A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要