T0 - Space Syntax Methodology

This teaching guide was first released in 2013 under
the joint authorship of K. Al Sayed, B. Hillier, A. Penn and A. Turner.

The front matter was amended in its current form in 2018;
not for citation and with K. Al Sayed as the principal author
Copyright @ 2018 Kinda Al_Sayed
This is a working document. It should not be used for teaching

outside of the MRes/MSc Space Syntax course without the author's
written permission.
No commercial use is allowed without permission.
The author makes absolutely no warranty that the

information in this document is correct.
Graphic design: Lina Hakim
Book Management: Sam Griffiths and Kayvan Karimi
Illustrations: Kinda Al_Sayed

SPACE
SYNTAX
METHODOLOGY
by Kinda Al_Sayed
With contributions from:

Bill Hillier, Shinichi Iida, Sam Griffiths, Kayvan Karimi, Nick Dalton, Alan Penn
Laura Vaughan, Kerstin Sailer, Tao Yang, Ava Fatah gen. Schieck
A teaching guide for the MRes/MSc Space Syntax course (version 5),
Bartlett School of Architecture, UCL
CONTENTS 4
SPACE SYNTAX METHODOLOGY 7

Introduction 7
What is Space Syntax? 7
CONVEX AND AXIAL ANALYSIS 9
THEORETICAL BACKGROUND ON AXIAL AND CONVEX ANALYSIS 11

Introduction 11
Two representations of buildings and urban spaces 11
Syntactic measures 15
DEPTHMAP EXERCISE 16
Files available for you to use in the exercise 16
TASKS 16
Before you start 16
TASK 1: STEPS TO PERFORM CONVEX ANALYSIS IN DEPTHMAP 16
TASK 2: STEPS TO PERFORM AXIAL ANALYSIS IN DEPTHMAP 21
Notes 26
ISOVIST AND VGA ANALYSIS 27
THEORETICAL BACKGROUND ON ISOVIST AND VGA ANALYSIS 29

Introduction 29
Isovist and VGA analysis 29
Visibility graph measures 30

TASK 31
STEPS TO PERFORM ISOVIST and VGA ANALYSIS IN DEPTHMAP 31
OBSERVATION TECHNIQUES 39
INTRODUCTION TO OBSERVATION TECHNIQUES 41

Introduction 41
Why do we observe? 41
Observation methods overview 41
Observation tools 47
DATA ANALYSIS 49
INTRODUCTION ON DATA AND STATISTICS 51

Introduction 51
Aspects of data 51
Primary vs. Secondary Data 52
Population Data and Sample Data 52
Thinking about where the data came from 52
Statistical Methods 53
Variables 53
DATA ANALYSIS 56
APPENDIX: GUIDE TO DETERMINING QUESTIONNAIRE SAMPLE SIZES 60
TASK 61
STEPS TO ANALYSE DATA IN DEPTHMAP 61
SEGMENT ANALYSIS 71
THEORETICAL BACKGROUND ON SEGMENT ANALYSIS 73

Introduction 73
Segment maps: A linear representation of urban spaces 73
Angular segment analysis with metric radius 73
The Principle Of Angular Depth Calculation In Segment Analysis 74
Tulip Analysis 74
Angular Segment Analysis Measures 75
Task 79
Steps to perform axial and segment analysis in Depthmap 79
Notes 84
ADVANCED AXIAL AND SEGMENT ANALYSIS 85
THEORETICAL BACKGROUND ON ADVANCED AXIAL AND SEGMENT ANALYSIS 87
Foreground vs. Background analysis 87

Introduction 87
What are the main components of cities that distinguish the global 87
from the local structure?
Background Analysis: Producing patchwork maps 88
Foreground Analysis: Producing topo-geometric analysis 89
Tasks 92
Steps to perform axial and segment analysis in Depthmap 92
Notes 98
AGENT ANALYSIS 99
THEORETICAL BACKGROUND ON THE AGENT MODEL 101

Introduction 101
AGENT ANALYSIS IN DEPTHMAP 103

Visualising agents movement in 3D view 105

REFERENCES 107
ADDITIONAL WEB SOURCES 111
APPENDICES 112
APPENDIX ! 113
APPENDIX 2 116
A PRACTICAL GUIDE TO SPACE SYNTAX METHODOLOGY
Introduction
This 'Practical Guide to Space Syntax Methodology' is intended to give an accessible

explanation of network representations of space for the purpose of architectural
and urban research and design. It provides some basics on Space Syntax theory,
both as a conceptual framework and as a tool for thinking the relationship between
1
Copyright @ space and society via the UCL Depthmap1 programme, alongside explanations
University College of the concepts underlying spatial analyses models and descriptions of how these
London 2000-2010
all rights reserved.
analyses are applied in the context of architecture and urban research.
Programme by
Alasdair Turner. The guide takes students from the basics of spatial analyses techniques,
observation methods and data modelling through to more advanced agent-
based modelling. The techniques are described roughly in the order they were
first discovered, and the guides indicates how they may be used in research
and also suggests how such tools might inform best practices in architectural and
urban design. Theoretical introductions for each chapter explain the representa-
tions, models and constructs of measurement. Exercises and tips throughout
the book allow students to practice using UCL Depthmap.
This guide is a supplement to the UCL MSc/MRes workshops that

run throughout the two terms of the academic year. Each workshop will
involve slides introduction and exercise demo as well as question and answer
sessions, which need to be studied alongside this manual.This guide is intentionally
parsimonious in its academic content. You will need to consult the academic literature
to understand the theories underpinning the methods described here.
What is Space Syntax?
It starts with a certain description of the spatial architecture of buildings and

cities. In Space Syntax, spaces are understood as voids (streets, squares, rooms,
parks, etc.). Voids are defined by obstructions that might either constrain access
and/or occlude vision (such as walls, fences, furniture, partitions and other
impediments). Buildings are composed of a series of spaces; each space has at
least one link to other spaces. The structural properties that comprise these
spaces and links might have an embedded social meaning that has implications
on the overall behaviour of human habitat (figure 1.1).
The same description might also apply on an urban scale. Cities are aggregates
of buildings held together by a network of spaces flowing in-between the blocks.
This network connects a set of street spaces that form together a discrete
structure. The structure is the optimum result of shortest paths from all origins
to all destinations in the spatial system. It is what holds it all together. It has an
architecture, and by this we mean a certain geometry and a certain topology,
that is, a certain pattern of connections (figure 1.2).
Buildings can have different structures that relate strongly to their functional-
ity. Prisons are normally hierarchical reinforcing power and control in the form
of access and visibility relationships. Prison cells appear at the very end of these
hierarchies. On the contrary, museums are made up of continuous spaces that
follow some sort of narratives. These kinds of building organisations have their
recognisable spatial characteristics. To expose these spatial characteristics we
7
use graph-based representations and measure their structural properties. The
structural properties might then be indicative to how the social organisation
functions.
On the urban scale, spatial structures can take an organic, uniform or deformed
shape. These universal types of urban grid vary in the way they interweave
connecting the part-whole structure. They emerge on different scales, and as a
result, have different geometric properties. Topological and geometric analysis
of urban grids using UCL Depthmap software helps us understand the configu-
rational structure of urban spaces and its potential impact on social behaviour
and economic activity (Hillier, 1996a).
1750
1850
2000
Figure 1.1. The transformation of the Guildhall of the City

of London. Source: Kinda Al_Sayed, 2007 @UCL.
Figure 1.2. Angular choice in Manhattan. Source: Kinda

Al_Sayed, 2007 @UCL.
8
CONVEX
AND AXIAL
ANALYSIS
THEORETICAL BACKGROUND ON AXIAL AND CONVEX
ANALYSIS
Introduction
In this chapter, we will explain some basic topological graph representations of

2
See wiki page on buildings and cities. Along with other software2, UCL Depthmap has the capac-
spatial network ity to produce different sorts of spatial analysis on different scales. Here, we are
analysis software
[http://en.wikipedia. going to learn about axial and convex analysis, what it can do and how powerful
org/wiki/Spatial_ it is as a tool for predicting social and economic activity, along with the different
network_analysis_ scales of measurement associated with it. We will also introduce integration and
software]
choice along with few other measures that will help understanding the social
logic that space might afford and the link between spatial measures and the
likelihood for movement and occupation in a layout.
Two representations of buildings and urban spaces
3
See appendix Space Syntax starts with defining movement and occupation as the fundamental
1 for a functions of a layout, where permeability of all spaces is the priority condition for a
mathematical
definition of the functioning layout structure. A proposed representation of a spatial structure might
axial and convex either be interpreted in a convex map or an axial map3. An example for both types
network. of representations is displayed in figure 2.1.
higher
integration
An analysed convex map An analysed axial map
Figure 2.1. An example of how convex and axial representations are mapped on House at Creek Vean,
Team 4. Source: Sarah Parsons, MSc AAS student work 2007 @UCL
1. A representational scheme of axial networks
On an urban scale, Space Syntax regards movement as the generic function of

street spaces and hence; reduces these spaces to the longest accessible lines that
cover all convex spaces in a map, that is; the axial lines or “lines of sight”. These
elementary components and their adjacency relationships can be represented by a
network (nodes or vertices of a morphological graph GA). The graph GA will consist
of two sets of information; graph vertices (representing axial lines) VA = {vA1, vA2,
… vAn}, and a set of lines LI = {lI1, lI2, … lIL}, each line in the graph GA represents an
intersection between two axial lines (two Vertices) in the spatial network. Spatial
adjacency is the fundamental relationship that characterises how structures might
be configured in a spatial layout. Two spaces, i and j, are considered as adjacent in
the dual graph GA when it is possible to access one space directly from another,
without having to pass through intervening spaces. In graph-theoretic, GA graphs is
regarded as non-planar dual graphs. It is non-directional in that;
lk = (vi, vj) = (vj, vi)
11
In axial representations, depth is identified as the change in direction between one
axial line and another. Depth is topological, in other words, it has no geometric
value. Axial maps are fundamental syntactic representations theoretically, because
they reflect many structural properties of urban street networks– i.e. line lengths,
intelligibility and synergy.
An example for how an urban area might be represented using the Space Syntax
model is demonstrated in (figure 2.2.). The urban space (a.) might be represented by
the set of fewest, longest, and walkable axial lines (b.), the axial lines are then
represented by a graph (c.), the different Connectivity (degree) values for each
vertix is then highlighted; vertices that have more connections to their immediate
neighbours will have higher Connectivity values (d.), these values of Connectivity
are then illuminated on the axial map to reveal the local network structure of street
spaces (e.).
a. b. c. d. e.
Figure 2.2 The axial representation of Space Syntax. An urban space represented by the fewest and
longest axial lines (b), axial lines are represented by a graph (c), the graph Connectivity is by high-
lighted in (d & e).
2. A representational scheme of convex maps
4
Note that this is Another syntactic representation of architectural space is that of convex map. A dis-
only one possible crete convex map represents adjacency relationships by reducing the spatial com-
representation
of convex spaces plexity of a layout to the fewest and fattest convex spaces4. In each convex space,
(see Peponis all pairs of points are inter-visible. Spaces that are immediately adjacent will have
et. al., 1997 for one step of depth in-between, spaces that have a minimum of one space separat-
other alternative
descriptions of
ing them will have two steps of depth in-between, and so on. In other words, depth
convex break-up). between two spaces is defined as the least number of syntactic steps –or shortest
topological distance- in a graph that are needed to move from one space to the
other.
We can attribute the value of topological depth to each node (vertex) in an adja-
cency graph GC. The graph GC will consist of two sets of information; graph vertices
(representing convex spaces) VC = {vC1, vC2, … vCn}, and a set of lines LA = {lA1, lA2, …
lAL}, each line in the graph GC represents an adjacency relationship between one
convex space and another. Spatial adjacency is the fundamental relationship that
characterises how structures might be configured in a spatial layout. Two spaces, i
and j, are considered as adjacent in the dual graph GC when it is possible to access
one space directly from another, without having to pass through intervening
spaces. The mathematical description of the network representation of convex
spaces is similar to that of axial networks.
12
Figure 2.3 shows an example for decoding an architectural layout designed by
Frank Gehry using the convex space representation. The architectural space (a.)
might be represented by the set of fewest and fattest convex spaces. These spaces
are linked where there is direct access from one space to another forming a convex
map (b.), the convex map is then represented by a graph (c.), the different Con-
nectivity (degree) values for each vertix is then highlighted; vertices that have more
connections to their immediate neighbours will have higher Connectivity values
(d.), these values of Connectivity are then illuminated on the convex map to reveal
the spatial structure of the building organisation (e.).
a. b. c. d. e.
Figure 2.3 The convex representation of Space Syntax. An architectural space represented by the
fewest and fattest convex spaces (b), convex spaces are represented by a graph (c), the graph Con-
nectivity is by highlighted in (d & e).
5
A justified Spatial relations between adjacent spaces in a layout can be represented using the
graph could be descriptive methods of justified graphs5 first presented by Hillier & Hanson (1984). A
constructed using
JASS or PAJEK
justified graph reads a spatial network of convex spaces from one space (root) to all
tools. others; representing each convex space with a circle and each permeable connec-
tion between two spaces with a line as in (figure 2.4). From a root space, all spaces
that are one syntactic step away are put on the first level above the root space,
all spaces that are two steps away are levelled on the second row, etc. A justified
graph might be deep or shallow depending on the relationship of the root space
to other spaces. Spatial relationships might form branching trees or looping rings.
A spatial relationship between two spaces might be ‘symmetrical’ if for example: “A
connects to B” is equal to “B connects to A”. Otherwise the relationship is considered
as ‘asymmetrical’. The total amount of asymmetry in a plan from any point relates to
its mean depth from that point, measured by its ‘relative asymmetry’ (RA). Spaces
that are, in sum, spatially closest to all spaces (low RA) are the most integrated in
a spatial network. They characteristically afford dense traffic through them due to
their central position in the spatial network. Spaces that locate in deeper locations
(high RA) are the most segregated. Integration and segregation are global attrib-
utes of the spatial network.
Spatial layouts are often a combination of hierarchical structures and circulation

rings. Normally the tree-like structure of space reflects a deep and controlled spatial
structure and perhaps a hierarchy in the social organization that occupies a build-
ing. Conversely, the provision of interconnected rings of movement in a layout
offers choices for movement routes reducing the depth of space. The spatial typol-
ogy of each convex space is likely to differ depending on its relationship to move-
ment rings (figure 2.4).
13
Hillier (1996) differentiated between four types of spaces:
• a-types; which are characterised as dead-end spaces and

connect to no more than one space in a graph
• b-types connect to two or more spaces in a graph without
being part of any ring of movement.
• c-types are usually positioned on one ring of movement.
• d-type of spaces must be in a joint location connecting two or
more rings.
The positioning of a, b, c, d types of spaces within the local and global settings
of the whole network can determine the overall spatial depth in a layout. A local
increase in the number of a-type of spaces and a global increase in d-type of spaces
would consequently minimise spatial depth, creating an integrated system, while
a global increase of b-type spaces and a local increase in c-type spaces are likely to
lead to a maximised depth, resulting in a segregated system.
a. b.
Figure 2.4 Different spatial typologies marked on a graph representing the relationships between
convex spaces. The two graphs elucidated here are; a justified graph that is being laid from the point
connecting the exteria to the interia of Frank Gehry’s house (a.) and an adjacency graph overlaid on
top of a convex map (b.).
It is possible to apply the representational scheme of justified graphs to both axial

and convex graphs. The ringiness of axial graphs is crucial to distinguish patterns of
order and structure in street networks, For the purpose of quantifying these prop-
erties, a measure of axiality in the axial graph was proposed in (Hillier & Hanson,
1984:91); that is the ratio of circuits/rings in the axial graph to the number of axial
lines. Grid axiality might follow the following formula;
�√I∗2�+2
Grid axiality
Grid = =
axiality (1)
L
where I is the number of islands, L is the number of axial lines. The results vary
between 0 and 1 with high values approximating to a regular grid and low values to
an axialy deformed system.
14
Syntactic measures
In this section we will be explaining four main topological measures that can
explain structural properties of a spatial graph. The measures are used to quantify
the configurational properties of a layout. The calculation might account for the
neighbourhood size of each node in a graph. By neighbourhood we mean the
nodes that are linked to each node within a certain graph distance that might either
be topological, metric or angular. For axial and convex analysis we use a topological
distance that is calculated from each node to define the radius within which dif-
ferent measures are calculated. Radius n is usually used to find measure values for
each node in relation to the whole system. Radius 2 (sometimes called radius 3), is
used to measure the relationship between each node and the neighbours located
two steps away from it.
During the last four decades, Space Syntax researchers have developed many
measures (see appendix 1) for the purpose of explaining social behaviour, some of
the most important ones are listed here;
Connectivity (degree) measures the number of immediate neigh-

bours that are directly connected to a space.
Integration is a measure that describes relativized asymmetry in

the graph network. It is a measure of mean depth that is specifi-
cally adapted for architectural layouts. The global measure shows
how deep or shallow a space is in relation to all other spaces.
Using integration, spaces are ranked from the most integrated to
the most segregated. Integration is usually indicative to how many
people are likely to be in a space, and is thought to correspond to
rates of social encounter and retail activities (Hillier, 1996a). It is
sometimes helpful to illuminate higher values in a system (i.e. the
highest 10% values) in order to illuminate the integration core in
a city. The integration core might take different shapes (a spine, a
deformed wheel, diffused, and concentrated).
Control measures the degree to which a space controls access

to its immediate neighbours taking into account the number of
alternative connections that each of these neighbours has.
Choice measures movement flows through spaces. Spaces that

record high global choice are located on the shortest paths from
all origins to all destinations. Choice is a powerful measure at
forecasting pedestrian and vehicular movement potentials. It
is usually applied to segment analysis rather than convex analysis,
because it is descriptive of movement rather than occupation.
The correlation between some of these measures might describe some char-
acteristic properties of layouts that relate to wayfinding (Conroy Dalton, 2000).
Intelligibility, for example, is the correlation coefficient between axial connectivity and
axial global integration. It helps identify how easy it is for one in a local position to
comprehend the global structure. Synergy is the relationship between smaller radii
of integration (i.e. integration HH R2) and larger radii (i.e. integration HH Rn) - also in
axial analysis. A relationship between smaller and larger radii is illustrative of the relation
between the parts and the whole in an urban system.
15
DEPTHMAP EXERCISE
Axial and Convex Analysis Using Depthmap
Files available for you to use in the exercise
FILES DESCRIPTION SOURCE COPYRIGHTS

https://github.com/
Cad drawing file con-
SpaceGroupUCL/
GALLERY.DXF taining an architec- The original CAD
Depthmap/wiki/
tural layout data copyright ©
Depthmap-Tutorials
Cambridge Uni-
Cad drawing file versity Press 1984,
GALLERY _convex. reproduced here with
containing a convex
dxf kind permission.
break-up map
http://moodle.ucl. Original drawings by
Cad drawing file ac.uk/ J Hanson, digitised by
GALLERY _axial. containing intersect- A Turner.
dxf ing series of line
elements
TASKS
The first task is to reduce an architectural layout into a convex map and analyse
its graph properties using Depthmap.
The second task in this exercise is to reduce an urban layout to an axial map and
analyse its topological configurations.
Before you start
Download Depthmap 10.14 from https://github.com/Space-

GroupUCL/Depthmap/downloads
Note that Depthmap only runs on the Windows operating

system. If you have a Mac, you can use DepthmapX.
TASK 1: STEPS TO PERFORM CONVEX ANALYSIS IN DEPTHMAP
In this exercise you will learn to do the following activities in Depthmap in order
to draw convex break up maps manually and perform convex analysis.
1. Download your files
Go to Moodle and download the file GALLERY.

dxf from the website onto your desktop
16
2. Prepare your convex map
Create a new file: Go to FILE---NEW. This gives a

blank page.
Save the file you are using as “GALLERY_

convex_axial.graph”: FILE---SAVE
Choose the name and location of your file
Import the architectural layout into Depthmap:

go to MAP--- IMPORT--- look in your Desktop for
a file titled GALLERY.dxf. Choose the DXF file and
click import. Now you will have the file imported
into your Depthmap file as a drawing layer6.
6
Note that the layout is carefully
drawn in such a way as to not leave
gaps within the boundaries of the
street structure. This is important for
automatic generation of the axial
lines.
3. Draw and connect convex spaces
Set up a new layer to draw your convex spaces.

