A Geospatial Framework for Retail Suitability Modelling and Opportunity Identification in Germany

This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. It combines multi-criteria suitability modelling with spatial autocorrelation and Geographically Weighted Regression (GWR). Using fine-scale demographic and retail data, the results show clear regional differences in how drivers operate. Population density is most influential around large metropolitan areas, while the role of points of interest is stronger in smaller regional towns. A separate gap analysis identified forty grid cells with high suitability but no existing retail infrastructure. These locations are spread across both rural and urban contexts, from peri-urban districts in Baden-Württemberg to underserved municipalities in Brandenburg and Bavaria. The pattern is consistent under different model specifications and echoes earlier studies that reported supply deficits in comparable communities. The results are useful in two directions. Retailers can see places with demand that has gone unnoticed, while planners gain evidence that service shortages are not just an urban issue but often show up in smaller towns as well. Taken together, the maps and diagnostics give a grounded picture of where gaps remain, and suggest where investment could bring both commercial returns and community benefits. This study develops an open, reproducible geospatial workflow to identify high-potential retail locations across Germany using a 1 km census grid and OpenStreetMap points of interest. A multi-criteria suitability surface is constructed from demographic and retail indicators and then subjected to spatial diagnostics to separate visually high values from statistically coherent clusters. “White-spots” are defined as cells in the top decile of suitability with zero (strict) or ≤1 (relaxed) existing shops, yielding actionable opportunity candidates. Global autocorrelation confirms strong clustering of suitability, and Local Indicators of Spatial Association isolate hot- and cold-spots robust to neighbourhood size. To explain regional heterogeneity in drivers, Geographically Weighted Regression maps local coefficients for population, age structure, and shop density, revealing pronounced intra-urban contrasts around Hamburg and more muted variation in Berlin. Sensitivity analyses indicate that suitability patterns and priority cells stay consistent with reasonable reweighting of indicators. The comprehensive pipeline comprising suitability mapping, cluster diagnostics, spatially variable coefficients, and gap analysis provides clear, code-centric data for retailers and planners. The findings point to underserved areas in smaller towns and peri-urban districts where investment could both increase access and business feasibility.