Towards real-time population estimates: introducing Twitter daily estimates of residents and non-residents at the county level
This research provides a more temporally granular picture of local and non-local populations for demographers and policymakers, addressing the historical limitation of sparse data in migration and mobility studies.
This study developed a near real-time (one-day lag) Twitter census to estimate daily populations of residents and non-residents at the county level using geotagged tweets. The method achieved over 80% accuracy in internal validation against self-reported home locations and successfully reflected both regular seasonal tourism and non-regular events like the 2017 Great American Solar Eclipse.
The study of migrations and mobility 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). Internal validation reveals over 80% of accuracy when compared with users self-reported home location. External validation 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 the Great American Solar Eclipse of 2017. The findings demonstrate that Twitter holds potential to introduce the dynamic component often lacking in population estimates.