Depthmap - InfAR - Bauhaus-UniversitÃ¤t Weimar

Depthmap 

Introduction to Space Syntax Analysis Software 

Dipl.-Ing. Sven Schneider 

Bauhaus-Universität Weimar 1 Decoding Spaces

Download Depthmap 

https://github.com/downloads/SpaceGroupUCL/Depthmap/DepthmapSetup1014.exe 


What is Depthmap? 

“It is a tool for topological analysis 

The analysis of layouts is achieved through the 

juxtaposition of graphs 

The graphs are analysed“ 

Possible Types of Analysis are: 

• Convex Space Analysis 

• Axial Line Analysis 

• Segment Analysis 

• Visibility Graph Analysis (including single 

Isovists & Isovist Fields) 

• Agent Analysis 


Tutorial 

Download at: 

http://www.vr.ucl.ac.uk/depthmap/tutorials/ 


Depthmap – User Interface 

Layer List 

Graphical 

Output 

Measures 


Importing Plans 

Depthmap is a pure Analysis Tool, it is not 

possible to draw plans! 

Depthmap can import 2-dimensional Plans. 

Fileformat working best is DXF (AutoCAD 2000 

Standard) 

After creating a new file (File New), you can 

import a plan (Map Import…) 

After importing, the plan is visible in the 

„Drawing Layers“ list. 

It is possible to import a number of plans. For 

each a new folder is added to the „Drawing 

Layers“ list. 

If your DXF File has different layers, these 

layers will be obtained in Depthmap. (This can 

be useful for analysing different design 

variants) 


Representations of space: 

Axial Lines, Convex Spaces und Isovists 


Convex Analysis 

It is not possible to detect Convex Spaces 

automatically! 

To make a Convex Analysis all the convex 

spaces have to be drawn „by hand“. 

For drawing a Convex Map in Depthmap it is 

useful to import a plan as a „template“. 

Then create a new map (Map New…) of the 

type „Convex Map“ 

For drawing Click on the - icon 

and draw the convex spaces „above“ the plan 

After this you have to connect all the spaces 

which touch, by using the Join-tool 

Start the Analysis (Tools Axial / Convex / 

Pesh Run Graph Analysis Ok) 


Exercise: Convex Map 

Create new file 

Import the plan „InfAR_office.dxf“ 

Create new Map (Map Type: Convex Map) 

Draw Convex Spaces 

Connect Convex Spaces 

Run Graph Analysis (Tools Axial / Convex / 

Pesh) 

Check Measures (Integration, Connectivity) 





Axial Analysis 

Axial lines can be drawn by hand, as well as 

being generated automatically. 

After creating an Axial Map it can be analysed 

(Tools Axial / Convex / Pesh Run Graph 

Analysis Ok) 

The measures are the same as in the Convex 

Analysis 


Drawing Axial Maps 

Axial Maps can be drawn by hand, as well as 

generated automatically. 

To draw an Axial Map first a new map must be 

created (Map New… Axial Map) 

After clicking on the drawing icon 

Lines can be drawn. 

Axial 

Before drawing the Axial Map it is useful to 

import a plan as a „template“. 

Note: You can also import a „hand-drawn“ Map 

as an Axial Map. This way you can use your 

favourite CAD-Tool. 


Generating Axial Maps 

To automatically derive an Axial Map of a plan 

use Axial Map Tool 

This creates an All-Axial Line Map, means a 

map which evenly covers the open space with 

Axial Lines (e.g. Lines of sight). 

This All Line Map can be analyed: Tools 

Axial / Convex /Pesh Run Graph Analsyis 

By clicking on Tools Axial / Convex / Pesh 

Reduce to fewest line map the All-Line-Map 

can be converted to a fewest line map. 


Algorithm for Generating All Line Maps 

Taken from: Turner, Penn & Hillier (2005) 


All-Line-Map & Fewest-Line-Map 

Advantage: 

divides the space into many possible lines of 

sight ( higher resolution) 

Disadvantage: 

Number of lines depend on the number of 

vertices in the plan (can lead to uneven 

weightings of certain areas) 

Advantage: 

Number of lines does not depend on the 

number of vertices but on connecting convex 

spaces 

Disadvantage: 

Low resolution can lead to missing some 

important lines 


Overview: Graph Measures (Axial Maps, Convex Maps) 

Connectivity 

Integration 

Choice 

Number of elements, which are connected to a certain element 

Distance of an element to (all) other elements in relation 

to the number of elements in the complete system 

(To-Movement, Centrality) 

Indicates how often a element is passed, when calculating the 

shortest paths between elements 

(Through-Movement) 

Control / Controllability 

Entropy / Relativised Entropy 

Intensity 


Connectivity 

Connectivity measures the number of elements, 

which are connected to a certain element. 

