Master Research Project : Visual Dislocation

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University College London The Bartlett School of Architecture 22 Gordon Street, London, WC1H 0AJ 2018-2019 Project Name: Visual Dislocation Team members: Lingzhao Wei Piyush Prajapati Han Wu Xi Wang

Design Tutors: Roberto Bottazzi and Tasos Varoudis

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Phase 1

This section brings in the understanding and deepens the theoretical knowledge of Visual Perception. Its relationship to Visual Dislocation, Past, Present and Future of the same.

Phase 2

This section deals with the researchexploration at the Macro and the Micro level of the City. Conclusively finding the site for the design Intervention.

Phase 3

At this step, we explore the tools for understanding the visual perception, its implementation, adaptation, illustrating the visual importance. The aim here is to understand the visual perception and dislocation through various methodologies, which thereby leads to a deeper understanding of visual cognition.

Phase 4

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The final phase is divided into two sections, amalgamating the research and the design tools for visual dislocation in two major cities, namely, London and Paris. Detailed understanding of design interpretation, prototyping and reverse-engineering is processed through trained neural networks.

PHASE 01 1. INTRODUCTION

1.1 VISUAL PERCEPTION : DEFINITION

1.2 THEORIES AND PRINCIPLES

1.3 VISION: PAST - PRESENT - FUTURE

1.4 VISUAL DISLOCATION

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PHASE 02 2. ANALYSIS

MACRO ANALYSIS

2.1 LONDON IN LAYERS

2.2 SOCIAL MEDIA ANALYSIS

MICRO ANALYSIS

2.3 SITE UNDERSTANDING

2.4 VISUAL ANALYSIS

2.5 DATASETS

2.6 PCA ANALYSIS

2.7 SITE SELECTION

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PHASE 03 3. DESIGN TOOL KIT

3.1 SOCIAL MEDIA ANALYSIS

3.2 GLOBAL POSITIONING SYSTEM (GPS)

3.3 GOOGLE STREET VIEWS

3.3.1 COLOR ANALYSIS

3.3.2 OBJECT DETECTION

3.4 A.I. NAVIGATION

3.5 VIEW ANALYSIS 3.6 DEEP LEARNING

48 50 56 62 66 74 78 82 86

PHASE 04 4.A DESIGN - LONDON

4.1 SPATIAL ARRANGEMENT

4.2 VOLUMETRIC ASSESSMENT

4.3 DESIGN STRATEGY

4.4 DESIGN VISUALS 4.B DESIGN - PARIS

4.5 PARIS IN LAYERS

4.6 DESIGN STRATEGY

4.7 DESIGN VISUALS

96 98 102 108 116 122 124 128 140

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1. INTRODUCTION

01 PHASE 1

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To understand and deepen the theoretical knowledge of Visual Perception, and its relation to Visual Dislocation, Past , Present and Future of the same.

1.1 Visual Perception : Definition 1.2 Theories and Principles 1.3 Vision: Past - Present - Future 1.4 Visual Dislocation

1.1 DEFINITIONS - A CONNOTATION : VISUAL -adjective relating to seeing or sight. “visual perception” -noun a picture, piece of film, or display used to illustrate or accompany something.

CONNOTATION : PERCEPTION -noun 1. the ability to see, hear, or become aware of something through the senses.

2. the way in which something is regarded, understood, or interpreted.

CONNOTATION : DISLOCATION -noun 1. disturbance from a proper, original, or usual place or state.

2. a displacement of part of a crystal lattice structure.

1.1 DEFINITIONS - B UNDERSTANDING Visual perception is the process of absorbing what one sees, organizing it in the brain, and making sense of it. Visual perception is the ability to interpret the surrounding environment using light in the visible spectrum reflected by the objects in the environment.

The project defines and underlines the importance of visual perception in the city through visual dislocation. It is done so to assimilate the information from surrounding and how this governs the conscious and subconscious human decisions.

PROXIMITY

CLOSURE

SIMILARITY

CONTINUANCE

FIGURE & GROUND

1.2 THEORIES AND PRINCIPLES There are many theories where people tends to organize visual elements in form of groups or unified whole when certain principles are applied, such as : GESTALT THEORY OF PERCEPTION Gestalt psychologists argued that these principles exist because the mind has an innate disposition to perceive patterns in the stimulus based on certain rules. These principles are organized into five categories: • Proximity, • Similarity, • Continuity, • Closure, and • Connectedness.

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1.2 THEORIES AND PRINCIPLES THEORY OF AFFORDANCES OPTICAL ARRAY: The patterns of light that reach the eye from the environment.

RELATIVE BRIGHTNESS: Objects with brighter, clearer images are perceived as closer TEXTURE GRADIENT: The grain of texture gets smaller as the object recedes. Gives the impression of surfaces receding into the distance.

RELATIVE SIZE: When an object moves further away from the eye the image gets smaller. Objects with smaller images are seen as more distant.

SUPERIMPOSITION: If the image of one object blocks the image of another, the first object is seen as closer.

HEIGHT IN THE VISUAL FIELD: Objects further away are generally higher in the visual field

BOTTOM-UP

TOP-DOWN

BOTTOM UP VS. TOP DOWN THEORY Gibson (1966) who has proposed a direct theory of perception which is a ‘bottom-up’ theory, and Gregory (1970) who has proposed a constructivist (indirect) theory of perception which is a ‘top-down’ theory.

Bottom up theory suggests that perception involves innate mechanisms forged by evolution and that no learning is required. This suggests that perception is necessary for survival – without perception we would live in a very dangerous environment.

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1.3 VISION: PAST-PRESENT-FUTURE PAST Alhazen (965 – c. 1040) carried out many investigations and experiments on visual perception, He was the first person to explain that vision occurs when light bounces on an object and then is directed to one’s eyes.

An experiment by Isaac Newton to isolate each colour of spectrum of light by passing it through prism gave character to the light and vision.

PRESENT David Marr developed a multi-level theory of vision, which analysed the process of vision at different levels of abstraction. In order to focus on the understanding of specific problems in vision, vision can be identified in three levels of analysis: the computational, algorithmic and implementation levels.

Many vision scientists, including Tomaso Poggio, have embraced these levels of analysis and employed them to further characterize vision from a computational perspective.

FUTURE

Theories and observations of visual perception from past and present have been the main source of inspiration for computer vision (also called machine vision, computational vision or cyberception).

The amalgamation of computer vision and computation has opened up new horizons to analyse the Visual perception in many Meticulous and neural levels.

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Issac Newton (1642–1726/27) was the first to discover through experimentation, by isolating individual colors of the spectrum of light passing through a prism, that the visually perceived color of objects appeared due to the character of light the objects reflected, and that these divided colors could not be changed into any other color, which was contrary to scientific expectation of the day.

- Vision and art : the biology of seeing

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Convolutional Neural Network (CNN) is composed of an input layer, an output layer and multiple hidden layers in between. The hidden layers generally include convolutional layers, pooling layers, fully connected layers and normalization layers.