You can do that by going to MAP→NEW... and a
NEW MAP window will appear where you can
choose what kind of map you are intending to
establish.
Choose CONVEX MAP from the MAP TYPE
drop down menu and give your convex map a
unique name.
After clicking OK you will have your new

convex map layer on the left within a category
called shape graphs.
Once you have checked the + button next to
it you will be able to see the layer is set to
be editable by default and this means that
you can draw polygons on the screen and the
polygons will be recorded as separate elements
within this layer.
The term EVERYTHING that you see next to the
eye icon means that you can see all the convex
spaces you have drawn on the screen.
At the moment you have nothing drawn in
that layer but once you have completed more
than one polygon you will see these polygons
assigned different colours if you are pointing
to the reference number.
17
In order to draw the convex spaces you will
have to check the polygon icon on top of your
MAP window . Then you can draw the
convex spaces by clicking with your left mouse
key to identify every single point that defines
the boundary of the convex space and close
the polygon by clicking on the starting point.
Meanwhile, you can snap the end points to the
layout by holding CTRL+SHIFT while you are
drawing.
You can cancel your polygon while you are
drawing it by clicking the right hand mouse
button.
For drawing convex spaces you will have to

follow the fewest and fattest rule in which the
fewest prevail over the fattest7. The minimum
convex space would contain a space for two
people within. The grid scale of it would be
equivalent to 1.2 metres if the original CAD
layout is set to a metric scale.
7
Decisions may vary in relation to
defining a convex space. You can
reason about your decisions based on
the change in space functionality or
usage.
Once you have done your convex breakup map,

you have to connect your convex spaces to
create a graph out of them. You can do that by
clicking the join icon in your tool bar and
linking spaces where accessible openings and
doorways exist in-between. Just select one
polygon (convex space) and select the polygon
it links to and you will see an edge linking the
centres of both polygons. After you have linked
all spaces in your convex map you will be able
to analyse convex relationships that are based
on the adjacency criteria.
In case you have done some mistakes in the

linking process, you can unlink convex spaces
by choosing the unlink icon from the drop
down menu next to the join tool and unlink
where necessary.
For linking again you need to go back to the
join icon and click on it to activate it. You can
skip this mode by clicking the select icon .
On the lower left part you will be able to see

the set of measures you have analysed so far.
For every convex space you have drawn on
the screen you will have a unique reference
number assigned to it automatically (the
18
colours do not mean anything at this stage).
For every convex space you will also have a
connectivity attribute;
- Connectivity indicates the number of ele-

ments each element is directly connected
to. After you have linked your convex map
all together, the colours of connectivity indi-
cate in which band the value of the convex
space falls within the range of spaces
drawn on the screen.
This attribute will not be calculated until

you start linking the convex spaces together.
The element that is not yet connected to the
network will be in gray colour. If you point to
one of the connected polygons on the screen
you will be able to see its connectivity value.
The one that is not connected will show ‘no
value’.
You can undo recently drawn convex spaces

by going to EDIT→ UNDO or you can use the
shortcut CTRL+Z. You can also delete polygons
but be careful though! Save your file before
you do that as this very often resolve with an
error. To delete polygons, you need to click the
selection icon and select the polygon that you
want to delete. The elements you select will
be highlighted with bright yellow colour. Click
the delete button on your keyboard or go to
EDIT→ CLEAR.
Do you feel that your convex map is precisely

drawn? You are now advised to turn off the
editable on option by clicking on it. You can
do that after you have finished drawing you
convex map to avoid accidental editing of your
convex map elements. Please remember to
save your file before you move to the analysis
stage.
4. Import your convex break-up map from a drawing file
If you have failed to produce a convex map in

your Depthmap file for any reason, don’t worry
there is a convex map prepared for you in a
DXF file. Just go to Map→ IMPORT and import
a DXF file called GALLERY-convex.DXF from the
Depthmap folder in Moodle. You will find that
in the samples folder within exercise 1. You will
have it imported as a drawing map.
Go to MAP→ CONVERT DRAWING MAP and
19
select NEW MAP TYPE to be CONVEX MAP. The
default name is CONVEX MAP. Give your convex
break-up map a unique name.
Start linking your convex spaces together as

explained in the previous section
Once you have got all your convex spaces

linked together in a convex map, you will be
able to see the basic attributes of the convex
map, which are; REFERENCE NUMBER, CON-
NECTIVITY in the lower left corner of your
screen.
5. Convex analysis
At the moment, the only analytical operation

you can perform with the convex maps you
have is to calculate step depth (topological).
You can do that by selecting one convex space
and clicking on the step depth icon in your
tool bar . You will need to invert the colour
range by choosing the icon specified for it on
the main tool bar . The blue colour will
represent then the deepest element from the
selected convex spaces in the adjacency graph
network.
If you need to check further local and global

measures, you will have to go to TOOLS→
AXIAL/CONVEX/PESH→RUN GRAPH ANALYSIS.
A window will appear where you could select
your convex analysis options. In the window,
leave everything as it is and just make sure
to check the boxes next to INCLUDE LOCAL
MEASURES.
After you click OK you will see different measures

added on the lower left corner of your screen.
The most meaningful measure8 is INTEGRATION
(HH).
8
Additionally, CONTROL and CON-
TROLLABILITY measures can be of
interest but may be more indicative in
VGA than in convex analysis.
- Integration is representative of potentially

core functionality in the layout. Integrated
spaces are highlighted as red and appear
in the shallowest areas of the graph. Segre-
gated spaces fall within the range of blue.
Integration and segregation are suggestive
of parallel social meaning in the built form.
20
Segregation in the architectural layout
might be an indication to a higher degree of
power or a lower degree of power depend-
ing upon the social organisation that occu-
pies that space (i. e. Head office, prisoners).
Now that you are done with convex map

analysis, please remember to save your file.
You can also export your convex graph as a

Pajek file. You will be able to use this Pajek file
for further exploration into topological graph
measures and for the purpose of producing
justified graphs.
6. Changing your colour range
In order to read differences more clearly between high low values of each
measure you can change the colour range. This is just for the purpose of data
visualisation and will have no effects on the values of elements themselves.
Go to WINDOW---COLOUR RANGE.
A window will appear.
Use the browser to move to Depthmap classic
as a banding range type.
Adjust the sliders in such a way as to find a
satisfactory representation of the data values
in the axial or convex map.
Please bear in mind that when comparing dif-

ferent systems you need to unify the ranges by
setting the same min and max limits.
TASK 2: STEPS TO PERFORM AXIAL ANALYSIS IN DEPTHMAP
In this exercise you will learn to do the following activities in Depthmap in

order to perform axial analysis from axial lines you have drawn or automatically
generated.
7. Draw your axial lines
Set up a new layer to draw your axial lines. You

can do that by going to MAP→ NEW... and a
NEW MAP window will appear where you can
choose what kind of map you are intending
to establish. Choose AXIAL MAP from the MAP
21
TYPE drop down menu and give your axial map
a unique name.
After clicking OK you will have your new axial

map layer on the left within a category called
shape graphs. Once you have checked the +
button next to it you will be able to see the
layer is set to be editable by default and this
means that you can draw lines on the screen
and the lines will be recorded in this layer. The
term EVERYTHING that you see next to the eye
icon means that you can see all the lines you
have drawn on the screen.
In order to draw lines you will have to check

the line icon on top of your MAP window. Then
you can draw the lines by clicking with your
left mouse key and dragging until you reach
the second point of the line.
Draw axial lines on the layout by following

the rule the fewest and longest axial lines that
would cover all convex spaces in the system, the
fewest to prevail. One tip is to start by drawing
the longest lines and then the shorter ones until
you cover all the permeable spaces in the layout.
Always try to get the axial lines to intersect
where possible. Where lines are considered to be
nodes in the axial graph network, Intersections
between lines are the links that connect these
nodes. Having missed an intersection between
two lines means necessarily further depth
between the two lines is to be considered in the
calculations. This will have implications over the
overall graph network. Draw the lines diagonally
across the street spaces, and lines that are likely
to extend further to cover other street spaces are
more preferred9. During the process of drawing
you can turn on the object snapping by pressing
CTRL + SHIFT will trying to find end points or
mid points on your layout.
9
In cases where you are conducting
observations in certain urban areas
you are encouraged to check the
real urban or architectural set up and
adjust your axial map accordingly.
On the lower left part you will be able to see

the set of measures you have analysed so far.
For every line you have drawn on the screen
you will have a unique reference number
assigned to it automatically (the colours do
not mean anything at this stage).
For every line you will also have connectivity
22
and line length calculated for it immediately
after you have drawn it (The colours indicate
in which band the value of the line falls within
the range on lines drawn on the screen).
If you feel that you have done some mistakes

don’t worry you can always edit your lines.
You can undo recently drawn lines by going to
EDIT→ UNDO or you can use the shortcut CTRL
+Z. You can also delete lines but be careful
though to save your file before you do that as
this very often resolve with an error. To delete
lines, you need to click the selection icon and
select the line that you want to delete and
click the delete button on your keyboard or
go to EDIT→ CLEAR. Try to avoid deleting lines;
instead, you can edit certain lines to fit other
spaces by clicking on the selection tool and
selecting a line on the screen. You will then be
able to see the handles on each line you select.
Just move the handles to any direction you
want using your left mouse key (click, drag and
drop).
Do you feel that your axial map is precisely

drawn? You are now advised to turn off the
editable on option by clicking on it. You can do
that after you have finished drawing you axial
lines to avoid accidental editing of your axial
map elements. Please remember to save your
file before you move to the analysis stage.
8. Generate your axial lines automatically
As an alternative to the manual drawing of

axial lines you can generate your axial map
automatically from your drawing file.
To do that you should have the drawing layout
precisely drawn and make sure that you have
no gaps left in the boundaries that enclose
your spaces.
First you need to create an ALL-LINE MAP by

clicking on the icon where four axial lines
appear in your tool box. After doing that a
dropper type of cursor will appear with which
you will point to the inner space of your
architectural layout and you will see an all-line
map generated within that boundary. At the
moment the map looks too dense and covering
most of the areas in the spaces. Individual lines
can hardly be distinguished at this stage. The
measures of connectivity and line length are
already attributed to this map.
23
In order to reduce this map to the fewest lines
you need to go to TOOLS→ AXIAL/CONVEX/
PESH→ REDUCE TO FEWEST LINE MAP.
You will be able to see two types of reduced

axial maps. The SUBSETS map is more repre-
sentative of curvy spaces.
The MINIMAL is the fewest-line map you
normally use.
It is recognised that in the absence of correct

base drawings, this might be more difficult
and time consuming than drawing an axial
map manually.
You might disagree with the solution provided

by the process of automatic reduction of all
line maps. In this case, it is important to note
that the automatic generation of axial lines
does not always resolve with the fewest map.
There is an acceptable margin of error in the
calculation especially with instances where
you have curvy patterns of street structures.
You can always turn editable on and edit the
lines yourself.
9. Import your axial map from a drawing file
If you have failed to produce axial maps in

your Depthmap file for any reason, don’t worry
there is an axial map prepared for you in a DXF
file. Just go to Map→ IMPORT and import a DXF
24
file called GALLERY-axial.dxf from the Depth-
map workshop’s Moodle Folder. You will find
that in the samples folder within exercise 1.
You will have it imported as a drawing map.
Go to MAP→ CONVERT DRAWING MAP and

select NEW MAP TYPE to be AXIAL MAP. The
default is AXIAL MAP and it will number it
if you have more than one axial map. Name
your AXIAL MAP to avoid confusion between
numbers. You will be able to see the basic
attributes of an axial map, which are; REFER-
ENCE NUMBER, CONNECTIVITY, LINE LENGTH
in the lower left corner of your screen.
10. Axial analysis
At the moment, the only analytical operation

you can perform with both the manually and
automatically drawn axial maps is to calculate
step depth (topological). You can do that by
selecting one axial line and clicking on the step
depth icon in your tool bar . You will need
to invert the colour range by choosing the icon
specified for it on the main tool bar . The
blue colour will represent then the deepest
element from the selected axial lines in the
axial graph network.
If you need to check further local and global

measures, you will have to go to TOOLS→
AXIAL/CONVEX/PESH→RUN GRAPH ANALYSIS.
A window will appear where you could select
your axial analysis options. You can select a
local topological radius to be added to the
radius you already have in there (Radius n) by
typing (n, 2, 3, 5, 7). The larger radii you choose
the closer you get to radius n, depending on
the node count (number of elements) you
have in the system. Radius 2 stands for two
steps away from each element; radius 5 means
five steps away...etc. A step means a change
in directional turns from each element in the
system. Normally we use radius 2 for calculat-
ing local depth within walkable urban regions.
Radii 5 & 7 are more likely to be used to
observe vehicular movement on a global level.
In the window, make sure you check the boxes
next to INCLUDE CHOICE (BETWEENESS) and
next to INCLUDE LOCAL MEASURES. You can
also INCLUDE WEIGHTED MEASURES if you feel
that any certain properties of the system can
be revealed by building a relationship between
two different measures.
25
After you click OK you will see different meas-
ures added on the lower left corner of your
screen. The most meaningful in the first instance
are INTEGRATION (HH) and CONNECTIVITY.
- Integration is representative of potential

destinations in the system. These destina-
tions are highlighted as red and appear in
the shallowest areas of the graph. Segre-
gated spaces fall within the range of blue.
Integration and segregation are suggestive
of parallel social meaning in the built form.
In many cases integrated areas turn out to
be active economic centres whereas seg-
regated areas are associated with poverty
and crime. However, it is important to note
that these associations are too complex and
case-sensitive to be generalised in simple
terms.
- The rest of the measures are still under

experimentation hence their observed
meaning is yet to be proven and goes
beyond our scope here.
Save your file
Notes
As per usual, try to follow the rules from the Social Logic of Space to draw each
axial line. However, if you are more adventurous, you may want to think a little
more about how to draw the axial map. See Turner et al. (2005) and in particular
section 4.1 for a discussion of the role of depth minimization.
Always check the integrity of your axial map before piling straight into the
analysis of your axial map, but wait! Check that your axial lines are properly
connected to each other. You can go through the map looking at the number of
connections (just hover the mouse over a line), or check all lines are connected
by performing a ‘step depth’ calculation. Both of these methods can be found in
the Depthmap axial analysis tutorial on the UCL Bartlett website.
Similar to what we have done with convex spaces linking/unlinking, you may
need to unlink lines where it is not possible to get from road to another – for
example, if there is a bridge. Go through the axial analysis tutorial to find out
how to unlink lines, and unlink where necessary.
26
ISOVIST AND
VGA ANALYSIS
THEORETICAL BACKGROUND ON ISOVIST AND VGA ANALYSIS
Introduction
In this chapter, we will present another type of representation that has to do

with the visual properties of a layout, that is the inter-visibility between each
pair of points in a layout and how that build into the visual configurations of
the built environment. This finer grained representation is particularly impor-
tant to address issues related to spatial cognition from a situated position. An
understanding of the visual perception of the built environment might help
forecasting how accessible spaces afford movement. This static representation
makes the ground for the dynamic agent model that will be introduced in the
final chapter.
Isovist and VGA analysis
An isovist, or viewshed, first introduced by Benedikt (1979), is the area in a spatial

environment directly visible from a point (see figure 4.1). An isovist is a physical
body bound by a closed polygon; hence it has geometric properties such as area
and length of perimeter. The spatial properties of the visibility field can be attrib-
uted to that point and a graph can be constructed linking this point with other
points. Isovists can also be constructed from an area (such as a convex space) to
display the field visible from that area. In addition, isovists can be constructed
from a façade, to illuminate the part of the environment that is visible from the
façade surface. A set of isovists can also be constructed from a path at regular
intervals to show how a user experiences space walking through the layout
from a point of origin (i.e. entrance) to a certain destination (i. e. the deepest
convex space). The set of isovists that cover this path are usually referred to as
the Minkowski model (see figure 4.2).
To reproduce a representation that is similar to that of Hillier and Hanson (1984),

Turner et. al. (2001) have constructed an undirected graph connecting all the
inter-visible points in a human-scale grid. The product of this representation is
a visibility graph where each point is notated as a node and inter-visibility is the
condition for linking one node to the other. The visual relationships between
different nodes in the system can be calculated using different local and global
measures in Depthmap. The programme enables the application of visibility
graph analysis on spatial layouts on different storey levels through the join tool.
29
Figure 4.1. An isovist field for the corridor space in Maggie
Edmond & Peter Corrigan Athan House 1989. Source: MSc
AAS student work 2007 @UCL
Figure 4.2. A Minkowski model for Frank Gehry house.

