PROJECT RE-SURGE

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RE-SURGE TUTOR: R O B E R T O B O T TA Z Z I TA S O S VA R O U D I S STUDENT: ABDIMAJID HASSAN KEYU SU MOHAMMED SAIFIZ NANXI ZHOU YUBIN LIAO MARCH URBAN DESIGN RESEARCH CLUSTER 14 B-PRO BARTLETT SCHOOL OF ARCHITECTURE UNIVERSITY COLLEGE LONDON

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Rain, Steam & Speed

Painting by J.M.William Turner

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. INTRODUCTION 1.1. Urban Fragmentation 1.2. Fragmentation across Europe 1.3. Fragments in the UK 1.4. Impacts of Fragmentation 1.5. Noise and Network 1.6. Pollution and Network 1.7. London in Layers and Site Identification . ANALYSIS 2.1. Micro Analysis 2.2.1. Historic Timeline 2.2.2. Patch Dynamics 2.2.3. Characteristics of Fragments 2.2.4. Land Use 2.2.5. Social Context 2.2.6. Space Syntax Analysis 2.2.7. Visibility Analysis 2.2.7. Noise Analysis 2.2.8. Movement Analysis 2.2. Data Analytics 2.3.1. Data Remapping 2.3.2. Correlations 2.3.3. Principle Component Analysis 2.3.4. K-Means Clustering 2.3.5. Overlaying PCA and K-Mean 2.3.6. Cluster Optimization 2.3.7. Boundary Definition

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CONTENTS

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. DESIGN EXPERIMENTATION 3.1. Re-writing 3.2. Cellular Automata

. DESIGN : KING’S CROSS 4.1. Design Strategy 4.1.1. Defragmentation Strategies 4.1.2. Strategy map and Conceptual Sections 4.2. Urban Glacier 4.2.1. Intelligent Surface 4.2.2. Surface Noise 4.2.3. Surface Visibility 4.2.4. Surface Integration 4.2.5. Surface Data 4.3. Flow & Debris 4.2.1. The Glacier 4.2.2. Flow & Debris 4.4. Design Evaluation 4.5. Virtual Exhibition

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INTRODUCTION

FRAGMENTATION ACROSS EUROPE The map above illustrates the degree of fragmentation across Europe.

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URBAN FRAGMENTATION

GLOBAL IMPACT

The term Fragmentation can be defined as a process of breaking or being separated into parts which form new individual units. In an urban context, Urban fragmentation refers to the fragmentation of city fabric by infrastructure. The urban fragments are a result of a process and cannot be reversed, leading to an inefficient urban scape.

As a city’s population grows and boundaries sprawl, infrastructure expansion, high car usage, poor road and rail network, and limited public transports causes various urban problems. Congestion, Pollution, Polarization, Social segregation etc are a few examples of the urban conditions that cities across the globe face due to fragmentation.

Urban fragments here define spaces or collection of spaces within the city which may either be isolated from their neighboring unit or might be interdependent on definite terms and activities. Urban Fragmentation could be a result of the historical pattern, economic systems, natural systems or spatial arrangement.

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Is fragmentation reversible? Can these “wounds” and “scars” actually heal? Is there a way to “restitch” the city? Is it feasible?


INTRODUCTION

Shanghai

London

New Delhi

Los Angeles

Kiev

Osaka

Minneapolis

Melbourne

Chicago

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. INTRODUCTION 1.1. Urban Fragmentation 1.2. Fragmentation across Europe 1.3. Fragments in the UK 1.4. Impacts of Fragmentation

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URBAN PARADOX

AIM

After two centuries of implacable urbanization, cities have inherited massive lines of infrastructure that divide neighborhoods otherwise connected. Railways, expressways and industrial yards were placed with no regard for everyday urban life and communities.

Project “Re-Surge” aims to tackle this growing concern around Urban Fragmentation and explores prototypical urban design strategies that can be implemented in multiple cities across the globe.

At the same time, this also effectively connected cities, regions and complete continents in ways that would have been unimaginable. This is one of the great paradoxes of contemporary cities: connection at the large-scale creates disconnection at the community level.

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“Resurge” is a term that means, to come back from a low point, to rise again. It’s often related to, the concept of emergence, a force of great influence yet beyond human perception. In this context, the project aims to identify undelying patterns and interactions related to the cause and effect of the fragmentation. The main strategy is driven by collecting, visualising, analysing and simulating data concerning urban fragmentation and its components, the element of dynamic agents and various cognitive factors.


EVOLUTION OF FRAGMENTS The above diagram illustrates the formation of the urban fragments and its evolution phases.

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FRAGMENT EXTRACTION

FRAGMENTS IN THE UK The maps above illustrates the various nodes of fragmentation caused by the rail and road network across the UK. Based on the outcome of this analysis, 108 similar fragments were identified.

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FRAGEMENT EXTRACTION

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IMPACTS OF FRAGMENTATION

Infrastructure such as elevated highways, multi-level vehicular intersections, (post) industrial complexes, railroads and railyards as well as on small urban spaces in parks, plazas and service alleyways often create interstitial spaces. The interruptions that these spaces create in the urban fabric are not only due to their physical condition, but also because there is a difference in the social, spatial and institutional scale. These spaces are often under national or provincial/state government jurisdictions (i.e. highways and railroads) or even transnational regulations (i.e. international corporations), and therefore are detached from community and neighborhood-level dynamics. Seen from the sky these structures look like “wounds� in the urban tissue. This analogy of the city as a body is helpful to rethink fragmentation. In Re-Surge the traditional interpretation of Fragmentation is replaced by the incorporation of various datasets for diagnosis, analysis and design. The Impacts of Urban fragmentation on the city are Congestion, Air and Noise Pollution, Industrial wastes, Low visibility, Low Integration, Higher crime rate, Social discrimination, Poor living conditions and Polarization. Based on the previously identified fragmentation nodes, the search radius was reduced to London which has the highest number of fragmentation nodes.

