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Modeling intra-city functional regions is essential for understanding socio-economic spatial organization and optimizing urban planning, infrastructure, and resource allocation. While existing research has extensively examined intra-city functional regions in current and past contexts, the development of predictive modeling approaches to forecast their future evolution remains largely unexplored. This study introduces an integrated approach to forecast the evolution of intra-city functional regions in Chicago using taxi trip data. The method involves two stages: (1) Calibrating the Flow-Focused Spatially Weighted Interaction Model (SWIM) with historical taxi trips to estimate time-dependent trip distribution parameters across 48 half-hour intervals, then generating predicted trips for validation; and (2) applying the Infomap algorithm to predicted trip networks to delineate functional regions temporally. Validation via hindcasting revealed strong agreement between modeled and actual regions, with stable counts, consistent boundaries, and diurnal trends reflecting Chicago’s mobility patterns. Spatial autocorrelation further confirmed the model’s ability to replicate spatiotemporal structures. This framework advances predictive urban analytics, offering planners a tool to anticipate functional region dynamics.
Ghanbari et al. (Thu,) studied this question.