Source: MSc AAS student work 2007 @UCL
Visibility graph measures
In this section, we will be explaining three main topological measures for visibil-
ity graph that explain a high resolution picture of the spatial configurations of
a layout. The measures depend on the neighbourhood size. We will explain here
the local measure of clustering coefficient, the global measure of integration and
the local measure of control.
Clustering coefficient is derived from the local configurations of each node and
calculates the degree to which nodes that are visible from one location are
themselves inter-visible. Clustering coefficient is indicative to how much one
loses in terms of visual information when moving from one location to another.
Isovists that are closer to convex retain high clustering coefficient, hence little
visual information is lost when moving from these locations. Contrary to convex
isovists, spiky ones correspond to low clustering coefficient; hence more visual
information is lost when moving away from these locations. Understanding
these properties is vital for illuminating the relationship between navigation
and wayfinding types of movement and how visual information changes in the
system. For example, spaces that have low correlation coefficient tend to corre-
spond to locations where pedestrians make decisions on directions. The cluster-
ing coefficient might be representative of convexity in a layout, by illuminating
how ‘self-contained’ visual information is in an isovist field. The measure also
exposes how disruptive certain objects are to the visual perception of a layout.
Integration is regarded as a global graph measure that computes the mean

shortest path length for all the nodes in the graph. The shortest path in a graph
is the least number of links or steps that need to be traversed to reach one node
from another. The mean shortest path length for a node is the average value of
all the shortest path lengths from this node to all other nodes. No normalised
real relative asymmetry is considered here, since this measure is only used to
compare location to location within a spatial setting.
Control calculates the area of the current neighbourhood with respect to the
total area of the immediately adjoining neighbourhood. Control is useful for
highlighting areas where observers can have a large view of the spatial layout.
30
DEPTHMAP EXERCISE
Isovist and VGA Analysis Using Depthmap

The original CAD
data copyright ©
Cambridge Uni-
Depthmap file versity Press 1984,
Gallery-convex File saved from last
containing analysed reproduced here with
_axial.graph exercise
architectural layout kind permission.
Original drawings by
J Hanson, digitised by
A Turner.
TASK
The task is to produce isovist and visibility graph analysis VGA to analyse the
visual configurations of the layouts.
STEPS TO PERFORM ISOVIST and VGA ANALYSIS IN DEPTHMAP
to perform isovist and VGA analysis.
Ideally, what is required for analysis is a closed

polygon for the envelope of the building, com-
plete with open doorways within the building.
As an example for that we will use the plan of
the National GALLERY that we used for the first
Depthmap workshop.
2. Prepare your file for isovist and VGA analysis
Open the Depthmap file you have done in the

last workshop and use save as to create a new
version of the file on which you will add the
layer of isovists and visibility graph analysis.
Choose the name as “GALLERY_vga.graph” and

location of your file
3. Create isovists
Click the button that symbolises an isovist

in your tool bar and press the left-hand
mouse key once the eye-dropper tool is within
the boundary of the layout. A window will
appear on your screen offering you several
options.
31
You can choose to have; quarter an isovist,
third isovist, half isovist and full isovist.
You can choose one of these options and an
isovist is going to be created at the point you
have located.
At the moment choosing one of these options

won’t make any difference on your isovists.
You will be able to see directly several isovist
properties on your lower left hand side of
the screen. Two of these properties are based
on the geometry of the isovists such as area
and perimeter; others are built on Benedikt’s
measures.
You can also choose the partial isovist tool

; for which you will need to define with eye
dropper tool a point of origin for the isovist
and a direction.
You will find that window again where you can

choose one of the options for the isovist angle.
If you choose quarter isovist (90o) you will see
the red isovist in figure .
In addition to creating a point isovist, you can

create an isovist path. To do that you have to
create a new data layer in which you will have
to draw some lines. You can spare this step if
you have already drawn a path for your isovist
in the CAD package you have and imported
that into Depthmap as a drawing layer. Since
you have not done that you have to resort to
drawing your path manually in Depthmap.
Create a new layer from MAP→ NEW. Choose
the data map and name it isovist path.
32
Using the line icon on your tool bar draw
the lines that define the path on your layout.
Make sure that you draw the lines following
one direction and make sure that the lines
intersect at their ends.
Make sure you select these lines and go to

TOOLS→VISIBILITY→MAKE ISOVIST PATH.
A window will appear providing you with

several options on how to define the isovist’s
field of view.
You can keep the default (quarter isovist) and

the results appear.
They will be added to isovists you have created
previously.
If you don’t want to see other isovists you can

try to delete them by turning editable on in
the isovist layer and then selecting isovist one
by one and deleting them. Make sure you save
your file before that.
Figure 5.3 Figure 5.4
33
4. Prepare your graph for calculating visibility properties
Up to this stage you were dealing with the direct layout boundary
to produce basic and localised visual properties of it. In order to
analyse visibility relationships on a complex and global level you
will have to use the visibility graph analysis model10.
10
The model has been developed by Alasdair Turner and implemented by him
into UCL Depthmap. It aims to calculate visibility relationships that are similar to
those of Space Syntax basing that on a finer level of representation; that is the
scale of the human body represented by a grid unit. The connections are then
made on the basis of inter-visibility between two units in the grid that fills an
area within a predefined boundary.
Create a grid by clicking the icon . Define

the grid spacing in order to define the unit
proportions. This will enable you to control
the resolution of the analysis. Remember to
choose a sensible grid spacing that matches
the human scale (0,6 to 0.7m). I have chosen
0.5m.
After you have defined the grid unit click OK.

You will be able to see the grid overlaid above
the layout and you will also find a new layer
created on your left-hand side called VGA .
Click the fill icon on your toolbar to fill

in the spaces you want to analyse within the
layout. And click somewhere within the layout
to fill in the area .
You can also try the context fill from the drop-
down list next to the filling tool, although this
tool is designed specifically for low resolution
grid.
Also, you can fill in grid spaces in case you
had a very precise pattern that you want to
analyse. You can do that using the pencil tool
in your toolbar . you can also block grid
spaces by right clicking them.
34
After you are done defining the spaces
you want to analyse, go to TOOLS→
VISIBILITY→MAKE VISIBILITY GRAPH.
A window will appear giving you the option

of restricting graph distances to be calculated
within a certain metric radius. This is in case
you had large areas to analyse and you did not
find the standard graph analysis to be repre-
sentative of the real situation.
Another option given by this window is to
analyse the boundary of the graph rather than
the spaces. This is in case you were interested
in the boundary visual configurations of the
occluding walls.
For the scope of standard analysis, you can
ignore these two options and proceed by click-
ing OK.
At this point a visibility graph has been made.

You will be able to see some graph measures
on the lower left hand corner. Simply, they
include the reference number, the connectivity
value and two other measures called point first
moment11 and point second moment.
11
The first moment is a mathematical
concept. For mathematicians: it is the
first moment about the generating
point for the isovist, not its centroid.
Depthmap defines it as the sum of the
distances from the generating point to
every visible VGA grid point.
After you have arrived at a visibility graph you

will see the joining tool enabled. You can
now select grid points on one end and join
them to the same number of points on the
other end that look for the first instance as not
directly visible. This is in case you had certain
transparent material separating spaces, also
in case you had some opening between two
different levels. This also includes cases where
you have staircases, although in this case the
decision of joining is left to the convenience
of inter-visibility between levels having them
joined by stair cases. If you have done mistakes
joining nodes in the visibility graph you can
always unlink them using the unlink tool .
Once you are done joining your graph nodes,
35
you will be ready to analyse visibility relation-
ships in your graph. You can click the selection
tool but you will still be able to see the green
linking lines in the background of your visibil-
ity graph. If this lines disappear after a while
don’t worry you can always check them again
when you switch to the joining mode.
5. Moving on to Visibility Graph Analysis
At the moment, the only measure you can

calculate is visual step depth. In order to do
so, you will have to select a node or more and
calculate step depth by pressing on the icon
. The red colour indicates deeper areas from
the selected nodes. It means that you will have
to go through several visual steps to be able
to see these areas. You can still reverse the
colours using to keep the conception of red
as the most shallow/integrated locality in a
spatial or visual setting.
In order for you to go further with the visibility

graph analysis go to TOOLS→ VISIBILITY→RUN
VISIBILITY GRAPH ANALYSIS. A window will
appear giving you several options.
One option is to calculate isovist properties;
this would render the aggregate isovist prop-
erties for every single point in the grid as a
generating point of an isovist.
Another option is to calculate visibility rela-
tionships. This is the standard option that you
will use for calculating graph measures similar
to those of convex and axial analysis. The node
here stands for a node in a graph instead of an
axial line or a convex space. The edges repre-
sent inter-visibility between two nodes rather
than intersection between two axial lines or
doorway between two convex spaces.
The rest of the measures are intended to
mimic the effects and measure of axial and
segment analysis.
Metric and angular analysis will calculate
physical distance and angular turns between
the nodes in the visibility graph; creating effect
a model that is similar to segment maps.
Through vision will look for longer lines of
vision and hence act as an axial map.
36
If you choose to analyse Isovist properties you
will find that several isovists measures appear
on the lower left hand side.
Some of these are physical properties that we
have introduced previously. Other measures
include; compactness, drift, radial and occlusiv-
ity. Explanation for these measures is in the
previous section.
If you go back to visibility graph analysis

window and you choose to calculate visibility
relationships. Make sure you check the include
local measures box, this is to include meas-
ures such as visual control, controllability, and
clustering coefficient.
Once you click OK, you will be able to see all

other measures listed in addition to the isovist
measures you have analysed previously. You
will see some measures that might sound
interesting to you like entropy. However, the
most profound measures are integration and
visual clustering coefficient. Visual control
and controllability might be of value to you
depending on the scope of your research.
Similar to integration in a convex map, visual
Integration is representative of potentially core
area in the layout where one can see much
of the layout and can be easily seen. We can’t
really establish that people will want to be in
such areas, may be if they want to see much
of the layout and communicate with others.
Visual clustering coefficient is indicative of
both convex maps and axial maps if you put
them together follow the representation of the
visibility graphs. By default red areas are more
convex like and might be potentially occupa-
tional spaces. Blue areas are more elongated
and might prove the nature of these areas to
afford high movement activity.
Save your file
37
6. Changing your colour range
In order to read differences more clearly between high low

values of each measure you can change the colour range. This
is just for the purpose of data visualisation and will have no
effects on the values of elements themselves.
Go to WINDOW---COLOUR RANGE.
A window will appear. Use the browser to move

to Depthmap classic as a banding range type.
Adjust the sliders in such a way as to find a
in the visibility graph.
Please bear in mind that when comparing dif-

ferent systems you need to unify the ranges by
setting the same min and max limits.
Save your file if you intend to keep the amend-

ments you have made on the colour range.
38
OBSERVATION
TECHNIQUES
INTRODUCTION TO OBSERVATION TECHNIQUES12
12
This section on
observations is
adapted from a
manual written Introduction
for the Space
Syntax Labora- This chapter addresses the description of field observations on pedestrian flow
tory by Tad Gra-
jewski in 1992 and static activities in the urban environment. Before designing and conducting
and rewritten by observations, field visits are normally organised to build a preliminary under-
Laura Vaughan in standing of the site conditions and settings, marking key functionalities or land
2001. uses in the layout and making initial decisions on where to allocate observation
areas. For the purpose of constructing a quantitative description of the move-
ment behaviour in the public realm, consistent and well-structured observations
on site are usually designed to measure real movement and occupational behav-
iour and test spatial predictions. In the sections that follow, we will explain the
observation methods and how these observations are conducted on site. The
observations are normally allocated in certain locations to ensure a comprehen-
sive coverage of the movement and occupation activities within target areas.
The methodologies explain can only act as guidance tips, special considerations
can be made to the particularities of each research or design project.
Why do we observe?
We observe in order to see how much we can learn about the environment
without taking account of people’s intentions. People normally say why they
are going somewhere if asked, not how they plan to get there, or what they
are going to do on their way. The collective activity within buildings or urban
contexts gives rise to a pattern of use and movement that is independent of
the intentions of individuals. Observations allow you to retrieve something that
might be considered as an objective view of human behaviour in the built envi-
ronment. However, in doing observations, some precautious measures should
be taken especially when an observer is regarded as a participant in the social
sphere that occupies the spatial scene.
Observation methods overview
Space Syntax Observations are a set of techniques to observe movement flows

and patterns of space usage in complex buildings or urban contexts. These
techniques are developed specifically for Space Syntax Research, yet they do
resemble methods used in other disciplines and fields.
To design our research we use a combination of methods (e.g. surveys and

landuse maps, time-lapse photography, questionnaires, interviews etc.) that are
most suitable to our research question. There are different methods to perform
an investigation into social behaviour. We will discuss some of them in the fol-
lowing sections.
1. Gate counts
Gate counts are usually directed to observe the density of pedestrian or

vehicular movement flows in a building or an urban layout. Gate counts allow
researchers to collect a great deal of data which can be represented graphically
and statistically.
41
The method must be applied with rigour and consistency at many locations.
To conduct observations, we usually choose a number of locations that cover an

area under study. We cover a range of well-used, moderately-used and poorly-
used spaces in and around the boundaries of the target area. We then choose a
reasonable number of ‘gate’ positions, around 25 gates or more. Each gate can
be observed for 5 or 2.5 minutes –depending on how quite a gate is- over regular
intervals (i. e. four or five times) during working days and weekends. Observers
are to stand at the edge of each gate to maximize their visual field and count
people crossing an imaginary line that ideally connects two parts of the built
environment e.g. columns, walls (figure 6.1). They may use stopwatch to perform
the counting precisely. Some gates can be divided into two or more when there
are dense movement flows that affect the feasibility of counting. Information
can be recorded on special tables, where numbers of pedestrians passing by
might be logged in addition to special notes on the time, date, weather condi-
tions and other special factors that might affect the course of observations
(figure 6.2). On the gate count tables, it helps to note categories as accurately
as possible, although erroneous assignment of categories is inevitable where
there some ambiguities in the characterization of a certain type of pedestrians.
Categories can be delimited by age, profession, status such as students, visitors,
tourists, locals, …etc. It is always a good practice to do as many categories as pos-
sible. This helps formulating the research question in some cases. It also helps
understanding the problem conditions of the site under study. Researchers may
aggregate the categories later to inform their own investigation (figure 6.3).
The counts of movement are usually helpful in understanding the relationship
between spatial structure and human behaviour (see figure 6.4).
In designing the research, observers should account for what is relevant in rela-
tion to their research question, how the site is used. They should also note that
in the UK, Fridays, Saturdays and Sundays tend to be different. So before starting
their research they should think about how often and when they should conduct
observations, and whether weekends are relevant at all.
Figure 6.1. Position of observer in relation to the observed gate. The observer
was to count pedestrian movement that crosses an imaginary line (adapted from
Laura Vaughan).
42
Figure 6.2. A sample of the gate counts table marking gate number, time of
observation task, age category, and a tally count of the number of people
passing by the gate. Special notes were also recorded in relation to weather,
use of IT equipment, and special site-related conditions such as closure of
underground station…etc. Source: Screens in the Wild @UCL.
Figure 6.3. A map showing the average number of pedestrians per hour
observed at the weekday crossing specific cordon lines in Covent Garden,
London. Pedestrian flows are categorise to recognise tourists, locals and
workers dressed in suits. Source: Space Syntax Ltd. 2007.
Figure 6.4. A map showing the average number of pedestrians per hour
observed at the weekend crossing specific cordon lines in Covent Garden,
London. Source: Space Syntax Ltd. 2007.
43
2. Static snapshots
Normally, static snapshots are conducted to record the use pattern of spaces
within buildings or public spaces in an urban context. The method is useful for
comparing static activities (standing, sitting) and movement. By tracking and
mapping these activities in time we may outline the patterns of space use in an
area and spot the locations where more potential interaction takes place natu-
rally. In general, snapshots might be comparable to a photograph taken from
above showing one moment of activities mapped onto the floor plan. They are
usually taken at consistent intervals during the day, to provide an objective view
of the invariant patterns of activity as well as different and peculiar behaviour
throughout the day.
To conduct snapshots, we predefine areas that can be easily observed and

positions at which an observer could maximise visual exposure to the observed
field of study and at the same time minimise his/her own visibility to the users.
We use a large-scale (1:50 minimum) floor plan to note categories and activities
(sitting, standing, moving, interacting) for a period of five minutes over regular
intervals during the day (see figure 6.5 for an example on snapshots). Other
particularities that relate to weather conditions, peculiar behavioural patterns,
IT use, or site settings can be marked in written text on observation sheets.
Figure 6.5. Movement traces and static activities are drawn on a 1:50 plan of the target area.
Notes on behavioural patterns and special features and conditions are recorded. Source: Screens in the Wild @UCL.
44
3. Movement traces
Movement traces enables tracking and mapping the collective flow dynam-
ics through a predefined area. It helps understanding movement patterns and
where people are likely to enter/exit the area from (see figure 6.6). Observers
might also be able to outline islands where no movement traffic is recorded.
Similar to snapshots, target areas are normally chosen to have a convex layout
that is easy to observe. The observers position themselves in locations that
maximize their vision of the layout and record movement for 5 minutes at
several time intervals throughout the day. They are encouraged to use coloured
pens to mark different categories on the layout.
Observations are usually conducted on site to empirically track and map human
behaviour. They are mainly directed to test the spatial models we have derived
earlier from the visual configurations of the urban layouts.
Where a correspondence between both observation and space exists, it comes
as to validate and support our assumptions on the role of spatial visibility and
access in promoting certain spaces to be more hostile for human encounter and
interaction. Where there is less correspondence, further investigation is needed
to define any external attractors or outliers in the environment.
Figure 6.6. Movement traces and some static patterns are

drawn on a 1:50 plan of an urban area. Source: Screens in
the Wild @UCL.
4. Traces (People-Following)
Tracing people is an important technique to observe movement flows that are

‘dispersed’ from a specific movement distributor, e.g. a train station, a shopping
mall, entrances to buildings. It can be used in urban contexts as well as in build-
ings. There are three distinct issues that can be investigated through the use of
this technique: 1) patterns of movement from a specific location; 2) relationship
of one route to other routes; 3) average distance people walk from one location
(e.g. to study the catchment area).
For the purpose of tracing, we first use plan of the whole area of interest. In
urban contexts it will be useful to arrange the plan so that the pick-up point is at
the centre of the plan.
45
To rule out bias in the reading of movement behaviour, observers should pick
up people randomly as they start a journey from a predefined point of origin
and follow them tracing their route. The tracing might be stopped either when
people leave the area of interest, reach a pre-defined destination, or after a
fixed period of time (e.g. ten minutes). It is important to be discreet in this
process – people should not become aware that someone is following them. It
is always good to account for a mix of people (age, gender, other categories of
interest) and note details for each trace. Tracing is a very useful technique when
comparing movement behaviour from a particular point of origin in a layout
(an entrance). It is usually used to display visual comparisons between spatial
analysis (VGA) and movement traces (see figure 6.7).
Figure 6.7. A comparison between agent traces (left) and observed movement traces (right), drawn
on the Tate Modern layout space. Source: Alasdair Turner@UCL.
5. Ethnographic Observations
Ethnographic observations are especially relevant to understanding qualitative

issues of space usage within buildings, but are also applicable to the observa-
tion of the public realm. They are useful for understanding and unpacking social
phenomena and complex relationships of cause and rationales. Further to the
patterns revealed by previous methodologies, ethnographic observations add
depth and richness to a research narrative.
To conduct ethnographic observations, an observer needs to take part in the

life of the observation field as discreetly and unobtrusively as possible – behave
like one of the observed subjects (role as “participant observer”). The observer
should seek as much detail as possible in the description of events and occur-
rences taking place in the observed environment. This will add to the richness
and precision of the observations. The observers are encouraged to take notes /
pictures, make sketches of what they see, hear, smell or feel. The observers are
also encouraged to let their observations guide them from a more open-ended
observation to a more structured observation of certain phenomena. In repre-
senting data, observers are required to be careful about how they interpret what
they see and how they make sense of their notes. When possible, observers can
combine the information they acquire from the environment with other relevant
data and information.
46
Observation tools
Alongside manual counting of movement, different technologies were used (i. e.