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INTRODUCTION

MAJOR IMPACTS The collage above portrays the negative impact resulting from Urban fragmentation.

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Overlap Congestion Polarization King’s cross

Noise

Waterloo

Air Pollution

Battersea

Integration Industries

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LAYERS OF IMPACT

The above diagram portrays the various data layers and their various contributions to the Fragmentation score. 26


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Identified Site

SITE IDENTIFICATION

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The following graphic illustrates areas that indicate overlap of selected datasets : Building Density, Integration,Railway Density and noise.It also show the fragmentation score.This overlapping process allowed for the identification of potential sites for intervention.

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. ANALYSIS 2.1. Micro Analysis 2.2. Data Analytics

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ANALYSIS

MICRO ANALYSIS

In this section the aim was to understand the city of London at a deeper cognitive and human level within the context of the micro boundary. After the overlapping of the various chosen dataset in the Macro level a smaller boundary was selected. This boundary was determined as the location with the most overlapping datasets. The site identified was the area between Kings cross and St.Pancaras stations which is currently undergoing an extensive urban redevelopment. The site is enclosed in-between railway lines in 3 direction resulting in the formation of various groups of isolated islands ‘’Patches’’. Looking back at its rich history and its central location Kings Cross stands out as a potential site for intervention.

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HISTORIC TIMELINE The collage above illustrates the historic timeline of the site.

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ANALYSIS

UNFORESEEN CONSEQUENCE The Redevelopment framed by the railway line has created a sense of polarisation, both spatially and socially.

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PATCH DYNAMICS

Patch dynamics is an ecological perspective that the structure, function, and dynamics of ecological systems can be understood through studying their interactive patches. Patch dynamics refers to the concept that landscapes are dynamic. There are three states that a patch can exist in: potential, active, and degraded. The Patch refers to a relatively homogeneous area that differs from its surroundings. In our case, every single fragmentation is a patch that is fragmented by corridors, which are small and dense enough. Patches are also linked. Although patches may be separated in space, migration can occur from one patch to another. This migration maintains the population of some patches, and can be the mechanism by which some agents interact. This implies that ecological systems within landscapes are open, rather than closed and isolated. Corridors are distinguished from patches by their linear nature and can be defined on the basis of either structure or function or both. As a consequence of their form and context, corridors may function as habitat, dispersal conduits, or barriers. In our case, the railway lines act as barriers in dividing the spaces into patches and fragments.

LAYERS ON SITE The diagram above illustrates the overlay of the various entities that form a patch. These include the patch, corridor and fragments.

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ANALYSIS

IDENTIFYING FRAGMENTS The map above shows the different distributions of land use caused by fragmentation. It portrays the impacts caused by the railway infrastructure in the distribution of functions.

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CHARACTERISTICS OF FRAGMENTS

As an attempt to generate a structure within the fragments, the fragments are characterized on the basis of density, Land-use, built vs open ration, space character etc., namely large impregnable voids, small insignificant patches, high density areas, inherently structured and lost spaces. The fragments can be structured through strategies of densification, network of movement, system of open spaces and appropriate accessibility. Large Impregnable: The fragments include Industrial buildings, Industrial zones, Large Warehouses, and Factories. They also include Privately oned estates, restricted Public realm and Undegoing Redevelopment. Inherently Structured: Housing develpments and areas with a structured Movement Network can be characterized as Inherently structured. These areas have uniform densities and grain sizes. The public realm is strong and unrestricted, making it self sufficent communities. Textured High Density: These fragments include slums, squatter settlements and low lying govt. owned lands. These areas have a relatively poor living conditions with lack of basic amenities. Small Insignificant Scale: Recent redevelopments and Single use building that are networked by information falls under this characterization. Lost Spaces: Lost spaces Includes vague terrain, leftover spaces due to adjacent development and key open spaces. CHARACTERIZED FRAGMENTS The diagram above shows the various uniquely characterized fragments within the site.

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ANALYSIS

FRAGMENTATION DENSITY The above is an illustration of fragment density across the site. The fragment density is caused by the rail and road network densities.

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LAND USE

A physical context analysis was done on the site to investigate and allow for a better understanding of the physical structure in the urban environment. The data used was based on the Green spaces, Building Height and Land-use.The data shows the disconnection green spaces, visual blockage due to elevated railway line and high buildings.All these mentioned situations result in the land-use separation within the fragmented spaces.

Green Spaces

Building Heights

Land Use

DEMOGRAPHICS The diagram above illustrates an overlay of 3 layers of data;Green spaces, Building Heights and Land Use within their respective LSOA boundaries.

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SOCIAL CONTEXT

A social context analysis was done on the site to investigate and allow for a better understanding of the existing social structure in the urban environment. The data used was based on the index of multiple deprivation primarily focus on; Crime, Income, Health and employment. The crime domain is measuring the risk of personal and material victimization of a local level. The employment deprivation domain measures the working age population in an area with comparison its employment rate. The health domain focuses in measuring the health risk brought about by the quality of life in the urban setting.

Economic Activity

Age

Population Density

DEMOGRAPHICS The diagram above illustrates an overlay of 3 layers of data; Population density, Age and economic activity within their respective LSOA boundaries.

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ANALYSIS

INDEX OF MULTIPLE DEPRIVATION [IMD 2015] The maps above shows the various selected social datasets in the Kings Cross area. It is based on the Index of Multiple Deprivation data (IMD 2015)

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SPACE SYNTAX ANALYSIS

Space syntax is a science-based, human-focused approach that investigates relationships between spatial layout and a range of social, economic and environmental phenomena. These phenomena include patterns of movement, awareness and interaction; density, land use and land value; urban growth and societal differentiation; safety and crime distribution. In this context space syntax was used to analyze the existing road network and its effect on the site. Two methodologies were applied at local radii of 1000. These methodologies are; Angular Choice 1000 and Angular Integration 1000. Integration measures the amount of street-to-street transitions needed from a street segment, to reach all other street segments in the network, using shortest paths. While choice measures the through movement potential of linear structures (e.g. Streets and corridors).This analysis was done in an attempt to indicate zones in the site that correlate or characterized by poor connection.

