Perceptive Datascapes by Apostolos Apostolopoulos

PERCEPTIVE DATASCAPES

APOSTOLOS APOSTOLOPOULOS CAIT BROCK ANNA KAMPANI

PERCEPTIVE DATASCAPES APOSTOLOS APOSTOLOPOULOS CAIT BROCK ANNA KAMPANI

THE BARTLETT SCHOOL OF ARCHITECTURE MArch Urban Design RC14 BIG DATA CITY: MACHINE THINKING AND URBANISM BEYOND THE VOID Tutors Roberto Bottazzi, Tasos Varoudis

CONTENTS

Visual Perception

Site Analysis

Visual Field

Lighting

Colour

Data Crossings

Data Analytics

The Perceptive Machine

Recursive Evaluation

108

Perceptive Datascapes

140

Sources

166

01 CHAPTER TITLE

VISUAL PERCEPTION

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Visual Perception

PERCEIVING OUR SURROUNDINGS

That Which We Perceive Visual perception is, the interpretation of ones surrounding environment. It includes various elements of which we interact with on a daily basis. These elements influence the way we interpret and understand space. This project identifies and explores a set of parameters catagorised as components of visual perception. Based on site visits, interpretation, and analysis a specific set of parameters have been identified and used to map new understandings of the city. The three main parameters studied included;

Visual Field Lighting Colour

â&#x20AC;&#x153;To better use space, you first need to become consciously aware of it. Learn to recognize space in different designs. Notice the shape it forms, and think about what the space is communicationâ&#x20AC;? Steven Bradley

The concept behind our method of analysis is to understand the visual perception of space by studying the various stimuli perceived by the human brain as a 3-dimensional collage. This project performs diverse analyses, transcending from two dimensions to three, exploring multidimensional clustering in order to develop a design that is directly influenced by data. In this way, Perceptive Datascapes represents our discovery of new and innovative interpretations of the cityscape and our exploration of data optimisation to provide strategic design decisions.

Right page:

The Perceptive Process This graphic conceptualises the topic of visual perception by illustrating the process or experience of interpretation. 6

Chapter title

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Network of Perception Concept collage of the process of visual perception, the process of the brain interpreting external stimuli. 8

Chapter title

PERCEPTIVE DATASCAPES

Perceptive Machine The aim of this project is to explore the possibility of constructing a computational mechanism which could offer new and innovative design strategies by fusing together digital and natural processes to create a mixed reality environment. The potential of this system seeks to expand our understanding and perception of space. The Perceptive Machine can act in parallel with the human brain and acquire the role of the counselor proposing a two-part brain arrangement. To put it differently, the Machine is consisted of various partial states of mind. 9

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Perceptive Datascapes The final result of this research is the design of public space through the amalgamation of human and computational means. This collaborative design suggests an innovative approach in designing public spaces for contemporary societies where the threshold between private and public is extended into a porous zone. 10

Chapter title

PERCEPTIVE DATASCAPES

02 CHAPTER TITLE SITE ANALYSIS

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Site Analysis

MULTIDIMENSIONAL ANALYSIS

From Global to Local In an effort to understand the relationship between multiple layers, an Angular Segment Analysis was performed in conjunction with a land use density map. Integration and Choice maps with a radius of 2000 were overlayed to establish connections between node centrality and closeness centrality. By layering these three maps we are able to indicate zones of the site that correlate, penetrating multiple layers.

Integration | Radius 2000 | Quantile Breaks

Choice | Radius 2000 | Natural Breaks

Land Use Density

Legend

Low Values

Low Variation

High Values

High Variation

Site Analysis

PERCEPTIVE DATASCAPES

Overlay of Integration, Choice (Radius 2000), and Land Use Density Map | Site Selection The following map indicates node centrality within the network. In combination with the land use density map, a site is selected based on versatility of land use, and the relationship of Integration and Choice in the Angular Segment Analysis. 15

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Site Analysis

Chapter title

PERCEPTIVE DATASCAPES

Hackney Tower Hamlets

Synthesizing Data Layer | Site Selection The following graphic illustrates areas that indicate overlap based on our Angular Segment Analysis (Integration and Choice at a radius of 2000) as well as the land use density map. Legend Indicates areas overlap between the values from the Angular Segment Analysis at a radius of 2000, and the land use density map Selected Site

03 CHAPTER TITLE VISUAL FIELD

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Visual Field

ISOVIST ANALYSIS

Single Isovist Isovist Field of View

Visual perception is inherently affected by the space people see and are able to reach. Thus, the visual field could be used as a tool to better understand the complex way we perceive space. The parameters that it consists of are analysed separately so as to highlight a different interpretation of space. The analysis aims to visualise the field of view in multiple positions within the city. The isovist raycasting is spread along the roads of the city, highlighting the areas that are most visible within the urban fabric as well as the areas that are almost or completely invisible.

Isovist Lines of Sight

Occlusivity

Occlusive Spaces

Occlusivity 400 70.8 0.0 1.0

Occlusivity = 0 20

400 80 0.0 0.78

Occlusivity > 0

289.1 486.6 146.5 0.015

Occlusivity >> 0

Area Perimeter Occlusivity Compactness

Occlusivity is the negative of the isovist field of view and calculates the space that is not visible from a specific point of view. It is a measurement that shows the potential. High value of occlusivity means that it is acknowledged that there are places to explore but they are not seen. For instance, a road with multiple crossroads would have a high values of occlusivity because we can see the potential of existence of other spaces and movement.