This methodology expands the horizons of visual analysis and perception which are beyond human cognition

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LONDON AS NETWORKS

1.4 VISUAL DISLOCATION Visual Perception is a method of conscious and subconscious understanding of spaces and environment.

In consideration of previous learnings, the research study turns actively into a research design project. The Project “Visual Dislocation� aims to train a computational brain to navigate in the city based on Data and Design.

This process unfolds in two phases. Exploratory and Conclusive.

The environment in which we live in includes social and physical factors. In the first Exploratory phase, the design research explores different sets of datasets and urban layers that have an impact on the built environment. With comprehensive understanding, key layers/datasets are carried forward for Visuospatial Quantification.

In the second Conclusive phase of design research, using the toolkit of the neural network, the interdependencies of datasets are established and executed, to simulate the built environment.

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02 PHASE 2 : PART 1

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London, U.K. • Elevation: 11 Mts • Coordinates: 51.5074° N, 0.1278° W

2. MACRO ANALYSIS This section deals with the research exploration at the Macro and the Micro level of the City.

Conclusively finding the site for the design Intervention.

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N

HIGH

0M 750M

3000M

GREEN SPACE London’s parks, canals, reservoirs and riversides form an important network of spaces and public places. Alongside London’s trees, these green and riverside spaces play a valuable role in improving the quality, character and economy of the capital.

LOW

N

RESIDENTIAL AND COMMERCIAL As illustrated in the map, it is inferred that the northern part of the area, especially near the central London, Oxford street has a higher building density than the southern part of the City.

0M 750M

3000M

N

ROAD CHOICE 800M As indicated in Choice Map, it is inferred that the area near the Oxford street, The Shard and the Hyde park has much higher value. The area, for example, near the Battersea Power Station has lower value. the area which has higher value has much more social interactions than the area has lower value, and is more vibrant.

0M 750M

3000M

N

BUILDING HEIGHT This map illustrates the building height for London. In a broader perspective, the commercial area concentrates on central London and Oxford Street. The residential area and green space distribute in the rest part evenly.

0M 750M

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3000M

A

B

C

D

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F

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2.1 LONDON IN LAYERS A. CHOICE MAP (REMAPPED) E. COMMERCIAL SPACES

B. CHOICE MAP C. DENSITY MAP F. RESIDENTIAL SPACES

D. GREEN SPACES G. BUILDING HEIGHTS

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Camden Market

Oxford Street

London Eye

Buckingham Palace Big Ben

Battersea Power Station

Battersea Park

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Barbican Center

The Gherkin St. Paul

Tate Modern

London Bridge

Tower of London

The Shard

Canary Wharf

2.2 SOCIAL MEDIA ANALYSIS UNDERSTANDING Social media analytics is the process of gathering data from stakeholder conversations on digital media and processing into structured insights leading to more information-driven decisions and increased customer centrality for cognitive understanding.

Mapping it at urban level, illustrates the potential and the active zones of social interaction.

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Battersea Power Station “Historical landscape” “Urban regeneration” “Really cool”

Westminster Cathedral “Lovely building” “Historic heritage” “Red brick”

Hyde Park “Nice place” “Walkable” “Rest”

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The

“C “B “M Tate Britain “Quite place” “Nice place” “Fabulous interior”

The Shard “Landmarks” “Light my way” “Nice view”

e Oxford Street

Crowded” Busy” Many people”

St Paul “Enjoying the view” "Spectacular" "Beautiful"

SOCIAL MEDIA SPATIAL INFLUENCE DATA GATHERING With internet users having an average of 5.54 social media accounts, the user’s interest, behavioural patterns, likes and dislikes can be easily mapped and understood. To understand it better, we have accumulated the data from Flickr, which is one of the pioneers of Social Media image collection.

This map demonstrates the catchment area of different landmarks in London. Based on the big data analysis for the landmarks, the areas such as the Oxford Street and the Hyde Park have limited social media dominance. On the contrary, The Shard and the Battersea Power Station has a much higher social media dominance due to the height of these landmarks. These landmarks can be seen in different urban areas.

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OXFORD STREET

BIG BEN

HYDE PARK

TATE MOD LONDON EYE

THE WESTMINSTER CATHEDRAL

BATTERSEA POWER STATION

The number of social media points in each pixel HIGH

LOW

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THE SHARD

ST. PAUL

HYDE PARK

LONDON EYE

BIG BEN

BATTERSEA PW. STATION

OXFORD ST.

WESTMINSTER CATHEDRAL

THE BARBICAN CENTRE

ST PAUL THE GHERKIN

TOWER BRIDGE

BRIDGE OF LONDON

DERN

CANARY WHARF THE SHARD

SOCIAL MEDIA ANALYSIS The map contains all the social media points verses its spatial dominance. Each blue hexagon defines social media impressions, with more than 5 geo-located points. It can be seen that the urban area in the central part is the most popular and more vibrant than the other urban space as the majority of landmarks concentrate. The white hatches explain the social media points of the most famous landmarks in central London.

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02 PHASE 2 : PART 2

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MICRO ANALYSIS These are based on the interrelationship between people, transport and amenities. They address the degree of user-friendliness in terms of mobility and access to services and facilities.

These are agglomerations of neighbourhoods integrated by shared infrastructure and amenities e.g public transport system; together they could form a regional urban centre.

Cluster model favours high-density areas.

In this section, we understand the City of London at a deeper cognitive and human level. Thereby analysing the attributes that function at Micro Levels.

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LIST OF ATTRIBUTES SITE UNDERSTANDING - Figure Ground - Choice Map - Site Attractors VISUAL ANALYSIS - View Rose Diagrams - View Rose Length - View Rose Area - Visual Connectivity - Visual Integration (r = n, 2, 4) The methodology of PCA (Principal component analysis) are used to understand the site better.

Following are the attributes being used and tested. The attributes have been grouped under three sections namely: ROAD NETWORKS - GPS - Choice Map

- R = 800 Metric Units

- R = 1600 Metric Units

- R = 3000 Metric Units - 2D Isovist

- Area (Metric Units)

- Length (Metric Units) BUILDINGS - Housing Prices - Building Heights - Visual Connectivity along Building edge LSOA (Lower Layer Super Output Area) - Greenspace - IMD (Index of Multiple Deprivation)

- Imcome deprivation: IMD

- Income: IMD

- Employment: IMD

- Education: IMD

- Health: IMD

- Cime: IMD - Land use - 2014 population - LSOA Housing prices2017 - Visual Integration [HH] R4

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A

ST PAUL

TATE MODERN

30 St Mary Axe

B THE SHARD

TOWER OF BRIDGE

C

30 St Mary Axe

ST PAUL

A. Site B. Site Attractors C. Choice Map (R=800) D. Selected Routes

2.3 SITE UNDERSTANDING

TOWER OF BRIDGE

TATE MODERN

THE SHARD

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SITE 01 Central London spans over several boroughs. It hosts to a huge amount tourist attractions and high rises such as The Shard, The Gherkin, The Big Ben, The Tower of London, etc.