VISIBILITY GRAPH ANALYSIS [VGA] The fundamental parameters of the visual field are being measured for every center point within a 5*5 grid. Each point receives a value based on its visual connectivity with other center points.

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ANALYSIS

Converting Street network into planes

Voxel generation based on data

Data fragmentation

High integration Volume

CHOICE 1000

INTEGRATION 1000

The map above show the Angular choice analysis within a radius of 1000.

The map above show the Angular Integration analysis within a radius of 1000.

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VISIBILITY ANALYSIS

Visual Perception is inherently affected by the spaces people are able to see and access.Visual field plays an important role allowing a better understanding of the perception of space. The various parameters that it consist of are analysed and visualised to highlight a different interpretation of space through visibility.

Isovist

Area

Perimeter

VGA

The isovist raycast is spread across different locations along the roads.This analyis highlights areas that are most visible and least visible within the urban fabric.

VISIBILITY GRAPH ANALYSIS [VGA] The fundamental parameters of the visual field are being measured for every center point within a 5*5 grid. Each point receives a value based on its connectivity with other center points.

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ANALYSIS

Area

Area

Area

Area

VISIBILITY MAPS OVERLAY The diagram above show an overlay of the different visibility analyses mapped on the site.

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ISOVIST ALONG PATHS The fundamental parameters of the visual field are being measured for every isovist center point along a street within the micro boundary.The exterior parts represent isovist area analysis done through VGA.This map is a hybrid map combining the two forms of visual analysis into one.

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ANALYSIS

FIELD OF VIEW The diagram above is a breakdown of the previous map. It represents the isovists areas of each focal point along the streets. This allows for a better understanding of how much area is visible from the various focal points along the streets.

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NOISE ANALYSIS

The negative impact of noise forces the buildings away from its source creating barren land in between. This space overtime evolves into slums, junkyards, warehouses etc. The reconfiguration of landuse around the noise factor creates an even larger gap between the fragments and the public realm. The two major sources of noise that worsen the condition of urban fragmentation are Rail noise and Road Noise. In this chapter, we analyse the impact of Noise in the King’s Cross region with relation to the built form.

NOISE MAP The images above explore 3 different instersection conditions and the influence of noise spread in shaping the built form around it.

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ANALYSIS

Sound Reflection 1

Rail Noise

Sound Reflection 2

Road Noise

NOISE MAP ON SITE The images above show Noise values mapped on site. It also show the spread and Noise intensity caused by both road and rail

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MOVEMENT ANALYSIS

This part primarily focuses on the concept of movement in the Urban setting. To satisfy the needs of a car-dependent population, the form of the city has transformed over time from the walkable, people-friendly traditional city into a place where the streets have be-come multi modal and public urban spaces “spaces for parking”, ignoring their significance as spaces for interaction, diversity and exchange. This development has resulted in the substitution of “access-by-proximity” with “access-by–movement”.

Pedestrian Runners Skates

Cyclists Segways

Drawing from the features of existing street network in the site and the speed of the various modes of transportation a movement models was developed. The model describes an urban environment using movement systems—Cycling, pedestrian, vehicles and train. This model offers a unifying framework that allows the use of a range of analysis metrics and conceptions of distance and aims to be a simple and applicable method of determining the dynamic value of the urban space.

Motorbikes Cars Buses

Overground rail Underground rail

DYNAMIC AGENTS The diagram above illustrate the various dynamic agents located in the site. These are; Pedestrian, Cycling, vehicles and train.

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ANALYSIS

Speed

Overground rail Underground rail

Mode of Movement SPEED VALUE The graph above show the various speed values drawn against the different modes of movement along the x and y axises respectively.

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ANALYSIS

MOVEMENT DENSITY The maps illustrates the movement density of different dynamic agents involved within the area of analysis.

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x>60 km/hr Rail speed

20<x<60 km/hr Vehicular speed

10<x<20 km/hr Cycling speed

x<10 km/hr Pedestrian speed

DYNAMIC VALUE The dynamic value is a value which intends to not only take into consideration movement as a whole but rather break it down and focus on its entities such as agents, speed and density. The project site is characterized by various movement agents such as train, cycles, and pedestrians. All these agents posses a different speed rate as well as different densities across the site. The dynamic value is summation of the product of speed of the agent and it movement density .It also takes into consideration the possibility of having multiple agents. This analysis aims to create a deeper understanding of the relationship movement and the different data sets such as ; visibility, building density, noise, integration.

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AREAS OF HIGHEST MOVEMENT The map above illustrates the identified six areas having the highest dynamic value.

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ANALYSIS

DATA ANALYTICS

Urban Environment operate as dynamic networks of special practices and evolve through circulation, repetition and interaction. The city and the urban elements modify and adjust themselves to the logic of the surrounding environment.In an attempt to understand the city as a multi-layered organism and to explore its dimensionality, we introduced machine learning technics to collect and analyses data related to the urban environment and sound. Two machine learning algorithms are used: Principal Component Analysis (PCA) and K-Mean clustering algorithm. These algorithms can help us determine the degree of data interaction and cluster analysis. By using machine learning, we aim to analyze collected data in an extensive way. In order to identify the spatial patterns in the site that need intervention. The data was then remapped to a 5*5 grid which allowed for it to be projected on a geographical space. Lastly, the results from the PCA and the K-Mean analysis was projected to the site and overlaid. Based on the outcome a preliminary boundary for intervention was selected.