Visual Field

PERCEPTIVE DATASCAPES

Lines of Sight

Area

Perimeter

Occlusivity

Field of View Parameters Within this diagram, the fundamental parameters of the visual field are being measured for every isovist center point along a street. The analysis aims to visualise the field of view in multiple positions within the city. The isovist raycasting is spread along the roads of the city, highlighting the areas that are the most visible within the urban fabric as well as the areas that are invisible. 21

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Visual Field

FROM POINTS TO OBJECT

Towards 3-Dimensional Isovists Overlapping layers of isovist field arrays represent an approach the third dimension. This layering at different heights adds dimension and depth to the analysis. The footprints of the visual field overlap revealing the degree of permeability in the various areas. These different levels explore the way we perceive space not as a flat surface but as a multi-layered cityscape. The rich and diverse amount of data deriving from this process will be evaluated and produce a new spatial interpretation of the way we perceive the world around us. Overlaying the isovist raycasting within a neighborhood reveals the degree of permeability and accessibility of the area. The layers represent different levels or heights so as to depict the differences and fluctuations of the section as opposed to simply the plan view. The density of the visibility footprint challenges the porosity of the urban fabric. The three isovist layers are deconstructed and reassembled into a point cloud of the visual field. Each point incorporates the attributes of the analysis as an integral part of its structure. At a secondary level, these bits of information are employed to shape a surface and volume that represents the visible space, embedded with all the data.

Raycasting in 3-Dimensions

3-Layer Isovist Point Cloud Elevation 22

Overlaying the isovist raycasting along a neighborhood reveals the degree of permeability and accessibility of the area.

Measurements of Each Point Reference Number 1517 X Value 60.1069105 Y Value 307.8714026 Z Value 1 Length 20.50210232 Area 6211.638501 Perimeter 746.6229628 Special Character 1

Point Cloud | The Visual Field The visual field is represented as a 3-dimensional point cloud based on the isovist layers. Afterwards, it takes the form of an object which connects all the points, leaving holes where the actual space is invisible.

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NEGATIVE SPACE

Architectural Voids In order to deeply understand the notion of visual perception and its potentials, the isovist field of view is extracted from the urban fabric and studied separately. The space of the city is actually the negative of the designed space, the void of architecture. It is a fluid, dynamic space, the leftover of the urban tissue.

The Physicality of the Negative The following model represents the negative of the visible space. The void in this sense is objectified into a 3-dimensional structure to help us to better understand the invisible spaces within the city. 24

Visual Field

PERCEPTIVE DATASCAPES

Isolating the Central Area

Area of focus

Top View

The Negative Space Area of Focus The area of focus is isolated and studied as a 3-dimensional solid space. The images show the visible space of the city as an object. The reverse condition of perceived space helps us to understand and explore different interpretations of space. 25

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Visual Field

Urban Fabric

Grid of points infilling space

Lines of sight spread across the open space

Void to Object 3-Dimensional Isovist Analysis The visible space of the city is infilled and represented with points. In this way, the negative space is perceived as an assemblage of particles that we are able to move in between. The lines of sight are extended in three dimensions and are graduated according to their length so as to better understand the distance in the visual field. 26

Object of the visible space in 3 dimensions

Visual Field

PERCEPTIVE DATASCAPES

Lower Perspective of the Visible Space as an Object

Transformation of the Visible Space of a Street

Bottom View of the Visible Space

The immaterial space of the city is isolated and studied as a self-existing object that could generate a new interpretation of the city itself. The way we perceive the negative space of the urban condition is being reconsidered through the production of various aggregations of the visible space of a street. 27

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Chapter title

Urban Fabric Raycasting Isovist Path

Isovist Points

Lines of Sight

Outline of Visual Field

Area Chapter title

PERCEPTIVE DATASCAPES

Perimeter 3 - Layers with Different Z Value

Footprint of Visual Field

Point Cloud of 3-Dimensional Visible Space

Visible Space Negative Space as an Object

Length

Occlusivity

Isovist Analysis | Process Diagram Calculating the Visual Field The process of the analysis is organised in steps. The algorithm of computing each one will be created and applied so that the final result will be a continuous computational process that can be used as a methodology. In this way, the algorithm could be employed to multiple areas with only changing the input data, the urban fabric and the selected path. 29

LIGHT

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Light

EXPLORING THE EPHEMERAL

Illuminating the Shadows During initial visits to site, we perceived there to be a lack of street lighting on the walking paths and some of the smaller neighbourhood roads. Analyzing two rich datasets provided by the borough of Hackney and Tower Hamlets, this analysis explores methods of visualising and studying light to understand how visual perception may be affected by it. In addition to lighting patterns, visual indicators on site also suggested that there were issues of crime within the area. Initial analysis sought to explore whether there was a correlation between crime and lighting within the area.

â&#x20AC;&#x153;...the first thing that is important is that light is used as material, and that it has a physical presence as such, and that space is solid and filled and never empty.â&#x20AC;? James Turrell

Studying lighting within these neighbourhoods has the potential to serve as a method of investigation into situating qualitative research (phenomenology: the interpretive study of human experience) within quantitative inquiry (big data) in order to explore the validity of affect in relation to environmental behavior. More specifically, considering the data to understand the relationship between light and crime may indicate how fear affects environmental interaction, and how affect and behavior is influenced by elements of visual perception.

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Lighting Patterns in Tower Hamlets and Hackney This image visualises lighting patterns in the boroughs of Tower Hamlets and Hackney indicating the strength of lighting. 32

Chapter title

PERCEPTIVE DATASCAPES

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Light in Darkness Figure Ground Study Using the concept of the positive and negative, we considered the role of light in dictating usage of space. The following image illustrates light as vacant space. This can be interpreted as accessible space, or indicate streets/ areas that are better lit. 34

Chapter title

Light

PERCEPTIVE DATASCAPES

Darkness from Light Figure Ground The reverse of the previous image, this graphic illustrates lighting as occupied space. From this image we can assess areas that require better lighting and potentially look at them as spaces that do not necessarily require immediate intervention. 35

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FORM FINDING

Shaping Light Through studying the negative of space, it was important to assign a form to the element of light in order to understand the condition created by its presence. Due to the rich datasets provided by the borough, there were many qualities that could be map lighting. We chose to focus on both the height and wattage of street lights within the area as these two characteristics could be applied to a form. Initially we created models that explored the form itself, and continued in an excavation subtracting this form from the existing condition. As a result, various iterations were created in the effort to understand the spatial condition created by the ephemeral element.

Light Finding Shape The following series represents particle studies exploring the characteristics of the emissive element. 36

Light

PERCEPTIVE DATASCAPES

The Physicality of Light 3D Printed Model The following model explores form finding of the ephemeral element of light. The conical shape represent the natural decay of light and as such a model is printed that physically connects the parameter not typically associated with form. 37

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The Ambiance of the Ephemeral The model represents the connection of street lighting heights, and the effect of light passing through angled perforations, which represent the lighting intensity. 38

Light

Chapter title

PERCEPTIVE DATASCAPES

A Tangible Representation | 3D Printed Lighting Cones This image illustrate one of the 3D printed versions of the cones that were created to understand the physical relationship between street lights. The models explored different scales for height and base radius which was determined by the light wattage. 39

COLOUR

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Colour

COLOUR DATA MINING

Social Media Extraction Upon initial site visit, it was noted that various areas could be described with sample colour palettes. The colours and textures of the different materials, street art and natural elements are a reflection of the ongoing development in this neighbourhood. This begged the question as to the role colour and art played in public visual perception. In order delve deeper into this idea, photographs from Flickr that were extracted using their geolocation through Python scripting. This process led to the creation of a database of 1,426 photos. Then, by filtering the photographs through tags and using images that reflect the cityscape, colour-maps were created. After the filtering process, the databased was downsized to 841 photos with multiple attributes. This analysis focused on mapping the correlation between colour and location to better understand connections and patterns.

Geolocated Colour Map Every image is geolocated creating a representative collage of the colour in the area. Afterwards, the average colour value is extracted from each pixel so as to create a colour map of our site and correlate it with the negative space. 42

Chapter title

PERCEPTIVE DATASCAPES

Colour Mapping Trails The following image represents a particle based simulation that maps values based on RGB numbers. The form is dictated by the image which is projected onto a sphere. 43

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Colour

4D Hue/Saturation/ Intensity Analysis [Custom Python Code]

A sample of the images is analysed though Python code to extract the hue, saturation and intensity values of each pixel for each photograph. Then, these values are represented in 3-dimensional diagrams, illustrating the distribution of colour in the urban space. 44

Right page:

Colour Mapping Sequence The following series illustrates the process dictated by the algorithm, mapping RGB values based on images from Flickr.

Colour

PERCEPTIVE DATASCAPES

RGB Cityscapes The purpose of this study is to explore the importance of colour in the way we perceive the space around us. 45

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RED

Colour

GREEN

BLUE

Right page:

Colour Cloud Sequences Analysing and Interpreting The Attributes These diagrams show the multiple and diverse attributes of the parameter colour as extracted through custom python scripts. 46

These sequences represent colour as a 3-dimensional point cloud which extracts the hue and saturation values from the photo-dataset. The image illustrates multiple such clouds and merges them together in a distorted multidimensional space.

Chapter title

PERCEPTIVE DATASCAPES

DATA CROSSINGS

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Data Crossings

PERFORATING MULTIPLE LAYERS

Light Visual Field

Stratification of the Datasets

Light Twitter Visual Field

In an effort to understand the correlations between these parameters, a series of analysis are performed to observe the way these elements interact.

Light Twitter Visual Field Light

In a first model, the datasets are stratified to highlight overlap and connection between the layers. The ability to remove each layer and analyse the reflection is a useful tool in helping us move forward.

Twitter Visual Field Light Twitter Visual Field Light Twitter

Data Overlay This model is a combination of the data. The different layers are situated one after the other and, using light projection, shadows are cast onto a single plane. Simultaneously, these layers are in fact sections of the study-area with collected data at each location. 50

Data Crossings

PERCEPTIVE DATASCAPES

Data Based Modifications | Particle Simulation With the intent to explore form finding and modification based on data, the following image represents a concept drawing for a structure altered by force. 51

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Data Crossings

Placing a Voxel to Each Point

Rotation | Public Perception Data Applied

Rotation | Colour Data Applied

Displacement

Data Morphing the Structure

Ephemeral Data Structure

Morphing the Parameters The visualizations presented demonstrate the process of creating this structure from different types of data. Initially, the point cloud of visual field and light parameters are represented as voxels. Then, these voxels are twisted and rotated according to activity and colour data. 52

Rotation

Displacement

Morphing

Data Crossings

PERCEPTIVE DATASCAPES

Ephemeral Data Structure This configuration is one of the multiple possible combinations of data. The fact that part of the data is live-streaming is a dominant factor as consequently the final structure is constantly changing. 53

DATA ANALYTICS

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Data Analytics

SPATIAL MACHINE THINKING

Machine Learning | Data Preprocessing

The main purpose of this process is to identify areas within the urban fabric that represent the ‘negative space’. In other words, the analysis is looking for hidden, or less integrated parts of city. It indicates pockets within the urban network that are less accessible or visually integrated and thus we identify them as the invisible, or the negative spaces. We classify these areas as ‘vulnerable spaces’ because of the inherent characteristics (e.g.; low accessibility, low lighting). In analyzing layers of data, we will try to identify these vulnerable spaces consistent within each parameter. Through the exploration of the datasets, we will build a list of criteria that will define a vulnerable space. The first step is to prepare the data for the analysis. This means unifying the datasets so that each layer holds the same size and dimensions. In order to accomplish this, the data is projected onto a dense grid that is set and generated based on a Visibility Graph Analysis. As a result of this data preparation, each point or pixel of the grid holds each layer of the data embedded into it. This process is accomplished through a series of custom Python scripts that perform each stage of the analysis. The following pages illustrate various studies in our efforts to understand the structure of the data. We work towards understanding the distribution of values and the correlations between different measurements.

