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Existing Spatial Interaction Models (SIMs) are limited in capturing thecomplex and context-aware interactions between business clusters and tradeareas. To address the limitation, we propose a SIM-GAT model to predictspatiotemporal visitation flows between community business clusters and theirtrade areas. The model innovatively represents the integrated system ofbusiness clusters, trade areas, and transportation infrastructure within anurban region using a connected graph. Then, a graph-based deep learning model,i.e., Graph AttenTion network (GAT), is used to capture the complexity andinterdependencies of business clusters. We developed this model with datacollected from the Miami metropolitan area in Florida. We then demonstrated itseffectiveness in capturing varying attractiveness of business clusters todifferent residential neighborhoods and across scenarios with an eXplainable AIapproach. We contribute a novel method supplementing conventional SIMs topredict and analyze the dynamics of inter-connected community businessclusters. The analysis results can inform data-evidenced and place-specificplanning strategies helping community business clusters better accommodatetheir customers across scenarios, and hence improve the resilience of communitybusinesses.

A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters