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Illustrates how function-form analysis can be used to automatically make explicit what is implicit in the map. Ambition is to create hierarchies of objects more 'in tune' with our perception of the world.
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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