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4.0 DATA DATA REMAPPING REMAPPING

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LAYERS OF VALUE DATA DYNAMIC The above illustrates the process Thisdiagram will be the overall description for the page and of remapping of the data to a grid. will add a brief introduction transitioning towards

the next spread. [futura bk 14pt]

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ANALYSIS

DATA GEOGRAPHICAL EMBEDDING: The above maps show the remapped data points visualized in their geographical locations. They show the distribution of values for each measurement both in 2d and 3d.

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ANALYSIS

DATA CORRELATION: The initial dataset was analyzed based on its correlations. This allowed for the first elimination process of the datasets before proceeding to machine learning.

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PCA ANALYSIS As the next step, machine learning algorithms were implemented to help in defining the interaction between the selected datasets. Our dataset contains; Isovist area, road & rail noise, integration and movement. The datasets used were mostly cognitive dataset that would contribute both with the urban context and human factor. The Principal component Analysis (PCA) through mathematical procedures converts a set of variables that are possibly related into a set of linearly unrelated variables (principal components). This analysis aims to spot similarities that penetrate the layers of data provided and evaluate the deviation from the prevalent trend. The primary focus was to find the areas of unique characteristics.

PCA ANALYSIS The scatter plot above is a graphical illustration of PCA analyzed data points. It show the different coorelations between the data points.

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ANALYSIS

PCA VALUE The above map shows the output of the PCA visualized. The high values indicate areas with unique values and characteristics. On the other hand, the low values indicate areas with standard values.

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K-MEAN CLUSTERING After determining areas with unique characteristics, the next step of machine analysis was applied using unsupervised machine learning K-Means Clustering. This allowed for the prediction and detection of subgroups with similar characteristics within the imported dataset. The inputs used were similar to that of the PCA, this is because the next step would include a combination of the two outputs. Through K-Means analysis the spatial similarity in key intervention areas from PCA analysis can further be determined. Since the dataset mainly contains data about road, rail and movement the data points are mainly located in the vicinity of the areas.

K-MEAN ANALYSIS The graphs above are illustrations of PKMean analysis. It show the different coorelations between the data points in graphic form.

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GEOLOCATED CLUSTERS The above map shows the output of the K-Mean geographically visualized. The values were clustered into 4 subgroups based on their similarities.

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EXPLANATORY DIAGRAM The diagram explains the interrelation of PCA and Clustering analysis.

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CLUSTER ZONING The diagram portray the various clusters divided into various zones based on fragmentation values.

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ANALYSIS

CLUSTER OPTIMIZATION The clusters are optimized based on several factors of proximity and deprivation values to finalise the area of intervention.

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BOUNDARY DEFINITION After determining areas with unique characteristics, both in the PCA and K-MEAN these points were then mapped. This allowed for easier interpretation of where intervention is required. The approach is to connect these zones together and incorporate them within the area of intervention. A boundary street is created on the existing street network by connecting the clusters. Boundary nodes are then generated based on the intersection of the boundary with the existing city network.

Intersection Streets

Intersection Nodes

Cluster Points

Potential Site Boundary

BOUNDARY ELEMENTS The diagram above shows the different elements used in determining the design boundary.

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01. St. Pancras Station 02. King’s Cross Station 03. St. Pancras Park 04. Camley Park 05. Coal Drops Yard

INTERVENTION BOUNDARY The map above shows the generated intervention boundary that would be used in the next phase of formal exploration.

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03

. FORM EXPLORATION 3.1. Data Rewriting 3.2. Cellular Automata

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DESIGN EXPERIMENT

DATA TO FORM

This chapter focuses on the different methodologies that could be applied in the process of form exploration. Various computational algorithms were experimented with in the network generation and form generation parts. For the network generation the main focus was finding a method that would allow generation of paths based on various rules such as shortest path this method include wooly path and decoding spaces. Furthermore , to understand the complexity of movement agent based algorithms were use to determine the relationship of the path to agents and the agents to agents. For the form generation part cellular automata was used to experiment on the deprived point creating a rather emergent form. The other method studied is aggregation which allowed for the connection of modules in a various way based on set rules. The outcomes of this experimentation was then evaluate and the most fitting ones were selected for the next chapter.

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REWRITING

Re-writing is the process of constant folding of data and form allowing for a different dimension in form generation. In this method, we considered 4 datasets; health, building height, building density and pollution. The relationship between data and form was strictly guided by 4 steps which resulted in multiple iterations. The resulting 24 formal variation exhibit different formal behavior based on the order of application of the dataset.

DATASETS The diagram above illustrates the different dataset used in the rewriting. These are: health, building height, building density and pollution.

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DESIGN EXPERIMENT

DATASET 1

DATASET 2

DATASET 3

DATASET 4

DATA OVERLAY The diagram above illustrates the overlay of the dataset used in the rewriting.

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DESIGN EXPERIMENT

SURFACE STRUCTURE

DEEP STRUCTURE [symantics]

RE-WRITING PROCESS The diagram above show the different steps of re-writing and the formal manipulation of each step.

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DESIGN METHODOLOGIES

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CELLULAR AUTOMATA

The universe of the Game of Life is an infinite, two-dimensional orthogonal grid of square cells, each of which is in one of two possible states, alive or dead, (or populated and unpopulated, respectively). Every cell interacts with its eight neighbours, which are the cells that are horizontally, vertically, or diagonally adjacent. At each step in time, the following transitions occur: 1] Any live cell with fewer than two live neighbours dies, as if by underpopulation. 2] Any live cell with two or three live neighbours lives on to the next generation. 3] Any live cell with more than three live neighbours dies, as if by overpopulation. 4] Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction. These rules, which compare the behavior of the automaton to real life, can be condensed into the following: 1] Any live cell with two or three live neighbors survives. 2] Any dead cell with three live neighbors becomes a live cell. 3] All other live cells die in the next generation. Similarly, all other dead cells stay dead.

