Beyond the Idyll: Unveiling the realities of risk perception and neighborhood influence on rural in-migrants in the COVID-19 Era in Dali, China

While the COVID-19 pandemic has intensified counter-urban migration trends globally, with rural areas often portrayed as ideal havens, the post-migration realities and risks faced by rural in-migrants remain understudied. Addressing this research gap, this research investigates the neighborhood factors influencing risk perceptions of rural in-migrants during the COVID-19 pandemic, focusing on four rural destinations in Dali, China. Using a mixed-method approach combining questionnaires, participatory mapping, and regression analysis, we surveyed 234 rural migrants. Our study incorporates objective measurements of neighborhood environments through Participatory GIS. Findings reveal that reduced life quality and economic income are the main perceived risks among rural in-migrants during the pandemic. Neighborhood factors significantly mitigating perceived risks include green spaces, accessible living services, government satisfaction, and strong local community networks. The study also identifies varying vulnerability levels among migrant groups, with migrant workers showing higher risk perceptions than entrepreneurs or unemployed individuals. This research addresses a critical gap in migration studies by examining post-relocation risks faced by rural in-migrants, especially during global crises. It provides empirical evidence on the role of neighborhood environments in mitigating migrants' risk perceptions, offering insights for enhancing rural community resilience. The findings contribute to our understanding of rural in-migration dynamics in the context of the COVID-19 pandemic and beyond, informing strategies for sustainable rural development and migrant integration in the face of future crises. • Post-pandemic rural in-migrants face economic and lifestyle risks, challenging the idealized "rural idyll" narrative. • Neighborhood environments significantly influence rural in-migrants' risk perceptions during the COVID-19 pandemic. • Green spaces, services, government satisfaction, and community networks mitigate migrants' pandemic risk perceptions. • Occupation, gender, and education level influence pandemic risk perception among rural in-migrants.

Authors

Institutions

Publication Details

Journal
Journal of Urban Management
Published
2024-09-30
DOI
https://doi.org/10.1016/j.jum.2024.09.001
Citations
2
Primary Topic
Disaster Management and Resilience
Type
article
Field-Weighted Citation Impact
2.26
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond the Idyll: Unveiling the realities of risk perception and neighborhood influence on rural in-migrants in the COVID-19 Era in Dali, China

Zhengxu Zhou, Yali Li, N. Zhang, Makoto Yokohari et al.
2 citations
Journal of Urban Management
Disaster Management and Resilience
2.26
article

Beyond the Idyll: Unveiling the realities of risk perception and neighborhood influence on rural in-migrants in the COVID-19 Era in Dali, China

Zhengxu Zhou, Yali Li, N. Zhang, Makoto Yokohari, Song Han, Yan Ni, Canxu Zeng
article en
2 citations

Abstract

While the COVID-19 pandemic has intensified counter-urban migration trends globally, with rural areas often portrayed as ideal havens, the post-migration realities and risks faced by rural in-migrants remain understudied. Addressing this research gap, this research investigates the neighborhood factors influencing risk perceptions of rural in-migrants during the COVID-19 pandemic, focusing on four rural destinations in Dali, China. Using a mixed-method approach combining questionnaires, participatory mapping, and regression analysis, we surveyed 234 rural migrants. Our study incorporates objective measurements of neighborhood environments through Participatory GIS. Findings reveal that reduced life quality and economic income are the main perceived risks among rural in-migrants during the pandemic. Neighborhood factors significantly mitigating perceived risks include green spaces, accessible living services, government satisfaction, and strong local community networks. The study also identifies varying vulnerability levels among migrant groups, with migrant workers showing higher risk perceptions than entrepreneurs or unemployed individuals. This research addresses a critical gap in migration studies by examining post-relocation risks faced by rural in-migrants, especially during global crises. It provides empirical evidence on the role of neighborhood environments in mitigating migrants' risk perceptions, offering insights for enhancing rural community resilience. The findings contribute to our understanding of rural in-migration dynamics in the context of the COVID-19 pandemic and beyond, informing strategies for sustainable rural development and migrant integration in the face of future crises. • Post-pandemic rural in-migrants face economic and lifestyle risks, challenging the idealized "rural idyll" narrative. • Neighborhood environments significantly influence rural in-migrants' risk perceptions during the COVID-19 pandemic. • Green spaces, services, government satisfaction, and community networks mitigate migrants' pandemic risk perceptions. • Occupation, gender, and education level influence pandemic risk perception among rural in-migrants.

Journal of Urban ManagementVol. 14(1)
Chiba University (JP), Beijing Forestry University (CN), Beijing University of Civil Engineering and Architecture (CN), The University of Tokyo (JP), South China University of Technology (CN)
Reduced inequalities
Openalex Percentile: Top 10%
Disaster Management and Resilience
2.26
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.