Dr Crispin Cooper - QGIS Plugin ‘sDNA’

• www.cardiff.ac.uk/sdnawww.cardiff.ac.uk/sdna

Spatial Design Network AnalysisPlugin for QGIS

Crispin Cooper

October 2016

• 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



"Konigsberg bridges" by Bogdan Giuşcă - Public domain (PD), based on the image. Licensed under CC BY-SA 3.0

Euler, 17363

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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


The sDNA Toolbox

sDNA toolbox

Free & Open Source

“Free as in beer"(not open source)

Proprietary

ArcGIS

Autocad

Command Line

QGIS


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


Discrete Space Analysis










Continuous Space Analysis












Demo


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


Epidemiology


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


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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s PO 400 m PDF

Shopping %Street length %

Shopping and street network length as functions of predicted flow

Primary road network27% of the shopping location are on 18% of most accessible street network.

Local super grid37% of the shopping location are on 14% of most accessible part of the street network

Pedestrian super-grid37% of the shopping location are on 11% of then most accessible part of street network.

Pedestrian local grid20% of shopping location are on 5% of the most accessible part of the street network.

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s PO 800 m PDF


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s PO 2000 m PDF


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s PO 10,000 m PDF


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Alain Chiaradia 2016


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 – Minimizes a mixture of relative and absolute error– aims to minimize GEH as favoured by transport modellers– GEH thresholds need reconsidering for small flows but balance still taken

as appropriate


Combining multiple behaviours through machine learning…






























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)



45% of cycle-to-work

decisions explainedby urban design

…without knowing places of work



Software

Dr Crispin Cooper - QGIS Plugin ‘sDNA’