving ranking to the countries: Nutritional standards, Urban absorption capacity, Food consumption as a share of household expenditure, Food loss; Protein quality, Agricultural import tariffs; Diet diversification, Agricultural infrastructure, Volatility of agricultural production, Proportion of population under the global poverty line, Gross domestic product per capita (US$), Presence of food safety net programs, Access to financing for farmers, Public expenditure on agricultural R&D, Corruption, Political stability risk, Sufficiency of supply, Food safety. EUROPE SCALE ANALYTICS | Food Transportation Map 10 BACKGROUND AND CONCEPT
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UK SCALE ANALYTICS | Mapping of England Farm Quality
The mapping of UK food print shows the farm quality into 4 classes: excellent quality, good quality, poor quality, very poor quality. As the result shows, the built-up London area's farm quality is low.
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UK SCALE ANALYTICS | Historical Timeline 10,000 years ago: Agriculture and cities occurred 14th century: Development of maritime transport 19th century: Train transport-Food transported to the city from rural slaughter 20th century: The car replaces the train for food transportation) 14 BACKGROUND AND CONCEPT
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CONCEPT GENERATION | How Food Shapes the City?
Cities have always revolved and evolved around food. Carolyn Steel in her book Sitopia mentions that people gathered to get and distribute food in the past. London streets have been named after food such as bread, fish poultry, etc. Even the book Food City talks about the rich history of the smell of spices at the Docklands area in London. According to Carolyn Steel, tasting is an act of triangulation in which we experience the flavour, in our orbitofrontal cortex which is responsible for memory and emotion and hence certain tastes can trigger a powerful nostalgia. London has such a rich history of food, it is disappointing to know that this history no longer thrives.
16 BACKGROUND AND CONCEPT
Source: Hungry city by carolyn Steel
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A lot of people in the UK in food neighbourhoods. The scarcity of fast-food neighbourhoods is helping
Keeping in mind the need for supermarkets and fresh food in most neighbourhoods along with food nostalgia we aim to design food-centric communities that transition to a dominant in London which would grow it's own and infuse well with the or take-away to create a healthy and balanced lifestyle.
We intend to find overlap with polluted to and allow participatory consumption and of Creating a food-matrix which call Agro-Matrix. In the Agro-Matrix we aim to connect different urban to these We intend to use interconnected and to at the We would that act as to of all towards to
live
desert
supermarkets and more
restaurants in
fuel problems like obesity and diabetes.
supermarket culture
food
restaurant
culture
areas
grow food
design for production,
recycling
food.
we
nodes
apply
different design strategies.
Urban agriculture techniques which include Rooftop farming, Vertical farming, street farming
underground farming
grow food
city level.
like to propose urban interventions
catalysts
pull users
communities
supermarkets
promote healthy eating and living. CONCEPT GENERATION | Neighbourhood as Supermarket ON GROUND FARMING VERTICAL FARMING ROOF FARMING 18 BACKGROUND AND CONCEPT
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CONCEPT GENERATION | Production, Consumption, Recycling System
In our design proposal we aim to connect different urban nodes to apply these different design strategies. We intend to use interconnected Urban agriculture techniques which include Rooftop farming, Vertical farming, and on-ground farming to grow food at the city level. We would like to propose urban interventions that act as catalysts to pull users of all communities towards supermarkets to promote healthy eating and living.
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DATA ANALYTIC AND MACHINE LEARNING SELECTION
This part shows our research in data including principal component analysis, suitability analysis and represents the method of how we zoom in from macro to micro scale.