It is known to be the most famous zone amongst other zones.

A

VICTORIA STATION

WESTMINSTER CATHDRAL St George’s Square

BATTERSEA PARK

TATE BRITAIN

BATTERSEA POWER STATION RIVERSIDE WALK GARDEN

B

C WESTMINSTER CATHDRAL

TATE BRITAIN

VICTORIA STATION

RIVERSIDE WALK GARDEN

A. Site B. Site Attractors C. Choice Map (R=800) D. Selected Routes

St George’s Square

BATTERSEA POWER STATION

BATTERSEA PARK

D

SITE 02 Located on the south bank of the River Thames, the major attractions are The Battersea Park, Battersea Power Station, The Oval, etc. The iovist values here are comparatively vast due to loosely placed buildings which gives more room for movement and visibility

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SITE 01: ROUTE A

SITE 01: ROUTE B

SITE 01: ROUTE C

SITE 01: ROUTE D

2.4 VISUAL ANALYSIS SITE 01 The Isovist values in these areas are low as the buildings are densely packed and does not offer much space for interpretation and alteration.

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SITE 02: ROUTE A

SITE 02: ROUTE C

SITE 02: ROUTE B

SITE 02: ROUTE D

SITE 02 The Isovist values here are comparatively vast due to loosely placed buildings which gives more room for movement and view angles.

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VIEW ROSE AREA

VIEW ROSE LENGTH

SITE 01 The diagrams above illustrate the Visual analysis which includes view rose and view angle analysis in site 1 for routes selected by Choice Map. An isovist, or viewshed, is the area in a spatial environment directly visible from a location within the space. Here we show how a set of isovists can be used to generate a graph of mutual visibility between locations.

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VIEW ROSE AREA

VIEW ROSE LENGTH

SITE 02 As illustrated in Site 1, the four routes show in the above diagrams for Site 2 are also determined based on the road choice analysis.

In comparison with the view analysis of Site 1, the visual accessibility is higher in site 2 due to the vastness of open spaces and the building density as compared to denser spatial organisation in the Site 2.

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SITE 01 The majority of urban space in site 1 has a lower visual connectivity and integration value due to the higher building density. The area along the riverside get much higher value, and these urban areas have higher visual accessibility.

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SITE 02 The urban space with higher visual connectivity value in site 2 concentrates along the riverside and the west-northern part. In terms of the visual integration, the urban space near the Battersea power station and the northern part of this area.

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2.5 DATASETS

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-0.8 - 1.0

-1.0 - 2.8

2.8 - 4.6

4.6 - 6.4

6.4 - 8.2

2.6 PCA ANALYSIS ROAD NETWORK PCA ANALYSIS From the picture on the left, we can see that the area has a much higher PCA value compared with the other road. It shows that there are more social interactions generated in these places.

PCA Variance Ratio (0.44, 0.29)

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ROAD NETWORK PCA ANALYSIS From the road network PCA analysis, we can see that the northern part of the area also has a lower value. It could be seen that in these areas which have lower PCA value, there are fewer social interactions and less liable than other areas.

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2.0094 - 0.6212

2.0094 - 0.6212

0.7670 - 2.1552

2.1552 - 3.5434

3.5434 - 4.5316

LSOA PCA ANALYSIS From the picture on the left, we can see that the area near the Battersea power station has a lower PCA value. It could mean that this area has lower value in different data aspects and has low-quality social conditions compared with the other areas.

PCA Variance Ratio (0.48, 0.12)

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LSOA PCA ANALYSIS Based on the PCA analysis of LSOA, we can see that the area in the south part gets a lower value, which means that there is a low-quality built environment and social conditions in these areas.

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0M

10M

20M

BUILDING PCA ANALYSIS

30M

Based on the building PCA analysis, the areas along the riverside have higher PCA value. The higher the building height it has, the widen the vision and the higher the housing prices it will get.

PCA Variance Ratio (0.46, 0.18)

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BUILDING PCA ANALYSIS

LSOA PCA ANALYSIS

ROAD NETWORK PCA ANALYSIS

OVERLAYING PCA DATASETS

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Crime CRIME Crime

2.8 2.8

30 30 25 25 20 20 15 15 10 10 5 5

6.4 6.4

4.6 4.6

8.2 8.2

2.7 SITE SELECTION 0 0

2 2

EDUCATION Education Education

5.2 5.2

20 20

4 4

6 6

8 8

6.4 6.4

7.6 7.6

8.8 8.8

4 4

6 6

8 8

10 10

15 15

In this part, the three PCA analyses are used to be the factors that affect our site selection. And then we overlap the different PCA layers to select several sites based on the mixed lowest and highest value.

10 10 5 5

HISTOGRAM 0 0

2 2

EMPLOYMENT Employment Employment

2.8 2.8

20 20

4.6 4.6

6.4 6.4

10 10

8.2 8.2

15 15 10 10 5 5 0 0

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2 2

4 4

6 6

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10 10

The y-axis indicates the number count of the particular domain, the x-axis represents the value of IMD. From the histogram, it can be seen that the selected site has a lower value in different deprivation aspects. It has lower accessibility to education, employment, and good medical condition.

ZONE 4 ZONE 1

ZONE 2

ZONE 5 ZONE 3

Upon combining three PCA layers, it is observed that some locations have some areas fall under lower values of each PCA layers. These areas are identified and highlighted in the map.

Moreover, we divided these areas into 5 Zones according to the urban environmental features and data comparison of each zone. For instance, Zone 1 has a lower value of IMD, but has a higher value in isovist analysis and visual integration.

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03 PHASE 3

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PHASE 3: DESIGN TOOL KIT 3.1 Social Media Analysis (Flickr) 3.2 Global Positioning System(GPS) 3.3 Google Street View:

3.3.1 Color Analysis

3.3.2 Object Detection 3.4 AI Navigation 3.5 View Analysis 3.6 Deep Learning

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3.1 SOCIAL MEDIA ANALYSIS DATA GATHERING With internet users having an average of 5.54 Million social media accounts, the user’s interest, behavioural patterns, likes and dislikes can be easily mapped and understood. To understand it better, we have accumulated the data from Flickr, which is one of the pioneers of Social Media image collection.

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SOCIAL MEDIA ANALYSIS The algorithms used here is to fetch the 5000+ images from the social media with their Geo Locations, these are then tested for socially active zones in the urban fabric.

The Key word used: “London Street�

Time Frame: 2018 - Present

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COLUMN 1 54

COLUMN 2

COLUMN3

COLUMN 4

COLUMN 5

GEO LOCATED SOCIAL MEDIA IMAGE TITLES COLUMN 1 Oxford Circus Welbeck Street car park. Marylebone.

Everyone Else Takes This Why Not Me Woodstock Street Victoria Palace 3211 Victoria Palace 3115 Victoria Palace 3156a Ghost Train Camden. London Victoria Palace 3121 Camden. London Camden. London Camden. London Welbeck Rd 2 Welbeck Rd 1 Welbeck Rd Christmas is calling - Oxford Street.