Bluetooth devices were used by Ava Fatah Gen Schieck in her research on digital
identity and performative interactions). Recently, there were developments on
pedestrian movement count tools that led to the introduction of new software
tools such as PedCount and People Watcher. PedCount is software-as-a-service
by Strategic Spatial Solutions, Inc. The software is available at https://github.
com/s3sol and is free for academic use. People Watcher is a data collection tool
written by Sheep Dalton and available as a free application on iPad at https://
itunes.apple.com/gb/app/people-watcher/id523155791?mt=8
47
48
DATA
ANALYSIS
INTRODUCTION ON DATA AND STATISTICS13
13
This section
was originally
prepared by Alan
Penn. Introduction
Parts of this
section are Data are collections of information. Traditionally in Space Syntax data are used
adapted from a
paper produced to understand and test the relationship between space and society. Data can
by the Cathie take any form, for example interview transcripts and field notes. The type of data
Marsh Centre collected will influence the sort of analysis that can be used to interpret them,
for Census and and academic disciplines develop research methods to reflect this.
Survey Research
at the University
of Manchester.
Other mate- Aspects of data
rial is adapted
from JMP and A key distinction made in social research is that between quantitative and quali-
the Statview
manual ‘Using tative methods. Although these methods have much in common, they differ in
Statview’, the sorts of data that are collected and the techniques applied to understand
Abacus Con- them.
cepts Inc, Berke-
ley, California.
Qualitative data is descriptive data, which can be collected by means of in-depth
interview, field observations or from other sources such as newspapers. The data
is generally quite detailed and is aimed to understand motives, understandings,
feelings and social processes, particularly at the small scale. Researchers using
this sort of data usually are attempting to explain social behaviour within a
set context, and rarely attempt to make claims about behaviour outside of this
context.
By contrast, quantitative data is data that is being used to understand questions

like:
- How many children under the age of 11 truant from school?
- In general, do people living in inner cities prefer to drive or to

use other forms of transport?
- Data used for this sort of research question has to contain

either information that is measured, or data that is suitable for
being counted.
One very important type of quantitative data is that which contains a standard
amount of information about a number of different things or people. This is
the sort of information that is gathered by a survey; interviewers ask the same
questions of a large number of people so that they can investigate the extent to
which individuals’ responses differ, and whether there are discernible patterns.
This sort of data is generally (although not necessarily) collected from a repre-
sentative sample of individuals so that conclusions can be applied to a wider
population; a common way to choose individuals who are representative is to
take a random sample. When data is produced in this way it is appropriate to use
statistical methods and conclusions subsequently drawn are said to be ‘general-
isable’ to a specified population.
Quantitative data comes in different varieties. The extent to which each is used
and the way in which they are classified varies from discipline to discipline.
Some key distinctions are as follows:
51
Primary vs. Secondary Data
Primary data are data that have been specifically collected for a particular study,
either via a survey or other means (including interviews and observation).
Secondary data are those which have been collected by some other person or
organisation, but which can be re-analysed for other purposes.
There are advantages and disadvantages to each approach; primary analysis
allows researchers an in-depth understanding of how the data were collected,
but is expensive and time consuming.
Secondary data are generally cheaper to use and permit researchers to explore
very large datasets. However, the usefulness of the secondary data is limited by
the secondary analyst’s ability to identify a dataset that is fit for purpose and to
understand how the data was collected and what the values mean. Key sources
of secondary data include the large government datasets (e.g. the General
Household Survey, Farm Business Survey, British Household Panel Study and
Labour Force Survey) and the Census.
Population Data and Sample Data
If one is interested in studying all the people living in the U.K it is very easy to
define what the population is – it is simply the population of the UK!
When researchers design a research project they decide which set of people they
are interested in, so for example, a researcher interested in patterns of travel
to work may be interested in every individual aged over 18, or every individual
between 18 and retirement age.
Every single individual in that set of people (or households or businesses... etc.)
collectively constitute the population of interest. So if, for example, we were
interested in studying patterns of travel to work within central London, our
population would be everyone currently working in Central London. If we were
a governmental institute interested in discovering transportation needs for Lon-
don’s commuters, we would want to ask this population a set of question about
their travel patterns and anticipated needs.
However, when we are collecting data we only very rarely try to collect informa-
tion from everyone in the population, it is usually too costly and too difficult. We
know that if we take a representative sample of the population we can cut our
costs, but obtain reliable estimates of the true characteristics of the population
as a whole.
Surveys obtain data for only a sample of the population. The extent to which the
sample is representative will depend on the sampling method chosen. There are
standard tables for determining a statistically significant sample. See appendix
to this section.
Thinking about where the data came from
Unless you collected your data yourself it is quite possible to be mistaken about
its nature unless you do some research to find out where the data came from,
how it was collected, what it was collected for and how the data was coded.
The answers to these questions will be helpful in determining how useful the
dataset is likely to be for your own research.
52
Statistical Methods
Many statistical techniques are designed for analysing a representative sample

of micro-data from a population. Statistical methods are a ‘toolbox’ of formal
techniques for analysing quantitative data; that is, data that can be expressed
numerically. Statistical techniques can be used to undertake a wide range of
tasks including:
- summarising characteristics of a sample,
- producing estimates of population characteristics,
- quantifying the accuracy of these estimates and,
- testing hypotheses.
Some techniques, especially the first two listed above, are designed for exploring
and describing data, and characteristics of the individuals from whom data was
collected. This is understandably known as ‘exploratory data analysis). Others,
particularly the second pair in the list, are designed to confirm conclusions
drawn using exploratory analysis or test theories.
An understanding of the research question is as important to doing good

research as an understanding of the concepts behind the methods. It is impor-
tant to understand the nature of the statistical method and, importantly, its
limitations, but most real world statisticians undertake their analysis using
computer applications like Excel (which is not primarily for statistical analysis)
or dedicated statistical analysis packages like SPSS, STATA, SAS, JMP, STATVIEW or
MINITAB.
Variables
A variable is simply something that can vary, it is a variable characteristic

of some sort (e.g. gender). In undertaking a statistical analysis we are often
interested in finding out how things vary and whether these variations can be
accounted for.
Variables may be categorical or continuous (the former being the more common
in social survey or census data).
Categorical Variables (also known as discrete or qualitative

variables)
Variables in which the values can be understood as categories are called categor-
ical variables. All the values of the variable are defined and given a value label to
indicate the meaning of the value.
An example of a variable of this type might be categorising streets in an urban

study according to their principal land-use. You might categorise residential as
(1) and shopping as (2) and office as (3). You can normally set up your statistics
software to translate numerical categories into meaningful labels (‘residential’)
and (‘shopping’) and (‘office’).
53
Categorical values have particular qualities. Most importantly, the numerical
values in the data, because they represent categories, have no significance. It
would be total nonsense to claim that, for example;
1 shopping street + 2 residential streets = 1 office
Categorical values can have a natural order. For example, the class classifications
given by Booth, which amount to 7 categories, from ‘1-vicious lower class’ to ‘7-
upper class’, have a natural order from lowest to highest class.
If you were categorising streets according to these values, you would normally
have the lowest number for the lowest class, but this does not imply that the
difference between being a member of the lowest class and of the second
lowest class is the same as the difference between the two top classes.
- A categorical variable that has a natural order

is called ordinal variable.
- A categorical variable that does not have any

natural order is called a nominal value.
- A categorical variable that only takes two

possible values is called a binary variable.
The easiest way to summarise the values for a categorical variable is to look at
the frequencies.
A statistical package will produce a table of all valid values with a tabulation of
the number of times the value occurs in the data (see table 7.1).
In JMP you need to select distribution, in the new window you will need to
select the column for which you want to display the frequency of data - here the
number of elements that have segment integration values falling within certain
bands.
Table 7.1 This histogram shows the statistical distribution of segment integra-
tion values (R 2000metric) for Barcelona 1714. Source: Kinda Al Sayed@UCL.
Continuous variables (also known as metrical or quantitative

variables)
In contrast, continuous variables have valid values that fall between a minimum
and maximum and any value between the two is possible. The minimum and
maximum do not have to be defined.
54
Age is generally measured as a continuous variable. The youngest age possible
is zero years, the oldest ever person at the time of writing was 122. It is possible
for a persons age to fall anywhere between these extremes. Where, as in age,
a continuous variable has a meaningful zero point the variable is said to be a
‘scalar’ or ‘ratio’ variable. You can do multiplication and division sums with these
variables as well as addition and subtraction. Being 0 years means that you’ve
only just arrived on the planet; being 4 years old means that you’re twice as old
as a 2-year-old.
If the exact date of birth had been recorded then age could be defined in exact
days of months. But if we look at year of birth instead of age in years the vari-
able would have different characteristics. The 2-year-old and 4-year-olds given
in the example above could have been born in 1989 and 1987 respectively. The
difference between 1989 and 1987 is still 2, addition and subtraction can still be
done, but multiplication and division cannot. The zero of year relates to a date
in the Christian calendar 1987 rather there being a total absence of time, or age,
before 0AD, so we say that the zero is arbitrary. Only when there is a real and
meaningful zero point is it possible to do multiplication and division calcula-
tions with the values.
If we were simply to produce a table (table 7.2), with density as a continuous

variable we would find that the output would be unwieldy, unless we defined
other bands (such as 0≤10%, 10%≤20%, etc.).
Variable Types
Variable Values Type
Street with Immigrant 1=yes Binary

Settlement? 2=no
Have only 2 possible values e.g. yes/no
( or male/female)
Marital status 1=single Nominal (most limited data type)

2=married
3=divorced Categories but there is no natural
order.
Social class 1=Upper Ordinal

2=Intermediate
3=Middle Categories which have a natural order,
4=Partly skilled but it does not make sense to do
5=Unskilled algebra with the values.
Age 0-120 Continuous (most flexible data type)
The number is meaningful and can be

used for calculations.
Table 7.2.
55
Looking at variables
An obvious occasion on which the differences between categorical and continu-

ous variables are apparent occurs when one wants simply to look at a summary
of the values for a variable.
Why use graphics?
A single image can convey some very complex observations about the character-
istics of a dataset. While you will normally be expected to produce commentary
and analysis as well as data summaries, a well thought out and well-presented
graphic can be more quickly assimilated than a complex table or commentary.
Graphics are also a very helpful means of understanding the structure of a
dataset and therefore form an important part of exploratory data analysis.
Computer packages often require only a few clicks to produce graphics that are
pleasing to the eye and professional looking. But perhaps the most important
stage in producing any graphic or other statistical output is deciding which tech-
nique to use and why. The decision that you make will depend on your research
question.
DATA ANALYSIS
You are likely to have a variety of types of questions about your data:
What?
Data resulting from a spatial and observation analysis of an area of London

would produce results on line length, radius n integration and number of men,
women, children per street (perhaps categorised by age groups), male/female
etc., all of which might be addressed graphically:
1. What is the average (mean) control value for the map?
2. What proportion of streets on the map have movement rates

above average?
3. What is the frequency of each person type? In other words,

how is user category distributed?
4. How do the ages of person types vary? I.e. are men’s and
women’s age distributions different?
5. What proportion of streets are shopping streets?
Why?
Some of these questions require further clarification. A seemingly simple ques-

tion like Q3. might lead us to ask whether we are more interested in the dis-
tribution of age groups or of the type of person using the streets. Sometimes
grouping categories allows us to see more easily what we are interested in, and
sometimes we are only interested in certain age ranges. Decisions of this type
can only be made if we ask why we are interested in a particular question.
56
1. Data types and analytical results (graphics)
Research questions may need different numbers of variables or different types

of variables to answer them. Those using a single variable are univariate; those
using two variables are called bivariate and those using more are called multi-
variate. You need to select the analysis which best suits the research question
and which is most appropriate to the type of data collected (sometimes you can
transform data to a different form to allow them to be analysed).
2. Univariate Techniques for Categorical Data
Univariate techniques show the frequencies with which each category of a

variable is observed in the data – the spread of observed values across possible
values for a variable is called a distribution. Looking at a distribution is an impor-
tant starting point in getting to grips with any dataset.
Univariate techniques are excellent at showing how variables are distributed.

Remember that when dealing with categorical data the values that are recorded
are given labels and, if nominal, will not have a natural order.
- Pie Charts
Pie charts are all about shares, proportions and

percentages. The pie chart is drawn as a circle
(the supposed pie) which represents 100% of
all cases and 360°. Slices represent the share
of the cases with which fall into each category
of the variable in question. The slices are sized
so that 1% of cases are represented by a 3.6°
sector of the pie. Pie charts are useful when
you are considering a whole population as they
show all cases.
- Bar charts
Like pie charts, bar charts can easily be pro-

duced for small datasets, or from a frequency
table. As with pie charts the size of the bar is
representative of either the number or per-
centage of the cases falling into the category
in question. The categories do not have to have
a natural order, although where the variable is
ordinal the order will be given in the chart.
Most packages allow you to define the scale

(on the y-axis) against which the bars are
measured – and the wrong choice of scale
could be misleading. Whether you choose to
represent the data with raw figures or percent-
ages will depend on the size of your sample
and whether there is a logic to making the
sub-groupings.
57
In general: you might want to lock the scale to
compare different samples for which the same
measure has been taken (e.g. control for a
whole map and a sub area of the map).
3. Univariate techniques for continuous data
The techniques described above are those suited to variables with a small
number of discrete values. If continuous data were used in either a bar chart
or pie chart you would obtain a bar or slice for each value and the important
characteristics of the data would get lost in the (cramped) detail. To get around
this problem, there are other techniques which either group the values or which
summarise the characteristics of the variable, rather than simply counting the
frequencies for each value. These include histograms and box plots. We will only
describe Histograms here.
- Histograms
The simplest approach to graphically repre-

senting a continuous variable is a histogram.
This technique is very similar to that of a bar
chart, however, while a bar chart represents
each discrete value with a bar, a histogram
groups together adjoining values. Because a
bar chart deals with discrete data, good statis-
tical packages will usually place a gap between
neighbouring bars. In contrast the bars in a
histogram touch, indicating that the data is
continuous. Histograms have the same issues
of presentation as bar charts, but, in addition,
one has to consider how to group values.
Statistical packages will group values naturally
by some default rule, but most will allow you
to change the way in which values are grouped
using an option or subcommand.
4. Bivariate or Multivariate analysis: Scattergrams
Sometimes a research question requires you to look at more than one variable,
often to see if there is a relationship between the two. Your questions might
include: Is there a relationship between radius n and radius 3 integration?
Scattergrams are primarily used to explore relationships between 2 continuous

variables. Simple scattergrams are composed of a multitude of points marked on
a graph with two axes, one axis for each variable. Each point represents a single
case. For illustration we can plot segment integration (see chapter on segment
analysis) radius n against radius 2000metric for the map of Barcelona, 1714. In
JMP this is known as Bivariate Fit. The scattergram in Depthmap is probably
equivalent to that. It is useful to find how recognized clusters might have certain
spatial distribution. In our case we see elements that represent high correspond-
ence between the local and global segment integration are more representative
of compact urbanized areas. An example for that is in figures 7.1 and 7.2.
58
Figure 7.1. This scattergram shows us Figure 7.2. The Regression here is similar to figure 7.2. UCL Depthmap is used
an R-squared value of 0.56 between to produce the correlation scattergram. By selecting the group of elements
segment integration radius 2000 that have the highest correspondence (closer to regression line) you could see
metric and segment integration where they are placed on the map. Source: Kinda Al Sayed@UCL.
radius n for Barcelona 1714. The
Bivariate Fit is produced using JMP
software.
There are many other ways of ‘interrogating’ scattergrams. First, you should
always check the p-value, to see the probability that a result occurred by chance.
Second, look at outliers: which values are under-performing or over-performing
(are not following the regression line). Does their exclusion create a better cor-
respondence (only exclude if there is a logic, such as excluding one-connected
streets and always mention you have done this in the text).
Why do you think these values are not performing like the others – is your data
accurate? Is there an anomaly in the results – you may discover new things
about your data through this process.
5. T-Tests
A t-test works by comparing the average value of a group or sample with the
average value of the population as a whole, and asking how likely it is that the
average of the smaller sample would have been arrived at by chance. The degree
to which the two averages differ is indicated by a t-value where a high number
(positive or negative) indicates greater difference – this expresses the difference
between the mean and the hypothesized value in terms of the standard error.
The probability that this could have happened by chance is indicated by the
p-value, where the smaller the number, the less likely to have occurred by chance
and the greater the significance of the result. Probabilities of less than .05 are
generally considered to be statistically significant; p-values of close to 1 mean
that it is very likely that the hypothesized and sample means are the same.
59
APPENDIX: GUIDE TO DETERMINING QUESTIONNAIRE SAMPLE SIZES
Table of sample sizes for different sizes of population at a 95% level of certainty
(assuming data are collected from all cases in the sample14
MARGIN OF ERROR
Population 5% 3% 2% 1%
50 44 48 49 50
100 79 91 96 99
150 108 132 141 148
200 132 168 185 196
250 151 203 226 244
300 168 234 267 291
400 196 291 434 384
500 217 340 414 475
750 254 440 571 696
1000 278 516 706 906
2000 322 696 1091 1655
5000 357 879 1622 3288
10000 370 964 1936 4899
100000 383 1056 2345 8762
1000000 384 1066 2395 9513
10000000 384 1067 2400 9595
14
This table is
based on a docu-
ment What is a “margin of error”? If you remember that a sample of a population is
provided to meant to reflect the characteristics of the population as a whole, the margin
Laura Vaughan
by RM Consult-
reflects the percentage by which the results are likely to deviate (plus/minus)
from those characteristics. Thus, if your survey shows that 14% of visitors to
Camden Market who responded to the survey are from abroad, you would
expect that 14% of the entire population would also be visitors from abroad. If
your sample was within the 2% margin, you would know that the percentage of
visitors would range, at the most, between 12-16%.
The higher the margin, the more built-in error there is likely to be in your results,
so a 2% margin is better than a 4% margin. The higher the population size, the
smaller the change in sample size needed to obtain a good margin or error. For
example, if your population is somewhere between 100 000 and 1 000 000,
the change in sample size required to obtain a 2% margin of error for the larger
population is only 50 more people.
Note that this table assumes, in the case of a questionnaire, that you obtain
answers from all people asked. If you have non-responses, you need to find extra
respondents to reach the required sample size. It is also important to under-
stand the cause of non-responses: are they due to refusal to respond (typical of
street surveys); due to ineligibility to respond (can’t speak good enough English
or don’t fit the profile you’re seeking); or you haven’t managed to contact the
respondent (when you’re making house-to-house surveys, for example).
60
DEPTHMAP EXERCISE
Data Analysis Using Depthmap

The original CAD
data copyright ©
https://github.com/
Cambridge Uni-
Depthmap file SpaceGroupUCL/
versity Press 1984,
barnsbury_centre. containing analysed Depthmap/wiki/
reproduced here with
dxf architectural layout Depthmap-Tutorials
kind permission.
File saved from last
Original drawings by
exercise
J Hanson, digitised by
A Turner.
TASK
In this exercise, spatial and visual data will be used to find correlations between
different visual and spatial measures. This will help understanding the nature
of the measures we have learned so far. Following that we will use search for
other types of data and enter these data into the Depthmap table. We will then
analyse correspondences between space and other variables.
STEPS TO ANALYSE DATA IN DEPTHMAP
to find correlations between space to space and vision to vision data.
1. Prepare your files
Ideally, what is required for analysis is a previously processed Depthmap file that
contains some analysed spatial and visual data. We will use the file that was
produced in the last workshop.
2. Prepare your map for data analysis
Download and import the dxf file into Depth-

map. Make sure you save the file before you go
further.
Choose the name “barnsbury-data.graph” and

location of your file
3. Accessibility and visibility analyses
Follow the steps you have learned in the

previous workshops to produce axial analysis,
convex analysis and VGA analysis for the area
defined in the DXF file.
61
4. Looking for values rather than colours
Until now we were dealing with maps that render a colour spectrum. We were
also able to change the colour range to suit the representation that we need to
see. Whether with spatial or visual analysis, this allowed us to find out about
higher and lower values and how they distribute throughout the layout. If we
are to outline models that are less inferred from these maps, we need to go
further and look for values. There are several ways in which to check the values
of elements.
One way is to point on one element in the

layout, after making sure that the map and
column for which you want to see the value of
this element are active.
A small window will appear next to the

element showing you the value of it in relation
to the active column. If we select more than
one element and leave the cursor on them
we will be able to see the average value of
these selected elements as well as the count
(number of elements selected).
62
Another way of looking for values is to go to
ATTRIBUTES→ COLUMN PROPERTIES.
A window will appear showing you the statisti-

cal moments of the column including average,
minimum, maximum, and standard devia-
tion as well as the count and quantiles of the
range.
If you want to get a summary of all the

attributes in the layer, you can go to
VIEW→ATTRIBUTE SUMMARY.
A window will appear showing you all the

columns attributes and their maximum,
average and minimum values.
A more comprehensive view of all the values

of columns within one map can be showed
through going to WINDOW→ TABLE.
You will be able to see the elements you have

selected previously as checked. The rows
are associated with the ID number of each
element and the columns represent the visual
or spatial attribute measure of the graph
configurations.
63
You can export this table as a txt. or as a csv.
file if you intend to perform more advanced
statistical analysis using specialised statistical
packages.
To do that you need to go to MAP→export, and

preferably choose a comma separated values
file csv. format.
5. Pushing values
In case your convex map was active, activate

15
Fewest-Line your axial15 map by making sure that the box
Map minimal if near it looks like this . Make sure that the
you have used column integration [HH] is the active one. In case
the all lines
method you wanted to push the values of your convex
map against an axial map, you need to rename
your column by write clicking it and choose
rename. Change the name to integration[HH]-
convex given that this column has the same
name in both the axial and convex maps so if we
try to merge it with an axial map for Barnsbury
area it might overwrite the integration column
there.
Go to ATTRIBUTES→ PUSH VALUES TO MAP.