SITE
[PART TWO] 2 23
Low Low Multiple Deprivation Food Desert, Income Food OSM High High FOOD POI HOUSEHOLD INCOME HOUSING DENSITY MULTIPLE DEPRIVATION SITE SELECTION | Macro Scale Analytics At the London scale, we study social make up and food related data such as Housing density, Multiple deprivations, and Household Income. Through the overlaps in this dataset we understand that the economic makeup of a place is directly associated with accessibility and density of food locations in the city. 24 DATA ANALYTICS AND MACHINE LEARNING
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Low Low Multiple Deprivation Food Desert, Income Food OSM High High The previous map helped us identify problems such as high food desert, low incomes and poor health in the East side of London. Hence we zoom into the region at the Meso scale where we studied datasets such as Supermarkets, Food Desert, Health Deprivation and Transport stations for food accessibility. SUPERMARKET FOOD DESERT TRANSPORT STATIONS HEALTH DEPRIVATION SITE SELECTION | Meso Scale Analytics 26 DATA ANALYTICS AND MACHINE LEARNING
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We tried to zoom in the Micro area where we wanted to understand the relationship of food between suburbs and city dwellers. We studied data such as Red meat consumption, Ready made Consumption, Housing Density, Household Income, Food desert, NOX emissions, Urban Heat Island, Integration R1000, Tesco Transactions, Transport stations. The map on the right represents NOx emissions overlapped with air quality focus areas. The buildings are spatially joined with urban heat islands data. The map suggests that the centre of London is highly polluted and has a maximum heat island. SITE SELECTION | Micro Scale Analytics BIKE STATIONS HOUSEHOLD INCOME HOUSING DENSITY FOOD DESERT RED MEAT CONSUMPTION READY MEAT CONSUMPTION HEALTH DEPRIVATION NOX EMISSIONS URBAN HEAT ISLAND 28 DATA ANALYTICS AND MACHINE LEARNING
Low Intergration R1000, Household Income, Food Desert, NOx, Health Deprivation High MICRO MACRO MESO 29UCL BARTLETT B-PRO RC14
SITE SELECTION | Micro Scale Analytics We further studied data such as Supermarket types, restaurant poor ratings and reviews, types of restaurant cuisines, types of food options with mixed ethnicity, white ethnicity, asian ethnicity. On the right-hand side, the map explains about the red meat consumption which is represented on the buildings in relation to the Asian cuisine restaurants and the grill options provided by the restaurants. The overlay of high streets and integration roads help us understand the accessibility to supermarkets and restaurants. RESTAURANT POOR RATINGS & REVIEWS SUPERMARKET TYPES TESCO TRANSACTIONSTYPES OF FOOD OPTIONS TYPES OF RESTAURANT CUISINES WHITE ETHNICITY ASIAN ETHNICITY MIXED ETHNICITY 30 DATA ANALYTICS AND MACHINE LEARNING
Low MESO
Low Intergration R1000 Red Meat POI High High MICRO MACRO
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Angular segment analysis is one of the most fundamental analyses in space syntax practice that helps understand movement, land-use and other socio-economic patterns. It was initially applied in axial segment maps and later was used in road centre line maps as an attempt to overcome the 'segment problem' (Turner, 2005) INTEGRATION R500 METRIC INTEGRATION R1000 METRIC INTEGRATION R2000 METRIC CHOICE R500 METRIC CHOICE R1000 METRIC CHOICE R2000 METRIC SITE SELECTION | Agular Segment Analytics 32 DATA ANALYTICS AND MACHINE LEARNING
INTEGRATION R1000 METRIC 1-71 71-100 100-150 150-201 201-343 MICRO MACRO MESO 33UCL BARTLETT B-PRO RC14
According to the ‘movement.uber.com‘, the data shows the movement data of travel times, speed based on the time change. We simulate the micro scale potential mobility. HIGH DENSITY TRANSPORTATION HUB AGENT-BASED POINT FLOW SIMULATION SITE SELECTION | Agent-based Mobility Simulation 34 DATA ANALYTICS AND MACHINE LEARNING
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1 4 7 2 5 8 3 6 9 10 13 15 17 19 20 21 22 23 24 11 12 14 16 18 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. With the use of PCA and Suitability analysis we will further decide on the final site for intervention. SITE SELECTION | Data Heatmap and Pair Plot 36 DATA ANALYTICS AND MACHINE LEARNING
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SITE SELECTION | Principal Component and K-means Analysis The K-Means cluster heat map shows how distinct data sets are represented inside different clusters. Clusters 1 and 3 best depicted the problematic regions in need of action, after selecting the most relevant components to better illustrate them on the map. As a conclusion from the PCA analysis, the combination of High Food desert and Low Household Income further helps us narrow down the areas for interventions. PCA 1 PCA 2 K - MEANS 38 DATA ANALYTICS AND MACHINE LEARNING
CLUSTER 1 CLUSTER 2 CLUSTER 3 HEATMAP 39UCL BARTLETT B-PRO RC14
LANDSCAPE ANALYTICS | NDVI (Normalized difference vegetation index)
Done by using the remote sensing data and composing the bands of Landsat 8, which describes the reflectance of vegetation cover and can be used to estimate the density of green on an area of land.