London skyline from Primrose Hill War Graves. London Road Cemetery Hard to resist..

Circle Line Platform Beaded bags - fashion accessories shop.

Amy & tiny Betty - art shop.

Lighting shop. Camden Market.

Highgate Cemetery Pause d?jeuner au bout du monde HYDE PARK 2 Always Xmas in some places Highgate Cemetery Men’s Bathing Pond. Hampstead Heath Grave of Patrick Caulfield.

Still Water Arcade-Victoria-No2 - Copy The Piano Shop Superhero Abby Road Cereal Killer Cafe Whoops Whoops Camden Town Mannequin Tunnel Cereal Killer Cafe Mayfair 17/18 Walk in the park?

Launch. Rebecca Newnham (Sculptor).

Diagonal perspectives Pretty Girl at Church Duke Street.

Arriva London LT216. LTZ1126.

INSIDE Waiting For Duffy Regents Canal 3227 Regents Canal 3225 Duke Street.

Camden Lock. London I Study Nuclear Science Go Ahead London General WSD2.

Reading Buses 757. YX64VRT. Moon With The Shard Baker Street Underground Station.

Bad Ad Just Say No Tables at Cafe Nero Selfie on Bond Street Things Are Going Great Together DSQUARED2 Shove Off Gingers Against Trump Zero Tolerance Toxic all souls. langham place Baker Street Underground Station.

Abstract Architecture #35 Kings Road. Chelsea On A High Baker Street Billboards Camden Selfie Parrot Punk Rocker

COLUMN 6

COLUMN 7

Picadilly circus London Cityscape Photogram Regents Canal 8424 Carnaby Christmas LONDON Westminster London Eye - Westminster Clockwork Salute Say Cheese Foyles. Charing Cross Road Bookshop. Charing Cross Road Night Bus - West End. London. UK Neon Reflections. West End Kiosk. King’s Cross Neon. West End Leydis on tube - Colour Piccadilly Circus at sunset Urban Flying low Mighty King Lion London Coliseum 7593 London Coliseum 7598 London Coliseum 7625-HDR Car Repairs. Stock Orchard Street North Road. Islington Tyres. Agar Grove Fluorescent interloper at Trafalgar Square Granary Square 8458 Granary Square 8462 Granary Square 8466 Time in Motion Blue Pyramid Dark wolverines Found A Gap LONDON Peoples Vote 8313 People at Piccadilly Circus. London Damp Piccadilly Circus. London Damp Piccadilly Circus. London Cathedral and a bus...

Emerald City...

A New Dawn... #Inktober the Tenth - Soho Square Fly away 2533 SN66WRW Loretto Italian Organic Sausages Unfit Mother Face the Strange Brunswick Center Bus to King’s Cross Sunset City London Downstairs Euston Street Mural Down and Out Street Mural Above London The curve of Regent Street.

Underground Grille Ice Cream LONDON Trafalgar square London Skyline from BT Tower blondie Now You See Me! Covent Garden. London Broad Stripes And Bright Stars BUS LANE Tottenham Mews.

Coca Cola London Eye -LondonSt Pancras 4139a St Pancras 4141 Kings Cross 4132 Trafalgar Square - London At Saint James.

Shard Towers GLIDE Trains Trains Trains COLUMN 2 London Morning London Eye The tree is up.Christmas must be coming.... i was the future once The British Museum.

Angels & Angles Reach Out City lights bokeh London Transport Museum - London. UK Clouds Over the NG Yellow Peril Alamo Piccadilly Circus Carnaby Street Starry (II) - Strand. London. UK COLUMN 3 Vauxhall 8814 Cycle Vauxhall 8824 Stop and Save Vauxhall 8829 Blackfriars at night Westminster. London Shard Light Show....

Southbank. London On High Great Windmill Street Picturehouse Central Kennington Park Road An ordinary day on Westminster Bridge Turbine Hall @ Tate Modern Sunrise over the City of London Shopping Is A Religion Sheltering from the downpour Park Morning Champagne Bar Deer Santa - Covent Garden. London. UK Red Shard Point Smithfield Market The Passenger Film Forever Braided Reflecting on a hard life...

Bulging Bankside. London London Glimpse Merry & Happy - Covent Garden.

Bankside. London ..street drumming St Alphege London Wall King’s Cross A spooky photo call...

2531 SN66WRU Hall of Justice Starry - Strand. London. UK One London Wall Waterloo Bridge Ray Of Shadow Flying high Street Art of The Southbank National Portrait Gallery Street Art shelves Trafalgar Square Waterloo Grill London Eye The Thames at Night Golden Jubilee Bridge Southbank Kings Cross Tunnel Gutter Lane Neon Rhapsody @ London Blackfriars train station In The Pipe. 5 By 5 IMAX. Southbank 7AM at Piccadilly Circus BFI IMAX Coal Drop Yard No Exit Let’s Face It Blackfriars Tower Table Dancing - Soho.

St Pauls Lisle Street Night fog Armed And Dangerous London Blues Kings Cross Wobbly Shaftesbury Avenue

grafitti in red Stairs Up Newgate Street London St Paul??s Dome A classic view St Paul??s Cathedral London Thames One Second London #7 London construction Old and New london St Paul’s Reflections The Ascent London London Watching The City skyline from Southbank.

Lovers Heart Dawn has broken...

Rainy London Day City Dragon & Rolls Royce Cafe Rouge X Highbury Fields New Kids on The Block Twinkly Lights St Paul’s at Twilight St Paul’s Cathedral Stairs View from St. Paul’s River Thames 7742 A view from the bridge Hell broke loose upon Protestant City lines and circles City Nights Peek a Boo...

One Blackfriars. London St Paul and Royal Theatre Barbican Center 8 Barbican Center 1 Pointed The Seven Samurai London and the Thames River Bridewell Theatre 7683 London Under Construction Atlantic Road. Brixton Electric Avenue. Brixton London London Cityscape No compromises Royal National Theatre The city of London The Shard 2 Please.

Who could say no?

Space Barbican Center 7 Barbican Center 3

COLUMN 5 City Contrasts...

Leopard-print Beetle with wall art.

Wall/door art. Shoreditch.

Woskerski wall art. Shoreditch.

Bethnal Green Garden Alleyway St Pauls Nights Boats Pillars Lights at the end of the Tunnel Warehouse Gospel Hall St. James of Bermondsey wheels Tower Bridge United Colours of Aldgate Tower Bridge Noah’s Ark. Evelyn Court. Amhurst Road.

Jessam Avenue. Clapton Phone Box. Cazenove Road Boats and skyline Shoreditch Red Dead Redemption...

Vernacular architecture.

Jurassic Park.

Shoreditch COLUMN 6 The lady with the dog The lady with the dog Daren Bread.

Pub. Edward Road.

Homerton Shops Cityscape #238 South Dock Marina Greenland Quay. Rotherhithe Greenland Dock. Rotherhithe Bridge over A12. Victoria Park Victoria Park shopping

COLUMN 7 Metal. Concrete & Glass You ain’t seen me ....right?