A window will appear showing you the
origin map that is the convex map and origin
attributes that is integration [HH]-convex.
Below that you will find a drop down list of a
selection of destination maps to push values
to. You can choose the axial map you have
created. After you have selected the destina-
tion you will see underneath different condi-
tions of the intersection between objects from
the origin map and those from the destination
map. If one axial line intersects with more
than one convex element we could consider
either having the maximum value of the
convex elements that intersect with this axial
line, the minimum, the average or the total
values. We have to be careful though because
sometimes when we use the total values, the
64
numbers aggregate to a very high value and
might increase exponentially. For that we may
need to log the column. We will learn about
that later. We can tick the box that allows for
recording intersection counts just to have an
idea about how many objects from the origin
map intersect with destination objects.
Once we have clicked OK we can go to the

axial map we have chosen as a destination
and find two new columns there; object count
which represents the number of elements each
element from the destination map intersect
with in the origin map. The second column is
the integration [HH]-convex from the convex
analysis.
Once we had both columns within the same

layer which is here the axial map, we can
detect correlations -if any- between the origin
attribute value and the values in the destina-
tion map.
For plotting correlations go to

WINDOW→SCATTER PLOT.
The window of the scatter plot will appear. You

can now choose different column values to
check any correlations between them. Choose
the integration [HH]-convex on the x axis and
the integration [HH] for the y axis.
You can check the toolbars associated with the

scatter plot window to see the R2 value and
the formula as well as the linear correlation.
The R2 value you see here is about 0.4 indicat-
ing a moderate correlation. High correlations
would be 0.65 and above.
65
You can export the scatter Plot by going to
EDIT→COPY SCREEN.
You can go on and try finding correlations

between different measures. One thing that
you might encounter is the strange relation-
ship between choice and integration, looking
at the maximum value of both and noticing
the high difference. This can be corrected by
logging the value of choice. You can do that
through creating a new value and logging the
formula of choice.
First go to ATTRIBUTES→ADD COLUMN.
Name your column and then go to

ATTRIBUTES→EDIT COLUMN. Double click the
choice attribute on the right hand side of the
new window. The value will appear in the
empty window.
Add the term log to this value after you

have enclosed it between two brackets (i. e.
log(value(“Choice”))).
After you have done so you may be able to

see more correct reflection on the correlation
between choice and integration.
It is recommended that you save your file at

this point.
6. Modelling correlations
What makes Space Syntax interesting is the established relationship between a

mathematical model representing spatial structure and the corresponding social
behaviour that inhabits, uses and probably formulate this spatial structure. One
way to render this relationship out is through plotting scattergrams between
social data and spatial data. An example for that is the relationship between
occupancy rates in the real environment settings and convex integration, or
gate counts with axial lines. One only has to make sure that the distribution of
numbers is reasonable. With gate counts for example the counts per 5 minutes
66
are averaged out for the day and multiplied by an hourly rate. In this case and
because we did not count the number of people per gate we are going to use
another type of data.
You would normally create a new data layer

and record the gate data in accordance with
elements you have drawn to match areas of
gate counts for example. When you do that
you are required to have bigger polygons
that are specifically drawn to cover the gates
where you have counted the number of people
passing by. The polygons would intersect with
at least one element in a graph network. To do
that you would go to MAP→ NEW... Select data
map as a map type. In our case, we could just
make use of the convex map itself. Activate
your convex map and go to MAP→CONVERT
ACTIVE MAP. Select from the list that appear
in the window the data map option. You can
name it according to the type of data you are
investigating (i. e. twittermap) and then check
the option copy attributes to new map to have
the convex map attributes copied into that
layer.
Go to the new data map and make it active. Add

a new column by going to ATTRIBUTES→ ADD
COLUMN. Name your column ‘twitter rates’.
Find your analysed area on Google map and

zoom in to check the names for each street.
Now you will need to search for data using the
web browser. Either counting the frequency
of mentioning a particular street on Twitter
engine or Flickr engine, looking at the number
of images associated with each street on a
Google map engine, looking for particular
landuses or number of building blocks on
Open Street Map http://www.openstreetmap.
org/ (make sure to press the Map key option
to get the legends for all the land uses), or look
at traffic density ranks on Bing http://www.
bing.com/maps/ (this option might be difficult
given the small area you are covering)… There
is so much Data out there!!!
Go to WINDOW and turn off the scatter plot

option by clicking on it. Turn on the table
and you will see the table appearing on
your screen. In order for you to know which
element you are selecting on the screen and
its corresponding row on the table, go to
WINDOW→TILE.
Using this mode you will be able to find ele-
67
ments on the screen, and by selecting them
the boxes at their rows will be checked you
can edit or enter a value at a certain column
by clicking on it once, type the new value, and
click ENTER. At the moment each element
in the new column is assigned a value of -1.
You can now enter the values of twitter rates
into the new column you have created. After
you are done entering the values click on the
twitter rate attribute you will be able to see
the street spaces assigned colours according to
the higher twitter rates you have entered.
Once you have both the twitter data and

spatial data columns within the same layer
which is here the data map (twittermap),
we can detect correlations -if any- between
twitter rates and the configurational values in
the spatial attributes.
For plotting correlations go to

WINDOW→SCATTER PLOT.
The window of the scatter plot will appear.
You can now choose different column values
for the spatial data to check any correlations
between twitter data and spatial attributes.
Choose the integration [HH]-convex on the x
axis and the twitter rates for the y axis.
You can check the toolbars associated with

the scatter plot window to see the R2 value
and the formula as well as the linear correla-
tion. Remember that high correlations would
be 0.65 and above. In our case the correlation
is inverted and has a moderate correlation
coefficient R2=0.46. Remember that we have
decided about the social data arbitrarily. In a
real observation always try to check outliers in
your correlation. You can choose elements on
your screen and see them as yellow if you had
the colour range on your scatter plot, other-
wise if you decide to have no colours on the
scatter plot they will be red.
In the scatter plot you can see some data clus-
68
tered together and other floating away from
the regression line. You might hope that these
data will present a strong correlation itself
with its associated spatial attributes.
In order to investigate that, you can isolate

these data in a separate layer. Go to EDIT→
SELECTION TO LAYER.
Name your new layer ‘correlated data’ and plot

it in a scatter plot against integration.
Again and to our surprise that correlation

is weak R2=0.22. Experimenting with these
methods will help you understand the nature
of the relationship between data and spatial
variables.
Now go back to the layer ‘everything’. You will

be able to see the entire layer again.
You might want to consider correlations

between the visual configurations and the
data you have entered. To do that you need
to go to the visibility graph analysis you have
previously done and activate it. Also make
sure that ‘visual integration HH’ is the active
attribute. After you have done that you can go
to ATTRIBUTES→PUSH VALUES TO MAP.
Select the data map you have ‘twittermap’ as

the destination map. The method of intersec-
tion can be chosen to be average values for
a change. You can check the record object
intersection to see how many visibility graph
nodes are intersecting or contained within
each convex space in the data layer. After that
69
click OK.
You will be able to see the visual integration

HH attribute within the list of attributes in the
data layer once you have activated it.
You can now check the correlation between

the average integration values of the visibility
graph and the data of ‘twitter rates’ using the
scatter plot window.
In this approach you were considering the

integration value of the visibility graph nodes
without paying attention to the number of
nodes contained within each convex space. If
you want to do extra explorations and you are
not convinced by the accuracy of this approach
you may try to multiply the values of visual
intergration by the object intersection count
that is recorded within the data layer.
In order to do that you should go to

ATTRIBUTES→EDIT COLUMN. Double click the
visual integration HH attribute. You will see it
in the blank window. Type * after the closing
bracket and double click the object count. By
doing this, you are weighting the visual inte-
gration within a convex space by the number
of nodes contained within it. The formula
will look like this; value(“Visual Integration
[HH]”)*value(“Object Count”). If you click OK
the values of visual integration will be updated
with the weighted measure.
You might want to check the correlation

between axial map data and ‘occupancy rates’.
If you have an intuition into a possible relation-
ship then you may follow the same steps you
have gone through with the VGA data.
Of course you can always go to EDIT→EXPORT

and export the map data you have into a CSV
format to handle it within more advanced
statistical packages. What you will find out is
that data has a language of its own, the more
you investigate the relationship between
spatial attributes and other types of data, the
more you learn about regularities and outliers
and where they are more likely to occur. It is a
very exciting exploration that is worth going
through!
Save your file
70
SEGMENT
ANALYSIS
THEORETICAL BACKGROUND ON SEGMENT ANALYSIS
Introduction
In this chapter, we will explain a new syntactic representation of cities which applies
to both topological and geometric configurations of space. This representation is on
the level of street segments, considering their topological, metric and angular con-
nections. Along with other software, UCL Depthmap has the capacity to produce
different types of segment analysis using different radii. We are going to learn about
segment analysis, what it can do, how powerful it is as a tool for predicting social
and economic activity, the different scales of measurement and graph distance
associated with it. We are also going to list the reasons to shift from axial analysis to
segment analysis, and how the measurements differ in this case. We will highlight
the most powerful tool for measuring accessibility in street networks; that is angular
depth with metric radius. Using this type of graph representation, we will calculate
integration and choice to measure accessibility and compare configurational prop-
erties of space with observed urban activity. These two measures; integration and
choice, will be devised to identify to-movement and through-movement potentials.
Other measures such as metric and topological analysis of segment maps will be
explained in later chapter.
Segment maps: A linear representation of urban spaces
In previous chapters, we explained how an axial map representation of city spaces

might help forecasting potential movement activity in real cities, however, axial
lines appear to be less helpful when trying to detect semi-continuous lines in the
system. This was particularly the case with cities that have a uniform structure with
very little disruptions and with smooth linear streets that cross the regular streets
diagonally. An example for that is Broadway in Manhattan. In such cases, a different
representation of the spatial structure was found necessary (Dalton, 2001). Such a
representation was required to detect these linear or semi-linear connections in the
urban structure and therefore had to use angular rather than topological configura-
tions. For this purpose, a finer grained representation was introduced to the Space
Syntax model considering street segments as the elementary components of street
networks. Each street segment is defined by the interjunction between two inter-
section points. Segments have geometric properties marking the cumulative angle
between each pair of intersecting streets. One of the most useful configurational
methods of analysis is that of angular depth which outlines the shortest angular
journeys through the spatial network. Angular analysis was found to correspond
well with spatial navigation and wayfinding, since users are likely to minimise cog-
nitive distance as they walk through a foreign environment (Hillier & Iida, 2005).
Angular segment analysis with metric radius

16
A metric radius
is the physical As mentioned before, angular analysis, particularly the one constrained by metric
network distance
within which
radius16, was found to be instrumental in detecting major to and through-move-
angular turns ment routes in a street network. Research suggests that a metric radius is especially
might be calcu- needed to avoid the edge effect of the boundary that define a pre-selected cluster
lated for each of streets (Turner, 2001). The selection of the boundary may cause distortions in the
segment street values of graph distance.
element.
It is important to emphasise that the metric radius of the measure refers to the
73
metric distance from each segment along all the available streets and roads from
that segment up to the radius distance. Following this definition, radius ‘n’ means
that each segment is related to every other segment in a city without any radius
restriction. This pattern changes as we reduce the radius of the measure. So if we
are to consider a metric radius as a “cookie cutter” in a network of nodes and con-
nections, resembling a segment map with segments and points of intersections, the
system will be analysed around that particular node or segment. In this case, radius
400 metres (approximately 5 minutes walking distance) will only calculate angular
turns of all nodes within 400m from the current node, any nodes beyond that radius
will not be calculated. This means that the system will only identify the local rela-
tionships between segment elements within 400 metres along the neighbouring
segment lines starting from each one of them.
Deciding about the minimum and maximum radius in an analysis is not restricted
to certain norms. Before beginning an analysis a set of fundamental research
questions need to be posed depending on the nature of investigation: What radius
measure would best correlate with block size parameters, segment length, land
values, Twitter activity, pollution rates or observed patterns of pedestrian and
vehicular movement within a certain urban area? Local movement is normally
best represented by a local radius measure – 800 metres which is equivalent to 10
minutes walk. Market areas with finer scale grid pattern are better represented at a
lower radius such as 400metres. Higher radius measures are then needed to repre-
sent vehicular movement flows.
The principle of angular depth calculation in segment analysis
A segment map is a broken representation of an axial map, where segments are the
inter-junction lines between points of intersection in an axial map (stubs can be
considered if long enough). Angular depth calculation in segment analysis takes a
different form compared to that of axial analysis. Angular segment depth is calcu-
lated by adding up the weighted values of the edges, where each edge is weighted
by the angle of connection. To make it more clear, the intersection of two seg-
ments at an angle of incidence 47±, which might be approximated to 45± degrees,
might have a weight of 0.5. If one of these segments is intersecting with a different
segment at 107± degrees, then it will have a weight of 1. If these three segment
elements are connected in the same direction then the depth between these street
elements is the sum of their weighted angular intersections, that is; 0.5 +1=1.5.
This angular sum can be considered to be “the cost of a putative journey through
the graph”, and from it a “shortest path” is one that presents the least angular cost
from one segment to all others in a street network, (Turner, 2001). The turn angle
is always regarded as positive and the calculation accounts only for directional
movement, meaning that, the point where one leaves the segment, a “forward” link
should be in the same direction as the point at which one has previously arrived at
the segment’s “back” links.
Tulip Analysis
In principle, Tulip analysis is set to approximate angular turns to a segmented

circle. It calls this an 8 bin tulip analysis in which 360° is equivalent to 32bins. UCL
Depthmap gives the ability to segment to any degree within the range 4 to 1024
bins which in a way approximates full angular analysis, whilst also improving on the
speed of computation. The following example elucidates how Tulip analysis might
be used to approximate values;
74
- a turn of less than 22.5± might be assigned zero value
- In the same way, a turn of 97:5± could be rounded to 90±, and

assigned a value of 1.
17
See appendix Angular Segment Analysis Measures17
2. For further
description
1. Angular Connectivity
about angular
segment analysis
measures The segment analysis measure of angular connectivity is considered to be
please refer to the cumulative turn angle to other lines. The turn angle is weighted so that
Depthmap 4: a 180°±,angle will be equivalent to a cumulative weight of 2 and an angle of
A Researcher’s
Handbook (see
45°±, will correspond to a cumulative weight of 0.5. If we look at the example
references) in figure 9.1 we see that the calculation of the measure for a segment B is made
through adding angle x to angle y;
Angular connectivity B= x + y = 0.333 +0.5
18
Diagram
source: Turner, A.
(2005) Could A
Road-centre Line
Be An Axial Line
In Disguise?
Figure 9.1 . An example of a segment map and its associ-

ated graph18.
2. Step Depth
Step depth follows the shortest angular path from the selected segment to all other
segments within the system. The weighting used in the angular scale considers 1
step as a 90± angle. Angles are cumulative. The depth from segment A to segment
B in figure 1 is 0.5 (a turn of 45o).
75
3. Node Count
Node count is the number of segments encountered on the route from the current
segment to all others. In the case of figure 1 we consider node count (NC) to be 3
because the shortest angular path goes through three segments.
4. Total Angular Depth
Total angular depth is the cumulative total of the shortest angular paths to all seg-
ments. In the case of segment ‘A’ in figure 1, the angular total depth is;
TD ‘A’ = (B)0.5+(C)0.833+(D)0.833= 2.166
5. Mean Depth
The angular mean depth value for a line is the sum of the shortest angular paths
divided by the sum of all angular intersections in the system rather than the
number of lines in the system. Mean depth in general terms is indicative to how
deep or shallow a node is in relation to the rest of the graph, a measure defined
as centrality. However, Mean depth seems to have several problematic issues
with regards to its implementation and verification in angular segment analysis.
On a small radius such as radius 500 metres, angular mean depth is a meaning-
less measure. It will simply approximate node count. On radius n, node count has
a constant value, because we are simply considering all the nodes (segments) in
the system. In this case, angular mean depth will be proportional to angular total
depth. In the example illustrated in figure 9.1, angular mean depth for the segment
‘A’ is calculated in the following way;
MD ‘A’= TD ‘A’/NC = ((B)0.5+(C)0.833+(D)0.833)/3 = 0.722
6. Relative Asymmetry RA and Real Relative Asymmetry RRA
RA and RRA in axial analysis have no corresponding methods of calculation in

angular segment analysis. The problem with RRA is basically that there was no
method of relativisation for the type of fractional numbers that result from weight-
ing the angular values in the graph. Relativisation is normally needed to compare
systems and sometimes different parts of the same system which contains in both
cases different numbers of elements. A solution for the relativisation problem was
proposed recently (Hillier et al, 2012). This solution is explained in the last section.
7. Integration measure
Integration in angular segment analysis is a good predictor of the potentials for

each segment within a metric radius to be a highly desired destination. In other
words, the measure forecasts the to-movement potentials for each segment when
measuring on all the shortest angular paths in the system from all origins to all
destinations.
Hillier’s integration measure
Integration = (NC * NC) / TD
Integration for angular segment analysis = NC/MD = NC/(TD/ND) = NC * NC/ TD
76
Some approximations of the measure in special radius types;
Integration radius n = 1/Angular total depth, because node count

is a constant in this case.
Integration low radius (radius 400 metres) ≈ NC
8. Choice measure
Choice (betweeness) is calculated automatically in Depthmap for different radii.

