Depthmap - infar-de - Bauhaus

Transcription

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
2
Decoding Spaces
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
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Decoding Spaces
Tutorial
Download at:
http://www.vr.ucl.ac.uk/depthmap/tutorials/
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Decoding Spaces
Depthmap – User Interface
Layer List
Graphical
Output
Measures
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Decoding Spaces
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)
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Decoding Spaces
Representations of space:
Axial Lines, Convex Spaces und Isovists
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Decoding Spaces
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)
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Decoding Spaces
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)
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Decoding Spaces
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Decoding Spaces
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
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Decoding Spaces
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.
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Decoding Spaces
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.
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Decoding Spaces
Algorithm for Generating All Line Maps
Taken from: Turner, Penn & Hillier (2005)
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Decoding Spaces
All-Line-Map & Fewest-Line-Map
Advantage:
divides the space into many possible lines of
sight ( higher resolution)
Advantage:
Number of lines does not depend on the
number of vertices but on connecting convex
spaces
Disadvantage:
Number of lines depend on the number of
vertices in the plan (can lead to uneven
weightings of certain areas)
Disadvantage:
Low resolution can lead to missing some
important lines
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Decoding Spaces
Overview: Graph Measures (Axial Maps, Convex Maps)
Connectivity
Number of elements, which are connected to a certain element
Integration
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)
Control / Controllability
Entropy / Relativised Entropy
Intensity
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Decoding Spaces
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
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Decoding Spaces
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
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Decoding Spaces
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
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Decoding Spaces
To-Movement & Through-Movement (Integration & Choice)
Look at:
http://www.slideboom.com/presentations/29255
8/Intro-to-Space-Syntax_Day-1
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Decoding Spaces
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
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Decoding Spaces
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“:
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Decoding Spaces
Scattergrams
Measure plotted on the Y-Axis
Measure plotted on the X-Axis
Show regression line
Show Coefficient of
determination
Select the
Analysis
Type
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Decoding Spaces
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
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Decoding Spaces
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
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
1
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Decoding Spaces
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.
Axial Map
Advantages of Segment Analysis:
-
more detailed
better correlation with movement analysis
(Hillier & Iida, 2005)
Look at: http://www.slideboom.com/presentations/293659/Intro-to-Space-Syntax_Day-2
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Segment Map
Decoding Spaces
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
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Decoding Spaces
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
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)
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(To-Movement)
Decoding Spaces
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°
.
.
.
45°
.
.
.
90°
.
.
.
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
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Decoding Spaces
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
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Decoding Spaces
33
Decoding Spaces
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!)
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Decoding Spaces
Overview: Isovist Measures
Area
Flächeninhalt des Sichtfeldes
Perimeter
Umfang des Sichtfeldes
Occlusivity
Länge der „verdeckten“ Kanten
Compactness
Verhältnis von Area zu Perimeter in Relation zur idealen Kreisform
Drift Magnitude
Distanz vom Blickpunkt zum „Schwerpunkt“ des Isovist-Polygons
Drift Angle
Winkel des Vektors (Blickpunkt zu Schwerpunkt) zur X-Achse
Max Radial
längster „Sichtstrahl“
Min Radial
kürzester „Sichtstrahl“
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Decoding Spaces
Isovist Measures - Examples
Area:
Perimeter:
Occlusivity:
Compactness:
circle
square
400
70.8
0.0
1.0
400
80
0.0
0.78
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forest
298.1
486.6
416.5
0.015
Decoding Spaces
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”
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Decoding Spaces
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
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Decoding Spaces
Isovist Field
Create a Isovist Field by clicking on
Visibility  Run Visibility Graph Analysis
Click on „Calculate isovist properties“
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Decoding Spaces
Exercise - Isovist Field
Import „Campus.dxf“
Set Grid to 2
Fill the open space
Calculate the isovist properties (Visibility 
Run Visibility Graph Analysis)
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Decoding Spaces
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)
41
Decoding Spaces
Visibility Graph
Create a Isovist Field by clicking on
Visibility  Run Visibility Graph Analysis
Click on „Calculate visibility relationships“
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Decoding Spaces
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
43
Decoding Spaces
Colour Range
Sometimes you have to adjust the color-range
for making differences in the results visible
more clearly.
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Decoding Spaces
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)
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Decoding Spaces
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
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Decoding Spaces
Overview: Isovist Measures
Area
Flächeninhalt des Sichtfeldes
Perimeter
Umfang des Sichtfeldes
Occlusivity
Länge der „verdeckten“ Kanten
Compactness
Verhältnis von Area zu Perimeter in Relation zur idealen Kreisform
Drift Magnitude
Distanz vom Blickpunkt zum „Schwerpunkt“ des Isovist-Polygons
Drift Angle
Winkel des Vektors (Blickpunkt zu Schwerpunkt) zur X-Achse
Max Radial
längster „Sichtstrahl“
Min Radial
kürzester „Sichtstrahl“
47
Decoding Spaces
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 /
Controllability
gibt an wie kontrollierend bzw. kontrollierbar ein Element ist
Entropy /
Relativised Entropy
gibt an wie geordnet ein System an einem bestimmten Punkt ist
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Decoding Spaces
“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)
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Decoding Spaces
(Examples)
Red – High Clustering Coefficient
Blue – Low Clustering Coefficient
50
Decoding Spaces
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
51
Decoding Spaces
„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)
52
Decoding Spaces
“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)
Controllability = Connectivity R2 / Connectivity (R1)
=
“Controllable spaces, on the other hand, are
locations that can be easily seen from other
locations but themselves cannot see much.”
(Güney, 2007)
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.
53
Decoding Spaces
„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.”
(Turner, 2004)
Control
54
Controllability
Decoding Spaces
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
61
Decoding Spaces
Integration in Segment Maps
Global Integration
value("T1024 Node Count")/value("T1024
Total Depth")
Local Integration
(Bsp. R750)
(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))
62
Decoding Spaces
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.”
(Turner, 2007)
63
Decoding Spaces
Agent Analysis in Depthmap
Tools  Agent Analysis  Run Agent Analysis
(only available after a visibility graph is made)
64
Decoding Spaces
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 2: Screenshot of Rhino, Grasshopper and the new
Spatial Analysis Components
Figure 1. Designing is a cyclical process where artifacts are
created with the help of design tools (see Gänshirt, 2007)
65
Decoding Spaces
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)
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Decoding Spaces
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)
67
Figure 8. Relating centrality (integration) to the
height of buildings
Decoding Spaces
Space Syntax Mailinglist
68
Decoding Spaces

Depthmap - infar-de - Bauhaus

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