Montgomery Square Montgomery Square London Aquatics Centre Westfield Stratford Bridge over River Lea near Bow Locks Boss car Brothers Rear Empty City Decorated industrial - Hackney Wick.

Warehouse/factory graffiti/wall art Rear Empty City COLUMN 4 Poppy Stop Numen (Shifting Votive Three).

Mattress London_City_8V6A9682 Mirror Bridge Without touching the sides Aquatic Centre The Army & Navy Pub. Dalston Queen Elizabeth Olympic Park London.

Chinese Inland Mission.

Crossrail Place. Canary Wharf.

Londres by night Streets. people...

London Taxis Protest Streets. people...

London Skyline Lotus...

Corner Cafe. Matthias Road. Dalston Old and the new. Fish Island London.

London scene West Ham United.

Borough Market. London Seating Street walker Cabot square Warp Speed Half and Half.

The Dark Tower The Martians have landed!

Barbican Centre ArcelorMittal Orbit at Olympic Park.

The Scalpel Steps. skyline with the shard Stainless Steel.

Tagging Streets. people...

Leadenhall Market Streets. people...

Storm. Light. Shard The bottom of Cutty Sark Good Morning Remaining parts of Robin Hood Gardens Shard Lights Standing at Robin Hood Gardens Wordsworth Road Baptist Church.

Limehouse Cut from Violet Road Street Art. Queen Margaret’s Grove.

DLR Crossing Limehouse Cut City Boys Tommy Flowers Pub. Poplar Tagging In the space station Leadenhall Market Power Vortex Storm. Light. Shard Underneath the river St Alphege London Wall Chinooked Good Morning “According to the app. this is Heathrow!” Waterworks River Shopping Mall Broadgate Circle Urban Traveller Shoe Repairs. Liverpool Street.

Lamborghini Huracan Point of time Stratford City bus station Till Death Do Us Part West Ham United St Dunstan in the East Orbit Night city Cats. Albany Road. Leyton Week 45/52 - The Alley Stratford City bus station Dalston Supermarket Good News Shop.

The Shard The Carpenters Arms The Bridge...

Water Tower Phone Alone Beckenham Cafe South Bank at Dusk Head Down Liverpool Street Yellow London ` Lloyds Orbit Liverpool Street Shard Stripes Lime Street Greenwich 3297 Tower Torches Greenwich 3291 Spitalfields Market Look Both Ways Cathedral and the Ghosts...

Empson Street Bow Bloomberg building Bridge Over Troubled Water Ambulance. Bricklayers Arms More cityscapes Upper Norwood Methodist Church More cityscapes After the Rain Walthamstow Market Hair Salon. Thornton Heath Walthamstow Market Jasper Passage. Gipsy Hill Tower Blocks. Rainhill Road. skyline with the shard Street Abstract Ward of Walbrook Empson Street. Bow 22 Bishopsgate Hot Desking Fortnum & Mason. Royal Exchange Stratford Mansion House Place Hotel Leadenhall Market Entering the Underground Leadenhall Place West Ham United City of London Square Mile Canary Wharf Looking up...

Olympic Bell Batcave...

Tower Bridge and River Thames London. London All The Angles Beyond the Deepening Shadow 164 City of London High Rise Skyscraper Shade Isle of Dogs. London Shoreditch

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3.2 GLOBAL POSITIONING SYSTEM GPS DATA SET FOR NINE ELMS - 221 GPS Traces - Over 100000 counting points - Each counting points contains: - User ID - Time - Speed - User type (walking, driving, cycling...)

For site 2, we are dividing our site into grid, for each 10m * 10m grid, the counting points of GPS traces is looking like the left image. By analyze GPS data set, we are trying to get three people related emotions of our city environment.

- Number of Impression - Mean Time Spend - Dominant Direction

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NUMBER OF IMPRESSION This explains the how one city grid is accessible and busy.

Number of impression is the cumulative sum of all the counting points in side one grid.

MEAN TIME SPEND Time Spend is the average time spend that every GPS traces in a city grid. Time spend is the difference between when one enters and leaves the grid.

DOMINANT DIRECTION Dominant Direction explains the city migration movement.

Dominant Direction is the most prominent direction which most pedestrian choose amongst all 8 directions for one city grid which are North, North-East, East, South-East, South, South-West, West, North-West, North.

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NUMBER OF IMPRESSION This explains the how one city grid is accessible and busy.

Number of impression is the cumulative sum of all the counting points in side one grid.

It can be seen from the map above that the number of impression is massive at Vauxhall Station compared to other areas of the site.

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MEAN TIME SPEND Mean Time Spend explains the commute for every city grid, understanding if people like to stay or are passing through the grid.

Time Spend is the average time spend that every GPS traces in a city grid. Time spend is the difference between when one enters and leaves the grid.

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DOMINANT DIRECTION The feeling of orientation is essential for our wellbeing.

Dominant Direction explains the city migration movement. For each city grid, it also explains the surrounding environment and to which direction most people prefer to walk/commute.

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CONCLUSION GPS traces helped in understanding human interaction and movement in relation to the existing context, the next step deals with analysing the colour and image segmentation.

To evaluate the same, we grab the pictures from Google Street view for specific X,Y,Z coordinate.

Google Street View API’s (Application Programming Interface) provides the best platform to extract the navigable information in the form of images as observed by the user. These images are then examined for visual characteristics such as brightness, hue, saturation, dominant colour, lightness, displacement of colour, etc. Increase in the number of pictures refines the datasets and the information. Pictures when analysed in clusters, presents a comprehensive understanding of the environment.

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3.3 GOOGLE STREET VIEW Upon having the data collected from GPS with respect to Time Interval Spent, Dominant Direction and Number of Impressions, we used an algorithm to capture the images from Google Street View for the particular Grid Point.

The Aim here is to analyse and understand why certain grid point and the directions are prominent and are more preferred when compared to others.

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3.3.1 COLOR ANALYSIS UNDERSTANDING Color is one of the most important characteristic of human visual perception. The perception of colour derives from the stimulation of cone cells in the human eye by electromagnetic radiation in the visible spectrum.

The dominant colour is the one used in different shades for a majority of things, it acts as the ‘default’. Accents are colour that look nice with the base-colour and are sparingly and flattering, so that it draws attention.

Colour Analysis for the city can explain what is the base environment and what is the object what is drawing attention. The reality world or pictures of street views is filling with huge amount of different colours. Our aim is to find the dominant colour and accent colour among them, which are the colour of base of one sight, and the colour of objects which draws attention in one sight.

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Cluster 1 Cluster 2 Cluster 3

METHODOLOGY K-means clustering algorithm is used to identify the centre point of clusters as the main colour for each cluster. Using this, we divide pixels of one image into 5 clusters. The first cluster is the cluster of majority colours which stand for the dominant colour (the base colour) while the fifth cluster normally stands for the accent.

