Shadow-Shaped City

SHADOW-SHAPED CITY PORTFOLIO 2021

YANNI REN-20028702 | FLORA MISTICA SELVARA J-20073598 VALERIIA VOLKOVA- 20039100 | RUILU YU-20106450

THE BARTLETT SCHOOL OF ARCHITECTURE | UCL MARCH URBAN DESIGN | B-PRO | RC14

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MARCH URBAN DESIGN | RC14 TUTORS: ROBERTO BOTTAZZI, TASOS VAROUDIS , EIRINI TSOUKNIDA , VASILEIOS PAPALEXOPOULOS

SHADOW-SHAPED CITY

YA N N I R E N | F LO R A M I S T I CA S E LVA R A J VALERIIA VOLKOVA | RUILU YU

CONTENTS I. INTRODUCTION COMPONENTS OF THE URBAN ENVIRONMENT THE EVOLUTION OF LONDON’S BRIDGES BRING THE LIGHT TO SHADOWS AIM OF THE PROJECT

II. REDEFINE SHADOW LITERAL AND FIGURATIVE MEANING BRING THE LIGHT TO SHADOWS

I I I . S I T E A N A LY S I S D ATA A N A LY S I S A N G U L A R S E G M E N T A N A LY S I S DISTRIBUTION FOR NEGATIVE DATASET •

MACRO SCALE

•

MESO SCALE

DATA INTERSECTION S U N S H A D O W A N A LY S I S

IV. FIGURATIVE SHADOW MACRO SCALE •

PA I R P L O T S F O R M A C R O - S C A L E D ATA A N A LY T I C S

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DATA VISUALIZATION FOR SELECTED DATASETS

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DATA VISUALIZATION

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P R I N C I PA L C O M P O N E N T A N A LY S I S

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PROJECTING ACCORDING TO GEO-LOCATION

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KMEANS CLUSTERING

MASTER PLAN MESO SCALE

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D ATA A N A LY T I C S - P R I N C I PA L C O M P O N E N T A N A LY S I S

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MAJOR VARIABLES PRINCIPAL COMPONENTS

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K-MEANS CLUSTERING

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SECOND PRINCIPAL COMPONENT

V. S PA C E A N A LY S I S MESO SCALE •

I S O V I S T A N A LY S I S

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V I S I B I L I T Y G R A P H A N A LY S I S ( V G A )

MICRO SCALE •

SHADOW SIMULATIONS

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WIND SIMULATIONS

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ISOVIST SIMULATION

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V I S I B I L I T Y G R A P H A N A LY S I S ( V G A )

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SOUND SIMULATION

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ENVIRONMENTAL DATA: URBAN CONDITIONS

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PRELIMINARY AGENT-BASED SIMULATION

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K- M E A N S C LU ST E R I N G A N D CO M B I N E D R E S U LTS

VI. AGENT-BASED SIMULATION •

AGENT-BASED ALGORITHM

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TECHNICAL DESCRIPTION

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MAIN LAYERS OF SIMULATION

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PARTICLE OPTIMISATION BY VECTORS SUPERVISION

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COMBINATION OF SIMULATIONS

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WORKING FLOW OF THE DATA DEFRAGMENTATION

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POINT CLOUD AGGREGATION

VII. INITIAL FORM GENERATION •

CLUSTERING - POINT CLOUDS

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ACTIVE SPACE DISTRIBUTION

VIII.SHADOW-SHAPED DESIGN •

GAME OF SHADOWS (TYPE A)

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FLOATING PLATFORMS (TYPE B)

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ENTRANCE TO THE SHADOWS (LAND DESIGN)

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THE NEW SHADOW LANDSCAPE

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DESIGN MOVEMENT

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INTRODUCTION SHADOW-SHAPED CITY

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As cities are constantly evolving, the change of road networks, inf rastructure and built forms leading towards the reconf iguration of urban spaces. In the Age of Information, with the large amount of data transmitting through the urban environment daily, such changes are largely affected the invisible data layers, seeing as the ‘ghost’ twin of our city – the shadow. The fluidity of city and the beauty its ‘shadow’, both physically and metaphorically, should be revealed and praised in the design process of the urban environment.

‘Shadow’, f rom our perception, is the unseen quality of the city that is unusual and derelict. It refers to the f igurative shadow, the digital aspect of the city that recording the existing and emerging urban issues, of which the aesthetics as immaterial layer is hidden, awaiting to be revealed. On the other hand, it tends to emphasize the exact physical shadow as the guidance for the project, as the fundamental element of which’s beauty has been long forgotten in our contemporary design f ield according to Tanizaki (2001).

The project aims to study such controversial qualities of how we def ined ‘shadow’ urban space, by bringing in the bridge that not only establish physical links, but also connecting places via virtual networks. It will test and try to answer the question: how does the ‘shadow’ shape the city?

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COMPONENTS OF THE URBAN ENVIRONMENT MODERN USAGE OF BRIDGES IN LONDON

VEHICLE-INTERSECTION

PEOPLE-INTERSECTION

ENGINEERING COMMUNICATIONS

With the growth of cities, in the modern urban environment, bridges are most often used as a structure for crossing uneven ground, crossing a river, or unloading traff ic flows. There are also pedestrian bridges, but most often they do not contain any additional functions to attract residents.

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THE EVOLUTION OF LONDON’S BRIDGES FROM RESIDENTIAL BRIDGES TO IRON STRUCTURES

1894 WESTMINSTER BRIDGE

1913 SOUTHWARK BRIDG 1894 TOWER BRIDGE

1887 HAMMERSMITH BRIDGE

1819 WATERLOO BRIDGE 1176 OLD LONDON BRIDGE

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In the Middle Ages, the construction of living bridges was quite common. Such an example is the London Bridge built in 1176. The bridge was not only a structure connecting the two banks but was f illed with life and various functions. Until 1750, London Bridge was the only bridge that crossed the Thames in the city centre area. In 1967 it was replaced by the bridge that we can see today. Examples of such living bridges can be found to this day in the UK, Italy, Germany and other countries. This type of bridge is a part of the city’s life and a centre of attraction for the citizens.

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Towe r B r i d g e a l s o h a s a s p e c i f i c f u n c t i o n . T h i s i s n o t

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only a structure connecting the banks but also a muse-

MILLENNIUM BRIDGE

um.

The further evolution of bridges does not contain any special functions other than the main one - redirection of flows and tool of connection. The River Thames today is the centre of the city, therefore in our project, we want to use the connections and integrate them into the city so that the bridge becomes not just a platform to get f rom point A to point B, but to populate them with various functions and properties.

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AIM OF THE PROJECT SHADOW-SHAPED CITY

AIM OF THE PROJECT - to eff iciently expand the city fabric on to the water; - to establish the connection between districts; - bring light to the shady parts of the city, both literally and f iguratively; - create a unif ied urban environment around the Thames Rive and make it a point of attraction; - establish a large-scale connection with the rest of the city;

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URBAN STRATEGY

Based on our analysis, we determined the Shadows of the city by three specif ications: Literal shadows, Figurative shadows and Digi t a l S h a d ow s . We c o n d u c te d m u l t i - s c a l e a n a l y s e s a n d s i m u l a t i o n s to determine the most affected area of the city in order to improve the urban environment and communications in the city.