It basically calculates the potentials for each segment element to be selected by
pedestrians as the shortest path (when considering a small radius) or selected by
drivers (when considering a large radius) or both. So choice signifies the through-
movement potential of a segment in a spatial system. The calculation is defined
according to Turner (2001) as the following, “for all pairs of possible origin and desti-
nation locations, shortest path routes from one to other are constructed. Whenever
a node is passed through on a path from origin to destination, its choice value is
incremented.” In Depthmap, when calculating each shortest angular path within the
system, a value of ‘1’ is assigned to every segment in the route from any origin to
any destination. If the shortest paths go through an element twice the calculation
of angular choice records the value ‘2’ for that element. This adding up continues
until all shortest angular routes are identified and calculated in the system. Then the
system identifies randomly all the equivalent shortest angular paths and eliminates
one of each pairs.
Note that, both integration and choice could be combined in one measure. This
makes it possible to find the segments in a network which serve as both a potential
destination and route of movement. This measure will then narrow the focus on
fewer and more significant elements within the system that combine the attributes
of being a potentially desired destination and at the same time a desired route for
movement.
9. Weighting by segment length
Depthmap gives the possibility of weighting segment analysis to different meas-

ures. One of the most valuable weighting methods is to weight by segment length.
Mainly because longer segments are likely to have more blocks and entrances
adjacent to them on each side leading consequently to higher rates of movement
activity. This argument is valid for pedestrian movement and is more likely to apply
to smaller radius of analysis. When considering vehicular movment, we might
anticipate that drivers would preferentially choose to drive straight forward without
taking many turns, hence longer lines are more likely to afford higher speeds.
Longer street lines might characteristically define higher speed motorways, where
journeys are rarely disrupted by road junctions and traffic lights. Pedestrian and
vehicular journeys are, therefore, more likely to pick up longer segments if they fall
within a shortest angular path from all origins to all destinations. The arguments
for pedestrians or vehicles to choose the shortest path with longer lines would all
depend on the radius of analysis whether small or large.
10. Normalising choice and integration
Recently, new advancement on angular analysis was introduced by Hillier et al

(2012); that is the normalisation of angular choice and integration measures (see
appendix 2). The purpose was to enable the comparison between systems of dif-
77
ferent sizes. It was basically necessary to introduce new normalisation methods to
angular distance in a graph, since it was not possible to use the diamond-type of
value (D-value) that was initially used to normalise topological distance in axial and
convex graphs. The normalisation of choice was motivated by acknowledging the
relationship between high choice and high total depth, that is the more segregated
the system is, the higher choice values are. Choice was seen therefore as a neces-
sary condition to overcome the cost of segregation in the street network. This is
a cost-benefit principle that was introduced by Tao Yang in Hillier et al (2012). The
new normalised angular choice measure was named as NACH;
NACH = logCH+1/logTD+3
In Hillier et al experiments, NACH has proven to be independent from the size of

cities – quantified in number of segments- but was found to correspond well with
simple segment connectivity. Integration had a simpler explanation, where the
system is being compared to the urban average. The result is a normalised angular
integration measure (NAIN) computed as follows;
NAIN = NC^1.2/TD
These two methods of normalisations have immense advantages, making it easier

to expose the inner structure of urban form, and making it possible to compare
street configurations in different cities and in different locations within a city. In
principle, it is possible to characterise cities based on their maximum and mean
values of integration and choice, the maximum values defining the foreground
structure of the grid and the mean values defining the background structure.
When comparing different cities with regards to their maximum and mean values,
we find that higher values are always indicative to how structured a city is, whilst
mean values are indicative to how grid-like a city is, but are not determinant of how
structured a city is. Similar to the usual definition of integration, maximum and
mean values of NAIN are related to the ease of accessibility in the street network.
Mean values of NACH are associated with how continuous a structure is in relation
to the background, while maximum values of NACH are representative of how
deformed or interrupted is the foreground structure of the grid. The performance of
these measures vary from city to city. The characteristic values of these measures for
each city are thought to tell something about the distinctive typologies that cities
share. They might also be indicative to how cities emerge in time, whether through
deformations on a regular grid pattern like Manhattan or through a bottom up
mechanisms, the case with London and Tokyo.
The initial testing of the measures -as explained in Hillier et al (2012)- proved to be
very effective. However, few issues have arisen in some cases where the application
of the measures on the local scale in areas that are less urbanised was rendering out
some erroneous results. To deal with that, it is recommended that fully urbanised
areas and periurban regions should be analysed separately. Another issue was
emerging when dealing with Road-centre line maps, where multiple segments on
the same line were adding up to the values of choice, hence distorting the results.
For that, an automated procedure was developed at Space Syntax Ltd. to eliminate
this effect. This problem can also be solved by manually deleting the extra lines. It
is also advised, that stubs up to 40% should be cleared from segment maps before
running the analysis. This is to ensure that no unnecessary depth is introduced to
the system as that might increase segregation and distort the values of normalised
choice.
78
DEPTHMAP EXERCISE
Segment Analysis Using Depthmap

Cad drawing file
containing intersect- http://moodle.ucl. London original map
cityofLondon.dxf
ing series of line ac.uk/ data copyright © UCL.
elements
Task
The main task in this exercise is to get from an axial map, to segment map16, to
16
Do not convert
your map some analytic measures of the spatial structure of urban environments.
directly to a
segment map! Steps to perform axial and segment analysis in Depthmap
You must make
an axial map
first to preserve
the links/unlinks to do segment analysis from axial lines data.
if there are any
embedded in 1. Download your files
the file.
Go to http://moodle.ucl.ac.uk/mod/resource/
view.php?id=645834
Follow the link
UCLMoodle → TMSARCSAAS01 → Resources → PoAD_Depthmap

Workshops → workshop4
and download the file cityofLondon.dxf from the website onto

your desktop
Prepare your axial map

blank page.
Save the file you are using: FILE---SAVE
Import the axial map into Depthmap: go to

MAP--- IMPORT--- look in your Desktop for a file
titled cityofLondon.dxf. Choose the DXF file and
click import.
Now you will have the file imported into your
Depthmap file as a drawing layer.
79
2. Axial analysis
Convert your drawing map into an axial map.

To do that, go to MAP menu and choose
CONVERT DRAWING MAP.
A pop up window will appear. Under the NEW

MAP TYPE choose AXIAL MAP.
You could change the map name under NEW

MAP NAME. The default is AXIAL MAP and it
will number it if you have more than one axial
map.
Once you have done that you will be able to

see the basic attributes of an axial map, which
are; REFERENCE NUMBER, CONNECTIVITY, LINE
LENGTH.
3. Creating a segment map
Convert your axial map to a segment map. You

can do this by going to MAP menu again and
choose CONVERT ACTIVE MAP.
A pop up window will appear. Select SEGMENT

MAP from the NEW MAP TYPE menu.

MAP NAME. The default is SEGMENT MAP and
it will number it if you have more than one
segment map.
From the ANALYSIS TYPE choose the option to

REMOVE AXIAL STUBS LESS THAN 25% OF LINE
LENGTH (40% is the default if you choose this
option)17.
17
You could change this value in
a way that suits your map inter-
pretation. This often depends on
the length of axial lines you have
because you don’t want to remove
long axial stubs as that might lead
to missing important information in
the map you have.
80
4. Calculating different types of segment step depth
Choose the SELECT icon from the tool bar on

top of the MAP window.
Select one element from the segment map.
Go to TOOLS ---SEGMENT--- STEP DEPTH ---

ANGULAR STEP.
To explore other sorts of step depth go to

TOOLS --- SEGMENT --- STEP DEPTH---TOPO-
LOGICAL STEP. Also try out TOOLS --- SEGMENT
--- STEP DEPTH --- METRIC STEP.
To limit the analysis by metric radius click

anywhere in the map window to remove the
selection (this step is not vital to execute the
analysis). Then go to TOOLS --- SEGMENT ---
RUN TOPOLOGICAL OR METRIC ANALYSIS.
A pop up window will appear you could select

the type of analysis you want to perform by
either choosing the analysis type (either TOPO-
LOGICAL or METRIC analysis) and then insert
the metric radii you are interested in such as (n
or 400 or 800 or 1200m).
81
5. Segment analysis18
18
Most description of segment anal-
ysis is rather technical, but it would
make sense to read the papers about
it: Hillier and Iida (2005) and Turner
(2007). For some historical perspec-
tive, you might also like to look at
Dalton et al. (2003), although note
that Dalton et al.’s fractional analy-
sis differs in detail from the later
methods.
Run the segment analysis: by going to TOOLS---

SEGMENT---RUN SEGMENT ANALYSIS.
A pop up window will appear showing

SEGMENT ANALYSIS OPTIONS.
Choose the default TULIP ANALYSIS rather than

FULL ANGULAR. Tulip analysis does everything
that full angular does and much more. Also
ensure you select INCLUDE CHOICE (BETWEEN-
NESS).
For RADIUS TYPE, change to METRIC, and select

a range of suitable radii to measure. Everything
at this stage hinges on whether or not you made
sure you scaled your map correctly in metres.
If you did, then a list of sensible radii might be:
n,250,500,750,1000,1250,1500,1750,2000,250
0, 3000,4000,5000 with a comma but no gap
between each (no comma at the end) (another
sensible option will be (n,200,400,800,1200,1600
,2000,2400,3200 – it is up to you to decide about
the most suitable radius19).
19
The minimum and maximum radii
chosen will depend upon the grid
dimensions and spacing.
Also make sure that you tick the box
marked INCLUDE WEIGHTED MEAS-
URES and select weight by SEGMENT
LENGTH from the drop-down menu.
Click OK and the analysis will run. A box will

tell you how long it will take.
Once the analysis is finished you will have

a series of measures the most important of
which is T1024 CHOICE. Note that this is differ-
ent from axial choice measure.
Ignore for the time being those measures with

‘Norm’, and ‘Segment Length Weighted’ (and
the combination of the two).
You will be able to see an integration measure.
82
6. Combining integration and choice
You can create a combined measure of inte-

gration and choice by multiplying one by the
other. To do this go to ATTRIBUTES and select
ADD COLUMN from the menu.
A window will appear in which you will provide

the NEW COLUMN NAME. Then you have to
edit the column (either using EDIT COLUMN
from the ATTRIBUTES menu, or right clicking
on the new column and choose EDIT). In the
FORMULA part fill in the combined measure
formula, where XXX is the radius you want the
integration measure for:
(value(“T1024 Integration RXXX

metric”))*(log(value(“T1024 Choice RXXX
metric”)+2)).
7. Calculating normalised measures of integration and choice
You can create a normalised measure of

integration or choice by creating new columns
for each measure which we will name as NAIN,
NACH respectively. To do this go to ATTRIBUTES
and select ADD COLUMN from the menu.
A window will appear in which you will provide

the NEW COLUMN NAME. Type NAIN for nor-
malised integration and NACH for normalised
choice
Then you have to edit the column (either

using EDIT COLUMN from the ATTRIBUTES
menu, or right clicking on the new column
and choose EDIT). In the FORMULA part fill in
the normalisation formula for each measure
separately, where XXX is the metric radius for
each measure:
NAIN
value(“T1024 Node Count RXXX metric”)^ 1.2 /

(value(“T1024 Total Depth RXXX metric”)+2)
NACH
log(value(“T1024 Choice RXXX metric”)+1)/

log(value(“T1024 Total Depth RXXX metric”)+3)
83
Notes
NOTE 01
When you need to draw axial lines data using map image files;
You may use whichever drawing package you prefer to create a vector drawing
of the axial map to import into Depthmap, however, MapInfo or other GIS pack-
ages are strongly preferred as it will allow you to register your map images to
the OS national grid, and thus link your axial map to other axial maps.
If you are using MapInfo to produce an axial map out of a map image file,
consider how to register the image. That is, at the moment, the image is simply
pixels on the screen, but in fact, each pixel corresponds to a location on the UK
national grid, and this is the preferred format. To register it, you will need to
download contemporary vector map data from OS to which to register the map.
Please consult your GIS lectures for how to register images within MapInfo.
Once your image is registered, you should start drawing the axial map. Make
sure that you use a new layer within MapInfo for your axial map.
If you are unable to register your image, at the very least set the correct scale
within your GIS or CAD package. If OS national grid units are not available, use
metres instead.
NOTE 02
In order to import the axial lines drawing file you need to export it from the
drawing package (CAD, GIS) you have used to draw the lines. From CAD you can
export a DXF file; from MapInfo it is probably easiest to export a MIF/MID com-
bination (although you may also export DXF from MapInfo if you prefer).
Always Check the integrity of your axial map before piling straight into the
analysis of your axial map, but wait! Check that your axial lines are properly
connected to each other. You can go through the map looking at the number of
connections (just hover the mouse over a line), or check all lines are connected
by performing a ‘step depth’ calculation. Both of these methods can be found
in the Depthmap axial analysis tutorial at UCL Bartlett website. If you find lines
that are unconnected, that should be, fix the map either within Depthmap or
MapInfo(if you have imported a MapInfo file), once again, see the tutorial. Actu-
ally edit the lines rather than using the ‘link’ tool. You may want to export the
edited map as a MapInfo MIF/MID file if you make significant changes to your
original map.
In rare circumstances, you may need to unlink lines where it is not possible to
get from road to another – for example, if there is a bridge. Go through the axial
analysis tutorial to find out how to unlink lines, and unlink where necessary.
NOTE 03
if you don’t have a proper metric (scale) on your map, then Depthmap will allow
you to set integer radii i.e. 1,2,3…..n where 1 is a single one of the units in which
the map was drawn
84
ADVANCED
AXIAL AND
SEGMENT
ANALYSIS
THEORETICAL BACKGROUND ON ADVANCED AXIAL AND
SEGMENT ANALYSIS
Foreground vs. Background analysis
Introduction
This chapter is aimed to explain some of the aspects of metric analysis in cities
and how it helps to spot localities with homogeneous identity in terms of the
scale of grid structure. These localities appear as patchwork patterns in segment
maps. Along with these local metric signature of neighbourhoods there seems
to be an associated global structure which can only be identified by rendering
its topo-geometric attributes. We will introduce these two models of spatiotem-
poral segment maps. While a patchwork pattern can be approximated using
the metric depth tool in UCL Depthmap, the top-geometric structure of the
grid needs more experimentation on how to expose it. Generally, an angular
measure of integration or choice will do, however, this can vary according to the
type of grid and area that need to be represented. There is still not much to say
about how these studies can be beneficial. Yet, they do tend to help us acquire a
better understanding of cities and their generative mechanisms.
What are the main components of cities that distinguish the global structure from
the local structure?
Cities can be represented by large graph networks where linear representations of

street spaces might be regarded as nodes and where intersections between street
lines are denoted as links. If we consider street interjunctions as a finer level of
representation of street spaces, we might derive a richer description of urban space;
that is a segment map. Segment maps might be seen as to reduce the layout com-
plexity of streets, by accounting for the linear elements that make its structure. An
analysis of the topological and geometric properties of the segmental structure of
streets is likely to expose some of the processes that led to the formation of urban
grid, and the role of different economic and social forces in shaping urban form. The
syntactic model explains all these processes in a synchronic representation, whilst
also acknowledging how simple local dynamics might generate global structures
that are similar to those of cities. A proposed description of these local dynamics is
that of ”Centrality and Extension” identified by Hillier (1996) as: Don’t block longer
alignments if you can block shorter ones. By implementing this rule what we get is
a large aggregate structure of shorter lines intersecting with near right angles and
longer lines continuing to intersect with each others with smaller angles to connect
the local structures of the parts. The patches of uniform shorter lines nest in-
between the global semi-linear connections. The clustering of local structures and
the global pattern of major road infrastructure can be identified through rendering
the metric and angular properties of streets networks.
On defining the global structures or the foreground layer of street networks, the
original syntactic axial model presented some deficiencies, hence there was a need
for a methodological intervention. Previous studies by Figueiredo and Amorim
(2004, 2005) recognised that axial analysis cannot identify continuous linearity in
the system. In response to this problem, Figueiredo and Amorim suggested “con-
tinuity lines” as a method to unify the long axial lines that intersect with smaller
angles and hence consider these unified linear representations as single elements
87
when computing different configurational measures of Space Syntax. In this way,
“continuity lines” can be considered as to represent a global structure highlighting
roads that are likely to afford higher movement flows due to their configurational
properties. The issues presented by semi-continuous streets were also discussed
by Dalton (2001), who proposed fractional analysis as to recognise angular graph
distance in calculating depth in urban layouts. Through the use of fractional analy-
sis, it was possible to highlight the role of Broadway in the grid-iron street network
of Manhattan. Another contribution was made by Dalton (2007) to empirically
define local structures, in a dedicated effort to find some quantitative description of
urban identity. In his approach, Dalton plots the values of point synergy and point
intelligibility of axial lines and accordingly reaches a form of patchworks map that
shows fuzzy boundaries of neighbourhood vicinities. Synergy was recognised by
Hillier (1996) as the relationship between local and global integration highlighting
some underlying relationship between the parts and the whole in cities. Intelligibil-
ity is similarly relating a local measure such as connectivity with the measure of
integration in an axial map, and in this way the measure gives an idea about how
intelligible and permeable a space is for users.
More recent work by Hillier et. al. (2009) revealed an interesting manifestation of
both the local and global patterns of grid structures. The local to be highlighted as
patchworks of metrically similar areas, and the global to be represented by semi-
continuous lines in the grid highlighting shortest paths between all origins and
destinations in an urban system. The metric patchworks serve as a background
structure and correspond to some extent with known localities in politically-
defined neighbourhood areas. The metric patchwork patterns are highly sensitive
to the scale of measurement. The longer continuities serve as a foreground layer
and stand for the shortest connections. They very often match busy streets in the
urban fabric.
Background Analysis: Producing patchwork maps
Patchwork maps are interesting manifestations of the metric density of urban

