This document describes the Spatial Design Network Analysis (SDNA) Plugin for QGIS. It summarizes network analysis techniques like those developed by Euler, Burgess, and Christaller. It outlines the features of the sDNA toolbox, including statistics on network buffers and discrete and continuous space analysis. The document provides examples of using sDNA for epidemiology, modeling travel flows, and machine learning to combine multiple transportation behaviors. It demonstrates how sDNA can explain a large percentage of cycle transportation patterns based on urban design factors alone.

1. • www.cardiff.ac.uk/sdnawww.cardiff.ac.uk/sdna
Spatial Design Network Analysis
Plugin for QGIS
Crispin Cooper
October 2016

2. • www.cardiff.ac.uk/sdna
Spatial Network Analysis
"Konigsberg bridges" by Bogdan
Giuşcă - Public domain (PD), based
on the image. Licensed under CC
BY-SA 3.0
Euler, 1736

3. • www.cardiff.ac.uk/sdna
Spatial Network Analysis
"Konigsberg bridges" by Bogdan
Giuşcă - Public domain (PD), based
on the image. Licensed under CC
BY-SA 3.0
Euler, 1736
3
3
3
5

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Spatial Network Analysis
Euler
Burgess (Concentric city model)
Christaller (Central Place Theory)
Shimbel 1953
(Closeness)
Social network
analysis (trendy!) Space Syntax sDNA
Freeman 1977
(Betweenness)
MIT UNA, etc

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The sDNA Toolbox
sDNA
toolbox
Free & Open Source
“Free as in beer"
(not open source)
Proprietary
ArcGIS
Autocad
Command Line
QGIS

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Statistics on network buffers
•Accessibility within radius
• Closeness
• Improved accessibility measures
• Network gravity model
•Flows/Betweenness for radius
•Network density within radius
• Link count
• Junction count
• Total connectivity
• Total length
• Total angular cost
• Total weight
•Severance
• Total/mean geodesic length
• Total crow flight distance
• Mean geodesic diversion ratio
•Efficiency
• Convex hull area
• Convex hull perimeter
• Convex hull maximum radius
• Bearing of maximum radius
• Hull shape index
•Two phase (generation/assignment)
model
• Two phase betweenness
• Two phase destination weight

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Discrete Space Analysis

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Discrete Space Analysis

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Discrete Space Analysis

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Discrete Space Analysis

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Discrete Space Analysis

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Continuous Space Analysis

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Continuous Space Analysis

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Continuous Space Analysis

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Continuous Space Analysis

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Continuous Space Analysis

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Continuous Space Analysis

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Demo

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Why?
• Spatial analysis
– Based on spatial proximity rather than networks
– General toolbox
• Transport models
– Highly detailed, reductionist, technical
– Specialist – for non transport projects typically too
• Complex
• Expensive (calibration)
• Hard to adapt

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Epidemiology

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Convex Hull Maximum Radius 600m (HullR600)
“the furthest you can get (as the crow flies) from your point of origin,
by walking 600m (taking obstacles into account)”
…averaged across an enumeration district (major roads don’t count)
A measure of network efficiency for pedestrians

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Hay Festival
Home locations of survey
respondents travelling by car
Numbers where shown show
number of passengers per
vehicle

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Cardiff All non–residential address layer 2 2014
Three main patterns
- Cardiff central cluster
- Radial linear cluster
- Dispersed location
Alain Chiaradia 2016

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0%
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1 2 3 4 5 6 7 8 9 10
s PO 400 m PDF
Shopping % Street length %
Shopping and street network length as functions of predicted flow
Primary road network
27% of the shopping location
are on 18% of most
accessible street network.
Local super grid
37% of the shopping location
are on 14% of most
accessible part of the street
network
Pedestrian super-grid
37% of the shopping location
are on 11% of then most
accessible part of street
network.
Pedestrian local grid
20% of shopping location are
on 5% of the most
accessible part of the street
network.
1 most flow
10 least flow
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s PO 800 m PDF
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Shopping % Street length %
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s PO 2000 m PDF
Shopping % Street length %
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s PO 10,000 m PDF
Shopping % Street length %
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Shopping % Street length %
Alain Chiaradia 2016

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Machine learning
• Penalized Regression (ridge or lasso)
– To a transport modeller, it’s a mild form of entropy maximization
– To a Bayesian, it’s introducing priors for parameters to equal zero
– To a frequentist, it’s a technique for handling multicollinearity
• Generalized cross-validation with bootstrapping
– Avoids overfitting the model – used to tune penalty metaparameter only
• Weighting by
𝑦 𝜆
𝑦
– Minimizes a mixture of relative and absolute error
– 𝜆 = 0.7 aims to minimize GEH as favoured by transport modellers
– GEH thresholds need reconsidering for small flows but balance still taken
as appropriate

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Combining multiple behaviours through machine learning…

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Explains 78% of cycle flows based on
minimal data (cross validated r2)
• Visual reference for integrated
network planning
• Improve current models of cost
benefit (HEAT)
Combining multiple behaviours through machine learning…

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45%
of cycle-to-work
decisions explained
by urban design
…without knowing
places of work

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