Understanding collective human movement dynamics during large-scale events using big geosocial data analytics. (May 2021)

• Record Type: Journal Article
• Authors: Fan, Junchuan; Stewart, Kathleen
• Is Part Of: Computers, environment and urban systems. Volume 87(2021)
• Publication Date: 2021-05
• Subjects: Human mobility -- Big data -- Geosocial data -- Transportation -- Solar eclipse event; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2021.101605
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: 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. Highlights: Big geosocial data analytics to estimate collective human movement pattern Two-stage data collection process to augment data signal of geosocial data Variable KDE method to fuse georeference data with different spatial scales Reveal population response to large-scale event from big geosocial data and thus facilitate emergency response