Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. However, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

用于模拟人类活动模式的传统方法通常将注意力集中在其日常生活中的人类活动和运动动态，而无法捕获响应大规模事件的人类运动动态的变化。随着信息和通信技术的飞速发展，许多研究人员已经采用了来自私人数据供应商的替代数据源（例如，手机记录，GPS轨迹数据）来研究人类对大规模自然或社会事件做出的动态响应。大的地理社交数据诸如地理定位推文之类的信息是公开可用的，并且随着现实事件的发生而动态变化，这使得它更有可能捕获实时的情绪和人群的反应。但是，精确定位的地理社会数据很少，而且偏向城市人口中心。在这项研究中，我们开发了一个大的地理社会数据分析框架，以提取人类运动动态以响应来自公开可用的地理参考推文中的大规模事件。该框架包括一个两阶段的数据收集模块，该模块以更具针对性的方式收集数据，以减轻地理参考推文的数据稀缺性问题。另外，采用可变带宽核密度估计（VB-KDE）方法融合不同空间尺度的地理参考信息，进一步增强了地理定位推文中包含的人类运动动态信号。为了校正地理参考推文的采样偏差，我们按人口调整了不同空间单位（例如县，州）的推文数量。为了演示所提出的分析框架的性能，我们选择了一个在美国全国范围内发生的天文事件，例如2017年大食蚀，作为示例事件，并研究了响应此事件的人体运动动力学。但是，此分析框架可以轻松地应用于其他类型的大规模事件，例如飓风或地震。为了演示所提出的分析框架的性能，我们选择了一个在美国全国范围内发生的天文事件，例如2017年大食蚀，作为示例事件，并研究了响应此事件的人体运动动力学。但是，此分析框架可以轻松地应用于其他类型的大规模事件，例如飓风或地震。为了演示所提出的分析框架的性能，我们选择了一个在美国全国范围内发生的天文事件，例如2017年大食蚀，作为示例事件，并研究了响应此事件的人体运动动力学。但是，此分析框架可以轻松地应用于其他类型的大规模事件，例如飓风或地震。