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RGB CHANNEL DISLOCATION Analysing the frames in continuity is the direct translation of human perception. From human visible perception, the colours are dynamic in nature and are continually displaced. The Pixel direction is the linear subtraction of Colour channels of two consecutive frames. Thus it depicts the dislocation of coloured pixels based on the RGB channels.

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RGB CHANNEL DISLOCATION The Above diagrams explain the RGB distortion in volumetric space. The Pixel direction is the linear subtraction of Colour channels of two consecutive frames. Thus it depicts the dislocation of coloured pixels.

This illustrates the movement of pixels with momentary frame and passing time interval.

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Colour Wheel : Social Media

Colour Wheel : Street View

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THE COLOUR WHEEL OF LONDON This map illustrates the collection of 7000+ images which are grouped and rearranged with relation to Hue-Saturation-Levels; Red-GreenBlue values; Brightness-Contrast values.

The colour wheel obtained here holds the generic essence of London as whole. It can also be inferred from the map that the hues of Red and Blue are dominant along with the grey palette.

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TEST DATA FOR 1000 + IMAGES

OUTPUT: OBJECT DETECTION

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3.3.2 OBJECT DETECTION METHODOLOGY Step 01 A base image is acknowledged, which is to be tested Step 02 A model and algorithm is used to generate regions of interest or region proposals.

Step 03 These region proposals are a large set of bounding boxes spanning the full image (that is, an object localization component).

Step 04 Visual features are extracted for each of the bounding boxes, they are evaluated and it is determined whether and which objects are present in the proposals based on visual features (i.e. an object classification component).

Step 05 In the final post-processing step, overlapping boxes are combined into a single bounding box (that is, non maximum suppression).

SCORE = AREA OF OVERLAP /

AREA OF UNION

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UNDERSTANDING With an image classification model, we generate image features (through traditional or deep learning methods) of the full image. These features are aggregates of the image. With object detection, we do this on a more fine-grained, granular, regional level of the image.

OBJECTIVE • • •

Identifying object location (placement) in an image.

Understanding how spatially active an image is, through image classification model.

It suggests which elements of the perceptual world are interactive.

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3.4 A.I. NAVIGATION The GPS Traces incorporates various social and cultural user involvement and this ease of access and connection is vital.

To understand this we select 10 GPS traces and analyse the moving pattern. Our aim here is to comapre the same with respect to AI Navigation

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UNDERSTANDING GPS TRACES The GPS traces is a method to analyse human interaction with space and the environment.

There is a significant number of factors which may cater to human decisions due to varying human psychological needs.

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A.I. TRACES Upon comparing the AI tracks with GPS Agents, some deviations can be seen. The Agents use the algorithm to compute the shortest and the best visually accessible routes.

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A.I. NAVIGATION In order to validate the GPS tracks we select the same Start and the Destination points to test with AI Humanoid agents. A navigation mesh data structure (a polygon mesh) used for pathfinding through complicated spaces.

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“Central” “New” “Classical”

3.5 VIEW ANALYSIS VISILIBITY DIAGRAM - ST. PAUL The above map illustrates the geolocated points, from where people could see St Paul. These points are plotted with respect to the urban context and the existing social media points.

Based on the visual interpretation, it is observed that the number of potential viewpoints is larger in comparison to the social media point.

This highlights the urban space which could be improved and thus would have more human interactions.

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“Tallest” “New” “Skyline”

VISILIBITY DIAGRAM - THE SHARD The above map illustrates the geolocated points, from where people could see The Shard.

Upon comparing the social media’s influential range of St Paul with The Shard, The Shard has a larger spatial dominance.

Likewise, there are numerous urban nodes, from where people could observe the Shard even after having lower accessibility. These urban spaces can be improved and have the potential to attract people.

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RE-WRITING With the understanding of Isovist, View angles and Visual area, we attempted to rewrite The Shard for its maximum visibility from all the points.

Step 01: We Geo-locate all the points on the road network from where the tip of Shard could be seen.

Step 02: We then break the shard into smaller modules and frames for form visual calculations and adaptation.

Step 03: For the points where there is no visibility to the Shard, the Pixelated Shard then gets the field of attraction.

Step 04: It then deforms and repositions itself for visibility from all the points.

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VISIBILITY FIELD OF ATTRACTION DIAGRAM

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3.6 DEEP LEARNING UNDERSTANDING The research up until now has illustrated the role of visual perception from past and present.

Their collective understanding illuminates the value of human navigation and perceived spaces. This section of research highlights on the computational measure to amalgamate different urban layers to examine the change in the perceptual environment. More we dig in to the microenvironment, the complexities and difficulties increases.

This mode of computer simulation helps to solve complex problems and can build intricate relationships between data layers that are often unstructured and unlinked to each other. On the lines of Deep learning, Neural Network (NN) toolkit are the set of algorithms recognising patterns whilst mimicking the human brain.

The internal structuring of Neural Network are compared to human cognitive systems.

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Here, we AIM to derive a computer simulation to establish relationship between Visual preferences (vistas) and the spatial arrangements in the city. The neural output is in the form of Colours describing its efficiency.

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3568 Sets of Trainging Data

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16 * 16 Data for One Set

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2 Layers of Input Data

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4 Layers Neural Networks

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Input_Data.shape = [ 3568, 16, 16, 2]

Output_Data = Red-Green-Blue

CONVOLUTIONAL NEURAL NETWORK Upon having the form amalgamating design methodologies, the next important phase is to test the output.

In order to do so, for the design intervention is reverse engineered and analysed with respect to the site. The City grid is divided into small test modules (16m * 16 m), totalling up to 3568 unique matrices of data. We use this to train our neural algorithm with respect to Building Heights, Visual Integration, which is then compared with Number of GPS interactions and color values.

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Layer_1 = Building Heights Layer_2 = Visual Integration (R = 2)

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URBAN FORM (NEURAL INPUT) The map above explains the combination of two input layers of the neural network, first being the building height value and second implying to the visibility value for each grid.

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NEURAL NETWORK The image on the left illustrates pictures from Flickr of each urban form grid in London.

The mapping on the right shows the combination of three layers, the pictures from Flickr, the dominant colour of each urban form on a grid based on the colour analysis of social media picture and the urban form grid.

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04/A 04 PHASE4 4: :Design Design - London PHASE

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METHODOLOGY DESIGN INTENTION 4.1 Spatial arrangement 4.2 Volumetric Assessment 4.3 Design Strategy 4.4 Design Visuals

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4.1 SPATIAL ARRANGEMENT 3 * 3 SPATIAL CALCULATION To testify Spatial Arrangement, we use genetic algorithm to solve both constrained and unconstrained optimization problem for its best solution.

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3 * 3 SPATIAL CALCULATION For optimum spatial arrangement, we test the 3X3 module for Isovist (Viewrose) and Spatial Area For the 3*3 practice, we give two genes for the box: Length of x direction (-1 to 0, -1 to 1, 0 to 1, 0; 0 = box disappears) Length of y direction (-1 to 0, -1 to 1, 0 to 1, 0; 0 = box disappears) And two negatively related fitness objectives: • Highest Area of Isovist for Two given points • Maximum Area of Box

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ANALYSIS It is an approach to use the algorithm to simulate the best spatial arrangements. Upon considering these voxels as 2D arrangements for building volumes, the algorithm computes them based on visibility quotient.