“Shadow ” will not darken our life, on the contrary, we propose that it will enrich our experience, as another layer f rom another dimens i o n . We a re c o m m i tte d to t u r n t h e s h a d ow s o f t h e c i t y i n to p re dominant spaces with diverse functions. Our goal is to connect the resulting non-preferential areas of the region and create a system and integrate it into the urban environment using digital shadows and literal and f igurative shadows of the city.

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II. REDEFINE SHADOWS LITERAL AND FIGURATIVE MEANING BRING THE LIGHT TO SHADOWS

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LITERAL & FIGURATIVE SHADOWS REDEFINE SHADOW

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MESO SCALE

MACRO SCALE

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I I I . S I T E A N A LY S I S D A T A A N A LY S I S A N G U L A R S E G M E N T A N A LY S I S DISTRIBUTION FOR NEGATIVE DATASET DATA INTERSECTION S U N S H A D O W A N A LY S I S

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D ATA A N A LY S I S FIGURATIVE SHADOWS

RESIDENTS ‘ QUALIFICATION LEVEL

We s t a r te d a l a rg e - s c a l e a n a l y s i s by c o l l e c t i n g a n d i d e n tifying negative data. This step was the initial stage for identifying the most interesting area for the project.

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HIGH

LOW

MACRO SCALE

DATA CORRELATION

Broadband Accessibility

Pedestrians per km

Railway Light Rail

LOW

HIGH

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A N G U L A R S E G M E N T A N A LY S I S FIGURATIVE SHADOWS

INTEGRATION R1000

We a n a l y s e d a rea o f L o n d o n o n m a c ro s c a l e to s e e h ow the region is integrated into the city and which areas are least connected to the city centre and other parts of i t . To d o t h a t we u s e d A n g u l a r I n te g r a t i o n A n a l y s i s w i t h radius 1000 meters and Angular Choice with radius of 1000 meters.

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LOW

HIGH

MACRO SCALE

CHOICE R1000

LOW

HIGH

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DISTRIBUTION FOR NEGATIVE DATASET

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FIGURATIVE SHADOW

The next step was to combine the data we were interested in at the macro level in order to see the most exposed a rea s . We p a i d a tte n t i o n to t h e a rea n ea r B l a c kwa l l b a s e d on visual analysis and intersection data,

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we noticed negative trends in the area such as high unemployment, low accessibility of parks and recreational areas, low pedestrian accessibility, poor communication with the city, high noise and pollution etc .

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DATA INTERSECTION FIGURATIVE SHADOWS

MESO SCALE

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MESO SCALE

Water transpor t pollution Rail noise Pedestrians / 24H Underground network

Integration R1000

LOW

HIGH Bus stops

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S U N S H A D OW S A N A LY S I S LITERAL SHADOW

The next step was the analysis based on Literal shadows.

We r a n a s h a d ow s i m u l a t i o n o n a n i n te r m e d i a te s c a l e to determine Literals shadows of the city. As you can see, the River Thames is a huge area without shadows, which drew our attention to the River Thames.

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LIGHT

MESO SCALE

SHADOW

This analysis helped us to see the darkened areas of the territory and determine the future direction of the project. In the future, we will apply this result as a base layer.

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IV. FIGURATIVE SHADOW MACRO SCALE:

PAIRPLOTS FOR MACRO-SCALE D ATA A N A LY T I C S DATA VISUALIZATION P R I N C I PA L C O M P O N E N T A N A LY S I S PROJECTING ACCORDING TO GEO-LOCATION KMEANS CLUSTERING

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PA I R P L O T S F O R M A C R O - S C A L E D ATA A N A LY T I C S FIGURATIVE SHADOWS

PAIRPLOTS FOR SELECTED DATASETS

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C H O S E N D A T A S E T S F O R M A C R O - S C A L E D A T A A N A LY T I C S

FULL TIME STUDENT WORKING AGE AVERAGE DOWNLOAD SPEED AVERAGE DATA USAGE LINES: AVERAGE DOWNLOAD SPEED (MBIT/S) FOR LINES < 10MB/S TRAVEL TO WORK <10KM DRIVING TO WORK <10KM TRAVEL TO WORK VIA PUBLIC TRANSPORT CHOICE R1000 INTEGRATION R1000 PTAL (2014)_ POOR ACCESS: 0-1 DEFICIENT IN ACCESS TO: LOCAL , SMALL OR POCKET PARK DEFICIENCY IN ACCESS TO NATURE MENTAL DEPRIVATION PM2.5 ALL DEPRIVED HOUSEHOLD DAY-TO -DAY ACTIVITIES LIMITED A LOT E C O N O M I C A L LY I N A C T I V E

To u n d e r s t a n d t h e c o r re l a t i o n s a m o n g m u l t i p l e d a t a , t h e aim of running Principal Component Analysis is to identify areas that are more affected by the selected datasets. In our cases, the negative conditions are selected for data analytical algorithm. The most affected grids are seen as the f igurative shadows. For preparing this algorithm, data values are projected to the 100*100-metre grids, as the resampling process, for more precised results.

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DATA VISUALIZATION FOR SELECTED DATASETS FIGURATIVE SHADOWS

FULL TIME STUDENT

WORKING AGE

AVERAGE DOWNLOAD SPEED

DRIVING TO WORK <10KM

TRAVEL TO WORK VIA PUBLIC TRANSPORT

CHOICE R1000

DEFICIENCY IN ACCESS TO NATURE

MENTAL DEPRIVATION

PM2.5 LEVEL

Negative datasets, associated with are selected for our computational algorithm to f ind where in our macro scales are more affected by such negative conditions.

The above images show the original values for analyzing.

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MACRO SCALE

AVERAGE DATA USAGE

AVERAGE DOWNLOAD SPEED (LINES)

TRAVEL TO WORK <10KM

< 10MB/S

INTEGRATION R1000

POOR ACCESS TO PUBLIC TRANSPORT

POOR ACCESS TO LOCAL, SMALL OR P O C K ET PARK

ALL DEPRIVED HOUSEHOLD

LARGE LIMITATION OF DAY-TO -DAY

E C O N O M I C A L LY I N A C T I V E G R O U P

ACTIVITIES

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DATA VISUALIZATION FIGURATIVE SHADOWS

FIRST PRINCIPAL COMPONENT AND SECOND PRINCIPAL COMPONENT

-5.75

7.16

First Principal Component Value -6.91

7.48

Second Principal Component Value -3.51 Third Principal Component Value

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6.95

First Principal Component Selected Rule: Absolute Values > 4 .5

Second Principal Component Selected Rule: Absolute Values > 4 .0

Third Principal Component Selected Rule: Absolute Values > 3.0

The Principal Component Analysis algorithm aims to evaluate the level of contribution of each variances, in other words, input datasets. Higher PCA values, both positive and negative, refers to variance with greater impact to the set, indicating that the variance contribute more to the deviation trend. Finding areas with higher PCA values means identifying the f igurative shadows, as such areas are more affected by the negative conditions.

