Automatic Classification of Retail Spaces from a Large Scale Topographic Database

William A Mackaness, Omair Z Chaudhry

Research output: Contribution to journalArticlepeer-review

Abstract

There is considerable interest in understanding the distribution patterns of different types of retail space, over time, and doing so at a national scale. Yet a lack of suitable data, coupled with poor classification schemas, has stymied efforts to create such a national perspective. This research reports on metrics and classification methodologies that have been applied to large scale topographic data, that afford a systematic classification of certain retail spaces potentially at the national coverage. By analysing the form, composition, extent and patterns of buildings within retail spaces, together with their degree of centrality and levels of access, we demonstrate that it is possible to classify different types of retail space. The research illustrates the utility of fine scale topographic data beyond mere mapping. The article compares three methodologies used for classification (Boolean, fuzzy logic and Bayesian modelling) and evaluates them through comparison with known locations of various retail types as a way of assessing the validity of these approaches. The quality of the results are good, though the work highlights the inconsistency in definitions that currently exist – reflecting, as much as anything, the shifting sands of definitions of various retail spaces that ebb and flow according to consumer needs, and the ambitions of urban planners.
Original languageEnglish
Pages (from-to)291-307
Number of pages17
JournalTransactions in GIS
Volume15
Issue number3
DOIs
Publication statusPublished - 1 Jul 2011

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