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Uncovering spatial and social gaps in rural mobility via mobile phone big data
Rural mobility inequality is an important aspect of inequality-focused Sustainable Development Goals. To reduce inequality and promote global sustainable development, more insight is needed into human mobility patterns in rural areas. However, studies on rural human mobility are scarce, limiting our...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119190/ https://www.ncbi.nlm.nih.gov/pubmed/37081023 http://dx.doi.org/10.1038/s41598-023-33123-0 |
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author | Liu, Zhengying Zhao, Pengjun Liu, Qiyang He, Zhangyuan Kang, Tingting |
author_facet | Liu, Zhengying Zhao, Pengjun Liu, Qiyang He, Zhangyuan Kang, Tingting |
author_sort | Liu, Zhengying |
collection | PubMed |
description | Rural mobility inequality is an important aspect of inequality-focused Sustainable Development Goals. To reduce inequality and promote global sustainable development, more insight is needed into human mobility patterns in rural areas. However, studies on rural human mobility are scarce, limiting our understanding of the spatial and social gaps in rural human mobility and our ability to design policies for social equality and global sustainable development. This study, therefore, explores human mobility patterns in rural China using mobile phone data. Mapping the relative frequency of short-distance trips across rural towns, we observed that geographically peripheral populations tend to have a low percentage of short-distance flows. We further revealed social gaps in mobility by fitting statistical models: as travel distances increased, human movements declined more rapidly among vulnerable groups, including children, older people, women, and low-income people. In addition, we found that people living with low street density, or in rural towns in peripheral cities with long distances to city borders, are more likely to have low intercity movement. Our results show that children, older adults, women, low-income individuals, and geographically peripheral populations in rural areas are mobility-disadvantaged, providing insights for policymakers and rural planners for achieving social equality by targeting the right groups. |
format | Online Article Text |
id | pubmed-10119190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101191902023-04-22 Uncovering spatial and social gaps in rural mobility via mobile phone big data Liu, Zhengying Zhao, Pengjun Liu, Qiyang He, Zhangyuan Kang, Tingting Sci Rep Article Rural mobility inequality is an important aspect of inequality-focused Sustainable Development Goals. To reduce inequality and promote global sustainable development, more insight is needed into human mobility patterns in rural areas. However, studies on rural human mobility are scarce, limiting our understanding of the spatial and social gaps in rural human mobility and our ability to design policies for social equality and global sustainable development. This study, therefore, explores human mobility patterns in rural China using mobile phone data. Mapping the relative frequency of short-distance trips across rural towns, we observed that geographically peripheral populations tend to have a low percentage of short-distance flows. We further revealed social gaps in mobility by fitting statistical models: as travel distances increased, human movements declined more rapidly among vulnerable groups, including children, older people, women, and low-income people. In addition, we found that people living with low street density, or in rural towns in peripheral cities with long distances to city borders, are more likely to have low intercity movement. Our results show that children, older adults, women, low-income individuals, and geographically peripheral populations in rural areas are mobility-disadvantaged, providing insights for policymakers and rural planners for achieving social equality by targeting the right groups. Nature Publishing Group UK 2023-04-20 /pmc/articles/PMC10119190/ /pubmed/37081023 http://dx.doi.org/10.1038/s41598-023-33123-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Liu, Zhengying Zhao, Pengjun Liu, Qiyang He, Zhangyuan Kang, Tingting Uncovering spatial and social gaps in rural mobility via mobile phone big data |
title | Uncovering spatial and social gaps in rural mobility via mobile phone big data |
title_full | Uncovering spatial and social gaps in rural mobility via mobile phone big data |
title_fullStr | Uncovering spatial and social gaps in rural mobility via mobile phone big data |
title_full_unstemmed | Uncovering spatial and social gaps in rural mobility via mobile phone big data |
title_short | Uncovering spatial and social gaps in rural mobility via mobile phone big data |
title_sort | uncovering spatial and social gaps in rural mobility via mobile phone big data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119190/ https://www.ncbi.nlm.nih.gov/pubmed/37081023 http://dx.doi.org/10.1038/s41598-023-33123-0 |
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