  With the rapid advancement of information and communication technologies,
many researchers have adopted alternative data sources 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.
