Gisruk retail identapril2010

1April 2010

Automatic Classification of Retail Spaces from a Large Scale Topographc Database

William A Mackaness, Omair Z Chaudhry

School of GeoSciences, University of Edinburgh, william.mackaness@ed.ac.uk

Environmental and Geographical Sciences, Manchester Metropolitan University,

O.Chaudhry@mmu.ac.uk

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A need to classify retail spaces

• To view distribution patterns of different types of retail space at a national scale.

• ‘essential as a means of understanding and analysing relationships in the work of retailing’ (Guy 1998, p255).

• form follows function• How might we characterise Retail Space• .. And thus reason about Retail Space• 3 methodologies

– Boolean, Fuzzy and Bayesian• Results• Conclusion

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Characterising Retail Space

Shopping Malls

Factory outlet

Regional centre

Retail spaces

High St Retail parks

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Retail Space

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Measures to discern different Retail Spaces

• A measure of urban centrality

• Accessibility: Bus Roads

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Identifying Retail Spaces

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The High Street

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Boolean Approach

• Shopping mall

• Retail park

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Boolean Approach

• Retail park

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Boolean Logic

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Fuzzy Logic

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Outline

• Broad ambition• Characterising Retail Space• Reasoning about Retail Space• Results• Conclusion

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Bayesian Approach

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• ‘For a given, unclassified retail space with a specific set of characteristics, what is the likelihood that it belongs to the population of ‘shopping malls’ with their specific set of characteristics?’.

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Conclusion

• Sample size• Retail Spaces

– Need for classification national picture– Bayesian is best? (Fuzzy Logic less Black Box)

• Broader Issues– Green spaces, urban spaces– Automated derivation techniques (context of Open

Source Data)– Reasoning about space – making explicit what is

implicit