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NDVI: 0.5 ~ 1 NDVI: -0.5 ~ 0 NDVI: 0 ~ 0.5 NDVI: -1 ~ -0.5 41UCL BARTLETT B-PRO RC14
LANDSCAPE ANALYTICS | Rain Flow Analysis
We simulate the rain flow, the slope and the aspect by using the DEM data of our site as factors for site selection. Afterwards we use these data as an important factors to calculate the distance for site selection.
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LANDSCAPE (Contour and Slope) Analysis
ANALYTICS | DEM
We use spatial analysis to calculate the slope and contour based on the DEM data. Slope and elevation are two crucial information that contributes to out suitability analysis below. 43UCL BARTLETT B-PRO RC14
ENVIRONMENT ANALYSIS | London Wind Speed Analysis
The wind roses for the four seasons show that overall, north-easterly winds prevail in London. The urban wind environment is the key factor that dominates the urban climate and directly affects the living environment of the city. By analyzing the wind direction and wind speed in London, we can roughly infer the influence of pollutant transmission speed and other influences.
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SUITABILITY ANALYSIS | Dataset and Method
Suitability analysis is a process for evaluating and weighing the criteria required for scientific site selection using machine learning. We used food-related data and environment-related data to find an optimal site location.
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SUPERVISED SUITABILITY ANALYSIS After PCA and K means, we start our supervised suitability analysis. Firstly, we use the 4 most important food related data: Food Desert, Supermarket Distance, Food Market Distance, Restaurant Density. We use different method to deal with the data according to their types like kernel density, point density, euclidean distance etc. 48 DATA ANALYTICS AND MACHINE LEARNING
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SUITABILITY ANALYSIS | Site Selected by Food Related Factors In our Micro Scale, we analysis our food data include 4 crucial factors: Food Desert, Supermarket Distance, Food Market Distance, Restaurant Density. 50 DATA ANALYTICS AND MACHINE LEARNING
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SUITABILITY ANALYSIS | Site Selected by Environment Related Factors 52 DATA ANALYTICS AND MACHINE LEARNING
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Environment data include 9 crucial factors: Slope, Stream Distance, Building Density, Solar Radiation, NDVI, Tree Density, Street Distance, Aspect, NOX. We use different method to deal with the data according to their types like kernel density, point density, euclidean distance etc. SUITABILITY ANALYSIS | Environment Dataset Visulization 54 DATA ANALYTICS AND MACHINE LEARNING
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SUITABILITY ANALYSIS | Supervised Classification and Score
By giving the different weight to data, we score and reclassify the 9 environment data and then overlay them to one raster file. To make the result more tangible we join them to the city base map and finalize our site as the square shows. The two analysis methods helped us conclude that Poplar falls in the high food desert and low-income neighborhoods category and should be the site for our proposal.
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This
59 DATA CROSSING ANALYSIS [PART THREE] 3
part shows some special machine learning result to analyse and redefine the space of our site.
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ARTIFICIAL NEURAL NETWORK | Method of Satellite Image Segmentation
Image semantic segmentation is an important part of machine vision technology regarding image understanding. Semantic segmentation is the process of linking each pixel in an image to a class label. These labels may include buildings, trees, roads, etc. Semantic segmentation enables fast recognition, segmentation and processing of image data. In satellite image analysis, different types of spaces in an image can be automatically identified by training neural networks. The design feasibility of the site is assessed based on its results.
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MICRO-SCALE ANALYSIS | The Result for Rood and On-ground
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ARTIFICIAL NEURAL NETWORK | Google Street View for Vertical Analysis
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ARTIFICIAL NEURAL NETWORK | Google Street View Segmentation for Vertical Analysis
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ARTIFICIAL NEURAL NETWORK | Method of Google Street View Segmentation
The semantic segmentation labels of google street view images include road, pathway, car, building, sky, tree, wall etc. We got the google location point firstly and then examined four viewpoints of the each point.