The improvement in the spatial arrangement can is observed from the image on the left spread.

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STEP A Volume Arrangement

STEP B Volume Calculations

STEP C Volume Expansion

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4.2 VOLUMETRIC ASSESSMENT ANALYSIS With a frozen spatial arrangement, which is the 2D arrangement of layout, the visual perception is calculated for three dimensional volumes. The calculation runs in two steps.

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Firstly, the masses are subdivided into small Bounding Boxes.

They are then dislocated (removed/reduced) based on 3D visual calculations. Thousands of spatial arrangements are tested through genetic Algorithm for best solution.

The second step includes the expansion of surface area, which is achieved upon Morphing the voxels. This is calculated with respect to visual dislocation. The negative space thus produced increases the surface area thereby acting as visible spaces.

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SPATIAL OPTIONS (VIA G.A.) These diagrams define the design options by using the genetic algorithm. In this case, two fitness are identified, which are maximum building volume and maximum spacial viewable faรงade.

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SPATIAL OPTIONS VS. NEURAL COLOR Based on the genetic algorithm, numerous design options are created as recorded above. Moreover, this map also defines the colour of each design option which is generated by the Neural Network.

It is thus an evaluation process, where, the neural network creates and examines the colour of these design options. The suitable design option are then selected based on the outcomes.

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STAGE 1 GPS DATASET : NUMBER OF IMPRESSION IN THE SITE

STAGE 4 CALCULATE ISOVIST AREA TOWARDS THE DOMINANT DIRECTION FOR EACH POINTS

STAGE 7 OVERLAY ISOVIST AREA FOR EVERY COUNTING POINTS

STAGE 2 ACCORDING TO THE GPS DATA, WE CHOOSE THE PRIMARY ROUTE

STAGE 5 OVERLAY ISOVIST AREA FOR EVERY COUNTING POINTS

STAGE 6 OVERLAY ISOVIST AREA FOR EVERY COUNTING POINTS

STAGE 8 VISIBILITY MAP FOR THE SELECTED SITE

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STAGE 3 GPS DATASET : DOMINANT DIRECTION FOR THE PRIMARY ROUTE

A unique Visibility Map is generated for the area. These values are then worked along with Neural Calculations in Generative Alogrithms.

VISIBILITY MAP

To calculate the overall Visibility Index of urban space, the Isovist areas are added in together.

OVERLAPPING VISIBLE AREAS

Visible area (Isovist Area) is calculated for every point on the route.

MAPPING VISIBILITY ALONG THE DIRECTION FOR THE ROUTE

For the route, the dominant direction is mapped from Open Street Map to calculate the Visibility Index ROUTE’S DOMINANT DIRECTION

Based on the impression, the ideal route is identified IDENTIFIED ROUTE

Mapping of Human movement in the form of Number of Impressions, dataset being combined from Open Street Map GPS - NUMBER OF IMPRESSIONS

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STAGE 4 And lastly, the final layer is formulated by dynamic elements of the perceptual world. The spatial zoning in thus layer is positioned in an area with the highest visibility value.

STAGE 3 In this stage, the forms are organic in nature, comprising of translucent elements. The forms in this zoning have relatively higher-visibility value compared with the previous stage.

STAGE 2 The primary layer comprises of rigid building forms, which is positioned in a lower visibility zone.

STAGE 1 In this stage, based on the visibility map that is determined, we identify the spatial zoning of our design. The forms are grouped based on the spatial zoning.

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In this stage of the Neural Outcome, the massing is volatile and is subject to change as per human navigation.

DYNAMIC LAYER

The spatial arrangement achieved here states the permeability. Which means, the spaces should have unobstructed views.

TRANSLUCENT / ORGANIC BUILTFORMS

In this step of Neural network, the Algorithm aims to increase the building massing along with the visibility quotient.

VOLUMETRIC EXPANSION

This step caters to the primary outcome of the neural algorithm.

The initial massing is the key stage to derive unique spatial characteristics.

RIGID BUILTFORMS

The ground is the first contact of human navigation. It is deformed and rearranged to simulate the movement patterns.

MAPPING NAVIGATION

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STAGE 1

STAGE 2

In this stage, based on the calculation of the visibility map, the spatial arrangement is derived for the selected Zone.

Using the genetic Algorithm, the primary spatial organisation is derived. Upon overlaying it with Visibility map, it can be inferred that the central area has no volumetric obstructions to achieve maximum visibility.

STAGE 3

STAGE 4

Above diagram illustrates the patterns of the ground. the ground as a design element is in from of Tectonic Plates which steps down and directs to spaces with maximum visibility.

To increase building massing in relation to the visibility quotient, additional details are added to the rigid built-form.

STAGE 5

STAGE 6

This step adds the translucent and organic built forms. It accomplishes the unobstructed views in a contextual urban space. Some built-form are wire framed and translucent in nature.

Lastly, we simulate the dynamic layer, which is illustrated in blue colour. They are in the form of canopies, shelters, WiFi pods, Navigation boards, and other user interactive spaces such as benches, and Signages.

ANALYSIS It is an approach to use the algorithm to simulate the best Volumetric arrangements.

Voxels are considered as building volumes. The best volumetric arrangement are calculated using algorithm based on visibility quotient.

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SECTION 3

SECTION 2

SECTION 1

SECTION 3

SECTION 2

SECTION 1

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ANALYSIS The sectional view of the design manifests the relation between the design and existing surroundings.

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4.4 DESIGN VISUALS DESIGN SECTION The sectional view of the design manifests the relation between the design and existing surroundings.

It highlights the internal relations between the different elements of the design interventions.

The section above explains the internal layers of the design , such as the dynamic layer, permeable built forms, the volumetric expansion and the rigid built profiles.

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The view below exemplifies the spatial intelligence via human navigation. It understands the colour preferences and Visual perception as a whole. The design takes into consideration of an existing environment and simulates it to improve the visuospatial intelligence.

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04/B PHASE 4 : Design - Paris

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DESIGN TRANSLATION DESIGN ADAPTATION

The Phase 4A explains the evolution of design from the existing built form, and hence it is the most precise outcome of the qualitative knowledge representing as visuospatial cities.

With an executed design output, the intention is to create a simulation tool that unifies various independent datasets, by building them on the uniform platform, This is limited only to the physical and semantic understanding.

The design exploits the full potential of the created tool kit, whilst exploring the methodologies used in quantifying and qualifying Visuospatial Intelligence. “ It justifies that the scope of the toolkit is not local, rather global in nature and execution.�

The section is described in 4.5 Paris in Layers 4.6 Design Strategy 4.7 Design Visuals

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FIGURE GROUND This map illustrates the figure-ground of this urban area in Paris. It demonstrates the relationship between the built area and the unbuilt area. Based on this diagram, we can see that there are numerous open spaces concentrate on the east-northern part.