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DESIGN METHODOLOGIES

GAME OF LIFE The diagrams above illustrate the different rules and step configurations of Cellular Automata based on the Game of life.

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The following section represents the exploration of cellular automata. Based on the previous machine learning analysis done, unique and deprived areas were identified. The points within these areas were then used as the alive points that would initiate the growth of the cellular automata based on the rules of game of life. The algorithm was given a bound of 10 iterations to avoid uncontrolled growth over the site. The outcome of this exercise was the generation of a new urban setting created by aggregation of deprived points. Most of the buildings within close proximity were taken over by these voxels. The taken over building were then removed to create space for intervention.

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DESIGN METHODOLOGIES

2D CELLULAR AUTOMATA The deprived points were selected and a 2d cellular algorithm was run.This allowed for the points to move resulting in an emergent confuguration which could be interpreted as deprived voxels aggregating the built environment. 89


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VOXELS OF DEPREVATION This image shows the outcome of what the urban setting would look like if voxels of deprivation would be allowed to take overprived voxels. 90


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04

. DESIGN : KING’S CROSS 4.1. Design Strategy 4.2. Urban Glacier 4.3. Flow & Debris

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DESIGN STRATEGY

This chapter focuses on the design stage of the project. where by different algorithms were used in the generation of an Urban setting that creates a solution to urban fragmentation. It takes inspiration from different areas such as ature and computational approaches. It adopts the various concepts found in the creation and movement of Glacier. In this section the project is refered to as an Urban glacier and subdivided into 4 components. The various sub-divisions contain different methodologies that are used both for generation and optimization. Methodologies used include; slope analyisis, network generation, raytracing, sound simulation,isovist visibility analysis and field lines. These sub-divisions were then combined to create a coherent urban setting.

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DEFRAGMENTATION STRATEGIES Urban fragments are a result of a process and cannot be reversed. But completely isolated fragments do not add to an urban fabric rather create a broken urban scape. A basic structuring through strategic stitching of these can maintain the variation yet the continuity of experience and character. To validate the fragments existence and functioning, the site chosen was studied and the following observations were made: The site (Kings Cross) considered has distinct fragments which can be characterized on the basis of the following: Density; Built Vs. Open; Usage; Texture; Accessibility; Movement Network; Porosity of fabric. metropolises today are experiencing rapid change and very little consideration has been given on the character and structure of the surrounding agglomerations. It’s here that urban fragmentation is witnessed not

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only in the existing areas which are transforming at a high rate but also in new additions which do not directly comply with the surroundings. The idea is not to disagree with fragmentation but rather to achieve a suitable correlation between fragments. With this regard various strategies were adopted this include; Subdivision, diffusion, linkage,expansion and overlap.


SUBDIVISION

Large Impregnable

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Subdivision into smaller fragments

DIFFUSION

Inherently Structured

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Diffusion of Edges

LINKAGE

Textured High-density

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Intra-linkage and Inter-linkage

EXPANSION

Influx - Outflux

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Focal expansion

OVERLAP

Lost Space

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Overlapping green belt

STRATEGY FOR FRAGMENTS The map above shows the different strategies applicable to the design, an understanding of the fragments and its charecteristics thus providing an initial framework for the design strategy.

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STRATEGY MAP 01. 02. 03. 04. 05. 98

Sub-division Diffusion Linkage Expansion Overlap

This map above visualizes the different strategies map on the site.


CONCEPTUAL SECTIONS This diagram shows a conceptual section illustrating how a multilevel approach could be implemented in the design phase.

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DESIGN METHODOLOGY

URBAN GLACIER

The concept of Urban Glacier resonates with re-surge in its ability to overcome barriers and facilitate movement.We divide our concept into 4 elements which are directly inspired from the glaciers.These elements are : Mountain, Glacier,flow and Debris. These 3 elements represent different components within the design: the Mountain being the barrier which is the train, the Glacier being the intelligent surface, the flow representing the field lines while the debris represent the Urban elements. Combined together these elements create an equilibriam state typical to that seen in nature.

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INTELLIGENT SURFACE The intelligent surface is equated to the Glacier element which has a dynamic state. It forms an important aspect of the design where by circulation and most of the urban activities take place. It is refered to as intelligent due to it generation process whereby multiple methodologies and data sets are used to generate and optimize it to allow maximum visibility, minimum noise and comfortable walkable slope.

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Barrier [MOUNTAIN]

Intelligent Surface [GLACIER]

Field Lines [FLOW]

Urban Elements [DEBRIS]


INTELLIGENT SURFACE The diagram above shows the multiple layers of data used in generating and optimizing the surface.

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SURFACE NOISE One of the main factors causing fragmentation in the site is the railway line. The railway also produces highest amount of noise which impacts the quality of the urban environment. The surface is firstly manipulated using noise rays, where by the height is varied and constatly tested with the rays, with aim of mantaining minimum amount of sound ray intensity and spread. Given that the surface has to be walkable, the surface is further optimized to allow minimum sound intesity and spread and minimum walking slope.

Height: 2x Ray Density: 2.8%

Height: 4x Ray Density: 1.9%

Height: 6x Ray Density: 1.5%

Height: 8x Ray Density: 1.3%

70-75 dB 65-70 dB 60-65 dB 55-60 dB

NOISE CLASSES The diagram above show the different rail noise spread values mapped on site.

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Ray Length : Slope = 3.75

Ray Length : Slope = 3.62

Ray Length : Slope = 3.01

Ray Length : Slope = 3.91

Ray Length : Slope = 3.14

Ray Length : Slope = 3.87

Ray Length : Slope = 2.68

Ray Length : Slope = 2.77

Ray Length : Slope = 3.01

Ray Length : Slope = 2.84

Ray Length : Slope = 3.70

Ray Length : Slope = 3.73

RAY TRACING

SLOPE ANALYSIS

The diagram above shows ray tracing of noise rays from the source(train) within the site.

