Conducting Trade Area Analysis Using Mobile Data: The Case of Michigan’s Super-Regional Shopping Centres

Date
2024-01-03
Authors
Azmy, Ali
Aversa, Joe
Hernandez, Tony
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5611
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The increasing availability of spatial big data has revolutionized data analytics and provided valuable insights into consumer behaviour. Spatial big data has enabled retailers to optimize product assortment, pricing, site selection, and trade area analysis. Mobile location data has further enhanced the analysis of individual consumer mobility patterns, offering a more detailed understanding of movement in various contexts. However, using mobile location data for trade area analysis in retail remains understudied. This study aims to fill this gap by employing advanced methods of trade area analysis using mobile location data. Two research questions guide the study: 1) How effective is mobile location data in modelling shopping centre trade area activity? and 2) How reliable are the derived metrics in reflecting changes in trade area consumer traffic patterns during and after the global COVID-19 pandemic? By addressing these questions, this study enhances our understanding of the potential of mobile location data for trade area analysis in retail. It provides insights into consumer behaviour dynamics during the pandemic.
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Geospatial Big Data Analytics, mobile location data, shopping centers, spatial big data, trade area analysis
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8 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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