A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters
This work addresses the problem of improving resilience in community business clusters for urban planners, though it is incremental as it supplements conventional models with a novel method.
The authors tackled the limitation of existing Spatial Interaction Models in capturing complex interactions between business clusters and trade areas by proposing a SIM-GAT model, which uses a graph-based deep learning approach to predict spatiotemporal visitation flows, demonstrating its effectiveness with data from the Miami metropolitan area.
Existing Spatial Interaction Models (SIMs) are limited in capturing the complex and context-aware interactions between business clusters and trade areas. To address the limitation, we propose a SIM-GAT model to predict spatiotemporal visitation flows between community business clusters and their trade areas. The model innovatively represents the integrated system of business clusters, trade areas, and transportation infrastructure within an urban region using a connected graph. Then, a graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the complexity and interdependencies of business clusters. We developed this model with data collected from the Miami metropolitan area in Florida. We then demonstrated its effectiveness in capturing varying attractiveness of business clusters to different residential neighborhoods and across scenarios with an eXplainable AI approach. We contribute a novel method supplementing conventional SIMs to predict and analyze the dynamics of inter-connected community business clusters. The analysis results can inform data-evidenced and place-specific planning strategies helping community business clusters better accommodate their customers across scenarios, and hence improve the resilience of community businesses.