Connectivity is a local measure, means it only 

takes into account the direct neighbours of an 

element. Connectivity = 3 

Connectivity in an All-Line-Map 


Integration (To-Movement, Closeness) 

Integration misst wie weit entfernt sich ein 

Element (bspw. Axial Line) in Relation zu 

(allen) anderen Elementen befindet. 

Integration is a global measure, means it only 

takes into account the relations of all element to 

an element. 

Depth = 13 

Der Wert wird in der Graphenanalyse auch als 

Zentralität bezeichnet. 

RRA, RA, Total Depth, Mean Depth sind 

Werte, die sich nur durch Umrechnung 

(Normalisierung, etc.) von Integration 

unterscheiden. 

Integration in an All-Line-Map 


Choice (Through-Movement, Betweeness) 

Choice gibt an wie oft ein Element passiert 

wird, wenn alle kürzesten Wege (von jedem 

Element zu jedem anderen) im Graphen 

durchlaufen werden. 

Shortest path 

between 2 

elements 

Der Wert wird in der Graphenanalyse auch als 

Durchgangspotential bezeichnet. 

Standardmäßig ist die Choice-Berechnung in 

Depthmap deaktiviert, da sie verhältnismäßig 

viel Rechenzeit erfordert. 

Choice in an All-Line-Map 


To-Movement & Through-Movement (Integration & Choice) 

Look at: 

http://www.slideboom.com/presentations/29255 

8/Intro-to-Space-Syntax_Day-1 


Exercise: Axial Analysis 

Create new file 

Import „Grasse.dxf“ 

Create All-Line Map 

Run Graph Analysis (Tools Axial / Convex / 

Pesh) 

Check Measures (Connectivity, Integration, 

Choice) 

Reduce to fewest line map and analyse it. 

Compare the results with the All Line Map 


Scattergrams 

Scattergrams are used for examining 

correlations between different measures. 

A prominent example therefore is the 

correlation between integration and 

connectivity. The degree of correlation is called 

intelligibility. 

To visualize such correlations in Depthmap 

switch the window to „Scatter Plot“: 


Scattergrams 

Measure plotted on the Y-Axis 

Measure plotted on the X-Axis 

Show regression line 

Show Coefficient of 

determination 

Select the 

Analysis 

Type 


Global and local analysis 

Global analysis takes into account the relations 

of all elements to all elements. 

Local analysis takes into account relations in a 

certain distance / radius of each element 

Global Integration 

Local Integration 


Global and local integration 

The radius defines to which depth from the 

original element other elements are taken into 

account for calculating its depth in the system. 

“Radius-3 integration presents a localised 

picture of integration, and we can therefore 

think of it also as local integration, while radiusn 

integration presents a picture of integration at 

the largest scale, and we can therefore call it 

global integration.” (Hillier, 1996) 

Note: 

Integration R1 = Connectivity 

4 

3 

2 

1 

Depth, R2 = 1+1+2+2 = 6 

Depth, R3 = 1+1+2+2+3+3 = 12 

Depth, Rn = 1+1+2+2+3+3+4+4 = 20 


Segment Analysis 

In this type of Analysis not the longest lines of 

sight are taken into account, but the segments 

between intersecting lines. 

For calculating the graph measures not the 

steps from one to another element are 

counted, but the angles between the 

intersecting points of the elements. 

Advantages of Segment Analysis: 

Axial Map 

- more detailed 

- better correlation with movement analysis 

(Hillier & Iida, 2005) 

Look at: http://www.slideboom.com/presentations/293659/Intro-to-Space-Syntax_Day-2 

Segment Map 


Creating Segment Maps 

Once you have a fewest line map, you can 

easily convert it to a segment map. 