Data Projection | Value Mapping The following graphic illustrates the visualisation of values following the projection mapping. 56

Data Analytics

PERCEPTIVE DATASCAPES

Each point contains a value for each attributes.

List of Attributes Reference Number x, y, z Isovist Perimeter Isovist Area Visual Integration Visual Mean Depth Occlusivity Choice Radius 500 Choice Radius 2000 Choice Radius 3000 Crime Integration Radius 500 Integration Radius 2000

Spatial Data Quantification

Integration Radius 3000

[Custom Python Code]

Light - Wattage Light – Height Twitter – Favorites Twitter – ReTweet Flickr – Views Flickr – RGB - Blue Flickr – RGB – Green Flickr – RGB - Red Flickr - Perceived Luminance

Grid of 48,488 points

The data is represented in a grid of pixels. The site is thus divided and data is projected onto this grid so that each pixel contains all the attributes embedded. Consequently, the final result is a set of 48,488 points, each with 25 values, from the attributes listed to the left. 57

Data Analytics

Light Wattage

Perceived Luminance

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Isovist Area

Crime

Perceived Luminance

Integration | Radius 2000

Occlusivity

These diagrams illustrate the relationship between different measurements. Each one aims to establish a connection and indicate the part of the dataset that fits our criteria. This juxtaposition demonstrates that the attributes do not contribute mutually to the final outcome. More specifically, some layers play a more important role than others and consequently, it is necessary to proceed with either using weighted measurements, or filtering the data. As a result, 58

the dataset is divided into “primary” data and the “secondary” data. On a secondary level, by taking a closer look at the diagrams, even among the most important attributes, not all the values are relevant to our study. We selected the part of the dataset that could indicate a vulnerable space and this is demonstrated with the red colour. This means that we are searching for low occlusivity, low integration, low luminance and high crime values.

Right page:

All to All Measurements Legend Section of the data indicating vulnerable spaces

The image shows the correlations between the different attributes of the final dataset. Each attribute is tested against all others.

Data Analytics

Perceived Luminance

Colour RGB Blue

Colour RGB Green

Colour RGB Red

Crime Incidents Count

Twitter Retweet Count

Twitter Favourites Count

Integration r3000

Integration r2000

Integration r500

Choice r3000

Choice r2000

Choice r500

Light Height

Light Wattage

Visual Integration

Isovist Perimeter

Occlusivity

Isovist Area

PERCEPTIVE DATASCAPES

Isovist Area

Occlusivity

Isovist Perimeter

Visual Integration

Light Wattage

Light Height

Choice r500

Choice r2000

Choice r3000

Integration r500

Integration r2000

Integration Twitter r3000 Favourites Count

Twitter Retweet Count

Crime Incidents Count

Colour RGB Red

Colour RGB Green

Colour RGB Blue

Perceived Luminance

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Isovist Area

Data Analytics

Integration Radius 2000

Occlusivity

Crime

Perceived Luminance

Light Wattage

Histograms | Primary Data These diagrams illustrate the variance and the distribution of values in the datasets that have the strongest influence (Primary). 60

Occlusivity

Light Wattage

Integration | Radius 2000

Perceived Luminance

Crime

Data Analytics

PERCEPTIVE DATASCAPES

Isovist Area

Integration | Radius 2000

Occlusivity

Distribution of Values These maps show the distribution of values for each measurement. The areas that have the low values are indicated with the purple colour as they are the ones that we are interested in. (Exception to be made for the measurement of crime, where we are looking for the high values and so high values are indicated in blue.) Legend High Vulnerability

Perceived Luminance

Crime

Light Wattage

Low Vulnerability

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Isovist Area

Occlusivity 3-Dimensional Scatter Plots These plots show the distribution of values of each measurement in a 3-dimensional space. Because of the fact that the data we explore is in reality a complex and dense point could, it is considerably helpful to visualize it as such. This overview of the data as a structure in space will contribute in a more sophisticated interpretation of the analyses we are going to perform further on. 62

Data Analytics

Perceived Luminance

Light - Wattage

Integration Radius 2000

Light - Height

Chapter title

PERCEPTIVE DATASCAPES

Integration Values

Luminance Values

Transcending into 3-Dimensions

Occlusivity Values

The following images represent the visualisation of the values generated from the data projection remapping. 63

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Data Analytics

MACHINE INTUITION

Unsupervised Machine Learning and Dimensionality Reduction The result of the previous experimentation was the acknowledgment of the complexity of our multi-layered dataset. In order to delve deeper into the data, a multidimensional clustering analysis is performed with the intent to reveal similarities and patterns that penetrate the data-layers and connect the various parameters identified. From this correlations within the different layers are made to understand which are the most influential. As a result of this process, primary data is identified and pushed forward for further exploration. Further to that, two unsupervised machine learning algorithms which, with linear data transformations, identify similarities and patterns. Initially, the data in question is tested against K-means clustering in order to acquire an initial overall image of groups with similar characteristics. The next step is to perform exploratory data analysis for dimensionality reduction. In this case, PCA and t-SNE are selected to compare the result of a non linear, non deterministic algorithm against a linear, deterministic one. Iterations at this stage include various sub-parts of the dataset as well as the whole. Right page:

Finally, there is an attempt to combine PCA and K-means to extract conclusions about parts of the site. In other words, it has been noticed that two adjacent K-means groups were of high and low uniqueness accordingly. This specific area is characterised by contradictory conditions that constitute a complex collage. In this way we were able to recognize spaces with contradictory characteristics within the urban fabric. It is these spaces that we focus further on.

Data in the City The urban fabric transcends from physical space into a data landscape and further into a dynamic data point cloud. The city is transformed into a new type of topography that is no longer constrained to its physical substance but can become a dynamic organism that constantly changes its shape and internal structure.