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P R I N C I PA L C O M P O N E N T A N A LY S I S F I G U R AT I V E S H A D O W S | D ATA A N A LY T I C S

SECOND PRINCIPAL COMPONENT AND ASSOCIATED GEOLOCATIONS -3.51

For better comprehension of the result of Principal Component Analysis, it is important to associate the analysis with its actual geolocations. The values of second principal component indicates that the selected datasets have greater contributions along the river, indicating that the f igurative shadows in our urban environment are largely located along the river.

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Second Principal Component Value

6.95

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MACRO SCALE

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P R I N C I PA L C O M P O N E N T A N A LY S I S F I G U R AT I V E S H A D O W S | D ATA A N A LY T I C S

Working Age

Distance Travel to Work < 10km

pm2.5 Level

Full-time Student

Travel to Work via Public Transpor t

Economic Inactive

PAIRPLOTS COLOR-LABELLED BY PARTICULAR DATA VALUES Heatmaps and pairplots are used to compare the deviation trend, to identify which variances have major contributions to the site. Finding variances with higher PCA values is an important step for comprehending what are affecting the result of Principal Component Analysis.

Finding the extreme distributions help us to better understand our site in macro scale, and thus identify areas of interest by locating the outfliers and higher values in those original datasets.

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Economy Inactive

Day-to-day Activities Limited a Lot

Deprived Household (All Level)

pm 2.5 Level

Mental Deprivation

Poor Access to Nature

Poor Access to Local, Small or Pocket Park

Poor Access to Public Transpor t

Integration R1000

Choice R1000 (After Log)

Travel to Work via Public Transpor t

Drive to Work < 10km

Travel to Work < 10km

Average Download Speed via Lines < 10mb/s

Average Data Usage

Average Download Speed

Working Age

Full-time Student

-0.4

-0.3

HIGH CORRELATION

2

-0.2

-0.1

0

1

0.1

0.2

0.3

0.4

HIGH CORRELATION

0

PRINCIPAL COMPONENT HEATMAP

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P R I N C I PA L C O M P O N E N T A N A LY S I S A N D O R I G I N A L D ATA S E T S F I G U R AT I V E S H A D O W S | D ATA A N A LY T I C S

C O M PA R I N G P C A R E S U LT S A N D O R I G I N A L D ATA S E T S Filtering and selecting the higher values f rom the second principal component, cells with higher data correlations are left and plotted to the map. Applied with this rule and in reference to the previous heatmap, three major attributes are selected, and coloured by their higher values. The brighter colours indicates such cells are more influenced by our selected datasets, with more full-time students, economically inactive group, and less distance traveling to work.

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0

65.6%

Full Time Student 0

59.8%

Economy Inactive

0 Distance Travel to Work < 10km

82.2%

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PROJECTING ACCORDING TO GEO-LOCATION F I G U R AT I V E S H A D O W S | D ATA A N A LY T I C S low

high

Average Data Usage

Integration R1000

-5.75

First Principal Component Values

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7.17

Choice R1000

Mental Deprivation

SCATTER PLOT

Locating the lower data usage with poorer integration indicates the ‘digital shadows’ in our city, as such areas are places less wanted to be places that people tends to visit, and meanwhile, less connected to the digital world due to the low usage of Internet in such regions.

SELECTED DATA FOR COMPARISON AVERAGE DATA USAGE INTEGRATION R1000

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K-MEANS CLUSTERING F I G U R AT I V E S H A D O W S | D ATA A N A LY T I C S

The Kmeans Clustering algorithm is the next step for def ining f igurative shadow after Principal Component Analysis, in order to select areas for further investigation.

According to the Principal Component Analysis, 6 sets of data are shown as the major contributors with more severe influences. In this case, those 6 sets are selected for the algorithm to cluster the data according to their similarity. The site we chose for the next step contains 6 clusters, which indicates and proves that the areas that we chose has the potentials as it is applied with multiple dimensions f rom our negative urban conditions, implied by the datasets and the algorithm.

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0 1 2 3 4 5

MACRO SCALE

Cluster 0

Cluster 1

Cluster 2

Cluster 3

PCA VALUES

Cluster 4

Cluster 5

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MASTER PLAN

B

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FIGURATIVE SHADOWS

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MESO SCALE

● CONNECTIONS CONCEPT

● INTEGRATION | PUBLIC TRANSPORT FLOW

● FIRST PRIMCIPAL COMPONENT

● FIRST PRINCIPAL COMPONENT (HIGH VALUES)

● “DARK” SHADOWS - COLLAPSED URBAN CANYONS

For our master plan, leading by our concept, we used layers of fundamental datasets we have gathered, and the results of our data analytics of principal component va l u e s a n d o t h e r a l g o r i t h m s . To f i n d a f u r t h e r d i re c t i o n for our project and to determine the most interesting area for our design part.

As layers, we use a base layer such as the Literal Shadows of the city, the result of the First Principal component and some base maps. By summing up the macro scale and meso scale analysis, we determine the further area of interest for research at the marco level.

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IV. FIGURATIVE SHADOW MESO SCALE: RESAMPLING DATA P R I N C I PA L C O M P O N E N T A N A LY S I S MAJOR VARIABLES PCA K-MEANS CLUSTERING SECOND PRINCIPAL COMPONENT

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RESAMPLING DATA FIGURATIVE SHADOWS

Shadow

PopulationSq/Km

Choice R1000

Activities limited

Unemployed rate

Economically Inactive

Integration R1000

Driving to Work less 10km

Data usage

Household Income

No qualif ications

Unemployed rate

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MESO SCALE

Average download speed

Mental Deprivation

Def icient to nature

Travel to Work >30km

Ethnic group

Road noise

Public transpor tAll

Deprived Household

Full time Student%

PTAL poor access

PM 2.5

Elder people

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P R I N C I PA L C O M P O N E N T A N A LY S I S FIGURATIVE SHADOWS -NEGATIVE DATA

Populayion Density Sq/Km Ethnic Group Full time Student(%) Working Age(%) Household Income Average download speed (Mbit/s) Average data usage (GB) Travel to Work <10km (%) Driving to Work <10km (%) Travel to Work Via Public Transpor t (%) Choice R1000 Integration R1000 PTAL (2014) Poor access: 0-1 (%) Def icient in access to Nature (%) Mental Deprivation (%) PM2.5 (%) Road Noise>55db(%) All Deprived Household (%) Economically Inactive% Shadow

The result of the analysis using twenty negative date sets to get a high intersection of this data using Principal Component Analysis.

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-0.4 Heatmap Values

0

0.4

MESO SCALE

This stage gives us an understanding of how the data correlates with each other and, as a result, reveals the m o s t p ro n o u n c e d c o r re l a t i o n s . We u s e d 1 0 - by -1 0 - m e te r grids for the most accurate result.

