Enhancing geospatial retail analysis by integrating synthetic human mobility simulations

The accuracy of retail location models depends on their precise calibration, but the data necessary for such a key task is seldom available. In this research, we use synthetic human mobility data, which introduces commuting dynamics, to improve the reliability of such models. We use the origin-destination flows to distribute households' potential expenditures in their home and commuting locations with the aim of modeling non-residential-driven demand in the commercial streets of Tokyo. We estimate potential revenues of commercial streets using the Huff model with its conventional specification as well as a variation of it that adopts pedestrian trajectory counts as the deterrence variable. We found that redistributing the potential expenditures toward the households' daytime locations significantly increased the model's performance. Additionally, we found that our use of pedestrian trajectory counts is comparable to using distance within the Huff model framework, but our proposed model was still outperformed by the conventional Huff model specification. We conclude that combining synthetic human mobility simulations and retail location models significantly increases the reliability of analysis in data-constrained situations.