Chapter title

PERCEPTIVE DATASCAPES

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Principal Component Analysis [Custom Python Code]

This analysis aims to spot similarities that penetrate the layers of data provided and evaluate the deviation from this trend. In other words, the high values of the analysis indicate areas that have unique characteristics which make them stand out. On the other hand, low values indicate places that show nothing out of the ordinary and remain in the shadows. This means that the red points in the map are the most vulnerable places because they have the lowest PCA values. Legend Highest Values

Lowest Values

Chapter title

PERCEPTIVE DATASCAPES

K-Means Clustering Analysis [Custom Python Code]

This clustering analysis groups areas with similar characteristics. In this map we asked for four clusters. By contrasting this analysis with the results of the PCA, we can identify clusters of vulnerable spaces. Legend Cluster One Cluster Two Cluster Three Cluster Four Cluster Five

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K-Means Clustering Analysis with Filtered Data + Overlapped PCA Values The final step was to run, once again, this analysis but with filtered data. This means that the analysis takes only a portion of the data that we are interested in and characterizes vulnerable spaces. The result of the analysis indicates centre points of vulnerability in the area. Additionally, it is interesting to note that there are clusters within close proximity of each other, where one has high PCA values and the other has low. Interpreting this in the context of the site, we have two clusters, separated by a single building. These clusters stand out because it identifies an area with unique characteristics and one with more mundane or ordinary values. Legend [PCA]

Cluster 1

High Values Cluster 2 Low Values

Cluster 3

Chapter title

Data Analytics

PERCEPTIVE DATASCAPES

Occlusivity values PCA values

Principal Component Analysis and Clustering Explanatory Diagram

Cluster

The diagram explains the interrelations of the PCA and clustering analysis. The inner arc represents the fluctuations of the PCA values while the middle one shows the occlusivity filter. Simultaneously, for each point apart from the PCA and occlusivity value we can identify the cluster it corresponds to. 69

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Chapter title

Primary Data Layers | Dimensionality Reduction [t-SNE] 6 Dimensions 70

Data Analytics

PERCEPTIVE DATASCAPES

Exploratory Data Analysis Of High-Dimensional Data

Primary Data Layers: Occlusivity - Integration

The following images represent an investigation of more understandable ways to visualise high dimensional data so as to be able to interpret and incorporate them into architectural design. The algorithm employed for this research is t-SNE and the tested dataset includes only the primary data. The images on the left illustrate iterations of changing the number of dimensions used in the dimensionality reduction, whereas on the right depict iterations of different data layers. 71

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Chapter title

Primary Data Layers | Dimensionality Reduction [t-SNE] 4 Dimensions 72

Data Analytics

PERCEPTIVE DATASCAPES

Primary Data Layers: Occlusivity - Light Watts 73

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Chapter title

Primary Data Layers | Dimensionality Reduction [t-SNE] 10 Dimensions 74

Data Analytics

PERCEPTIVE DATASCAPES

Primary Data Layers: Integration - Isovist Area 75

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Dynamic Data Clustering Visualisation [t-SNE in Embedding Projector]

The following images represent visualisations of data transformations using a non linear - non deterministic algorithm. The presented outcome is the result of t-SNE in the Embedding Projector of TensorBoard. 76

Chapter title

PERCEPTIVE DATASCAPES

Summarising The Results The overlay of the data analytics produces a new topography over the existing urban fabric, where the peaks represent areas of potential intervention. These diverse data analyses provide a more thorough understanding of space seen from a new perspective which grasps the fusion of digital means into everyday life. 77

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Chapter title

PERCEPTIVE DATASCAPES

THE PERCEPTIVE MACHINE

BUILDING A SYSTEM

The Perceptive Machine Based on the previous analysis, parameters most prominently influencing the data have been identified. We move forward with the intension to explore principles of physical and visual connectivity as a means of evaluating and generating new spatial solutions for the public spaces. Trying to deconstruct the notion of visual perception, we have met certain constraints intrinsic to old spatial models, which are still widely repeated even for new social processes and are related with the appropriation of space and concept behind the sense of security within the public realm. Today, â&#x20AC;&#x2DC;defensible spaceâ&#x20AC;&#x2122; has become associated with militaristic urban environments, occupied by heavy surveillance. In identifying locations of vulnerability, this project challenges what it means to create defensible space, through the reconstruction of a physical layout, allowing for new forms of dwelling. We therefore design a system, a machine, which evaluates an environment, in real time, so as to assist in the design process, offering a new opportunity for architects in designing public spaces in contemporary urban environments.

ÎŁ 4

i =1

Light

Visibility

Luminance

Integration

Translating the Perceptive Machine The following image represents the combined values of the four identified parameters and maps them in a 3-dimensional data cloud. This visualisation represents areas with high and low accessibility, permeability, and sense of security. Legend Highest Values

Lowest Values

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MULTI-LAYERED SYSTEM OF ANALYSIS

LIGHT Sunlight Analysis

Process of Evaluation

In order to build out the ability of this formula, a series of tests and analyses allowed for the development of a recursive system. It was our intention to build a machine with real-time evaluation ability, and thus in order to reach this we had to create and adjust the formulas and scripts so as to allow for any form to be evaluated. The designed factor combines the values of four parameters, and as such requires an evaluation for each of these layers. The following pages detail this process of evaluation for each parameter.

VISIBILITY Isovist Analysis

COLOUR RGB Color Evaluation

Recursive Evaluation The following diagram illustrates the final stage of each layer of analysis. This drawing alludes to the uses of numerical values to evaluate form so as to assist in indicating where alteration s need to take place. 82

INTEGRATION Drainage Analysis

The Perceptive Machine

Light

PERCEPTIVE DATASCAPES

Visibility

Luminance

Integration

Step One Using the Ladybug Plug-in for Grasshopper, collect weather data for the specific site

Step One Identify points along a path.