structures. They look interesting and promising as they appear to mark out distinct
areas in cities Hillier et. al. (2009). The clustering of patchwork patterns is thought to
represent the spatial signature of dynamic local processes of attachment, pruning
and diffusion (Al_Sayed, 2013). The meaning of metric analysis and its relationship
to angular analysis are very much dependent on scale (figure 11. 1). Metric analysis
is very close to angular scale within a walkable radius distance from each street
segment. As we increase the radius, we find that the correlation between metric
distance and angular distance becomes weaker and weaker. This means and the
angular geometry and the metric geometry of urban grid are likely to highlight
different features on the global scale. The significant routes rendered by angular
analysis were often compatible with movement flows. However, when measuring
on metric distance, the analysis proved to be less powerful correlate to pedestrian
and vehicular movement (Hillier et. al., 2009). It is important to emphasize that
metric analysis of larger urban areas is not useful for the purpose of urban model-
ling and forecasting. Indices of metric distance might highlight walkable areas on
a local radius. Patchworks are normally highlighted by measuring the metric mean
depth of the system within a certain metric radius. It is important therefore, to be
aware of the kind of local areas one needs to reveal, because by making the radius
too small (radius 400 metres for example) the patchworks will pick smaller spots of
dense local structures. The larger the metric radius, the wider and larger these spots
are. A very large radius is likely to expose larger voids and compact districts in an
urbanised area, but bears no relationship with movement and its socioeconomic
88
consequences. If we take the metric mean depth of all elements within the analysed
urban system o the X axis and plot it against metric mean depth of radius 1000 -for
example- we might be able to highlight a pattern of peaks and troughs that is rep-
resentative of how far a metrically defined local cluster. It is important to remember
that the density of street structures and the performance of different radii are also
related to the distribution and size of urban blocks. At the centre of a spatial system,
as blocks shrink to a smaller size, street structures intensify and metric mean depth
decreases. The exact contrary happens when blocks grow larger in size at the centre
of an urban region. It must be recognised that city centres tend to intensify their
grid structures in central areas to minimize depth.
Where patchwork maps seem to highlight areas of analogous characteristics in

terms of scale and network distance, another model might be used to detect
discontinuities in maps (Yang & Hillier, 2007). Yang’s model accounts for the change
in node count values while changing the radius around each element in an urban
system. The spatial network starts picking discontinuities which in turn correspond
to patchwork maps. In general, metric depth and node count values play a crucial
role in determining the properties of local grid structure. However, as we increase
the scale of measurement this effect diminishes. At a very large radius, the global
attributes of an urban structure can best be represented by its angular topo-geo-
metric configurations.
Figure 11.1 . The relationship between metric and angular graph distance in Barcelona. A set of correla-
tion coefficients that mark the relationship between MMD and angular total depth, and between MMD
and choice on different metric radii. Adapted from (Al_Sayed, 2013).
Foreground Analysis: Producing topo-geometric analysis
Global structures in cities are often recognised as the set of near-continuous linear
connections that afford shorter journeys from all origins to all destinations. It is
perhaps reasonable to recognise a global road infrastructure network as a sepa-
rate foreground layer in an urban grid. As we have seen, metric measures can only
function well on a small local radius whereas global radius is mostly topological
or topo-geometric. If we start calculating metric mean depth or node count on a
small radius from each segment element the resulting values of physical distance
will start highlighting local structures (figure 11.2). Once we push the radius higher,
an overlapping effect starts to emerge (Hillier et. al., 2009). More overlaps between
the ‘buffer zones’ around each single element arise and this demands a less intuitive
measure to calculate the depth and journey cost relationships of the system as a
whole.
If we look at the integration measure, the best way to illustrate important linear
connections is by calculating angular turns within a metric radius. At radius ‘n’ the i
89
ments on this measure yield that a simple weighting of integration values by
segment length does not make that much difference on the whole. As the system
increases in size, the configurational values of street elements follow a lognormal
distribution, reducing the difference between mean values. This does not apply to
indices of choice, mainly because the values of choice are likely to follow an expo-
nential distribution.
Figure 11.2 . Overlaying the foreground structure on top of the background structure. Smaller radii
are applied to both metric and angular measures to render local patterns (top), whilst larger radii
are applied to show global patterns (down). The analysis reveals varying patterns of clustering in the
background and branching of semi-continuous lines in the foreground Source: Al_Sayed, 2013, LSE.
90
In a large scale system, choice does not seem to work well when applying metric
weighting or angular weighting. In fact, a seemingly trivial effect on a local segment
of the route can add up in computing values leaving a negative impact on the rep-
resentation of the system as a whole. Restricted choice radius, however, performs
much better in measuring the network affordances for movement traffic, especially
when applying metric weighting. The best method for weighting choice would be
to weight the origin and destination of the shortest angular turn journey (Hillier
et. al., 2009). In this way, it will allow for the inclusion of the block size variable in
weighting origin and destination segments. This solution might be less effective
when computing shortest journeys within radius ‘n’. Yet, the calculation of least
angular turns is more effective than calculating shortest metric distance. Hence,
whether calculated separately or combined in one measure, an analysis of angular
choice and angular integration are usually reliable in highlighting the foreground
layer of street structures.
91
DEPTHMAP EXERCISE
Advanced Segment and Axial Analysis: Foreground vs. Back-
ground analysis
CityLondon_axial-
map.MIF Fewest line map of
MapInfo exchange the city of London
CityLondon_axial-
file containing inter- data copyright ©
map.MIDI http://moodle.ucl.
secting series of line University College
CityLondon_un- ac.uk/
elements and MIDI
links_coord.MIF London
sequence
CityLondon_un-
links_coord.MIDI
Tasks
Import a dxf file into Depthmap with its associated unlinks data
Analyse the axial map
Get from axial analysis to segment analysis20

20
Do not
convert your
map directly to Select angular analysis to render out the foreground structure of the area high-
a segment map! lighting the longest semi-continuous lines.
You must make
an axial map
first to preserve
Produce metric analysis to illustrate the background structure of the urban area
the links/unlinks by highlighting the patchwork of shorter lines.
if there are any
embedded in Steps to perform axial and segment analysis in Depthmap
the file.
to do Produce Foreground analysis and Background analysis from your axial lines
map.
Go to http://moodle.ucl.ac.uk/
and download the zipped file named < london-

city.rar> from the website onto your desktop.
Once you unzip the folder you will find that it

contains these four files:
1. CityLondon_axialmap.MIF
2. CityLondon_axialmap.MIDI
3. CityLondon_unlinks_coord.MIF
4. CityLondon_unlinks_coord.MIDI
Copy the files into a new directory on your

desktop
92
2. Prepare your axial map

blank page.
Save the file you are using: FILE---SAVE
Import the axial map into Depthmap: go to

MAP--- IMPORT--- look in your Desktop for a file
titled CityLondon_axialmap and Choose the
MIF file and click import. Now you will have
the file imported into your Depthmap file as a
drawing layer. You will be able to see the basic
attributes of the file, which are; REFERENCE
NUMBER and ID.
3. Axial analysis
Convert your drawing map into an axial map.

To do that, go to MAP menu and choose
CONVERT ACTIVE MAP.
A pop up window will appear. Under the NEW

MAP TYPE choose AXIAL MAP21.
21
Please note that this step differs to
that of importing a DXF file in which
you have to convert drawing map
instead of active map. From this step
on, the procedures undertaken to
analyse the map is similar in both
DXF-based and MIF-based formats.

MAP NAME. The default is AXIAL MAP and it
will number it if you have more than one axial
map.
Once you have done that you will be able to

see the basic attributes of an axial map, which
are; REFERENCE NUMBER, CONNECTIVITY, LINE
LENGTH.
At this point, you may import the unlinks file

into your current graph file as a data layer. Go
to MAP--- IMPORT--- look in your data folder
for a file titled CityLondon_unlinks_coord and
choose the MIF file and click import. Now you
93
will have the file imported into your Depthmap
file as a data map containing the following
columns; REFERENCE NUMBER, X, Y.
Now make sure that you turn off the unlinks

data layer. This should activate your previous
axial map layer.
Go to TOOLS---AXIAL / CONVEX / PESH---

CONVERT DATA MAP POINTS TO UNLINKS. A
window should appear asking you to select
source layer for unlinks points.
Select from the browser the name of the data

layer containing unlinks data. The unlinks are
stored there in the form of points coordinates
(X,Y). Click OK.
In order for you to check your loaded unlinks

data, from the Icons in the toolbar above your
map window choose join icon which is
displayed as two boxes with a curvy arrow
in-between them. You will be able to see all
the unlinks data located on your map. Now
choose the select icon to view your axial
map again.
Make sure your axial map is activated and then

go to TOOLS---AXIAL / CONVEX / PESH---RUN
GRAPH ANALYSIS. A window will appear in
which you could fill in the different radii you
wish to analyse (n, 2, 4, 6 for example). Check
the three options if you wish to include choice,
local measures, RA, RRA and total depth meas-
ures.
You could also include weighted measures if
you think they might be useful in your case.
You will see so many measures on the left
hand of your screen, only few of them will be
of interest to you in the future.
Please save your file before you go further.
94
4. Creating a segment map
Convert your axial map to a segment map. You

can do this by going to MAP menu again and
choose CONVERT ACTIVE MAP.
A pop up window will appear. Select SEGMENT

MAP from the NEW MAP TYPE menu.

MAP NAME. The default is SEGMENT MAP and
it will number it if you have more than one
segment map.
From the ANALYSIS TYPE choose the option to

REMOVE AXIAL STUBS LESS THAN 25% OF LINE
LENGTH (25% is the default if you choose this
option)22.
22
You could change this value in
a way that suits your map inter-
pretation. This often depends on
the length of axial lines you have
because you don’t want to remove
long axial stubs as that might lead
to missing important information in
the map you have.
At this stage you will only see several columns

on the left hand side of the screen; REFERENCE
NUMBER, ANGULAR CONNECTIVITY, AXIAL LINE
REFERENCE, CONNECTIVITY, SEGMENT LENGTH,
note that the segment map will inherit the
unlinks data points from the axial map.
5. Background analysis: Calculating metric mean depth
Go to TOOLS ---SEGMENT--- TOPOLOGICAL AND

METRIC ANALYSIS.
A pop up window will appear. Select the type

of analysis you want to perform -in this case
it is the METRIC analysis- and then insert the
metric radius you are interested in such as
(1000). Click OK. You will be able to see a list of
measures on the left hand side of the screen.
The most important measure is the metric
mean depth measure. In our case it is the
“Metric mean depth R 1000 metric”.
At the first instance, when you try to view the

effects of metric mean depth on the map, you
95
might not distinguish any pattern. To start
distinguishing ‘patchworks’ in the map you will
have to adjust the colour range.
Go to WINDOW---COLOUR RANGE. A window

will appear.
Use the browser to move to Depthmap classic

as a banding range type. Adjust the cursors
in such a way as to invert the red-blue range
and keep applying the analysis until you get a
in the form of patchwork map. Note that you
are only trying to display the distribution of
numbers so you are not changing the numbers
themselves.
You could repeat that calculation by applying

radius ‘n’. Again you could do that again by
applying radius 1500 metres.
Once you have done that, you can start looking

for patterns and correlations in values. To do
that, go to WINDOW---SCATTER PLOT.
A different window will appear on top of your

map window. Change the X axes to represent
“metric mean depth” and change the Y axes to
represent “metric mean depth R1000 metres”.
You will be able to see the signature of area-
isation of the patchwork map plotted in the
form of peaks and troughs. You may also select
some of the peaks and check their locations on
the map window. The smaller the metric radius
you choose the sharper the peaks and troughs
will be.
At this stage you may want to check the corre-

spondence between the patchwork spots you
have and real areas on a real map by opening
a windows explorer window and going to
Google map. Zoom into London area. See if the
areas match existing areas.
Save your file.
96
6. Foreground analysis23: calculating global integration and
choice values
23
Most description of segment anal-
ysis is rather technical, but it would
make sense to read the papers about
it: Hillier and Iida (2005) and Turner
(2007). For some historical perspec-
tive, you might also like to look at
Dalton et al. (2003), although note
that Dalton et al.’s fractional analy-
sis differs in detail from the later
methods.
Run the segment analysis: by going to TOOLS---

SEGMENT---RUN SEGMENT ANALYSIS.
A pop up window will appear showing

SEGMENT ANALYSIS OPTIONS.
Choose the default TULIP ANALYSIS rather than

FULL ANGULAR. Tulip analysis does everything
that full angular does and much more. Also
ensure you select INCLUDE CHOICE (BETWEEN-
NESS).
For RADIUS TYPE, change to METRIC, and select

a range of suitable radii to measure. Every-
thing at this stage hinges on whether or not
you made sure you scaled your map correctly
in metres. If you did, then a list of sensible radii
to plot global connections might be: n, 3000,
5000, 7000 with a comma but no gap between
each (no comma at the end).
Also make sure that you tick the box marked

INCLUDE WEIGHTED MEASURES and select
weight by SEGMENT LENGTH from the drop-
down menu.
Click OK and the analysis will run. A box will

tell you how long it will take. If this will take
too long try to run the analysis at home and
save the file on your remote desktop driver.
Once the analysis is finished you will have

a series of measures the most important of
which is T1024 CHOICE, a possibly good repre-
sentation of the foreground layer. Note that
this is different from axial choice measure.
Ignore for the time being those measures with

‘Norm’, and ‘Segment Length Weighted’ (and
97
the combination of the two).
Another representation of the foreground
layer is identified through global integration
measure
At this stage you may want to check the cor-

respondence between the catchment area of
an important road and the lines highlighted by
the foreground layer. You can do so by opening
windows explorer and checking Google map.
Zoom into London area. See if the routes iden-
tified by choice Rn for example match highly
significant roads in the street network.

Notes
Save your file whenever possible. Depthmap tends to crash sometimes when
analysing large graphs.
If you stop your analysis before it is actually finished for whatever reason (often
because the analysis is taking too long) the graph will be analysed only partially.
So you will have to produce another active layer of analysis. If you don’t want to
confuse half-finished analysis with the finalized one within one layer you will
have to delete the columns of the partial analysis one by one. Note that some
columns cannot be deleted.
98
AGENT
ANALYSIS
THEORETICAL BACKGROUND ON THE AGENT MODEL
Introduction
This chapter introduces the cognitive agent-based model and simulation tech-
niques that are incorporated with the Space Syntax tools in UCL Depthmap10.14.
The cognitive agents, developed by Alasdair Turner, introduce a dynamic descrip-
tion to the static representation of Space Syntax model in that it accounts for the
adaptive behaviour of individuals/agents in relation to space. This section will be
dedicated to introduce the theoretical and modelling description of Turner’s cogni-
tive agents.
Different Agent tools are provided on the 2D and 3D graph windows to visualize
aggregate and individual agent movement. These tools were initially dedicated
to form the experimental part of Alasdair Turner’s research, for this reason further
testing is needed to validate the rules on different building typologies and different
architectural and urban scales. The manual explains the use of the Agent Analysis
Setup toolbox in the graph window. The toolbox enables users to generate differ-
ent patterns of aggregate behaviour by controlling the parameters and rules in
the toolbox window. In addition, the manual explains some of the tools provided
on the 3D view window that control the visualisation of the standard agents. The
3D view helps understanding how individual movement behaviour of standard
automata/agents builds into aggregate patterns that might then be compared to
human behaviour in space.
The chapter describes the application part of a theoretical framework developed

by Alasdair Turner; namely his theory on ‘Embodied Space’ in which he seeks to
explain the natural visual interaction between the individual and the environ-
ment. To test his theoretical investigation, Turner devised an Agent’s model
architecture to simulate natural movement patterns in buildings and cities. His
model reflects on several levels of investigation where he initially tests simple
automata that act by means of direct visual affordances using simple rules. He
then evolves his automata by assuming that his agents or animats can make
choices about certain visual characteristics of the environment. The last stage
of his investigation is where animats acquire the ability to learn and utilise
memory in different environments. This investigation is partly implemented in
the Agent analysis tool in Depthmap 10.14. The Agent model is instrumental to
acquire a better understanding of the cognitive basis of natural movement and
probably explain navigation and wayfinding.
In separate experiments that were implemented in previous EVAs application,

Turner et. al. (2004) has also demonstrated possibilities for the Agent model to
be devised as a generative design tool to coevolve agents and spaces. The design
was an emergent product of users’ movement using predefined rules. Turner’s
agents act upon their visual perception in relation to their position within the
environment. As they move through space, the visual information they receive
through their visual devices change and their reactions change accordingly. This
process involves a dynamic component, and in this way it differs from Space
Syntax representation. The Agent model starts from the irreducible elementary
actor in the system, that is the individual, and present the visual dynamics that
direct his movement aiming to understand and reproduce the process of inhabi-
tation and occupation in space.
101
The Agent analysis tools in the 2D view window (Map window) are used to gen-
erate aggregate models of agents’ movement in space. These aggregate models
are governed by global parameters as well as parameters defining the behaviour
of individual agents. The global parameters determine the duration of analysis,
when, where and how many agents are released into the system. They also
allow for externalising agents data to compare with movement traces and with
observed gate counts. The agents’ parameters will define their field of vision
and the number of steps at which they decide to change their directions. The
agents may follow different rules to see or take turns in the system. These rules
are in need of further testing by measuring the correlation between the model
and observed movement patterns. Empirical testing would help understanding
the basic cognitive mechanisms that drive explorative and planned movement
behaviour in relation to space.
The 3D view window provides the advantage of a 3D visualisation of agents in

action. The 3D view may cast more light on the individual and situated behav-
iour of agents; hence help to understand the notion of situated cognition, as
suggested by Alan Penn. It helps tracing the decision-making process embodied
in agents’ movement actions and reactions to spatial affordances. It allows for
the observer to track localised movement patterns, thus helping to design a
case-sensitive approach to simulate natural movement in a particular system. It
must be emphasised here that the 3D view in this version of Depthmap10.14 can
only show the standard automata, not the evolved or learning animats.
102
DEPTHMAP EXERCISE
Agent analysis in Depthmap
In this tutorial we are going to explain how to produce Agent analysis using the
different parameters settings in Depthmap. First, create a new file in Depthmap
and import a drawing file (either DXF or MIF file). The drawing you are import-
ing should have closed boundaries in order for you to be able to make Visibility
Graph before you start the agent tool. After you import the file follow the steps
to make a visibility graph, first by setting the grid using the SET GRID button .
The default value is 0.04. You could simply adjust this value by entering a differ-
ent number, preferably to be 0.02 to match human scale. This is up to you and it
depends on the resolution of the analysis you need to obtain. After you set the
grid, you can fill the enclosed spaces using the FILL button . Click inside the
area you want to analyse and it will fill it with a different colour marking the
enclosed space in which agents are going to move about. After you have done all
this steps to prepare a space for analysis, you have to make a visibility graph out
of two attributes the grid and the boundary that you have filled. In order for you
to do that, go to
TOOLS---VISIBILITY---MAKE VISIBILITY GRAPH
A window will appear providing you with visibility graph options. Just click OK
and it will make visibility graph. Now your settings are ready for Agent Analysis.
Go to TOOLS---AGENT TOOLS ---RUN AGENT ANALYSIS

