Authors: Yago Martin Gonzalez*, University of South Carolina
Topics: Hazards, Risks, and Disasters, Behavioral Geography
Keywords: mobility, tourism, population, social media, Big Data
Session Type: Paper
Presentation File: No File Uploaded
The study the study of population stocks and human movements has historically been severely limited by the absence of reliable data or the temporal sparsity of the available data. Using geospatial digital trace data, the study of population movements can be much more precisely and dynamically measured. Our research seeks to develop a near real-time (one-day lag) Twitter census that gives a more temporally granular picture of local and non-local population at the county level. Leveraging geotagged tweets to determine the home location of all active Twitter users, we contribute to the field of digital and computational demography by obtaining accurate daily Twitter population stocks (residents and non-residents). Preliminary results suggest these stocks correlate with available statistics of residents/non-residents at the county level and can accurately reflect regular (seasonal tourism) and non-regular events such as evacuations or post-disaster displacement.
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