Assessing the Relative and Combined Effects of Network, Demographic, and Suitability Patterns on Retail Store Sales

Junyi Wang and Derek T. Robinson ()
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Junyi Wang: Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada
Derek T. Robinson: Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L3G1, Canada

Land, 2023, vol. 12, issue 2, 1-23

Abstract: Despite challenges associated with acquiring proprietary sales data, there exists a wealth of literature using different types of data (e.g., spending, demographic, geographic) to understand or represent different drivers of retail store sales. We contribute to the spatial analysis of drivers of retail store sales by analyzing the relative influence of road networks, demographic, and suitability variables on retail store sales within the home-improvement sector. Results demonstrate that the inclusion of variables describing the road network pattern is more influential in predicting store sales than demographic and suitability variables with linear models (e.g., ordinary- and partial-least squares regression) as well as with a non-linear mathematical model derived using artificial intelligence. The analysis builds on previous research estimating consumer spending and a big-data suitability analysis for site selection that incorporates spatial interaction models, location quotient, and other unique criteria that are typically used in isolation. The overarching contribution of our results is the demonstration that network patterns can play a critical role in retail store sales, especially when regressions, analogs, and other simple methods for site selection are used.

Keywords: network analysis; regression; artificial intelligence; site selection; spatial analysis; retail (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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