The diagram above shows a slope analysis of the final generated surface.

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SURFACE VISIBILITY After the first draft generation of the surface based on sound and slope the next step was visibility.One of the main problem in the site was poor visibility caused by the elevated railway lines. A visibility analysis was run on the surface to maximize the visibility values from important points within the site. This was then folllowed by a combined optimization of the three aspects: noise, visibility and slope resulting in an adaptive and intelligent surface.

Ray Length : Slope = 3.14

Ray Length : Slope = 3.14

Ray Length : Slope = 3.14

VISIBILITY ANALYSIS

BOUNDARY NODES VISIBILITY

The map above show the visibility values of various areas in the site.

The diagram above shows a visiblity analysis of various boundary node.

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Ray Length : Slope = 3.75

Ray Length : Slope = 3.62

Ray Length : Slope = 3.01

Ray Length : Slope = 3.91

Ray Length : Slope = 3.14

Ray Length : Slope = 3.87

Ray Length : Slope = 2.68

Ray Length : Slope = 2.77

Ray Length : Slope = 3.01

Ray Length : Slope = 2.84

Ray Length : Slope = 3.70

Ray Length : Slope = 3.73

ISOVIST ANALYSIS

INTELLIGENT SURFACE

The diagram above shows raytracing from from a centarl point using an isovist analysis.

The surface above shows the resulting surface after generation and optimization using the 3 different layers.

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SURFACE INTEGRATION As the first step towards the street network synthesis, the existing street network was analyzed by comparing it with the output of the data analysis. The different K-means clusters show zones with different characteristics. The approach is to connect these zones together and incorporate them within the area of intervention. A boundary street is created on the existing street network by connecting the clusters. Boundary nodes are then generated based on the intersection of the boundary with the existing city network. These will provide as the entry point for the new network These will provide as the entry point for the new network These will provide as the entry point for the new network These will provide as the entry point for the new network These will provide as the entry point for the new network

Pedestrian Network

Vehicular Network

Nodes Boundary

NODE GENERATION Using the Decoding spaces tools, different network iterations were generated based on instruction tree.

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Iteration 1

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Iteration 54

GENERATED NETWORKS 54 different iterations were generated as a result of the network synthesis.

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Iteration 1

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Iteration 54

CENTRALITY BASED OPTIMIZATION The diagrams above show the different iteratons of choice values of the generated network.

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DESIGN STRATEGY

Integration

Choice

Network 21

Choice [1k]

CHOSEN ITERATIONS The map above shows the chosen iteration, its closeness centrality and betweeness centrality.

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The vehicular network will run parallel to the pedestrian network and will be underneath the surface.

The shortest path network is calculated for every vehicular node using Floyd Warshal Algorithm.

The vehicular nodes are generated by intersecting the boundary with the city grid.

Generated Network usind Decoding spaces tool

VEHICULAR GENERATION NETWORK NETWORK SIMPLIFICATION To generate network, a comThis will bethe thevehicular overall description for the page and bination of algorithms are used ontransitioning the pewill add a brief introduction towards destrian network. the next spread. [futura bk 14pt]

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DESIGN

Corridor Overlay

Street Offset

Patch formation

Surface Patch

Sunken Patch

Circulation

Network

Surface

Boundary

PATCH DEFINITION The diagram above illustrates the different layers of the intelligent surface. And how their various distinction is done.

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DATA SCAPE In this part the focus was on the patches and the assignment of function to these spaces. The surfaces were first subdivided into 5*5 grids. Data was then embedded onto the center points of the grid to allow a direct overlay of the different datasets. Noise

Visibility

Integration

Speed

The dataset; visibility, noise and integration were then clustered using machine learning, 7 clusters were identified. The clusters would then be used in the division of program across the site.

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DESIGN

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The design implements a logistic regression model to predict new speed values for the later stages of analysis. The dynamic value generated in the micro analysis was an output of the existing street network and thus, new values had to be generated for the new generated network.

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DESIGN: KING’S CROSS

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As a methodology of spatial configuration and function assignment, the dataset previously chosen as highest contributers and have influence on spatial quality were clustered. The resulting clusters were then mapped on to the site. The clusters were further analyzed to understand the contribution of each dataset. This would later be used in determining the type of urban function to be assigned in a particular cluster and location within the site.

K-MEAN CLUSTERING This map visualizes the analyzed clusters and the contribution of each dataset.

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DESIGN: KING’S CROSS

Cluster 6

Cluster 5

Cluster 4

Cluster 3

Cluster 2

Cluster 1

POTENTIAL FUNCTIONS The diagram above visualizes the different clusters and their locations within the site.

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In this part the focus was on the clusters, the various clusters were mapped onto the site. This allowed for an initial understanding of the characteristics of all the area in the site. Further analysis into the clusters allowed the understanding of how each dataset contributed to the cluster. This is then used to determine what type of function can be assigned to a specific area. Various functions were assigned to different clusters this include: static built, static landscape,dynamic built, landmark, temporary built and dynamic landscape.

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DESIGN: KING’S CROSS

CLUSTER 1

CLUSTER 2

CLUSTER 3

CLUSTER 4

CLUSTER 5

CLUSTER 6

Static Built:

Static Landscape:

Dynamic Built:

Landmark:

Temporary Built:

Dynamic Landscape:

Commercial Residential Industrial Educational

Green park Leisure zone Tree clusters

Noise Barrier Noise Panels Movement Axis Energy generator Dynamic walls

Watch Tower Statue Installation Connectivity Nodes

Canopies Kiosks Food stalls Pavilions Performance stage

Kid’s Playground Sports arena Video Screens Open Air Theatre

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Attractor Points [A]

MIXED USE BUILT Attractor Points [A]

Given that the surface is elevated various patches under the surface are designated as mixed built. This areas act as permanent built structure that are accessible both from the surface or directly from the existing streets.

