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

Social Sciences

Xiao Huang¹  Zhenlong Li²  Yue Ge³  Yago Martín³ 

[1] Department of Geosciences, University of Arkansas, Fayetteville, AR 72701, USA;Geoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC 29208, USA;School of Public Administration, University of Central Florida, Orlando, FL 32801, USA;

关键词: social media;    real-time;    population;    digital trace data;    tourism;    demography;   

DOI  :  10.3390/socsci10060227

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown