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Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level

Author

Listed:
  • Yago Martín

    (School of Public Administration, University of Central Florida, Orlando, FL 32801, USA)

  • Zhenlong Li

    (Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC 29208, USA)

  • Yue Ge

    (School of Public Administration, University of Central Florida, Orlando, FL 32801, USA)

  • Xiao Huang

    (Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA)

Abstract

The study of migrations and mobility has historically been severely limited by the absence of reliable data or the temporal sparsity of 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. Internal validation reveals over 80% 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 the potential to introduce the dynamic component often lacking in population estimates. This study could potentially benefit various fields such as demography, tourism, emergency management, and public health and create new opportunities for large-scale mobility analyses.

Suggested Citation

  • Yago Martín & Zhenlong Li & Yue Ge & Xiao Huang, 2021. "Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level," Social Sciences, MDPI, vol. 10(6), pages 1-20, June.
    • Handle: RePEc:gam:jscscx:v:10:y:2021:i:6:p:227-:d:574792
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    References listed on IDEAS

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    Cited by:

    1. Yang Yang & Xiang Chen & Song Gao & Zhenlong Li & Zhe Zhang & Bo Zhao, 2023. "Embracing geospatial analytical technologies in tourism studies," Information Technology & Tourism, Springer, vol. 25(2), pages 137-150, June.

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