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ARTIFICIAL NEURAL NETWORK | The Result of Google Street View Segmentation
We investigate four types based on the area of the building façade by segmenting 1,600 photos.
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ARTIFICIAL NEURAL NETWORK | Four Types Street Availability Map
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ARTIFICIAL NEURAL NETWORK | On-ground, Roof and Vertical
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We create an assessment model to calculate the percentage of different types (tree, shadow, landscape, building, road and open space) of space in each patches.
MICRO-SCALE ANALYSIS | Assessment Models for PCR System
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SOCIAL MEDIA | Crowdsourcing Dataset We combine the results with the food photos from social media as crowdsourcing data to locate the situation and predict the demand.
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We use UAV to record videos of the main crossings of the micro scale site and send it to Pixellib and Open CV to analyze the videos and street view images for instance segmentation. DESIGN SITE SELECTION | Instance Segmentation of UAV Videos and Google Street View Images Videos from UAV (unmanned aerial vehicle) BEFORE AFTER CAR PERSON 1.00 Street View Images from Google Map Raw image Instance Segmentation Object Detection Comprehensive Result 82 DATA CROSSING
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After all the results were overlayed, we finalized our design area according to convenience, energy, and comfort.
DESIGN SITE SELECTION | Overlay
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DESIGN STRATEGY PRODUCTION, CONSUMPTION AND RECYCLING [PART FOUR] This part shows our design strategies for our ideas: Consumption, Recycling and Production areas for Roof, Elevated, Vertical, On ground Farming. 4 87
SITE
March March July July August August January January February February September September 88 DESIGN STRATEGY
SIMULATION | Shadow and Sunlight
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DESIGN STRATEGY | Proposing Vertical Farms
For proposing vertical farms, we start with finding buildings that are near a 100m radius to the highest values of NOx, highest values away from shadow and highest values of solar radiation. After obtaining the buildings, we run a simulation on Evolutionary Optimizer to check for the optimal height above these buildings according to the average sunlight hours throughout different seasons in the year. We apply the incident radiation to the optimal heights received and we zoomed in a bit more. We select radiation values between 3 and 5 to get the high radiation values for each square facade of the buildings. We then propose vertical modules that can help grow food by gaining an appropriate amount of solar radiation throughout the year. The size of these modules depends on the amount of radiation received by each square facade.
BUILDINGS NEAR NOX
BUILDINGS NEAR SOLAR RADIATION
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BUILDINGS AWAY FROM SHADOW
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DESIGN STRATEGY | Proposing Vertical Farms
We first check for Optimal height (average sun hour for different seasons of the year) using Evolutionary Optimizer. Then we select incident radiation values between 3 and 5 on the facade. Incident solar radiation to normal refers to solar radiation falling perpendicular on a surface, ie, having an angle of 90 degree to the surface. Finally, we propose modules that can grow plants. Their size changes according to value of radiation.
