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.

翻译：现有空间交互模型（SIMs）在捕捉商业集群与贸易区域之间复杂且具有情境感知的交互作用方面存在局限性。为解决这一局限性，我们提出了一种SIM-GAT模型，用于预测社区商业集群及其贸易区域之间的时空访问流量。该模型创新性地利用连通图表示城市区域内商业集群、贸易区域和交通基础设施的集成系统。随后，采用基于图的深度学习模型——图注意力网络（GAT）来捕捉商业集群的复杂性和相互依赖性。我们利用佛罗里达州迈阿密大都市区收集的数据开发了该模型，并通过可解释人工智能方法展示了其在捕捉不同住宅社区吸引力的差异性以及多情境下的有效性。我们贡献了一种补充传统SIMs的新方法，用于预测和分析相互关联的社区商业集群的动态变化。分析结果可为基于数据且具地方针对性的规划策略提供依据，帮助社区商业集群在不同场景下更好地服务客户，从而提升社区商业的韧性。