Step One Identify points and run various paths connecting these points.

Step Two Allow the analysis to run to gather information for either a specific season or time period.

Step Two Calculate isovists at each point at three different heights.

Step Two Calculate RGB values at each point using an evaluation algorithm using Flickr photos.

Step Two Run a drainage analysis off this identified path and add a gradient to assign a value to the result.

Step Three Calculate the total values based on the sunlight analysis at each point on the surface.

Step Three Calculate the values based on the isovist analysis at each point on the surface.

Step Three Calculate the total values based on RGB analysis at each point on the surface.

Step Three Calculate the total values based on the drainage analysis at each point on the surface. 83

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VISIBILITY ANALYSIS Field of View Studies

Field of View

Lighting Value Particle Simulation Particle simulations exploring the reaction of light assists in the process of understanding where the values derive from. 84

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Visibility Value Particle Simulation The following graphic illustrates a particle simulation of points radiating out from a static source. This simulation corresponds with our field of view analysis from the site. 85

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Color Value Particle Simulation The following graphic illustrates a particle simulation along a path, mapping RGB values based on Flickr photos. This sequence visualises colour values along a given route. 86

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Integration Value Particle Simulation The following graphic illustrates a particle simulation of a drainage analysis. Particles naturally radiate and fall out from the main route, creating a network of drainage, one that gives insight to integration values. 87

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RECURSIVE EVALUATION

Creating the Feedback Loop

Using the Perceptive Machine, we create a process that allows for a real time evaluation of a form. Once each parameter has been tested, the data is projected onto the geometry and the process highlights a specific area with the lowest combined value, thus indicating the space that needs alteration. The algorithm can be applied to any type of geometry, evaluating the condition for each layer and then compressing them all in one. This strategy allows us to correlate multiple systems which excludes simple collage or imposition and encourages contributive coexistence of urban layers. Our purpose is to stretch the threshold between private and public into a porous zone with establishing a gradient of experience. The following pages illustrate our tests of this process in three stages;

Simple Surface

Surface in the City

Structure in the City

Visualisation of Evaluation Process This graphic illustrates the results of the multi-layered analysis and the projection of these combined values into a data placeholder. 88

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Evaluation Generative Iterations This series of studies iterate the process of evaluation on varying simple surfaces. The Machine generates a simple surface and then evaluates it . In this way it is possible to tune the algorithm so that the values are normalized in such a way that results into their mutual contribution to the final outcome. 90

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Adding Context to a Surface Moving the varying forms into the context of the city allows for the use of measurements relevant to the specific site. Indicated in this diagram are the locations of the lowest values for the three measurements and thus where the greatest change to form will need to take place. 92

Lowest Values of Integration

Lowest Values of Light

Lowest Values of Visibility

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Clustering in Relation to The Perceptive Machine This diagram visualises the interrelations of the clustering analysis, generated topography based on the Perceptive Machine, and the surface to be evaluated. 93

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Sunlight Evaluation

Visibility Evaluation

Topography One

Value: 665, 864

Value: 261.80

Topography Two

Value: 666, 632

Value: 70.13

Value: 666, 508

Value: 68.89

Value: 665, 126

Value: 79.70

Topography Three

Topography Four

Evaluation | Surface in the City Analysis of the three surfaces in the context of the city. Differing topographies provide alternative results after propagating through the factor. 94

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Colour Evaluation

Integration Evaluation

Value: 20

Value: 18

Value: 24

Value: 23

Value: 684.86

Value: 1559.46

Value: 3039.41

Value: 3116.27 95

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Concept Diagrams Form Exploration One The Perceptive Machine is tested against three design proposals each of whom is based on different principles.

Iteration One: - Multiple access points (all equal). - Centralized activity in the middle section. - Movement through the centre. 97

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Concept Diagrams Form Exploration Two Iteration Two: - Two main access points - Centralized activity along the West - Movement alongside the East 99

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Concept Diagrams Form Exploration Three Iteration Three: - Three main access points - Decentralised activity - Movement throughout the whole area.

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Sunlight Evaluation

Visibility Evaluation

Form One

Value: 14.35

Value: 31.96

Value: 8.51

Value: 38.94

Value: 20.62

Value: 39.75

Form Two

Form Three

Evaluation | Structure in the City Analysis of the three forms in the context of the city. Differing shapes provide alternative results after propagating through the factor. 102

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Colour Evaluation

Integration Evaluation

Value: 20

Value: 39.84

Value: 18

Value: 38.41

Value: 24

Value: 47.77

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The Perceptive Machine

Recursive Evaluation Refining the Structure Based on evaluation we move forward with the structure that generated the highest rating. The following diagram illustrates the process of refinement that the Perceptive Factor allows for. Areas in pink highlight sections of the form generating the lowest values, thus indicating a need for further refinement. This process illustrates the useful nature of the system not only as a method of evaluation but a tool in design. 104

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The Perceptive Machine The following is a concept drawing illustrating the multi-layered filtration process designed to generate a reading using an Arduino board. 106

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LIGHT Value dictated by an aperture style mechanism.

INTEGRATION Value dictated by rotating panels.

VISIBILITY Value dictated by sliding panels.

The Perceptive Machine The Evaluation Process

COLOUR Value dictated varying acrylic panels.

The following graphic illustrates the physicality of The Perceptive Machine. The process works using the medium of light, which runs through each layer of the machine. Through each level, the light undergoes a filtration, representing the varied values generated though altered form. A combined value reading is taken at the bottom using an Arduino board, RGB sensor, and light sensor. Values are live streamed into Arduino and moved into a third party platform where we see a geometric pattern formed based on generated values. Having an interactive machine, as well as a responsive geometric form allows users to understand the effect each parameter has on the generated pattern. 107

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Recursive Evaluation

INTERPRETING INTENSITY VALUES

The Voxel as a Data Placeholder To further challenge the Perceptive Factor we further refine the form being evaluated moving more specifically into patterns. Due to the fact that the parameters being considered are heavily related to permeability, we begin to work with patterns as a method of increasing accessibility, light and visibility. In order to accomplish this a mapping is generated to indicate current value levels of each of the parameters. This assists in the design process so as to indicate areas that may be zoned or clustered highlighting a need for a specific pattern. The following pages detail this process of analysis as well as the design refinement. With the ultimate goal of challenging what it means to create defensible space, this project suggest that through variation form, topography, and pattern we can design a space that offers a greater sense of security than that which currently exists.