OPTIMAL HEIGHT - EVOLUTIONARY ALGORITHM
INCIDENT RADIATION
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PRODUCTION: VERTICAL
94 DESIGN STRATEGY Using the Data from GIS, Environment Simulations and Grasshopper plug-in Elk, we use these data points as attractors. We locate areas of production, consumption and recycling in our site using parameters that are relevant to these areas. We define a node which has a supermarket proximity of 200m. EMOTIONS AND SMELL Source: https://goodcitylife.org/ ANGER SADNESS JOY FOOD SMELL EMISSIONS SMELL DESIGN STRATEGY | Developing the Agro-Matrix Script ENVIRONMENT SIMULATIONS NDVI SOLAR RADIATION SOUND NOx SHADOW SUNLIGHT HOURS
PROXIMITY OF FACILITIES
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96 DESIGN STRATEGY EMOTIONS AND SMELL PROXIMITY OF FACILITIES ENVIRONMENT SIMULATIONS PRODUCTION CONSUMPTION RECYCLING We wanted to locate Production areas that are near : Anger (associated lack of nutrition), sadness (green spaces will improve mental well-being), sunlight hours (to go food), noise (for plants to absorb sound) areas away from Shadow and NDVI. For Consumption areas we wanted them to be near: Joy, Food smell, areas near Shadow, retail spaces, street markets, transport stations for better accessibility, restaurants and supermarkets. For Recycling areas we wanted them to be near: Areas near Shadow, retail, street market, community centres, food facilities and schools (for promoting recycling activity). After putting in these objectives we use Wallaci to decide t the optimal location for our PCR points in the city. DESIGN STRATEGY | Agro-Matrix Script (Production, Consumption And Recycling)
CHRISP MARKET
POPLAR
LANGDON
LANSBURY LAWRENCE
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RECREATIONAL GROUND PRODUCTION: VERTICAL
PARK SCHOOL
PRIMARY SCHOOL P.O.S LANGDON PARK PRODUCTION: ON-GROUND CONSUMPTION RECYCLING EAST INDIA SQUARE
98 DESIGN STRATEGY PRODUCTION CONSUMPTION RECYCLING DESIGN STRATEGY | Agro-Matrix Script (Production, Consumption And Recycling)
LANGDON PARK
CHRISP MARKET
POPLAR RECREATIONAL GROUND
P.O.S
LANGDON PARK SCHOOL
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DESIGN STRATEGY | Food Study Requiremenrt Crop Farming Techniques Aquaponics System Aeroponics System Hydroponics System Nutrient Film Techniques System In this section, we look at some of the most important needs for growing a certain crop. The 11 crops on the list are the most often imported fruits and vegetables in London. After then, more research was carried out. The following parameters were studied: - Calories/100 grams - Soil PH Lifespan(Days) - Temperature (°C) - Sunlight Hour/ Day - Nutrition Provided By Every 100g Plant - Time Period To Plant - Time Period To Harvest - Yield Per Plant/ Kg - Water each week(Inches) - Colour - Live space/ Plant (M2) For a city with food as its main node we propose On-Ground farming, Rooftop Farming, Vertical Farming and Smart Farming. Furthermore, the use of different farming techniques like, Aquaponics, Pixel Cropping, Hydroponics, is also envisioned in the proposal. Another research was conducted based on the nutrition ingested according on Poplar's location, using pin codes on the left and nutritional kinds on the right. 100 DESIGN STRATEGY
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Carbohydrates Sugar Protein Fats Fibres DESIGN STRATEGY | Food Study Requirement Crop 102 DESIGN STRATEGY
Time Period To Plant Time Period To Harvest Yield Per Plant/ Kg Water each week(Inches) Colour Nutrition Provided By Every 100g Plant Live space/ Plant (M2) 103UCL BARTLETT B-PRO RC14
Using the production Grid from the PCR script, we work with an evolutionary algorithm to propose a variety of food crops in the region according to four objectives. - Each cell requires one hour of sunlight every day. - Daily Relative Humidity Requirement - Day-to-day Temperature Requirements - Volume of live space per cell Each food crop has various requirements, and by including these criteria into the script, we may identify multiple iterations of the most likely options for which crop will work best in a cell. DESIGN STRATEGY | Agro-Matrix Script Crop Produce 104 DESIGN STRATEGY
Tomatoes Carrots Lettuce Cucumber Spinach Cabbage Potato Broccoli Apple Strawberries Grapes ITERATIONS I ITERATIONS 2 ITERATIONS 3 ITERATIONS 4 ITERATIONS 5 105UCL BARTLETT B-PRO RC14
ITERATIONS 6ITERATIONS 4 Tomatoes Carrots Lettuce Cucumber Spinach Cabbage Potato Broccoli Apple Strawberries Grapes ITERATIONS 5 DESIGN STRATEGY | Agro-Matrix Script On Ground, Roof and Vertical Crop Produce From this part of Agro-Matrix algorithm different iteration of on ground,roof and vertical farming were produced. Here the algorithm works with the said rules on the ground and roof top: - Each cell requires one hour of sunlight every day. - Daily Relative Humidity Requirement - Day-to-day Temperature Requirements - Volume of live space per cell ITERATIONS 3ITERATIONS I ITERATIONS 2 106 DESIGN STRATEGY
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From this part of Agro-Matrix algorithm different iteration of on ground,roof and vertical farming were produced. Here the algorithm works with the said rules on the ground and roof top: - Each cell requires one hour of sunlight every day. - Daily Relative Humidity Requirement - Day-to-day Temperature Requirements - Volume of live space per cell DESIGN STRATEGY | Agro-Matrix Script Food Yeild 108 DESIGN STRATEGY
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APP DEVELOPMENT Interactive Community Farming
Welcome to AgroMatrix Detect your location Explore your parcels
The app’s primary goal is to locate places where food may be grown and markets where food can be purchased. Using external parameters like sunlight hours, Humidity, and crop Lifetime, our Agro-Matrix algorithm helps the user identify the best crop to plant on a parcel of land. Users can select any Parcel of land in the area and explore the crop type. Another feature of the app will be selecting crops based on the seasons.