Low

High

Principal Component Values

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PRINCIPAL COMPONENTS VALUES FIGURATIVE SHADOWS

PCA 0

PCA 1

PCA 2

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MESO SCALE

SECOND PRINCIPAL COMPONENT

The 3D visualisation indicates the combination of the 0,1 and 2 Principal Component Analysis (PCA)

Low

High

Principal Component Values

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MAJOR VARIABLES PRINCIPAL COMPONENTS FIGURATIVE SHADOWS

Cluster 0 Cluster 1 Cluster 2 Cluster 3 Cluster 4

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MESO SCALE

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K-MEANS CLUSTERING FIGURATIVE SHADOWS

Intergration level1

Intergration level2

Intergration level3

Intergration level4

Intergration level5

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MESO SCALE

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SECOND PRINCIPAL COMPONENT FIGURATIVE SHADOWS

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MESO SCALE

PCA 2 PCA 1

Former PCA Results

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III. FIGURATIVE SHADOW MESO SCALE: RESAMPLING DATA P R I N C I PA L C O M P O N E N T A N A LY S I S

V. S PA C E A N A LY S I S

MAJOR VARIABLES PCA E :G K - M E A N SM E C SL O U SSTCEAR LI N

S TC AI P NA S E C O N D I SPORVIIN ALLY SCI SO M P O N E N T

V I S I B I L I T Y G R A P H A N A LY S I S ( V G A )

MICRO SCALE: SHADOW SIMULATIONS WIND SIMULATIONS ISOVIST SIMULATION V I S I B I L I T Y G R A P H A N A LY S I S ( V G A ) SOUND SIMULATION ENVIRONMENTAL DATA: URBAN CONDITIONS PRELIMINARY AGENT-BASED SIMULATION K-MEANS CLUSTERING S W O T A N A LY S I S 65

I S OV I S T A N A LY S I S

PENINSULA EMB. BLACKWALL

O2

At this stage, we conducted an isovist analysis to see t h e v i s i b i l i t y o f t h e a rea o f i n te re s t . We u s e d t h e R i ve r Thames and areas of high interest f rom the PCA analysis as viewpoints to identify the visibility of the area.

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MESO SCALE

ROYAL DOCK EMB.

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I S OV I S T A N A LY S I S RIVER VISIBILITY

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MESO SCALE

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V I S I B I L I T Y G R A P H A N A LY S I S ( VG A ) MESO SCALE

VISUAL INTEGRATION Visual Graph Analysis was run to determine the degree of visualness of the area on meso scale. The grid was used by the size 5 by 5 meters.

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MESO SCALE

LOW

HIGH

VGA VALUES

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SHADOW SIMULATIONS LITERAL SHADOWS

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JAN

FEB

MARCH

APRIL

MAY

JUNE

JULY

AUG

SEPT

OCT

NOV

DEC

MICRO SCALE

Throughout our project, the analysis of sun shadows performs an important role in def ining the area and the f o u n d a t i o n f o r o u r c o n c e p t . We re - a n a l y z e d t h e l i te r a l shadows for every month of the year for 24 hours to examine the area of interest on a larger scale.

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WIND SIMULATIONS S PA C E A N A LY S I S

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JAN

FEB

MARCH

APRIL

MAY

JUNE

JULY

AUG

SEPT

OCT

NOV

DEC

MICRO SCALE

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The next step was to run climate simulations in this area such as the wind simulation for each month of the year, in order to determine the degree of auspiciousness of the area for the residents of the city and to identify weak locations for further solutions.

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ISOVIST SIMULATION ENVIRONMENTAL DATA: URBAN CONDITIONS

S I N G L E I S O V I S T A N A LY S I S | D E P T H M A P To u n d e r s t a n d o u r s i te m o re s p e c i f i c a l l y, t h e I s ov i s t analysis is chosen for one of the methods to evaluate our urban environment based on the visibility level f rom a series of vantage points. It is a very important step to understand the site before our design investigation, as the algorithm helps to comprehend the site f rom the perspective of how we perceive spaces, in regards to the given enviornment.

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We s e t t h e n o d e s o f t h e s t re e t n e t wo r ks w i t h i n o u r chosen site for vantage points, to evaluate the level of visibility. The Hot Pink regions in the map indicate larger areas calculated for Isovist f ields, in other words, better visibility. The Cyan regions are areas with less visibility level, which are mostly associated with denser tendency in terms of urban morphology.

MICRO SCALE

Point cloud

3D Visualisation

Base map

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V I S U A L G R A P H A N A LY S I S ENVIRONMENTAL DATA: URBAN CONDITIONS

V I S I B I L I T Y G R A P H A N A LY S I S | D E P T H M A P Visual graph analysis examines the spatial movements within our urban environment. Hence, this is another guidance before starting with our further progress in design. This step is to understand the spatial layouts, determining how people move according to their perception of the given environment. The analysis is based on the given shape of geometries, representing the urban environment for investigation.

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By running the algorithm, we are able to identify the critical locations where people tends to move to based on their sights and the surrounding urban forms.

In our understanding, we associate areas with lower values in the analysis, seen as the f igurative shadow as one of the negative urban conditions, with the literal s h a d ow f ro m f o r m e r s h a d ow s i m u l a t i o n . We s e e s u c h spaces as areas we proceed further for the next stage.

MICRO SCALE

Point cloud

3D Visualisation

Base map

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S PAT I A L A N A LY S I S ENVIRONMENTAL DATA: URBAN CONDITIONS

LIGHT AND DARK VOXELS WITHIN ‘VISIBILITY OBJECT’ Cellular automata is used as a method for a preliminary investigation to understand our site. The game of life rules applies to the lightest and darkest cells, f iltered f rom previous sun and shadow analysis, to explore the relationship between light and shadow.

The hot pink voxels represent the result growing f rom the light point, while the dark blue ones are the growth of shadow point.

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ISOVIST POINTS

VISIBILITY AS AN OBJECT

MICRO SCALE

ORIGINAL STATE

ITERATION 3

ITERATION 5

ITERATION 7

ITERATION 9

ITERATION 12

ITERATION 15

ITERATION 18

ITERATION 20

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K- M E A N S C LU ST E R I N G A N D CO M B I N E D R E S U LT S ENVIRONMENTAL DATA: URBAN CONDITIONS

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MICRO SCALE

Using the shortest path results generated the agentbased simulation, as the guide for catogorising the light and shadow voxels, we establish a preliminary concept for the logic to establish spatial structures, in respond to the shadow.

Kmeans Clustering also is used for a method to classify the point clouds f rom cellular automata, for identifying certain functions for each cluster.

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VI. AGENT-BASED SIMULATION TECHNICAL DESCRIPTION MAIN LAYERS OF SIMULATION SPACIAL GROWTH PARTICLE OPTIMISATION BY VECTORS SUPERVISION AGENTS DEFINING DESIGN LOCATIONS AGENTS TRAILS AGENTS DEFINING DESIGN LOCATIONS COMBINATION OF SIMULATIONS 85

AGENT-BASED ALGORITHM ACO(ANT COLONY OPTIMIZATION ALGORITHMS)

PHEROMONICS STRATEGIES Agents leave behind the pheromone trail in order to establish a movement agent flow similarly as ants in nature. By means of this network strategy it is possible to observe new networking and occupying process of an empty area.

PATH FOLLOWING Path following algorithm, partially flocking algorithm (Swarming behavior with separation, cohesion and alignment characteristics in order to def ine spatial conditions - spacing distances between elements), attraction (seeking targets) and pheromone path following.

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DEFINE IMPACT FACTORS

AGENT-BASED SIMULATION

LOCATIONS OF AGENTS AND ATTRACTORS

AGENTS (POI) AND ATTRACTORS(SHADOW) To explore the population flows among cities, the ant colony algorithm was used to simulate the movements of people on the ground, and the points of interest in the city were regarded as the star ting points in which agents are emitted. And shadows as the attractors for us to def ine the darkest par t of the urban environment f rom both the literature to the f igurative dimension. Our aim is to reconstruct these invisible layers of the shadows and project them to the existing urban environment.