GREEN SPACE As shown in the graph, there are numerous green spaces concentrate on the south riverside. In the central part of this urban area, there is a large green space surrounds the Notre-Dame.

BUILDING DENSITY MAP From the picture, we can see that the south riverside of this urban area has a higher building density than the north riverside. Additionally, the east part of the island has a much higher building density.

ROAD CHOICE 800M This map shows the road choice map of this urban space. We can see, in the central part, it has a higher value compared with the other urban space. It means that there is relatively higher connectivity and accessibility in these urban areas compared with the other space.

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4.5 PARIS IN LAYERS A. CHOICE MAP (REMAPPED) E. BUILDING HEIGHT

B. CHOICE MAP F. FIGURE GROUND

C. DENSITY MAP

D. GREEN SPACES

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SOCIAL MEDIA ANALYSIS - PARIS Geo-Located images as a dataset, collected from the navigable routes of the city. These are downloaded collectively from Google Street View Platform using Google’s API.

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DATA CONCLUSION A. SOCIAL MEDIA POINTS B. GPS DATA (NUMBER OF IMPRESSION) C. BUILDING HEIGHT

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NEURAL OPTION 01

OBSERVATION DECK

COMMERCIAL PODS

NEURAL OPTION 02

NEURAL OPTION 03

NEURAL OPTION 04

NEURAL OPTION 05

CANOPIES NEURAL OPTION 06 DWELLING PODS BRIDGE FORMATION NEURAL OPTION 07

INTERACTIVE SCREENS

NEURAL OPTION 08

NEURAL OPTION 09

NEURAL OPTION 10

NEURAL OPTION 11 GALLERY SCAPES

4.6 DESIGN STRATEGY- PARIS DESIGN VIA NEURAL OUTPUTS

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DESIGN : TERRACE - CANOPIES

NEURAL OUTPUT POTENTIAL OUTPUT

FINAL OUTPUT Spatial composition from Neural Calculations.

Voxels are transformed in this stage to form a Terrace - Canopy structure by validating its functionality in an open space.

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NEURAL OUTPUT Spatial arrangement from Neural Calculations.

Output A illustrates the best spatial organisation of voxels, providing optimum connectivity and circulation.

TRANSFORMATION A Spatial arrangement from Neural Calculations.

Output A illustrates the smooth spatial organisation of voxels, providing a continuous canopy structure, which is structurally fragile.

TRANSFORMATION B Spatial arrangement from Neural Calculations.

Output B illustrates the distorted spatial organisation of voxels, providing loose connectivity and circulation.

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DESIGN : SCREENS VIEW AREA HIGH

VISUAL ANALYSIS ON SCREEN / FACADE

FINAL OUTPUT Voxels are transformed to screen in Two Layers Bigger plates acts as backing, whereas thinner ones can rotate as per observer’s movement.

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NEURAL OUTPUT A Pattern for screen from Neural Calculations.

Output A illustrates the transformation of voxels to screens for existing facade.

NEURAL OUTPUT B Spatial arrangement from Neural Calculations.

Output B illustrates the different spatial arrangement of voxels, providing permeability +transparency.

NEURAL OUTPUT C Output C illustrates the best spatial arrangement of voxels, further being examined for figureground and fenestration calculations.

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DESIGN : BRIDGES

NEURAL OUTPUT FORMATION OF BRIDGE

FINAL OUTPUT Spatial composition from Neural Calculations.

Voxels are transformed in this stage to form a Bridge structure while retaining the nature of the spatial outputs.

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NEURAL OUTPUT Spatial arrangement from Neural Calculations.

Step A illustrates the spatial organisation of voxels, which are random outputs.

TRANSFORMATION A Spatial arrangement from Neural Calculations.

Step B illustrates the form-findng process while retaining the nature of the spatial organisation.

TRANSFORMATION B Spatial arrangement from Neural Calculations.

Step C illustrates the resultant output and the potential functionality of the neural outputs.

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DESIGN : PODS

VISUAL ANALYSIS ON SCREEN / FACADE

FINAL OUTPUT Voxels are transformed to screen in Two Layers Bigger plates acts as backing, whereas thinner ones can rotate as per observer’s movement.

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NEURAL OUTPUT A Spatial arrangement from Neural Calculations.

Output A illustrates the best spatial organisation of voxels, providing optimum connectivity and circulation.

NEURAL OUTPUT B Spatial arrangement from Neural Calculations.

Output B illustrates the distorted spatial organisation of voxels, providing average connectivity and circulation.

NEURAL OUTPUT C Spatial arrangement from Neural Calculations.

Output C illustrates the random spatial organisation of voxels, providing loose connectivity and circulation.

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VIEW 04

VIEW 03

VIEW 02

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VIEW 11

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VIEW 06

VIEW 01

VIEW 14 VIEW 08

4.7 DESIGN VISUALS This section caters to the set of design visuals explaining the final design output. The map here illustrates the position of the viewpoints/camera positions.

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VIEW 10 VIEW 09

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VIEW 01: SECTIONAL PERSPECTIVE The view exposes the section through central arena which gives the overview of the Ground Spatial Simulation. The design is determined to connect the ground level and the existing underground crypt. Doing so increase the permeability and the physical connectivity of the urban space.

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VIEW 02: SECTIONAL PERSPECTIVE The new spatial built from incorporates the intelligence from Human Navigation through the Neural model, Colour preferences and Visual perception as a whole. It takes into consideration of an existing environment and simulates it to improve the visuospatial intelligence.

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VIEW 03: BRIDGE VIEW (HUMAN EYE) VIEW 04: BRIDGE VIEW (CLOSEUP) VIEW 05: SECTIONAL PERSPECTIVE

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VIEW 06: VIEW ALONG CENTRAL PROMENADE The view highlights the connectivity to the ground and the underground crypt. The entrance is located in consideration of the spatial outputs generated from Neural Networks.

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VIEW 07: CRYPT ENTRANCE This visualisation illustrates the spatial details of the underground spaces.

Based on the spatial outputs from the design, the underground space is connected to improve the visual quality of this space. This connectivity extends and improvises the circulation to the existing cruise terminal.

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VIEW 08: CRUISE TERMINAL This space host to the passenger boats and the other vessels while providing direct connectivity to the Existing crypt. It renders the new spaces as exhibition decks and leisure spaces.

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VIEW 09: ACROSS THE RIVER LA SEINE This view exemplifies the functionality of the spaces such as open-air theatres, recreational areas, performance decks, amphitheatres, Sculpture Gardens etc.

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VIEW 10: LA SEINE VIEW (TOP LEFT) VIEW 11: NOTRE DAME VIEW (BOTTOM LEFT) VIEW 12: ENTRANCE NOTRE DAME (TOP RIGHT) VIEW 13: DWELLING PODS (BOTTOM RIGHT)

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“We make our buildings and our building makes us” - Winston Churchill

“Now we make our networks and our network makes us” - William Mitchell

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