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

Yago Martín,Xiao Huang,Yue Ge,Zhenlong Li

doi:10.3390/socsci10060227

Yago Martín, Xiao Huang + Show 2 more

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https://doi.org/10.3390/socsci10060227

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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.

Highlights

Spatial mobility has long been a matter of interest for many disciplines (e.g., Faggian and McCann 2008; Squire 2010; Colleoni 2016), and as a subject of study of geography (Cresswell 2011)
The study of population movements has historically been severely limited by the absence of reliable data or the temporal sparsity of available data (Laczko 2015; Rango and Vespe 2017)
In broad terms, changes of residence are rarely recorded and readily available at the time of the move, which precludes from connecting triggering events with population movements (Fussell et al 2014)

Summary

Introduction

Spatial mobility has long been a matter of interest for many disciplines (e.g., Faggian and McCann 2008; Squire 2010; Colleoni 2016), and as a subject of study of geography (Cresswell 2011). The study of population movements has historically been severely limited by the absence of reliable data or the temporal sparsity of available data (Laczko 2015; Rango and Vespe 2017). For long-term movements (e.g., displacement, migration), census data, with a multi-year periodicity, and household surveys, often collected on an annual basis, constitute the main data source for studying population movements. In broad terms, changes of residence are rarely recorded and readily available at the time of the move, which precludes from connecting triggering events with population movements (Fussell et al 2014). The need for a comparable, reliable, and dynamic (with a finer temporal resolution) source of data is, a condition to improve our understanding of human spatial mobility

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