Places of Interest Low Internet Usage PCA High Values Sun Shadows

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TECHNICAL DESCRIPTION

agent follows closest trail (not own)

future location agents leave behind a pherimone agent follows closest trail (not own)

target

pathway normal

r

target

future location target

agents leave behind a pherimone

pathway

normal

pheromone trail

agents

target

target(pheromone) r pheromone trail

agents

target(pheromone)

Attractions

Stigmergy

Fllowing

Distribution

DESCRIPTION OF THE SIMULATION MODEL

Non-occupied Volumns

Occupied Volumns

ENVIRONMENT TRANSCRIPTION INTO THE MODEL The agents will respond to attributes of the environment such as physical barriers or inf rastructure by adopting their behavior to the features of the modeled environment. These shadows def ined as attractors and centroids as initial positions of agents in the cell grid and these attractors influence behavior of agents in the flow layer.

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Centroids Attractors

The territor y will operate with a predef ined capacity of the shadow for the selected number of agents, while the population of agents is characterized by its objectives for a specif ic boundar y number of places. The environment def ined by the cell clusters will operate with proper basic capacities.

AGENT-BASED SIMULATION

Initial Environment Occupation

Extension

Addition Extending

SIMULATION RULES OF THE COLONIAL GROWTH

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W AR II N M T EL TA IYTELRES OOFF TSHI EM U PA LA G TEI O N TWHREI TM E OSDUEB TOI FT LAEC O T IFO TNHS EOPF ACGHE O S E N D A T A S E T S

AGENTS LINES Agent lines of seperate dominated attractors based on triple identif ied shadows

LITERAL SHADOW Distrubution of real-time shadows based on daily movement of the sun

FIGURATIVE SHADOW High PCA values of nagative datasets as the f igurative shadow of the city

DIGITAL SHADOW Areas with lowest data usage and download speed are the dark side of the information age

DISTRIBUTED AGENCY YIELDS Cubes grown f rom the agents lines and located on the real locations of shadows

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AGENT-BASED SIMULATION

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SPACIAL GROWTH THE DISTRIBUTION OF AGENTS GROWTH OF LITERAL SHADOW

SIMULATION RESULT: LITERAL SHADOW DISTRIBUTION The location of literal shadow distributed evenly in the urban areas and the agents are not homogeneous spread in the city under the attraction of the attractors.

Agents Lines Literal Shadow Figurative Shadow Digital Shadow

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AGENT-BASED SIMULATION

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SPACIAL GROWTH THE DISTRIBUTION OF AGENTS GROWTH OF FIGURATIVE SHADOW

SIMULATION RESULT: FIGURATIVE SHADOW DISTRIBUTION we chose both the highest and lowest value parts of our PCA results as the f igurative shadow and try to indicate the darkest area based on the negative datasets.

Agents Lines Literal Shadow Figurative Shadow Digital Shadow

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AGENT-BASED SIMULATION

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SPACIAL GROWTH THE DISTRIBUTION OF AGENTS GROWTH OF DIGITAL SHADOW

SIMULATION RESULT: DIGITAL SHADOW DISTRIBUTION We d e f i n e t h e d i g i t a l s h a d ow b a s e d o n t h e l ow d ow n load speed and less data usage as the digital absence in the city. These unseen spots in urban areas cause the invisible shadow and become the most potential part influencing people’s daily routine.

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Agents Lines Literal Shadow Figurative Shadow Digital Shadow

AGENT-BASED SIMULATION

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AGENTS DEFINING DESIGN LOCATIONS FROM VECTORS & TARGETS FOR LITERAL SHADOW Weightage- 1:2 (vectors- Target points)

Vectors Directing the agents

Initial Points - Places of Interest

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AGENT-BASED SIMULATION

AGENT BASED BEHAVIOUS

Origin - Points of interest

Diver ting f rom the Lights

In search of the Shadows

Brownf ields

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AGENTS DEFINING DESIGN LOCATIONS FROM VECTORS & TARGETS FOR FIGURATIVE SHADOW Weightage- 1:2 (vectors- Target points)

Vectors Directing the agents

Initial Points - Places of Interest

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AGENT-BASED SIMULATION

AGENT BASED BEHAVIOUS

Origin - Points of interest

Diver ting f rom PCA High Values

Leading to PCA low Values

Brownf ields

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AGENTS DEFINING DESIGN LOCATIONS FROM VECTORS & TARGETS FOR DIGITAL SHADOW Weightage- 1:2 (vectors- Target points)

Vectors Directing the agents

Initial Points - Places of Interest

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AGENT-BASED SIMULATION

AGENT BASED BEHAVIOUS

Origin - Points of interest

Diver ting f rom good network coverage

In search of poor connectivity

Brownf ields

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COMBINATION OF SIMULATIONS CO M B I N AT I O N O F T H E R E S U LTS F R O M T H E F O R M E R S I M U L AT I O N S

LITERAL SHADOW

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AGENT-BASED SIMULATION

FIGURATIVE SHADOW

DIGITAL

SHADOW

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COMBINATION OF SIMULATIONS CO M B I N AT I O N O F T H E R E S U LTS F R O M T H E F O R M E R S I M U L AT I O N S

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AGENT-BASED SIMULATION

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WORKING FLOW OF THE DATA DEFRAGMENTATION GET THE POINT CLOUDS FOR DESIGN ELEMENTS

Literal Shadow

Figurative Shadow

Digital Shadow

SPACIAL GROWTH OF CUBES

CENTROIDS OF CUBES

The growing cubes f rom the urban spacial contract simulation to show the distribution of the cubes demonstrate the shaded areas in urban spaces

Extract the center points of each cube and these shows the location of the cube not just on geometrical but z vector as the real data values

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AGENT-BASED SIMULATION

SET THE ENDING POINTS

PARTICLE SWARM OPTIMIZATION(PSO)

The points f rom the vector dominated simulation reach the most agminated parts which shadow located, we set them as the ending points

We s e t t h e s wa m s i m u l a t i o n a n d t rea t t h e m a s pedestrian movement to locate the shadowdominated conjunctions, after this step to get our triple layer structured point clouds

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POINT CLOUD AGGREGATION THE NODES AND STRUCTURE LINES GENERATION

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Iteration 25

Iteration 50

Iteration 75

Iteration 100

Iteration 125

Iteration 150

Iteration 175

Iteration 200

Iteration 225

Iteration 250

Iteration 275

Iteration 300

Iteration 300

Iteration 350

Iteration 375

Iteration 400

Iteration 425

Iteration 450

Iteration 475

Iteration 500

AGENT-BASED SIMULATION

LITERAL SHADOW For literal shadow, the river Thames is the brightest part of the city, it is spacious but can’t be used as open spaces. So we are creating some structured constructions to indicate the shadow in the city, and make it the dominated column of our design and the footstone of the whole design.

