ABSTRACT

People interact with each other in space and time. Improved understanding of human interactions in spatial, temporal, and social dimensions are highly beneficial for research and practices in public health, urban planning, and other fields. Traditional methods of collecting social interaction data are time-intensive and resource-consuming, resulting in relatively small sample sizes and limited information. Furthermore, traditional methods often oversimplify the dynamics of human interactions and fail to capture the characteristics of places where the interactions occur. With the popularity of location-based social media (LBSM) platforms, people can publish information about their social events such as time, location, and other participants. This research introduces a framework that formalizes terminologies and concepts related to spatial-social connections for the construction of spatial-social networks from LBSM data in GIS. Supported by the framework, the study presents methods of collecting, analyzing, and visualizing LBSM data in spatial-social dimensions. The methods are implemented and tested in a case study with Facebook data. The case study demonstrates that location-based social media data can be transformed into spatial-social networks and then be analyzed and visualized to answer innovative types of scientific inquiries.

摘要

人们在时空上相互交流。更好地理解人在空间，时间和社会维度上的相互作用，对于公共卫生，城市规划和其他领域的研究和实践非常有益。传统的收集社交互动数据的方法非常耗时且耗费资源，导致样本量相对较小且信息有限。此外，传统方法通常过分简化了人与人之间互动的过程，无法捕捉到人与人之间发生互动的地方的特征。随着基于位置的社交媒体（LBSM）平台的普及，人们可以发布有关其社交事件的信息，例如时间，位置和其他参与者。这项研究引入了一个框架，该框架正式确定了与空间-社会联系有关的术语和概念，以便根据GIS中的LBSM数据构建空间-社会网络。在该框架的支持下，这项研究提出了在空间社会维度上收集，分析和可视化LBSM数据的方法。该方法在使用Facebook数据的案例研究中得以实施和测试。案例研究表明，基于位置的社交媒体数据可以转换为空间社交网络，然后进行分析和可视化以回答创新型的科学询问。该方法在使用Facebook数据的案例研究中得以实施和测试。案例研究表明，基于位置的社交媒体数据可以转换为空间社交网络，然后进行分析和可视化以回答创新型的科学询问。该方法在使用Facebook数据的案例研究中得以实施和测试。案例研究表明，基于位置的社交媒体数据可以转换为空间社交网络，然后进行分析和可视化以回答创新型的科学询问。