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An open source delineation and hierarchical classification of UK retail agglomerations.
Macdonald, Jacob L; Dolega, Les; Singleton, Alex.
Afiliação
  • Macdonald JL; University of Sheffield - Department of Urban Studies and Planning, Sheffield, United Kingdom.
  • Dolega L; Geographic Data Science Lab, University of Liverpool - Department of Geography and Planning, Liverpool, United Kingdom. L.Dolega@liverpool.ac.uk.
  • Singleton A; Consumer Data Research Centre (CDRC), Liverpool, United Kingdom. L.Dolega@liverpool.ac.uk.
Sci Data ; 9(1): 541, 2022 09 03.
Article em En | MEDLINE | ID: mdl-36057644
ABSTRACT
Town centres and high streets typically form the social and commercial cores of UK cities and towns, yet, there is no uniform definition of what a town centre or high street is. In this study the spatial delineations of retail agglomerations are generated using open-source data for England, Wales, Scotland and Northern Ireland. The extent and boundaries of these physical retail areas are identified based on the density and connectivity patterns of individual retail units over space. A high resolution hexagonal grid is superimposed over spatial clusters of retail points and a network-based algorithm used to identify mutually exclusive tracts. Agglomerations are then pruned and fine-tuned according to a series of heuristic rules. Our retail agglomerations represent local commerce areas with shopping amenities and are assigned to a hierarchical classification ranking from the largest Regional Centres, Major Town Centres and Town Centres, down to Small Local Centres and Retail Parks. The classification into one of eleven hierarchies is based on a combination of relative rank in the local area and absolute size of retail units within the area. These retail agglomeration boundaries, hierarchical classification and lookups form an open-source spatial data product available for wide use and research implementation.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Data Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Sci Data Ano de publicação: 2022 Tipo de documento: Article