用于社区商业集群弹性空间规划的空间交互模型深度学习表示
A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters
AI总结：
提出SIM-GAT模型，利用图注意力网络捕捉商业集群与贸易区间复杂交互，预测时空访问流量，为社区商业集群的弹性规划提供数据驱动策略。
摘要：
现有的空间交互模型（SIMs）在捕捉商业集群与贸易区之间复杂的且依赖上下文的交互方面存在局限。为了解决这一局限，我们提出了一种SIM-GAT模型，用于预测社区商业集群与其贸易区之间的时空访问流量。该模型创新地使用连通图来表示城市区域内商业集群、贸易区和交通基础设施的综合系统。然后，使用基于图的深度学习模型，即图注意力网络（GAT），来捕捉商业集群的复杂性和相互依赖性。我们利用从佛罗里达州迈阿密都市区收集的数据开发了该模型。随后，我们利用可解释人工智能（eXplainable AI）方法，证明了该模型在捕捉商业集群对不同居住社区以及不同情景下不断变化的吸引力方面的有效性。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连接的社区商业集群的动态。分析结果能够为基于数据和特定地点的规划策略提供信息，帮助社区商业集群在各种情景下更好地服务其客户，从而提升社区商业的弹性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