VOXELISATION Evaluate the Geometry and apply voxels.

SEPARATION Select the voxels with the top 30% of values.

COLOUR Assign colour based on the parameter being represented.

SCALE Adjust the size based on the intensityvalue. 110

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Data Driven Patterns This drawing represents pattern application in the initial stages. Breaking down the structure into smaller surfaces allowed for a variation of patterns to be evaluated and applied. 111

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Form Finds Function Diagram illustrating the components of the final base structure. Highlighted in this diagram are the accessible routes, the exterior and interior spaces of the form. 112

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Perceptive Datascapes Rendering demonstrates the final result of using the Perceptive Factor as a tool and method for design.

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Intensity Value Assessment

Legend Visibility Integration Light Colour

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This diagram represents value intensity distribution for each of the four parameters contributing to the Perceptive Factor. Understanding where these values are situated assists in the design of a condition that better promotes permeability.

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Data Dictating Design Legend Visibility Integration Light Colour

The following image also illustrates an intensity diagram in a way, but pushes the evaluation further by suggesting programming that may correspond with these conditions. 115

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EVALUATING PATTERNS

Generative Algorithms and Optimisation Our final step of refinement includes the application of patterns to our structure which contribute to at least one of the parameters we were trying to improve. Visibility connects with patterns that allow for a clear field of view, light also plays with the porosity of form, while integration works with directionality. We apply these to our overall structure and work with further modifying them based on the data and refine this application manually. The final step of pattern application revolves around the idea of optimization. More precisely, the chosen patterns are modified through an algorithm so as to maximize visibility, light and integration. The algorithm presented alters the surface in question and tests it until the fitness value reaches a satisfactory point. For every generation, the algorithm tests 20 possible surfaces with an initial boost of 50. As a result, the process is consisted of 250 iterations. A small part of the design is taken as an example to run the simulations. The patterns start from an initial condition and are altered until they reach the optimal one. Finally, the generated iterations are clustered using t-SNE so as to discover which patterns have similar characteristics and could potentially influence more intensively each parameter.

Applicable Patterns Working with versatile patterns that offer various benefits. 116

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Perceptive Datascapes Rendering demonstrates the final result of using the Perceptive Factor as a tool and method for design.

Isolating Surfaces In order to test the optimisation process, three surfaces are selected to move forward with and undergo the process of refinement.

Recursive Evaluation

PERCEPTIVE DATASCAPES

2 The initial surface is analysed and refined to points.

3 This new surface is the one to be tested for every iteration.

The algorithm moves the points, creating a new surface.

Optimisation Process

4 Multiple surfaces will be generated and tested to reach the optimal result.

The algorithm runs the tests to check how well this surface serves the optimisation goal.

6 Optimal result is reached.

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Visibility - Light - Integration