Attractor Points [A]

Attractor Points [A]

The approach is to connect these zones both from the top surface and the existing network.The solid mass is subdivided by the railway line, however various linkage points are provided that allow for easy accesibility between the masses. The heights of these masses are constricted by the surface height to ensure there is no visual blockage.

Attractor Points [A]

Attractor Points [A]

Attractor Points [A]

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DESIGN: KING’S CROSS

Attractor Points [A]

Attractor Points [A]

Attractor Points [A]

Attractor Points [A]

Attractor Points [A]

NODE GENERATION POTENTIAL FUNCTIONS This map Using the visualizes Decoding the spaces Integration tools, different (closenetwork ness centrality) iterations of the werestreet generated networkbased and on instruction the industrial zones tree. in the city of London.

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DESIGN: KING’S CROSS

FLOW & DEBRIS

The flow of the glacier provides asense of directionality to its functions. This was achieved throughstudiesrelated to field lines and field strength.The forces weredivided into attraction, repulsion and spinforces. The centroid of the selected data points weregiven properties related to attraction, repulsion and spin. This wasused to generate the field linesresulting in a unified flow on the surface. The generated field lines werethenmanipulated through the datasculpting process of re-writing. The process gave rise to several iteration of line based dynamic urban elements. The urban elements were then matched with the data clusters to identify their potential function and configuration.

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FLOW

Flow of the glacier provides a sense of directionality to its functions. This was achieved throughstudies related to field lines and field strength.The forces were divided into attraction, repulsion and spinforces. In this part fields and forces where used to experiment . Various strengths and point configurations were experimented with which allowed for optimum study and understanding of behaviour of the flow.

FIELD LINES Tus am publiquius culinces los, se inpra? Palienihicae vide tero noxime terum inatum, Castandit; in sere acit gra vivissa coendensulto esilnem pra virions ceperri publicae nemprorei tem, Cupes maion diu morudenatque aperehem noster ut quam effrei

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DESIGN: KING’S CROSS

Repulsion 1

Repulsion 2

Repulsion 3

Repulsion 4

Attraction 1

Attraction 2

Attraction 3

Attraction 4

Spin Force 1

Spin Force 2

Spin Force 3

Spin Force 4

Repulsion + Attraction 1

Repulsion + Attraction 2

Repulsion + Attraction 3

Repulsion + Attraction 4

Repulsion + Spin Force 1

Repulsion + Spin Force 2

Repulsion + Spin Force 3

Repulsion + Spin Force 4

Attraction + Spin Force 1

Attraction + Spin Force 2

Attraction + Spin Force 3

Attraction + Spin Force 4

All forces 1

All forces 2

All forces 3

All forces 4

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Attractor points were placed in fragmented areas to allow the flow of people and activities toward this area. Repulsion points were placed in places of high activity and traffic. This includes path juctions within the site that have high choice value. This was done to dispers people and activity from highly active areas toward less active areas. Spin force points, these points were placed in large open patches and act as mass gathering areas. They are mostly located at the back end of the site and act as crowd pullers as well.

Attractor Points [A] Repulsion Points [R] Spin force Points [S] Spin force zones ML Cluster Output

FIELD LINE GENERATION The map above illustrates the different behavioural points mapped withing the site.

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DESIGN: KING’S CROSS

[A] Iteration 1

[A + R] Iteration 1

[A + R + S] Iteration 1

[A] Iteration 2

[A + R] Iteration 2

[A + R + S] Iteration 2

[A] Iteration 3

[A + R] Iteration 3

[A + R + S] Iteration 3

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[A] Iteration 8

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[A + R + S] Iteration 8

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DEBRIS [URBAN ELEMENTS]

In the context of an Urban Glacier, the debris corresponds to the urban elements. The debris inform the surface of the glacier and evolve over time due to the dynamic nature of the glacier. The urban glacier uses the field lines as an initial input to generate urban elements. This is achieved through the process of rewriting (mentioned in the design experimentation chapter), where the field lines are evaluated and divided into 4 types based on their curve typologies. These four curves are then voxelised and formally manipulated to generated a variety of urban elements.

FIELD LINES The diagrams illustrate the generated field lines and also the process of rewriting the field line curves into urban elements.

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DESIGN: KING’S CROSS

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Scenario 6

Scenario 5

Scenario 3

Scenario 1

Scenario 4

Scenario 2

SCENARIO IDENTIFICATION The above digram illustrates the identification of different cluster scenarios.

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DESIGN: KING’S CROSS

6

1

3

6

4

Scenario 1

3

Scenario 2

4

5

4

Scenario 3 4

2

Scenario 5

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Scenario 4

5

6

1

Scenario 6

CLUSTER COMBINATIONS The extracted cluster scenarios primaryily belong to one single cluster but are also assisted by supporting clusters that create variation in element function and behaviour.

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SCENARIO 1

Scenario 1 falls under the static built cluster. These are primarily permanent built functions supportinng the mixed use development beneath the surface.

STATIC BUILT Above diagrams show the rewriting of the elements from its extracted field line element.

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DESIGN: KING’S CROSS

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SCENARIO 2

The static landscape cluster form the main part of the surface landscape and scenario 2. The cluster is a combination of trees, groves, trail lights, light poles, bennches and other elements which are all guided by the field lines.

STATIC LANDSCAPE Above diagrams show the rewriting of the elements from its extracted field line element.

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DESIGN: KING’S CROSS

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SCENARIO 3

This scenarion primarily consists of the Dynamic Built Cluster. The dynamic built cluster can be envisioned as a permanent built elements that respond and interact with the help of data. The dynamic element extract noise and vibration data and responds to the movement of the train creating a dynamic urbanscape.

DYNAMIC BUILT Above diagrams show the rewriting of the elements from its extracted field line element.

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DESIGN: KING’S CROSS

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SCENARIO 4

This scenarion consists of the Landmark Cluster. The Landmark cluster can be envisioned as statues, watch towers, or focal points of movement.

