Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.

Identifying counter-urbanisation using Facebook's user count data / Q. Duan, J. Steele, Z. Cheng, E. Cleary, N. Ruktanonchai, H. Voepel, T. O'Riordan, A.J. Tatem, A. Sorichetta, S. Lai, F. Eigenbrod. - 150:(2024), pp. 103113.1-103113.13. [10.1016/j.habitatint.2024.103113]

Identifying counter-urbanisation using Facebook's user count data

A. Sorichetta
Conceptualization
;
2024

Abstract

Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.
Diurnal population dynamics; Redistribution Counter-urbanisation; Facebook data; Data imputation; Decomposition analysis
Settore GEO/05 - Geologia Applicata
Settore SECS-S/04 - Demografia
Settore BIO/07 - Ecologia
2024
https://www.sciencedirect.com/science/article/pii/S0197397524001139?via=ihub
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1075628
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