Advances in Geospatial Analysis: Predictive Analytics Methods for Store Location Selection and Market Entry Return on Investment
Abstract
Store location remains one of the few strategic decisions in retailing that is simultaneously capital intensive, difficult to reverse, and amenable to formal prediction, and the past three decades have transformed how firms model it. This review traces the migration of location science from deterministic gravity formulations toward probabilistic, data rich, and machine learning based prediction, and it connects that methodological evolution to the financial question that ultimately governs expansion, namely the return generated by entering a new market or opening a new outlet. The paper organizes the field into three developmental eras. The first established analog reasoning, gravity and spatial interaction models, and location allocation optimization as the formal vocabulary of site selection. The second integrated these models with geographic information systems and geodemographic classification, producing decision support environments that practitioners could operate at scale. The third, driven by mobile positioning data, location based social networks, point of interest databases, satellite imagery, and supervised learning, reframed prediction as an inductive exercise in which patterns are learned from large behavioral datasets rather than imposed by theory. Across these eras the review argues that geospatial prediction and return on investment have become inseparable, because revenue forecasts, cannibalization estimates, and network effects are now embedded directly in expansion appraisal. The synthesis identifies persistent controversies regarding model transferability, the displacement of theory by predictive accuracy, the treatment of competition and cannibalization, and the validity of behavioral proxies for genuine demand. It concludes that the field has converged on hybrid approaches that retain theoretical structure while exploiting data abundance, and that the most urgent priority is rigorous out of sample validation of return on investment forecasts across heterogeneous market contexts.
Keywords
retail location analysisspatial interaction modelsgeomarketinglocation allocationpredictive analyticsmarket entryreturn on investmentgeographic information systems
References
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