Map Convert Active Map Select Segment 

Map as new Map Type 


Overview: Segment Analysis Measures 

Connectivity 

Number of elements, which are connected to a certain element (Note: in 

Segment-Analysis Connectivity is no indicator how many streets are 

connected to a street because it only takes into account the localised segment, 

connectivity mostly ranges between 2 and 6) 

Total Depth Angular Distance of an element to (all) other elements (To-Movement) 

Integration 

Angular Distance of an element to (all) other elements in relation 

to the number of elements in the complete system (To-Movement, Centrality) 

Choice 

Indicates how often a element is passed, when calculating the 

shortest paths between elements (Through-Movement) 


Total Depth (Segment Analysis) 

Total Depth in Segment Analysis does not take 

into account the steps from one segment to 

another, but angles between segmentintersections. 

0° means a step-depth of 0.0 

. 

. 

. 


. 

. 

. 


. 

. 

. 

td = 1.07 + 0.07 + 0.05 + 

1.1 + 0.06 + 0.1 + 0.47 + 

0.1 = 2,85 


Exercise – Segment Analysis 

Convert the Fewest Line Map of Grasse to a 

segment map (Map Convert Active Map 

Select Segment Map as new Map Type) 

Analyse the segment map (Tools Segment 

Run Segment Analysis) 

Compare the results to the ones of the Axial 

Analysis 





Isovists 

By Clicking on the Isovist Icon 

create Isovists. 

you can 

You can create multiple Isovists and compare 

their properties by clicking on the different 

Isovist Measures in the Measures-List. 

For deleting Isovists, you have to switch on the 

„Editibale“-Mode in the Layer-List. (Note that 

sometimes Depthmap is buggy and does not 

delete Items properly!) 


Overview: Isovist Measures 

Area 

Perimeter 

Occlusivity 

Compactness 

Drift Magnitude 

Drift Angle 

Max Radial 

Min Radial 

Flächeninhalt des Sichtfeldes 

Umfang des Sichtfeldes 

Länge der „verdeckten“ Kanten 

Verhältnis von Area zu Perimeter in Relation zur idealen Kreisform 

Distanz vom Blickpunkt zum „Schwerpunkt“ des Isovist-Polygons 

Winkel des Vektors (Blickpunkt zu Schwerpunkt) zur X-Achse 

längster „Sichtstrahl“ 

kürzester „Sichtstrahl“ 


Isovist Measures - Examples 

circle square forest 

Area: 

Perimeter: 

Occlusivity: 

Compactness: 

400 

70.8 

0.0 

1.0 

400 

80 

0.0 

0.78 

298.1 

486.6 

416.5 

0.015 


Isovist Field 

“to quantify a whole configuration, more than a 

single isovist is required and he suggests that 

the way in which we experience a space, and 

how we use it, is related to the interplay of 

isovists” 

“Isovist fields record a single isovist property for 

all locations in a configuration” 


Isovist Field 

For creating an Isovist Field, you first have to 

set up a grid: 

In a Dialogbox you can adjust the gridsize. If 

your Model is scaled 1:1 then the unit is meter 

(m). 

After setting up the grid you have to „fill“ the 

open space 

Now you can create the visibility graph: 

Tools Visibility Make Visibility Graph 


Isovist Field 

Create a Isovist Field by clicking on 

Visibility Run Visibility Graph Analysis 

Click on „Calculate isovist properties“ 


Exercise - Isovist Field 

Import „Campus.dxf“ 

Set Grid to 2 

Fill the open space 

Calculate the isovist properties (Visibility 

Run Visibility Graph Analysis) 


Visibility Graph 

is a graph of mutually visible points in space 

In mathematical terms, a graph consists of two 

sets: the set of the vertices in the 

Graph and the set of edge connections joining 

pairs of vertices. 

The graph edges are undirected (that is, if v1 

can see v2 , then v2 can see v1 ). 

(see Turner et al, 2001) 

Schematic plan and visibility graph (from: Krämer & Kunze, 2005) 


Visibility Graph 

Create a Isovist Field by clicking on 

Visibility Run Visibility Graph Analysis 

Click on „Calculate visibility relationships“ 


Overview: Visibility Graph Measures 

Connectivity 

gibt an wieviele Punkte im Raum mit einem Punkt verbunden 

sind (entspricht der Area eines Isovist) 

Integration 

gibt die durchschnittliche visuelle Distanz eines Punktes zu allen anderen 

Punkten an 

Clustering Coefficient 

Control / 

Controllability 

Entropy / 

Relativised Entropy 

Intensity 


Colour Range 

Sometimes you have to adjust the color-range 

for making differences in the results visible 

more clearly. 