|
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Seasons - Fall
Consumer profile
Seasons - Winter Urban Farmer profile Seasons - Spring
Sell a parcel Seasons - Summer
Buy a parcel
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APP DEVELOPMENT | Seasonal availability of crops 112 DESIGN STRATEGY
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FOND: FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT [PART FIVE]
This part shows our design strategies for our ideas: Consumption, Recycling and Production areas for Roof, Elevated, Vertical, On ground Farming. 5 115
116 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT LANGDON PARK SCHOOL RAILWAY STATION BUS STOP For masterplan proposal for FOND (Food oriented neighbourhood development) we zoom into the Langdon Park area which has accessibility from the railway station, bus stop, has community living on the right, commercial area on the left and a school below. A neighbourhood which caters to almost all functions. For our intervention proposal, the plan was to integrate food into the city in form of a food network. From integration points, points away from shadow and production cubes we formulated a network of lines using the woolly algorithm. DESIGN PROCESS | Area for Intervention using Woolly Algorithm SUNLIGH HOUR DATA VERTICAL SURFACES DATA INTEGRATION SURFACE FROM SUNLIGHT HOUR DATA
LINES
WOOLLY
PATHS SELECTED FROM WOOL
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CONNECTING THE DATA
PATH
FOR FIELD AGGREGATION
118 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT COMPOSTING UNIT PRODUCTION CONSUMPTION RECYCLING RECYCLING TOWER - ANAEROBIC DIGESTION FARM TO TABLE RESTAURANTS INCUBATOR HUB FARMERS MARKET COMMUNITY GARDEN GREENHOUSEPASSAGE WAY Discrete design tool uses the wool algorithm to shape the aggregation based on the connection rules. The final design consists of a food system that is intervening into the city. We use different modules to create an interplay of a mini-interconnected food-system. For consumption we have Farm to table restaurants that use produce directly from the neighbouring community garden and kiosks for farmers market. DESIGN PROCESS | Food Oriented Neighbourhood Development using Discrete Design
BRIDGES
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FOOD MODULES AGGREGATION
120 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT From our vertical analysis and PCR script we identify the places to grow food. In addition to this we propose areas to grow food inside our aggregation. FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT | Neighbourhood Production Areas
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Growing food is one of the best ways to reconnect with nature and the community. Color of food adds a vibrant environment where people can connect back to food at all levels of the food system. It is a genuine ‘hands-on’ endeavor that may re-educate us about our relationship with food and the living environment, offer us direct access to fresh goods, and aid in the growth of our social development.
FOOD ORIENTED
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NEIGHBOURHOOD DEVELOPMENT | Neighbourhood Production Areas
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Infrastructure for balconies is included for the vertical surfaces. By constructing balconies, we are planning a potential infrastructure for the neighbourhood vertical production.
FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT | Neighbourhood Production Areas
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From
From
126 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
Underground FromBusStop
Commercial Area MASTERPLAN | Site Accessibility
Underground Station
Langdon Park School
From Community Housing
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MASTERPLAN Introducing Colours to Neighbourhood
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130 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT MASTERPLAN | Introducing Colours to Neighbourhood ROOF VERTICAL AUTOMATION ON GROUND The aggregation design combines a series of production, consumption and recycling modules which can be interchanged through mobile cranes. The proposal envisions robots that can harvest the produce in a city which is capable of automation as a functionality.