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POINT CLOUD AGGREGATION THE NODES AND STRUCTURE LINES GENERATION

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Iteration 25

Iteration 50

Iteration 75

Iteration 100

Iteration 125

Iteration 150

Iteration 175

Iteration 200

Iteration 225

Iteration 250

Iteration 275

Iteration 300

Iteration 300

Iteration 350

Iteration 375

Iteration 400

Iteration 425

Iteration 450

Iteration 475

Iteration 500

AGENT-BASED SIMULATION

PCA

FIGURATIVE SHADOW Figurative shadow is the shadow that doesn’t exist and as an urban negativity origin f rom the datasets. These invisible layers indicate people’s insecurities, crime, mental health, and lack of connectivities. But it could be brought to light by def ining the reality and transformations of the urban environment.

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POINT CLOUD AGGREGATION THE NODES AND STRUCTURE LINES GENERATION

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Iteration 25

Iteration 50

Iteration 75

Iteration 100

Iteration 125

Iteration 150

Iteration 175

Iteration 200

Iteration 225

Iteration 250

Iteration 275

Iteration 300

Iteration 300

Iteration 350

Iteration 375

Iteration 400

Iteration 425

Iteration 450

Iteration 475

Iteration 500

AGENT-BASED SIMULATION

DIGITAL SHADOW The new paradigm of urban connectivity is dominant in the form of network coverage in terms of internet rather than physical communications which questions the realisation of spaces devoid of digital fabrics.

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VII. INITIAL FORM GENERATION

STRUCTURE BASED ON THE AGENT-BASED SIMULATION

CLUSTERING THE POINT CLOUDS ACTIVE SPACE DISTRIBUTION

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CLUSTERING - POINT CLOUDS K-MEANS CLUSTERING OF THE LITERAL SHADOW

Leisure spaces

Leisure spaces

Pedestrian walkway

Cycling

Cycling Docks for Ferries, Yachts, Cruise, water taxi

Installations Event spaces

Recreation

Literal Shadow Cube generations

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Installatiions Event spaces

Pedestrian walkway Docks for Ferries, Yachts, Cruise, water taxi Recreation

K-Means 1

INITIAL FORM GENERATION

Leisure spaces

Leisure spaces

Pedestrian walkway Cycling

Installations Event spaces

Pedestrian walkway Cycling

Docks for Ferries, Yachts, Cruise, water taxi

Docks for Ferries, Yachts, Cruise, water taxi

Installations

Event spaces

Recreation

K-Means 2

Recreation

K-Means 3

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CLUSTERING - POINT CLOUDS K-MEANS CLUSTERING OF THE FIGURATIVE SHADOW

Restaurants Interaction Zones Retail pods

Under water Pavilion Pubs

Piazza

Ski slopes

Small scale Business

Literal Shadow Cube generations

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Restaurants

Under water Pavilion

Interaction Zones

Pubs Retail pods

Piazza

Ski slopes

Small scale Business

K-Means 1

INITIAL FORM GENERATION

Restaurants

Restaurants

Under water Pavilion

Interaction Zones Pubs

Retail pods

Ski slopes

Under water Pavilion

Interaction Zones

Piazza

Pubs

Small scale Business

Retail pods

K-Means 2

Ski slopes

Piazza

Small scale Business

K-Means 3

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CLUSTERING - POINT CLOUDS K-MEANS CLUSTERING OF THE DIGITAL SHADOW

Creativity decks Stages AR Interaction Zone VR Gaming Zone

Scientific Exhibition venues Digital screens

Stages AR Interaction Zone

Projectors

Literal Shadow Cube generations

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Creativity decks

VR Gaming Zone

Scientific Exhibition venues Digital screens Projectors

K-Means 1

INITIAL FORM GENERATION

Creativity decks Stage AR Interaction Zone

Creativity decks

Scientific Exhibition venues

Digital screens

Digital screens

VR Gaming Zone

Stage

Scientific Exhibition venues

VR Gaming Zone

Projectors

Projectors

AR Interaction Zone

K-Means 2

K-Means 3

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ACTIVE SPACE DISTRIBUTION PLAN VIEW OF THE STRUCTURE

4 124

INITIAL FORM GENERATION

5 125

ACTIVE SPACE DISTRIBUTION PERSPECTIVE VIEW OF THE STRUCTURE

MA JOR SOCIAL INTERACTIONS AND CONNECTING SPACES

PASSIVE RECREATION SPACES

PLATFORMS AND CANOPIES

INFORMATION ZONES - HOTSPOTS - DIGITAL PROJECTORS

2 126

N II TT II AA LL FF O O RR M M G G EE N N EE RR AATT II O ON N II N

3 127

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VIII. SHADOW-SHAPED DESIGN FORM GENERATION GAME OF SHADOWS (TYPE A) FLOATING PLATFORMS (TYPE B) ENTRANCE TO THE SHADOWS (LAND DESIGN) THE NEW SHADOW LANDSCAPE

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FORM GENERATION VOXEL TO PLATFORMS

FRAMES

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VOXELS

FORM GENERATION

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VOXELS 01 | DIGITAL SHADOWS

FRAMES 01 | DIGITAL SHADOWS

VOXELS 02 | FIGURATIVE SHADOWS

FRAMES 02 | FIGURATIVE SHADOWS

VOXELS 03 | PHYSICAL SHADOWS

FRAMES 03 | PHYSICAL SHADOWS

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FORM GENERATION C H O I C E & I N T E G R AT I O N A N A LY S I S

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DIGITAL CHOICE | R=300M

DIGITAL CHOICE | R=1000M

FIGURATIVE CHOICE | R=300M

FIGURATIVE

PHYSICAL CHOICE | R=300M

PHYSICAL CHOICE | R=1000M

CHOICE | R=1000M

FORM GENERATION

FIGURATIVE INTEGRATION | R=500M

PHYSICAL INTEGRATION | R=1000M

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DIGITAL BRIDGE IDENTIFY THE PATH

GEN.0 INDV.0

GEN.0 INDV.5

GEN.0 INDV.8

GEN.1 INDV.5

GEN.1 INDV.7

GEN.1 INDV.8

GEN.2 INDV.1

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GEN.2 INDV.5

GEN.2 INDV.7

FORM GENERATION

ENTRANCE TO THE DIGITAL WORLD ENTRANCE TO THE PHYSICAL SHADOWS - PLATFORMS DIGITAL BRANCH THE DIGITAL CONNECTION - VIRTUAL PATH

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FORM GENERATION OCTREE GENERATION

GAME OF SHADOWS & FLOATING PLATFORMS

EXPLORATORY ROOMS

PLAYFUL ROOMS

GALLERY SPACES

INTERACTIVE SCREENS

BOATS

GAME OF SHADOWS

THE ENTRANCE TO THE SHADOWS

DIGITAL HINTS GATHERING SPACES

VIRTUAL EXPLORATIONS

PEDESTRIAN WALK

RIVER-FRONT LANDSCAPE

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FORM GENERATION DESIGN

GAME OF SHADOWS & FLOATING PLATFORMS

EXPLORATORY ROOMS

PLAYFUL ROOMS

GALLERY SPACES

INTERACTIVE SCREENS

BOATS

GAME OF SHADOWS

THE ENTRANCE TO THE SHADOWS

DIGITAL HINTS GATHERING SPACES

VIRTUAL EXPLORATIONS

PEDESTRIAN WALK

RIVER-FRONT LANDSCAPE

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SHADOW-SHAPED CITY DESIGN TOP VIEW OF THE STRUCTURE

GAME OF SHA

SHADOW EXPLORATORY ROOMS

SHADOW-SHAPED DESIGN In the design process, we formed a dynamic system of platforms on the water and on the ground, which integrate with each other on the principle of steppingstones.