Permeability / Visibility 

“Architecture might be defined as the process 

of giving definitions to otherwise undefined 

space by providing boundaries. The boundaries 

used to define spaces may be dynamic, like 

swinging doors, or static, like walls. They may 

be transparent, like glass windows, or opaque, 

like brick walls. Whatever the nature of the 

boundaries, once defined, they provide a 

structure that distinguishes inside and outside. 

Depending on the nature of the boundaries, the 

accessibility, i.e. permeability, and visibility 

between inside and outside can be controlled. 

Both permeability - where you can go - and 

visibility - what you can see - directly affects 

how buildings in general and houses in 

particular work spatially and how they are 

experienced by their occupants, inhabitants as 

well as visitors.” 

(Güney, 2007, p.2) 


De-connecting Links in an Axial Map 

When in an axial map 2 Lines intersect 

graphically, but are not connected in reality, as 

it is the case with bridges or tunnels, you have 

to de-connect the link(s) of the axial lines. 

Therefore press Unlink 

and click on the corresponding lines 


Overview: Isovist Measures 

Area 

Perimeter 

Occlusivity 

Compactness 

Drift Magnitude 

Drift Angle 

Max Radial 

Min Radial 

Flächeninhalt des Sichtfeldes 

Umfang des Sichtfeldes 

Länge der „verdeckten“ Kanten 

Verhältnis von Area zu Perimeter in Relation zur idealen Kreisform 

Distanz vom Blickpunkt zum „Schwerpunkt“ des Isovist-Polygons 

Winkel des Vektors (Blickpunkt zu Schwerpunkt) zur X-Achse 

längster „Sichtstrahl“ 

kürzester „Sichtstrahl“ 


Overview: Visibility Graph Measures 

Connectivity 

gibt an wieviele Punkte im Raum mit einem Punkt verbunden 

sind (entspricht der Area eines Isovist) 

Integration 

gibt die durchschnittliche visuelle Distanz eines Punktes zu allen 

anderen Punkten an 


gibt an, wie viele der Punkte, die von einem Punkt aus gesehen 

werden können, sich gegenseitig sehen (kann als Maß für die 

Konvexität eines Isovisten gesehen werden) 

Control / 


gibt an wie kontrollierend bzw. kontrollierbar ein Element ist 

Entropy / 

Relativised Entropy 

gibt an wie geordnet ein System an einem bestimmten Punkt ist 



“The clustering coeffcient is a measure of the 

extent to which all the lines of sight which 

could exist in the neighbourhood of a location in 

the visibility graph, do exist. 

If most of the locations visible from a location 

are mutually visible then c will approach 1. If 

many of the locations visible from a location are 

not mutually visible, then c will approach 0. “ 

High Clustering Coefficient 

(nearly all of the locations 

inside the isovist are 

mutally visible) 

(O’Sullivan & Turner, 2001) 

Low Clustering Coefficient 

(just some locations inside 

the isovist are mutally 

visible) 



(Examples) 

Red – High Clustering Coefficient 

Blue – Low Clustering Coefficient 


Step Depth 

Calculates the steps necessary to get from one 

single element to all the others 

Step Depth is available in every graph based 

analysis (Convex Spaces, Axial Maps, Visibility 

Graphs, Segment Maps) 

Shortcut: Strg+D 

Step Depth in a Visbility 

Graph from a node at the 

entrance of the flat 



„control picks out visually dominant areas, 

whereas controllability picks out areas that 

may be easily visually dominated. 

… 

For control, each location is first assigned an 

index of how much it can see, the reciprocal of 

its connectivity. Then, for each point, these 

indices are summed for all the locations it can 

see. As should be obvious, if a location has a 

large visual field will pick up a lot of points to 

sum, so initially it might seem controlling. 