Generation 4/100 Iteration 2/20 Fitness 1/10

Generation 8/100 Iteration 13/20 Fitness 2.5/10

Generation 12/100 Iteration 4/20 Fitness 3/10

Generation 16/100 Iteration 18/20 Fitness 3.2/10

Generation 20/100 Iteration 19/20 Fitness 3.5/10

Generation 24/100 Iteration 1/20 Fitness 4/10

Generation 28/100 Iteration 11/20 Fitness 4.1/10

Generation 32/100 Iteration 5/20 Fitness 4.6/10

Generation 36/100 Iteration 2/20 Fitness 4.8/10

Generation 40/100 Iteration 16/20 Fitness 5/10

Generation 44/100 Iteration 3/20 Fitness 5.4/10

Generation 48/100 Iteration 7/20 Fitness 5.9/10

Generation 52/100 Iteration 10/20 Fitness 6.2/10

Generation 56/100 Iteration 8/20 Fitness 6.6/10

Generation 60/100 Iteration 14/20 Fitness 7.1/10

Generation 64/100 Iteration 9/20 Fitness 7.5/10

Generation 68/100 Iteration 12/20 Fitness 7.9/10

Generation 72/100 Iteration 19/20 Fitness 8.2/10

Generation 76/100 Iteration 16/20 Fitness 8.6/10

Generation 80/100 Iteration 13/20 Fitness 8.8/10

Generation 84/100 Iteration 1/20 Fitness 9.1/10

Generation 88/100 Iteration 6/20 Fitness 9.3/10

Generation 92/100 Iteration 17/20 Fitness 9.5/10

Generation 96/100 Iteration 11/20 Fitness 9.8/10

Generation 100/100 Iteration 18/20 Fitness 10/10

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PERCEPTIVE DATASCAPES

Optimisation Process

Visibility - Light - Integration

Generation 4/100 Iteration 5/20 Fitness 1/10

Generation 8/100 Iteration 1/20 Fitness 2.3/10

Generation 12/100 Iteration 14/20 Fitness 2.9/10

Generation 16/100 Iteration 11/20 Fitness 3.3/10

Generation 20/100 Iteration 5/20 Fitness 3.9/10

Generation 24/100 Iteration 1/20 Fitness 4.2/10

Generation 28/100 Iteration 6/20 Fitness 4.5/10

Generation 32/100 Iteration 9/20 Fitness 4.9/10

Generation 36/100 Iteration 13/20 Fitness 5/10

Generation 40/100 Iteration 15/20 Fitness 5.2/10

Generation 44/100 Iteration 12/20 Fitness 5.6/10

Generation 48/100 Iteration 15/20 Fitness 6/10

Generation 52/100 Iteration 3/20 Fitness 6.3/10

Generation 56/100 Iteration 6/20 Fitness 6.8/10

Generation 60/100 Iteration 10/20 Fitness 7.3/10

Generation 64/100 Iteration 19/20 Fitness 7.6/10

Generation 68/100 Iteration 8/20 Fitness 8/10

Generation 72/100 Iteration 14/20 Fitness 8.3/10

Generation 76/100 Iteration 13/20 Fitness 8.7/10

Generation 84/100 Iteration 3/20 Fitness 9.2/10

Generation 88/100 Iteration 9/20 Fitness 9.3/10

Generation 92/100 Iteration 14/20 Fitness 9.6/10

Generation 96/100 Iteration 16/20 Fitness 9.9/10

Generation 80/100 Iteration 7/20 Fitness 8.9/10

Generation 100/100 Iteration 20/20 Fitness 10/10

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Optimisation Process

Visibility - Light - Integration

Generation 4/100 Iteration 4/20 Fitness 1.5/10

Generation 8/100 Iteration 8/20 Fitness 2.3/10

Generation 12/100 Iteration 10/20 Fitness 2.9/10

Generation 16/100 Iteration 11/20 Fitness 3.4/10

Generation 20/100 Iteration 7/20 Fitness 3.8/10

Generation 24/100 Iteration 12/20 Fitness 4.1/10

Generation 28/100 Iteration 9/20 Fitness 4.3/10

Generation 32/100 Iteration 13/20 Fitness 4.5/10

Generation 36/100 Iteration 18/20 Fitness 4.6/10

Generation 40/100 Iteration 6/20 Fitness 5.1/10

Generation 44/100 Iteration 19/20 Fitness 5.2/10

Generation 48/100 Iteration 2/20 Fitness 5.8/10

Generation 52/100 Iteration 1/20 Fitness 6.1/10

Generation 56/100 Iteration 13/20 Fitness 6.7/10

Generation 60/100 Iteration 15/20 Fitness 6.9/10

Generation 64/100 Iteration 7/20 Fitness 7.4/10

Generation 68/100 Iteration 1/20 Fitness 7.7/10

Generation 72/100 Iteration 6/20 Fitness 8.3/10

Generation 76/100 Iteration 15/20 Fitness 8.5/10

Generation 80/100 Iteration 12/20 Fitness 8.7/10

Generation 84/100 Iteration 16/20 Fitness 9/10

Generation 88/100 Iteration 3/20 Fitness 9.2/10

Generation 92/100 Iteration 4/20 Fitness 9.6/10

Generation 96/100 Iteration 6/20 Fitness 9.8/10

Generation 100/100 Iteration 20/20 Fitness 10/10

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PERCEPTIVE DATASCAPES

Pre-Optimised Surface 3D model of a surface at one point of the optimisation process. 123

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t-SNE Clustering One The generated surfaces are grouped according to their similar characteristics into neighbourhoods. 125

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t-SNE Clustering Two 127

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Snapshots Of Dynamic Clustering of Geometries Using t-distributed Stochastic Neighbor Embedding [t-SNE] 128

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Initial Configuration 130

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Optimised Configuration 131

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Perceptive Datascapes Rendering demonstrates the final result of using the Perceptive Factor as a tool and method for design. 133

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Perceptive Datascapes Machine Assistance to Human Design Rendering demonstrates the final design, and overlays data components that assist in the generation of form combined with the sense of human scale. 135

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The Perceptive Machine Detail illustrating the interaction between the accessibility and the patterns that correspond. 136

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Perceptive Datascapes Masterplan Context of Perceptive Datascapes. Creating a form of connection between London Fields park and the high traffic area of Mare Street. 138

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AUGMENTED REALITY

The Detail is in the Data This project presents designers with a method of locating and identifying spaces with less permeability and in a way sense of security. It offers a recursive system of evaluation, a feedback loop, to allow for constant alteration and re-evaluation. The influence of the mentioned principles propagates through the system, builds upon it, and acts as a correlative mechanism. We present not only the system but an iteration and example of what the systems creates and the potentials it may hold for the future. The final pages detail further the intricate design created by the combined ability of human and machine. Being tightly related with proximity of spaces, the internal rules of this computational mechanism have potential in terms of building spatial and social relationships by challenging the way we perceive space.

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Augmented Perception 3D models overlayed with some visuals alluding to the augmentation of perception of reality. 142

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The Perceptive Machine Detail illustrating the interaction between the accessibility and the patterns that correspond. 144

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Transversal Section

30m 147

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Longitudinal Section

30m 149

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Exterior View Perceptive Datascapes 151

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Feedback Loop

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Virtual Reality Experience Moments The concept of immersion of the digital world into the physical one is explored further through the creation of a VR experience with the aim to make more understandable the way data is translated into our design. 160

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Data produced by The Perceptive Machine

Produced data placeholders through the whole project.

Colour

Integration

Distribution Of Values The diagram represents the distribution of values for each of the parameters while changing the geometry using the Perceptive Machine during this project. Each of the produced voxels containing data is linked to the most dominant parameter. 162

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Data Driven Design

Light

Visibility

Quanitative data assisting in the design of a new form of public space, one that challenges form and topography to create accessible and secure spaces.

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Sources

REFERENCE LIST

Bradley, S. (2013) Design Fundamentals: Elements, Attributes, & Principles. Availabe from: https://vanseodesign.com/web-design/introducing-designfundamentals-book/. [25 November, 2017] Turrel, J. (2013) ‘Light Matters: Seeing the Light with James Turrell’. Interview by Schielke, T for ArchDaily 4 July. Available from: https://www.archdaily. com/380911/light-matters-seeing-the-light-with-james-turrell. [28 August, 2018]

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