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

Recognizing unexplored retail prospects requires an expert understanding of spatial and demographic dynamics. This study introduces an integrated geospatial framework for retail opportunity mapping in Germany, integrating multi-criteria suitability modeling with spatial autocorrelation analysis and Geographically Weighted Regression (GWR). Using high-resolution demographic and retail data, we demonstrate pronounced spatial variation in the influence of key drivers: for example, the local effect of population density on retail suitability peaks in the Hamburg metropolitan region (notably in Norderstedt and Ahrensburg), while point-of-interest clustering is most influential near Rotenburg (Wümme) in Lower Saxony. These findings highlight the regional variability of retail market determinants as revealed by GWR. The gap analysis discovered 40 "white spot" grid cells throughout rural and urban areas that exhibit extraordinarily high anticipated suitability yet lack any retail infrastructure. These priority locations are geographically diverse, including well-populated municipalities in areas such as the districts of Esslingen and Göppingen (Baden-Württemberg), northeastern Brandenburg, and southern Bavaria. The spatial logic of these “white spots” is robust across alternative model specifications and externally validated by independent studies that document substantial retail supply gaps even in thriving communities. These insights not only benefit retailers seeking to enter underserved markets but also provide urban planners with actionable data to balance service provision and enhance infrastructure in high-demand areas, optimizing both commercial and community outcomes. By pinpointing both established retail hotspots and emerging underserved markets, this approach provides important information for retai lers and urban planners alike, and advances the science of data-driven site selection in geomarketing.