We a re p ro p o s i n g a d y n a m i c s e l f - i n te l l e c t u a l s t r u c t u re w i t h multilayers of spaces usages, consist of units with different level of movements on varied urban conditions.

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SHADOW-SHAPED CITY DESIGN

SHADOW BRANCHES

ADOWS

THE PATH INTO SHADOWS

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SHADOW-SHAPED CITY DESIGN SHADOW DIAGRAM

SHADOW-SHAPED DESIGN The shadow-shaped city project is an urban space created f rom the shadows of the city. In the project, all three shadow concepts formed a single system of spaces, taking into account both the physical urban needs of urban residents and the virtual requirements of the modern world.

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SHADOW SHAPED CITY DESIGN

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SHADOW-SHAPED CITY DESIGN ZONING SCHEME | TOP VIEW

HIGH-SPEED INTERNET

GAME OF SHADOWS

‘PATHS INTO THE SHADOW ’ | FLOATING PLATFORMS

FORM GENERATION

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SHADOW-SHAPED CITY DESIGN

DATA CLUSTERS

SUN SHADOWS DATA

100m X 100m

The location was chosen based on analysis and simulations. Using integration analysis, we have established the most convenient location of the bridge path for the city and evaluated the spatial connectivity of each segment.

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GAME OF SHADOWS

SHADOW-SHAPED CITY DESIGN

MAIN STRUCTURE OF THE PROJECT

In the design process, we formed a dynamic system of platforms on the water and on the ground, which integrate with each other on the principle of steppingstones. The main structure of our project is the ‘Game of Shadows’ Model.

We are proposing a dynamic self-intellectual structure with multilayers of spaces usages, consist of units with different level of movements on varied urban conditions. The main connection across the river is based on the logic of f inding the shor test path among the networks of octree system and the simulation of the movement of the platforms.

The structure is the main element of the whole system aims to redef ine the concept of urban bridge in the Age of Information. The overing view shows the connection between the structure and urban surroundings.

The f irst level consists of floating platforms, the second - open urban spaces and f inally closed spaces such as VR rooms, cafes and educational hubs.

The next stage is the base for our floating platforms. The trajector y of the f irst type of platforms is based on the simulation of fast tracks. The design itself is a combination of spaces that are concentrated relative to the main path.

When you walk through the ‘Game of Shadows’, when you enter a darker place, you are able to see the distor ted reality with VR devices, while you get immersed in the dense shadows. The illustration moves relative to the shadow analysis and creates VR rooms and recreations.

Game of Shadow acts as the quickest connection across the river with richest programs, the rest of the platforms remain as supplementar y connections.

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STRUCTURE GENERATION THE FORM GENERATION OF THE GAME OF SHDOW

DATA - BASED ISLAND

PLAYFUL OUTDOOR SPACES

VR PROJECTIONS

ARCHITECTURAL ROOMS

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SHADOW-SHAPED CITY DESIGN

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GAME OF SHADOWS (TYPE ‘A’) ZONING SCHEME

RIVERSIDE CANOPIES HALF - OPEN ENTRY SPACES VR ROOMS

COMMERCIAL LANE VR ROOMS

STEPPING STONES PL AYFUL SHADOWSCAPE

LOCATION

Shadow paths lead us to the Shadow-Chasing Rooms, some of them could be accessed while it is closer to our paths, or by the shadow boats. These are adaptive rooms with shadows that form a virtual space for both communication and events.

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OPEN OUTD

SHADOW-SHAPED CITY DESIGN

SHADOWSCAPE ON WATER SHADOW PROJECTIONS

INTERRACTIVE FACILITIES PERMENANT ISLAND

WATER FRONT EXHIBITION ROOMS

N SPACES DOOR ACTIVITIES

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GAME OF SHADOWS ZOOM IN DIAGRAM

RIVERSIDE CANOPIES HALF - OPEN ENTRY SPACES

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SHADOW-SHAPED CITY DESIGN

VR ROOMS & CAFE

CONNECTION WITH FLOATING PLATFORMS (TYPE ‘B’)

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WARM G I TEE OTFI TSLHE AODFO W T HSE P A G E WOROI TME I SNU D Z B ITAI TG LREA M OF THE PAGE

COMMERCIAL LANE

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INTERRACTIVE FACILITIES PERMENANT ISLAND

CONNECTION WITH FLOATING PLATFORMS (TYPE ‘B’)

153 153

GAME OF SHADOWS THE MAIN CONNECTION

ENTRY SPACES Entr y spaces set several projection canopies and interactive facilities to enrich the enter tainment spaces, along with the stepping stones, these gapping areas connect the embankment with the waterbody.

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SHADOW-SHAPED CITY DESIGN

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GAME OF SHADOWS THE MAIN CONNECTION

COMMERCIAL LANE The central space creates a commercial center with open outdoor spaces and a small pool to heighten the commercial activities such as cafes, ar t galleries even with some small concer ts, we leed the light to the shadow.

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SHADOW-SHAPED CITY DESIGN

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FLOATING PLATFORMS

SHADOW-SHAPED CITY DESIGN

TYPE ‘B’

In the design process, we formed a dynamic system of platforms on the water and on the ground, which integrate with each other on the principle of steppingstones. The second ‘layer ’ of our bridge complex consist of floating units with different movements. ‘ The Shadow-explorator y Rooms’ is one categor y of the shadow bridge that demonstrates the quality of how people could experience the space in the darkness. Simultaneously, the rooms are par t of the smar t system. Together with the other types of unit platforms, they form the bridge. The essence of this type of the rooms is intelligent, engaging time factor, that is sentient, controlled by the digital world. They are vir tually connected, while physically separated as the floating units on the river.

‘Path Into the Shadow ‘ is a type of platform that changes its location relative to the data of sun shadows. They are passive recreational areas with comfor table micro climate and quiet zones. Those paths also lead us to the Shadow-Chasing Rooms, some of them could be accessed while it is closer to our paths, or by the shadow boats. Their movements generate energy, that will suppor t our digital installations utilised for generating /the new reality when digital and physical world /merges.

One type of the ‘Shadow-Chasing Rooms’ is the enclosed galler y show rooms. In the distance, the other types of rooms are visible with the 3D hologram projected f rom them.

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SHADOW BRANCHES: FLOATING PLATFORM AND PROJECTIONS TYPE B ZONING SCHEME

GAME OF SHADOWS (TYPE A) ENTRANCE 01

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PLATFORM’S TYPES:

01. PROJECTOR

02. V/R ROOM A

03. V/R ROOM B

04 . BOAT

05. SLAB

GAME OF SHADOWS (TYPE A) ENTRANCE 02

O2

06. GAMING

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SHADOW BRANCHES: FLOATING PLATFORM AND PROJECTIONS

162

SHADOW SHAPED CITY DESIGN

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SHADOW BRANCHES: ‘PATH INTO THE SHADOW ’

TYPES: 01. CANOPIES 02. DIGITAL WORLD PROJECTION 03. PUBLIC SPACES & SLABS 04 . SHADOW EXPLORATORY ROOMS

LOCATION IN A CERTAIN PERIOD OF TIME

‘Path Into the Shadow‘ allows passive recreations, digital screens occur occasionally along the path to enrich the idea of ‘digital shadow’ layer within the new urban space we area proposing.