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50.17795 43.67871 39.78376 38.47441 38.16034 38.02853 37.6636 37.50456 37.24523 36.80847 36.53077 36.4335 35.92328 35.86958 35.81017 35.59987 35.31905 35.00821 34.98343 34.41605 34.40123 34.27172 34.1205 34.08583 33.93551 33.93055 33.87653 33.82855 33.78447 33.69028 33.67579 33.36226 33.25369 33.2178 33.15641 32.99371 32.84023 32.82093 28.64854 28.6415 28.53091 28.52791 28.47203 28.46181 28.45401 28.40704 28.32066 28.27585 28.27132 28.25952 28.25836 28.25334 28.1916 28.18182 28.13962 28.11679 28.08702 28.04189 28.01407 27.99465 27.99026 27.97173 27.9711 27.95151 27.94878 27.94876 27.93248 27.92151 27.91486 27.88086 27.87701 27.8609 27.82814 27.8095 27.80871 27.71077 27.6956 27.68292 27.67944 27.64727 27.63776 27.60111 27.59577 27.47297 27.46885 27.42293 27.41486
136 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
137UCL BARTLETT B-PRO RC14
138 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
139UCL BARTLETT B-PRO RC14
12,211 12,333 13,621 11,030 12,257 11,228 12,029 13,998 10,562 4,876 60,301 1,582 213 91,157 215 668,277 249,511 38,998 10,562 501 6-8 Hrs 4-6 Hrs 3-5 Hrs 6-8 Hrs 3-5 Hrs 3-5 Hrs 4-6 Hrs 4-6 Hrs 6-8 Hrs 4-6 Hrs 1 to 1.5 inches 1 inch 1 to 2 inches 1 to 1.5 inches 6 inches 1 to 1.5 inches 2-3 inches 1 to 1.5 inches 1 inch 2-3 inches 5 140 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
12,211 12,333 13,621 11,030 12,257 11,228 12,029 13,998 10,562 4,876 Carrot Cucumber Cabbage Broccoli Strawberries Lettuce Spinach Potato Apple Grapes 141UCL BARTLETT B-PRO RC14
“Protein: 0.9 grams.
Carbs: 3.9 grams.
Sugar: 2.6 grams.
Fiber: 1.2 grams.
Fat: 0.2 grams.”
“Protein: 0.9 grams.
Carbs: 9.6 grams.
Sugar: 4.7 grams.
Fiber: 2.8 grams.
Fat: 0.2 grams.”
“Protein: 0.9 grams.
Carbs: 2.9 grams.
Sugar: 1.7 grams.
Fiber: 1.2 grams.
Fat: 0.14 grams.”
“Protein: 0.65 grams.
Carbs: 3.6 grams.
Sugar: 1.6 grams.
Fiber: 0.5 grams.
Fat: 0.11 grams.”
“Protein: 2.86 grams.
Carbs: 3.63 grams.
Sugar: 0.42 grams.
Fiber: 2.2 grams.
Fat: 0.39 grams.”
“Protein: 1.44 grams.
Carbs: 5.5 grams.
Sugar: 3.5 grams.
Fiber: 2.3 grams.
Fat: 0.12 grams.”
“Protein: 2.02 grams.
Carbs: 17.4 grams.
Sugar: 0.78 grams.
Fiber: 2.2 grams.
Fat: 0.09 grams.”
“Protein: 2.82 grams.
Carbs: 6.64 grams.
Sugar: 1.7 grams.
Fiber: 2.6 grams.
Fat: 0.37 grams.”
“Protein: 0.26 grams.
Carbs: 13.8 grams.
Sugar: 10.39 grams.
Fiber: 2.4 grams.
Fat: 0.17 grams.”
“Protein: 0.67 grams.
Carbs: 7.68 grams.
Sugar: 4.66 grams.
Fiber: 2 grams.
Fat: 0.3 grams.”
“Protein: 0.72 grams.
