Authors: Junchuan Fan*, Center for Geospatial Information Science, UMD, Kathleen Stewart, University of Maryland, College Park
Topics: Geographic Information Science and Systems, Urban and Regional Planning
Keywords: Human movement, big data, geosocial data, transportation, solar eclipse event
Session Type: Paper
Start / End Time: 11:10 AM / 12:25 PM
Room: Plaza Ballroom E, Sheraton, Concourse Level
Presentation File: No File Uploaded
Conventional approaches for modeling human movement dynamics often focus on movement dynamics in individuals’ 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 used geosocial data to study human movement dynamics in response to large-scale natural or societal events. Although geosocial data is publicly available, 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) module was developed 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.