The following window will appear.
1
1.1
1.2
2
2.1
2.2
2.3
3
3.1
3.2
3.3
4
5
Figure 13.1. Agent analysis setup in Depthmap.
In order to explain the window we have marked the different parameters with
numbers and a short explanation will be associated with each number.
103
7. Global setup
Sets the global attributes of the agent movement in the system
1.1. Analysis length (timesteps): Sets the period of the analysis in timesteps
1.2. Record gate counts in data map: records how many agents are passing
through predefined gates in a new column. These gate count values are stored
in a data map layer and can be compared to observed gate counts representing
pedestrian flow per time unit in the real built environment. Normally you will
need to log the values in both data map columns because the values will be
exponentially distributed and in order to calculate their correlation coefficient
they need to be normally distributed. You can log the value by just adding a log
function when updating the two columns.
8. Agent set parameters
Sets the attributes of agents releasing mechanism in the system
2.1. Release rate (agents per timestep): Sets how many agents are released to the
system within each time step.
2.2. Release from any location: A check box giving you the option of releasing
agents randomly from any location in the predefined space.
2.3. Release from selected locations: A check box giving you the option of
releasing agents from previously selected locations. You will have to select the
locations from which you want the agents to be released before you start your
Agent analysis. Normally you will need that to simulate the flow of people start-
ing from the entry points in the layout such as the main entrance, stair cases
or elevators. This technique also helps when the observer needs to compare
observed movement traces to traces that agents leave behind when moving
from the same point that the observer has followed people from. In order to
select locations on your grid you have to press the left mouse button and keep it
pressed while you define a window containing the points you need and release
it once you are done. If you want to add to your selection you will have to hold
the SHIFT button will you define a new selection using your mouse.
9. Agent program parameters
3.1. Field of view (bins): This attribute will define the field of view that each
agent can see when moving in certain direction. The default is 15 bins which is
equivalent to 170 degrees. It have proven to be most effective when comparing
to natural movement patterns. However, it is up to the researcher to change this
field of view subject to the particularities of the case under investigation.
3.2. Steps before turn decision: These are steps or grid points that the agent
passes through before choosing to randomly change direction in relation to the
environment surrounding them at the time it has arrived at the last step. The
default is 3 steps and it has proven to correlate best with natural movement pat-
terns. Again, it is up to the researcher to change that.
3.3. Timesteps in system: There are time steps that the agent move about in the
system before it disappears. Normally this will be relative to the distance chosen
104
between the grid points and the walking distance that a pedestrian may take in
a certain urban or building environment.
10. Record trials for
This option will export the movement traces to a file called trails that will be
stored within the folder where you have your original graph file. You can import
that file after you have done your agent analysis and Depthmap will store it as a
separate drawing layer.
11. Movement rule
There are different rules within this drop down list. It is advised that you use the
standard rule which is the default. The rest of these rules are part of an ongoing
research and need to be further tested before implementing in natural move-
ment simulations. Further information about these experiments might be fol-
lowed in Turner (2007a). The occlusion rules are of particular interest. However
these rules will not work properly unless you calculate isovist properties. For
that you have to go to
TOOLS --- VISIBILITY --- RUN VISIBILITY GRAPH ANALYSIS --- CALCULATE ISOVIST
PROPERTIES
Visualising agents movement in 3D view
In this section we will demonstrate how to use the Agent tools in the 3D view
window. The first step to do that is to open the 3D View from the Window Menu.
The 3D View window like the one you see in Figure 13.2 will appear and you will
be able to see on top of the window as highlighted a set of icons that may be
used to create, control, visualise and view Agents’ movement. The functionality
of each single tool is explained below.
Click this icon to enable you to drop a new agent within the scene.
Click this icon to enable agents’ movement after you have deliberately stopped
it
Click this icon to pause agents’ movement
Click this icon to stop agents’ movement
Click this icon to enable traces to be drawn tracking agents’ movement routes.
Click this icon to enable you to control the orbit zoom of the 3D view.
Click this icon to enable you to pan your view in different directions.
Click this icon to enable you to zoom in your view
Click this icon to enable you to have a continuous zoom
Click this icon to enable you to see the gate count values and how they
emerge and change as the agents move on the grid.
105
Figure 13.2. Agent tools that may be used to demonstrate realtime behaviour of individual agents
in a 3D view window in Depthmap.
106
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ADDITIONAL WEB SOURCES
Depthmap online training platform, Kayvan Karimi, Tim Stonor, Space Syntax Ltd
https://otp.spacesyntax.net/
Depthmap tutorials, Alasdair Turner, Joao Pinelo

https://github.com/SpaceGroupUCL/Depthmap/wiki/Depthmap-Tutorials
Depthmap 4, A Researcher’s Handbook, Alasdair Turner, June 2004

https://eprints.ucl.ac.uk/2651/1/2651.pdf
Space Syntax Network, Tim Stonor

https://www.spacesyntax.net/software/ucl-depthmap/
GitHub Depthmap Open Source Community

https://github.com/SpaceGroupUCL/Depthmap
UCL Depthmap website

https://www.bartlett.ucl.ac.uk/graduate/research/space/research/ucl-depthmap
People Watcher, Sheep Dalton

https://itunes.apple.com/gb/app/people-watcher/id523155791?mt=8
PedCount, Strategic Spatial Solutions

https://github.com/s3sol
Space Syntax journal, Sophia Psarra

https://joss.bartlett.ucl.ac.uk/journal/index.php/joss/index
111
APPENDICES
112
APPENDIX 1
Topological Measures of Space Syntax

The simplest graph measure of Space Syntax; that is Connectivity Ci = Ki, defines the
configurations of local structures in street networks; where Ci –in axial analysis- is
the number of axial lines connected to the i-th axial line. Beyond graph Connectiv-
ity, there is a set of global static and dynamic measures of the graph configurations
that are thought to be more predictive and postdective of pedestrian and vehicular
movement (Hillier et al, 2012), and perhaps the economic consequences of this
movement (Hillier, 1996a).
The global measures are derived from the graph topological depth, which accounts
for the distance between each axial line and all the others, where the shallowest
axial line is the closest to all other axial lines and the deepest is the furthest one.
Depth is a topological distance between vertices in the dual graph G. Two open
spaces, i and j, are said to be at depth dij if the least number of syntactic steps
needed to reach one vertix from the other is dij. The sum of all depths from a given
origin is computed as Total Depth;
𝑛𝑛−1
𝑇𝑇𝑇𝑇𝑖𝑖 = � 𝑑𝑑𝑖𝑖𝑖𝑖 , 𝑖𝑖 ≠ 𝑗𝑗 (10)

𝑗𝑗 =1

The Mean Depth of the graph representing average distance of the i-th axial line
from all the other n – 1 in the dual graph G is computed as follows;
𝑛𝑛−1
1
𝑀𝑀𝑀𝑀𝑖𝑖 = � 𝑑𝑑𝑖𝑖𝑖𝑖 , 𝑖𝑖 ≠ 𝑗𝑗 (11)
𝑛𝑛 − 1
𝑗𝑗 =1

Depth might be calculated for the whole graph G containing n vertices, or for a
certain number of neighbouring vertices within a predefined graph distance from
each vertex. For example, Mean Depth at radius 2 might be defined as the average
distance of the i-th axial lines from the other w-1 axial lines at a distance dij ≤ 2;
𝑤𝑤−1
1
𝑅𝑅2 𝑀𝑀𝑀𝑀𝑖𝑖 = � 𝑑𝑑𝑖𝑖𝑖𝑖 , 𝑖𝑖 ≠ 𝑗𝑗 (12)
𝑤𝑤 − 1
𝑗𝑗 =1

Using the topological measure of depth, it is possible to deduce the structural prop-
erties of the system by deducing the Relative Asymmetry RA! values. RA represents
the centrality of an axial line comparing its actual Mean Depth with the theoretical
highest and lowest values that Mean Depth could have in the given graph. Com-
pared to Mean Depth alone, Relativized Asymmetry is a normalization of depth to
fit values within the range [0 , 1]. RA! is the normalised value of being min (MD!) =1
and max(MD!) = n/2;
2(𝑀𝑀𝑀𝑀𝑖𝑖 − 1)
𝑅𝑅𝑅𝑅𝑖𝑖 = (13)
n−2
113
The problem arises with RA is that the limits that depth is being scaled to are quite
extreme. To enable a comparison between systems of different sizes and between
local and global structures within the same graph, a normalisation of the graph
measures is needed. For this purpose, a dedicated ‘Diamond’ D-Value was com-
puted to normalise graphs that are representative of architectural or urban spaces
(Kruger, 1989). Normalization using the D-Value is obtained through comparing a
centrality measure of the i-th vertix of a graph with n vertices with the centrality
measure we would get if the vertix was at the root of a graph of the same number
of n vertices but is justified in a diamond shape (Kruger, 1989; Teklenburg et al.
1993; Hillier & Hanson, 1984). In such a graph, depth values are thought to follow a
normal distribution; therefore, comparing the RA value of its root to that of a vertix
in a graph with the same number of vertices is a way to compare a normal distribu-
tion with the actual distribution. In this type of graphs the D-Value (Hillier & Hanson,
1984) is computed as:
𝑛𝑛 + 2
2{n �𝑙𝑙𝑙𝑙𝑙𝑙2 � � − 1� + 1}
𝐷𝐷𝑛𝑛 = 3 (14)
(n − 1)(n − 2)
In order to make the centrality measure of RA independent from the size of the
graph, Real Relative Asymmetry (RRA) values were computed to allow for a com-
parison between graphs of different sizes. RRA is derived by normalising RA values
by the D-Value;
𝑅𝑅𝑅𝑅𝑖𝑖
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 = (15)
𝐷𝐷𝑖𝑖
Using the aforementioned measures of street networks, Space Syntax has devel-
oped two major indices of Centrality that capture the relative structural importance
of a street represented by a vertex in a dual graph. The Centrality Closeness, defined
as Integration in Space Syntax terms, is expressed by a value that indicates the
degree to which a vertix is integrated or segregated from the urban system as a
whole (global Integration), or from a partial system consisting of vertices that reside
within a neighbourhood that is defined within a certain number of steps away from
each vertix (local Integration). The global measure of Integration is computed as
follows;
1 𝐷𝐷𝑖𝑖
𝐼𝐼𝐼𝐼𝐼𝐼𝑖𝑖 = = (16)
𝑅𝑅𝑅𝑅𝑅𝑅𝑖𝑖 𝑅𝑅𝑅𝑅𝑖𝑖
Another graph measure is Betweenness Centrality; redefined as Choice in syntactic

terms; that is the number of the intermediate vertices that stand on the shortest
path between i and j. Choice is a dynamic global measure of the flow through a
space i ∈G . It captures how often a vertex –an axial line- may be used in journeys
from all origins to all destinations in a street network. Vertices that occur on many
shortest paths between other vertices have higher Choice values than those that
do not. Global Choice can be computed as the ratio between the number of short-
est paths through vertix i and the total number of all shortest paths in a graph G.
To go back to the mathematical definitions, centrality ‘betweenness’ was defined
early in network theory as “the weighted frequency a point falls in the shortest path
between all origins and destinations in a given system” (Freeman, 1977). A calcula-
114
tion of Choice within different radii can render out the shortest path routes on dif-
ferent local and global scales. Unlike Integration which is normally or lognormally
distributed, Choice values are distributed exponentially. Most axial lines render very
low values of Choice, whilst a minority of axial lines reserve higher values than the
average and constitute the foreground of the urban fabric. Choice is computed as
follows;
𝜎𝜎𝑠𝑠,𝑡𝑡 (𝑖𝑖)
𝐶𝐶ℎ𝑖𝑖 = (17)
𝜎𝜎𝑠𝑠,𝑡𝑡
115
APPENDIX 2
Angular Analysis of Street Networks

This section explains the angular weighting concept as implemented in Space
Syntax to measure graph distance in street networks. The concept was originally
introduced in (Hillier & Iida, 2005) and was later applied in UCL Depthmap (Turner,
2011). To measure angular graph distance, the original axial map introduced in
(Hillier & Hanson, 1984) was again broken up into a segment map, were segments
are defined as the street lines between intersections (Figure 15.1. a). A graph repre-
sentation of the segment network is shown in (15.1.b), where each street segment
is represented as a vertix in a graph G’ and each intersection between two street
lines is considered to be a link.
a) A segment map b) A graph representation of a)
Figure 15.1 . A segment map model and its graph representation to elucidate how an angular graph
distance is calculated for a street network.
The distance cost between two line segments is measured by taking a “shortest
path” from one to the other, so the cost of travelling from a street segment s to a
street segment a can be notated as 𝜔𝜔(𝜋𝜋 − 𝜃𝜃) + 𝜔𝜔 (∅) , while the cost between s
and b can be defined as 𝜔𝜔(𝜃𝜃) + 𝜔𝜔(𝜋𝜋 − ∅) . The Least angular change (geometric)
distance cost is measured as “the sum of angular changes that are made on a route,
by assigning a weight to each intersection proportional to the angle of incidence of
two line segments at the intersection” (Hillier & Iida, 2005). The weight is defined so
that the distance gain will be 1 when the turn is a right angle or 90°, 2 if the angular
turn was 180°, and 0 would be the value of angular distance gain if two segments
are continuing straight – this is the case when segments belong to one axial line.
This description notated as follows;
𝜋𝜋
𝜔𝜔(𝜃𝜃) ∝ 𝜃𝜃 (0 ≤ 𝜃𝜃 < 𝜋𝜋), 𝜔𝜔(0) = 0, 𝜔𝜔 � � = 1 (18)
2
This angular cost can then be applied as a weighting function to the Centrality
measures of ‘Closeness’ and ‘Betweenness’, originally known in graph theory.
Closeness or Integration 𝐴𝐴𝐴𝐴𝐴𝐴𝜃𝜃 in Syntactic terms is defined as:
−1
𝐴𝐴𝐴𝐴𝐴𝐴𝜃𝜃 (𝑥𝑥) = �� 𝑑𝑑𝜃𝜃 (𝑥𝑥, 𝑖𝑖)� (19)

𝑖𝑖=1
116
(𝑛𝑛 + 2)1.2
𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝜃𝜃 (𝑥𝑥) = (21)
(∑𝑖𝑖=1 d
where 𝑑𝑑𝜃𝜃 (𝑥𝑥, 𝑖𝑖)) length of a geodesic (shortest path) between vertix x and i.
is the
Angular Betweenness or Angular Choice value for a segment x in a graph of n seg-
ments is calculated as follows:
∑𝑛𝑛𝑖𝑖=1 ′ ∑𝑛𝑛𝑗𝑗=1 𝜎𝜎 (𝑖𝑖, 𝑥𝑥, 𝑗𝑗)

𝐴𝐴𝐴𝐴𝐴𝐴𝐵𝐵 (𝑥𝑥) = (𝑖𝑖 ≠ 𝑥𝑥 ≠ 𝑗𝑗) (20)
(𝑛𝑛 − 1)(𝑛𝑛 − 2)
where (i, x, j) = 1 if the shortest path from i to j passes through x and 0 otherwise.
Normalising angular analysis of street networks
In order to enable cross scale comparisons between different parts of a city or

between different cities, Hillier et. al. (2012) suggested a normalisation procedure
for angular weighted graph distance considering a relationship between the ten-
dency of an urban system to1.2optimise travel distance from all origins to all destina-
tions𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁
and the cost of (𝑛𝑛 + 2)
segregation that is an effect of the system size. Normalised
(21)
𝜃𝜃 (𝑥𝑥) =
(∑
angular Integration NAIN 𝑑𝑑 (𝑥𝑥, 𝑖𝑖))
𝑖𝑖=1 𝜃𝜃 for a graph G’ with the size n is defined in Hillier et. al. as
follows;
(𝑛𝑛 + 2)1.2
(∑𝑖𝑖=1 𝑑𝑑𝜃𝜃 (𝑥𝑥, 𝑖𝑖))
(𝑛𝑛 + 2)1.2
(∑𝑖𝑖=1 d
where 𝑑𝑑𝜃𝜃 (𝑥𝑥, 𝑖𝑖)) length of a geodesic (shortest path) between vertix x and i.
is the
Normalised Angular Choice NACHB is defined as follows;
where (i, x, j) = 1if the shortest path from i to j passes through x and 0 otherwise.
117

T0 - Space Syntax Methodology

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This teaching guide was first released in 2013 under

the joint authorship of K. Al Sayed, B. Hillier, A. Penn and A. Turner.

Copyright @ 2018 Kinda Al_Sayed

This is a working document. It should not be used for teaching

No commercial use is allowed without permission.

The author makes absolutely no warranty that the

Graphic design: Lina Hakim

Book Management: Sam Griffiths and Kayvan Karimi

Illustrations: Kinda Al_Sayed

With contributions from:

SPACE SYNTAX METHODOLOGY 7

CONVEX AND AXIAL ANALYSIS 9

THEORETICAL BACKGROUND ON AXIAL AND CONVEX ANALYSIS 11

ISOVIST AND VGA ANALYSIS 27

THEORETICAL BACKGROUND ON ISOVIST AND VGA ANALYSIS 29

INTRODUCTION TO OBSERVATION TECHNIQUES 41

INTRODUCTION ON DATA AND STATISTICS 51

THEORETICAL BACKGROUND ON SEGMENT ANALYSIS 73

ADVANCED AXIAL AND SEGMENT ANALYSIS 85

THEORETICAL BACKGROUND ON ADVANCED AXIAL AND SEGMENT ANALYSIS 87

Foreground vs. Background analysis 87

THEORETICAL BACKGROUND ON THE AGENT MODEL 101

AGENT ANALYSIS IN DEPTHMAP 103

ADDITIONAL WEB SOURCES 111

This 'Practical Guide to Space Syntax Methodology' is intended to give an accessible

This guide is a supplement to the UCL MSc/MRes workshops that

What is Space Syntax?

It starts with a certain description of the spatial architecture of buildings and

Figure 1.1. The transformation of the Guildhall of the City

Figure 1.2. Angular choice in Manhattan. Source: Kinda

In this chapter, we will explain some basic topological graph representations of

Two representations of buildings and urban spaces

An analysed convex map An analysed axial map

1. A representational scheme of axial networks

On an urban scale, Space Syntax regards movement as the generic function of

2. A representational scheme of convex maps

Spatial layouts are often a combination of hierarchical structures and circulation

• a-types; which are characterised as dead-end spaces and

It is possible to apply the representational scheme of justified graphs to both axial

Connectivity (degree) measures the number of immediate neigh-

Integration is a measure that describes relativized asymmetry in

Control measures the degree to which a space controls access

Choice measures movement flows through spaces. Spaces that

Files available for you to use in the exercise

FILES DESCRIPTION SOURCE COPYRIGHTS

Before you start

Download Depthmap 10.14 from https://github.com/Space-

Note that Depthmap only runs on the Windows operating

TASK 1: STEPS TO PERFORM CONVEX ANALYSIS IN DEPTHMAP

1. Download your files

Go to Moodle and download the file GALLERY.

Create a new file: Go to FILE---NEW. This gives a

Save the file you are using as “GALLERY_

Choose the name and location of your file

Import the architectural layout into Depthmap:

3. Draw and connect convex spaces

Set up a new layer to draw your convex spaces.

After clicking OK you will have your new

For drawing convex spaces you will have to

Once you have done your convex breakup map,