142 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
41 14 15 23 24 77 34 52 32 69 Carrot Cucumber Cabbage Broccoli Strawberries Lettuce Spinach Potato Apple Grapes 143UCL BARTLETT B-PRO RC14
Spring Spring Spring Summer Spring Summer Spring Spring Fall Spring Spring Summer Fall Winter Fall Winter Winter Fall Winter Winter Fall Fall 65- 75 80 50-70 80 40-50 50-60 80 60-70 40-50 60-75 40-50 6.8-5.4 6.8-5.5 6 6.0-7.0 6.8-6.0 6.5-6.8 8.0-5.0 6.8-5.5 6.8-5.4 5.4-6.5 6.5-6.8 144 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
“Protein: 0.9 grams.
Carbs: 3.9 grams.
Sugar: 2.6 grams.
Fiber: 1.2 grams.
Fat: 0.2 grams.”
“Protein: 0.9 grams.
Carbs: 9.6 grams.
Sugar: 4.7 grams.
Fiber: 2.8 grams.
Fat: 0.2 grams.”
“Protein: 0.9 grams.
Carbs: 2.9 grams.
Sugar: 1.7 grams.
Fiber: 1.2 grams.
Fat: 0.14 grams.”
“Protein: 0.65 grams.
Carbs: 3.6 grams.
Sugar: 1.6 grams.
Fiber: 0.5 grams.
Fat: 0.11 grams.”
“Protein: 2.86 grams.
Carbs: 3.63 grams.
Sugar: 0.42 grams.
Fiber: 2.2 grams.
Fat: 0.39 grams.”
“Protein: 1.44 grams.
Carbs: 5.5 grams.
Sugar: 3.5 grams.
Fiber: 2.3 grams.
Fat: 0.12 grams.”
“Protein: 2.02 grams.
Carbs: 17.4 grams.
Sugar: 0.78 grams.
Fiber: 2.2 grams.
Fat: 0.09 grams.”
“Protein: 2.82 grams.
Carbs: 6.64 grams.
Sugar: 1.7 grams.
Fiber: 2.6 grams.
Fat: 0.37 grams.”
“Protein: 0.26 grams.
Carbs: 13.8 grams.
Sugar: 10.39 grams.
Fiber: 2.4 grams.
Fat: 0.17 grams.”
“Protein: 0.67 grams.
Carbs: 7.68 grams.
Sugar: 4.66 grams.
Fiber: 2 grams.
Fat: 0.3 grams.”
“Protein: 0.72 grams.
Carbs: 18.1 grams.
145UCL BARTLETT B-PRO RC14
146 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
147UCL BARTLETT B-PRO RC14
REFERENCES
Street network
Building footprint
Railway network
: Open street map
: Digimap.adina.ac.uk
: Open street map Movement movement.uber.com
Emotions Data
: https://goodcitylife.org/ Data : https://goodcitylife.org/
Health Deprivation score : https://data.london.gov.uk/ desert score : https://data.cdrc.ac.uk/ Household Income
Housing
: https://data.london.gov.uk/
: https://data.london.gov.uk/
RedMeat Consumption : https://www.nature.com/ Tesco Transaction : https://www.nature.com/
NDVI : USGS(A platform supported by NASA)
NoX emissions : https://data.london.gov.uk/ Urban Heat Island
Integration r1000
: https://data.london.gov.uk/
: calculated with depthmapX Station
: https://data.london.gov.uk/
Poor Restaurant Rating : https://www.tripadvisor.co.uk/
American Cusine
: https://www.tripadvisor.co.uk/
Asian Cusine : https://www.tripadvisor.co.uk/
Middle-Eastern Cusine : https://www.tripadvisor.co.uk/
European Cusine : https://www.tripadvisor.co.uk/ https://www.tripadvisor.co.uk/ https://www.tripadvisor.co.uk/ Produced
148 FOOD ORIENTED NEIGHBOURHOOD DEVELOPMENT
| Sources
Traffic
:
Smell
Food
Density
Transport
Vegan Food Option :
Fastfood Option :
3D buildings :
by Grasshopper Meerkat Image :@_foodstories_ Instagram
Aashi Mathur Mingze Chen Nermeen Abbas Zaidi Yanqi Zeng Shivani Sathiyamurthy AGRO-MATRIX RC 14: Machine Learning Urbanism B-Pro Bartlett