Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

TL;DR

This paper presents a novel analytical framework utilizing big geosocial data, specifically georeferenced tweets, to study human movement dynamics during large-scale events, addressing data scarcity and bias issues with targeted collection and advanced density estimation techniques.

Contribution

The study introduces a two-stage data collection process and a variable bandwidth kernel density estimation method to improve analysis of human movement from geosocial data during large-scale events.

Findings

Successfully analyzed human movement during the 2017 Great American Eclipse

Demonstrated the framework's adaptability to other large-scale events

Mitigated data scarcity and bias issues in georeferenced tweet analysis

Abstract

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.