However, if the locations it can see also have 

large visual fields, they will contribute very little 

to the value of control. 

ci = Control of a node i 

kj = Connectivity of the nodes that node i 

is connected to 

So, in order to be controlling, a point must 

see a large number of spaces, but these 

spaces should each see relatively little. The 

perfect example of a controlling location is the 

location at the centre of Bentham's panopticon.” 

(Turner, 2004) 



“Controllability (…) for a location it is simply 

the ratio of the total number of nodes up to 

radius 2 to the connectivity (i.e., the total 

number of nodes at radius 1). 

Applied to the panopticon example, it would 

seem to operate in a similar manner to control. 

Each of the cells is highly controllable, as the 

area of visual field is small compared to the 

area viewable from the centre to which it 

connects, while the centre is less controllable, 

as it links only to the cells within its field, and 

they add little extra visual field.” (Turner, 2004) 

“Controllable spaces, on the other hand, are 

locations that can be easily seen from other 

locations but themselves cannot see much.” 

(Güney, 2007) 

Controllability = Connectivity R2 / Connectivity (R1) 

= 

/ 

das was man auf den „zweiten Blick“ sieht 

/ 

das was man auf den ersten Blick sieht 

Wenn Controllability hoch ist, ist der Zugang zu 

entsprechenden Punkt im Raum sehr einfach und 

vice versa. 



„However, the panopticon is, of course, a 

contrived example. In reality, some spaces can 

be both controllable and controlling, and others 

uncontrollable and uncontrolling. It would seem 

an interesting avenue of research to see if 

anything further can be made from these 

measures, for example, to look at locations 

where crimes are committed, where a robber 

will want to control the victim, but at the same 

time be uncontrollable by the forces of law and 

order.” 


Control 



Adding measures in Depthmap 

Click on Attributes Add Column type in a 

name right-click on the new measure in the 

measures-list and chose edit 

http://www.vr.ucl.ac.uk/depthmap/scripting/salascript.pdf 


Integration in Segment Maps 

Global Integration 

Local Integration 

(Bsp. R750) 

value("T1024 Node Count")/value("T1024 

Total Depth") 

(value("T1024 Node Count R750 

metric")^2))/(value("T1024 Total Depth R750 

metric") 

Integration-Choice 

Combination 

(value("T1024 Node Count")/value("T1024 

Total Depth"))*(log(value("T1024 

Choice")+2)) 


Agent Analysis 

“In agent-based analysis virtual `people' (called 

agents) are released into the environment, and 

make decisions on where to move within it. The 

agents require a visibility graph in order for 

them to have vision of the environment. 

… 

The original agents from Turner and Penn 

(2002) simply select a destination at random 

from their field of view, take a few steps 

towards, before selecting another destination. 

… 

The analysis may be performed accurately, 

counting agents passing through gates just as 

people can be measured passing through gates 

in the real world. 

… 

If agents are programmed to move towards 

occluding edges rather than open space, then 

their movement patterns tend to be drawn 

between the lines joining those occluding 

edges. Since the occluding edges in an 

environment are simply the concave corners 

within it, the agents start to embody the axial 

system with their movement.” 



Agent Analysis in Depthmap 

Tools Agent Analysis Run Agent Analysis 

(only available after a visibility graph is made) 


New: Space Syntax in Grasshopper 

See: “The parametric exploration of spatial properties – 

Coupling parametric geometry modeling and the graphbased 

spatial analysis of urban street networks” 

(Schneider, Bielik & König, 2012) 

Figure 1. Designing is a cyclical process where artifacts are 

created with the help of design tools (see Gänshirt, 2007) 

Figure 2: Screenshot of Rhino, Grasshopper and the new 

Spatial Analysis Components 



Figure 3: Modular concept of the spatial analysis framework for Grasshopper 

Figure 4. The ConvertToSegmentMap Component 

converts geometric structures into segment maps 

Figure 5. Converting a segment map into a graph (for 

details see Hillier & Iida, 2005) 



Coupling Analysis & Modeling offers 2 

important advantages: 

1. Effectively comparing design variants 

2. Using analyis results as parameters for the 

parametric model 

Figure 7. Relating betweenness (choice) to 

the width of streets 

Figure 6. Three design variants deriving from a simple parametric 

model, analysed in terms of betweenness (first row) and centrality 

(second row) 

Figure 8. Relating centrality (integration) to the 

height of buildings 


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