D5.1 Measuring urban sprawl patterns: a large scale automated graph and
LISA approach : Software and technical report
› Authors: Caruso Geoffrey; Hilal Mohamed; Thomas Isabelle
› Nature: Prototype
› Dissemination: Program participants
› Due date: Month 12 (September 2014)
› Submission: Delivered
› Download: –
| Executive summary |
We propose an innovative method that combines graph theory and local spatial autocorrelation analysis in order to identify urban patterns within a large urban and periurban region.
Objectives:
A new method is proposed for characterising urban patterns and sprawl at the scale of a large urban region. The approach overcomes the difficulties of surface-based representations of builtup morphologies, hence providing a better way to account for the social value and ecological impacts of the very proximity of built and non-built land.
Methods:
The strength of the approach resides in the direct use of the coordinates of each building in a region, a very parsimonious input of external parameters, and a local spatial statistical perspective. The method consists in, first, constructing and clumping the minimum spanning tree that links each building to its neighbours in order to obtain connected urban and suburban graph subsets, and second, measuring a local spatial association index (LISA) on inter-building distances in order to characterise ‘morphotypes’, which differentiate heterogeneous settlements from homogeneous clusters of either high density (compact nuclei) or low density (sprawl).
Main research findings:
We contribute here an original method that is arguably more parsimonious in the type of data used and less case-study dependent than land-use and census based approaches. Only the coordinates of buildings are used as the primary data source here. We rely on a graph approach and local Moran’s I to derive homogenous subset of local morphologies based on inter-building distances. We use very few exogenous parametric decisions, and the ‘morphotypes’ so derived do not rely on any pre-specified spatial units.
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