An evaluation of geo-located Twitter data for measuring human migration. Issue 9 (2nd September 2022)

• Record Type: Journal Article
• Authors: Yin, Junjun; Gao, Yizhao; Chi, Guangqing
• Is Part Of: International journal of geographical information science. Volume 36:Issue 9(2022)
• Page Start: 1830
• Page End: 1852
• Publication Date: 2022-09-02
• Subjects: Human migration -- geo-located Twitter data -- migration flows -- spatial scan statistics -- multidimensional Poisson process; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2075878
• Languages: English
• ISSNs: 1365-8816

Abstract

Abstract: This study evaluates the spatial patterns of flows generated from geo-located Twitter data to measure human migration. Using geo-located tweets continuously collected in the U.S. from 2013 to 2015, we identified Twitter users who migrated per changes in county-of-residence every two years and compared the Twitter-estimated county-to-county migration flows with the ones from the U.S. Internal Revenue Service (IRS). To evaluate the spatial patterns of Twitter migration flows when representing the IRS counterparts, we developed a normalized difference representation index to visualize and identify those counties of over-/under-representations in the Twitter estimates. Further, we applied a multidimensional spatial scan statistic approach based on a Poisson process model to detect pairs of origin and destination regions where the over-/under-representativeness occurred. The results suggest that Twitter migration flows tend to under-represent the IRS estimates in regions with a large population and over-represent them in metropolitan regions adjacent to tourist attractions. This study demonstrated that geo-located Twitter data could be a sound statistical proxy for measuring human migration. Given that the spatial patterns of Twitter-estimated migration flows vary significantly across the geographic space, related studies will benefit from our approach by identifying those regions where data calibration is necessary.