Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level
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 de...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Social Sciences |
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| Online Access: | https://www.mdpi.com/2076-0760/10/6/227 |
| _version_ | 1797530203306590208 |
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| author | Yago Martín Zhenlong Li Yue Ge Xiao Huang |
| author_facet | Yago Martín Zhenlong Li Yue Ge Xiao Huang |
| author_sort | Yago Martín |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T10:25:39Z |
| format | Article |
| id | doaj.art-b515aad5cb654cb48acd18666d4431f5 |
| institution | Directory Open Access Journal |
| issn | 2076-0760 |
| language | English |
| last_indexed | 2024-03-10T10:25:39Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Social Sciences |
| spelling | doaj.art-b515aad5cb654cb48acd18666d4431f52023-11-22T00:04:36ZengMDPI AGSocial Sciences2076-07602021-06-0110622710.3390/socsci10060227Introducing Twitter Daily Estimates of Residents and Non-Residents at the County LevelYago Martín0Zhenlong Li1Yue Ge2Xiao Huang3School of Public Administration, University of Central Florida, Orlando, FL 32801, USAGeoinformation and Big Data Research Laboratory, Department of Geography, University of South Carolina, Columbia, SC 29208, USASchool of Public Administration, University of Central Florida, Orlando, FL 32801, USADepartment of Geosciences, University of Arkansas, Fayetteville, AR 72701, USAThe 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.https://www.mdpi.com/2076-0760/10/6/227social mediareal-timepopulationdigital trace datatourismdemography |
| spellingShingle | Yago Martín Zhenlong Li Yue Ge Xiao Huang Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level Social Sciences social media real-time population digital trace data tourism demography |
| title | Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level |
| title_full | Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level |
| title_fullStr | Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level |
| title_full_unstemmed | Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level |
| title_short | Introducing Twitter Daily Estimates of Residents and Non-Residents at the County Level |
| title_sort | introducing twitter daily estimates of residents and non residents at the county level |
| topic | social media real-time population digital trace data tourism demography |
| url | https://www.mdpi.com/2076-0760/10/6/227 |
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