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, [email protected]
Environmental and Geographical Sciences, Manchester Metropolitan University,
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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