LANDMARK Above diagrams show the rewriting of the elements from its extracted field line element.

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DESIGN: KING’S CROSS

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SCENARIO 5

This scenarion primarily consists of the Temporary built Cluster. The Temporary built cluster can be envisioned as a space for temporary events and function. These spaces can be in the form of exhibition spaces, canopies, art installations, temporary stalls and markets.

TEMPORARY BUILT Above diagrams show the rewriting of the elements from its extracted field line element.

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DESIGN: KING’S CROSS

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SCENARIO 6

This scenarion primarily consists of the Dynamic Landscape Cluster. The dynamic landscape cluster can be envisioned as a space for public gathering and events. The cluster represents a space wit high noise and high visibility, and hence, they can also form sports arenas and playgrounds.

DYNAMIC LANDSCAPE Above diagrams show the rewriting of the elements from its extracted field line element.

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DESIGN: KING’S CROSS

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LANDSCAPE FORMATION

The landscape form the major part of the surface texturing. The field lines are voxelized to generate trees, benches, light poles, steps and other urban elements . The cluster guides the flooring patterns as it guides the functionality of the surface.

Trees

Light strips

Skylights, Benches

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DESIGN: KING’S CROSS

Static Landscape Cluster

Trees

Benches

LANDSCAPE ELEMENTS The above isometric view illustrates the formation of several landscape elements onto the urban glacier.

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SITE PLAN

The design forms an amalgamation of the landscape elements and the urban elements generated through data. The design is primarily and artificial topography generated to overcome the urban condition of fragmentation.

SUBDIVISION

Subdivision into smaller fragments

DIFFUSION

Diffusion of Edges

LINKAGE

Intra-linkage and Interlinkage

EXPANSION

Focal expansion

OVERLAP

Overlapping green belt

IMPLEMENTED STRATEGIES The above diagrams illustrate the strategies and solutions applied in the design.

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DESIGN: KING’S CROSS

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ST. PANCRAS GARDEN

MOVEMEN

PROPOSED PARK ST. PANCRAS BASIN

VIEW 1

CAMLEY STREET PARK ST. PANCRAS STATION

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GRAN


DESIGN: KING’S CROSS

ST. PANCRAS STATION COMMUNITY HOUSING

VIEW 2 SPORTS ARENA

MIXED USE DEVELOPMENT

OPEN AIR THEATRE

NT CORRIDOR

GAS HOLDER PARK

COAL DROPS YARD

NARY SQUARE

DESIGN OVERVIEW The view above shows an overview of the whole design and the important destinations in and around the site.

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DESIGN: KING’S CROSS

VIEW 1 The view showcases the part of the design involving the Landscape link and the connection to the St. Pancras Station.

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DESIGN: KING’S CROSS

VIEW 2 The view showcases the part of the design involving the mixed use development, and sports arena.

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DESIGN: KING’S CROSS

PERSPECTIVE SECTION The above diagram illustrates a perspective sectional view along the St. Pancras garden and the Coal drops Yard.

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View 3 View 2 View 1

1

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Regent Canal Edge


DESIGN: KING’S CROSS

2

3

Elevated Urban Corridor

Mixed Use Complex Entrance lobby

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DESIGN: KING’S CROSS

FRAGMENT RE-EVALUATION

The final design proposed aims to resolve the problems around urban fragmentation and proposes a computational design structure and methodology. As part of the final stage of the project, the finalized design is further evaluated for its performance. The evaluation process follows a similar procedure implemented in identifying the fragmented areas. The parameters considered during the fragment identifications stage were Rail line density, Building heights, Street network integration, Visibility and Noise levels. The evaluation aims to compare the impact of the design on the situated fragment and derive a comparison between pre and post-design scenario. This would also allow in generating a computational model in resolving the urban fragment across the globe.

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DESIGN EVALUATION

The data for each analysis parameter is gathered and subsequent kernel density scores are calculated. The desnsity scores are compared based on their respective weights and a final fragment score is generated. The exposed line length have been reduced but creating underground tunnels and facilitating pedestrian and vehicular movement. The polarisation was on outcome of the extreme difference in the heights of the buildings due to the lack of gradual height increments. The surface bridges this gap by respecting the contextual building heights and completes the gradient. The new generated network , optimized besed on the analysis of highest fragment integration makes sure the surface and movement remains well

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integrated and efficient. The visibility on and around the surface is calculated and optimized. The project tries to also create a relation between visibility and accessibility, also avoiding barriers in the process. The final fragment score obtained was 0.46, compared to previous 0.86. The design achieves a 46% reduction in the fragment score.


DESIGN: KING’S CROSS

Line density Building Heights Integration Visibility Noise

Design

0.3

0.4

0.8

0.7

0.3

High

Low

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King’s Cross Fragment

51.534725, -0.128624

GOOGLE EARTH OVERLAY The above image illustrates an overlay of the final design on the Google earth view of the site.

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DESIGN: KING’S CROSS

Identified Site

Before

After

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DESIGN: KING’S CROSS

VIRTUAL ENVIRONMENT

Virtual installations of projects dealing with Machine Learning and public space, developed by Team Resurge as part of 2020 ARS Electronica Theme.

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ARS ELECTRONICA EXHIBITION Virtual installations of projects dealing with Machine Learning and public space, developed by Team Resurge as part of 2020 ARS Electronica Theme.

OVERALL ISOMETRIC VIEW The isometric view above illustrates the final design of the virtual environment created for the ARS Electronica virtual exhibition.

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DESIGN: KING’S CROSS

EXPLODED ISOMETRIC VIEW The above diagram explains the various layers of the virtual exhibition space.

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View 5 View 4 View 3 View 2 View 1

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Entrance

1

Data Garden

2


DESIGN: KING’S CROSS

UK Fragments

3

Surface Elements

4

Final Design Exhibits

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