Those paths also lead us to the Shadow-Chasing Rooms, some of them could be accessed while it is closer to our paths, or by the shadow boats. Their movements generate energy, that will support our digital installations utilised for generating / the new reality when digital and physical w o r l d /m e r g e s .

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SHADOW-SHAPED DESIGN

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SHADOW BRANCHES: ‘PATH INTO THE SHADOW ’

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SHADOW SHAPED CITY DESIGN

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SHADOW BRANCHES: SHADOW EXPLORATORY ROOMS TYPE B

TYPES: 01. CANOPIES 02. DIGITAL WORLD PROJECTION 03. PUBLIC SPACES & SLABS 04 . SHADOW EXPLORATORY ROOMS

LOCATION IN A CERTAIN PERIOD OF TIME

Shadow exploratory rooms are adaptive rooms with shadows that form a virtual space for both communication and events.

A mirror or virtual world occurs when the physical world has a re-flection in the digital world. There may also be objects or entities in the mirror world that affect the physical world such objects can be reproduced using AR.

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SHADOW-SHAPED DESIGN

MOOD REFLECTIONS

HOW DOES IT WORK? ELECTRONIC DEVICE VR GLASSES

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SHADOW BRANCHES: SHADOW EXPLORATORY ROOMS

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SHADOW BRANCHES: A HOLOGRAM OF VIRTUAL PROCESSES ZONING AND FORMATION SCHEMES

TYPES: 01. CANOPIES 02. DIGITAL WORLD PROJECTION 03. PUBLIC SPACES & SLABS

HOW DOES IT WORK? VOLUME & BRIGHTNESS OF THE PROJECTION RELATIVE TO THE USE OF DIGITAL GADGETS

04 . SHADOW EXPLORATORY ROOMS

BOAT SURFACE

PUMP TI SUCK WATER

BATTERY M E C H A N I C A L F I LT E R T O R E TA I N THE SOLID WASTE

LOCATION IN A CERTAIN PERIOD OF TIME

In the Shadow-Shaped city design, virtual realities transform the data of digital shadows. In the project there are two worlds: real and virtual, which form a hybrid space.

It is a spatial environment, where two realities meet and unite, forming a comfortable space and carrying many functions.

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F I LT E R O U T L E T

GENERATOR - MECHANICAL TO ELECTRICAL ENERGY CONVERSION

CHEMICAL-CARBON ENERGY IS CREATED FROM THE MECHANISM OF THE SUSPENDERS CREATED BY WAVE HEIGHT DIFFERENCE

SHADOW-SHAPED DESIGN

TRANSPORT COMMUNICATION BOATS (4Х2М)

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SHADOW BRANCHES: A HOLOGRAM OF VIRTUAL PROCESSES

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SHADOW-SHAPED DESIGN

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THE ENTRANCE TO THE SHADOWS ZONING | LAND DESIGN

LEISURE SPACE

ACTIVE SPACE LEISURE SPACE

ACTIVE SPACE

DIGITAL HINTS DIGITAL HINTS

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LEISURE SPACE

SHADOW-SHAPED CITY DESIGN

DIGITAL COMMUNICATION - HINTS

SEMI- OPEN - PUBLIC SPACES

SEMI- OPEN - GATHERING SPACES

PLATFORMS - PEDESTRIAN WALK

CLOSED - VIRTUAL EXPLORATIONS

RIVER FRONT LANDSCAPE

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THE ENTRANCE TO THE SHADOWS LAND DETAIL

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SHADOW-SHAPED CITY DESIGN

With Augmented Reality, Virtual Reality, digital projections, etc., the application of digital technologies leads to the def inition, ‘augmented space’, which refers to the ‘physical space overlaid with dynamically changing information’ (Manovich, 2006, p. 220).

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THE ENTRANCE TO THE SHADOWS LAND DETAIL

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SHADOW-SHAPED CITY DESIGN

The dynamism of space responds to the idea of adding the digital layer to the bridge, as the digital shadow f rom the virtual world casted to its physical form, responds and integrates the Sentient City concept, that the city reconf igures to the information which is in ‘localised’ and ‘multimedia form’ (Shephard, 2011; Manovich 2006, p. 220) This can be perceived in the digital hologram structure that creates invisible connections in the networks linking the places where viewer stands to places in the rest of the world.

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GAME OF SHADOWS (TYPE A) MOVEMENT MONDAY - SUNDAY | FERRY ENTRANCE

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SHADOW-SHAPED CITY DESIGN

GAME OF SHADOWS ‘Game Of Shadows’ behave as a prime connecting Bridge across the river, hence, the movement is minimal and happens only when the ferries or ships has to pass through.

The characteristics of movement is similar to that of the Ice-burgs. The Ferries can pass through by hitting the f ragments, while the f ragments distribute itself based on natural force and tide factors.

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TYPE B MOVEMENT MONDAY - SUNDAY | 24H

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SHADOW-SHAPED CITY DESIGN

FLOATING PLATFORMS | TYPE ‘B’ CURVES The Shortest path chosen f rom wallacai analysis tends to act as secondary bridge with the elements moving at certain intervals along the path.

The elements are public and gathering spaces with digital communications and activities.

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TYPE B MOVEMENT (SHADOW DATA) MONDAY - SUNDAY | 24H

FLOATING PLATFORMS | TYPE ‘B’ SHADOW DATA The thirtiary movement rules are based on sun angle and real-time data associated with the dynamism of shadows.

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The Exploratory rooms and boats are trained to follow these shadows to trace the darkness and achieve its spacial qualities f rom it. Hence the positions are different throughout the day and throught the year.

SHADOW-SHAPED CITY DESIGN

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THE NEW SHADOW LANDSCAPE INTENSITY OF SHADOWS

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SHADOW-SHAPED CITY DESIGN

1 PM

3 PM

6 PM

8 PM

10 PM

12 AM

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LONGITUDINAL AXONOMETRIC SECTION 1-1

The Longitudinal section describes the relationship between the Design and the Site surroundings. The site environment is as dynamic as design. The different concepts of design adapting to the environment is emphasized within these sections.

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SHADOW-SHAPED CITY DESIGN

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PERSPECTIVE SECTION OF THE ‘SHADOW BRIDGE’

SECTION VIEW The section shows the relationship between the whole installation and the urban environment, the most impor tant of which is its connection with the River Thames.

Our entire device is movable except for par ts of Game of Shadow. Our structure includes both above and beneath water, and we aim to make water as par t of our design.

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SHADOW-SHAPED CITY DESIGN

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VR ROOM & FLOATING PLATFORMS

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SHADOW-SHAPED CITY DESIGN

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SHADOW-SHAPED CITY BIRD VIEW DESIGN

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SHADOW-SHAPED CITY DESIGN

THUS , THE SHADOWS FORM THE CITY.

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