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 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.

The effects of urban land use on energy-related CO2 emissions in China.

Land use change caused by urbanization is widely believed to be the primary way human activities affect energy use and, thus, CO2 emissions (CEs) in China. However, there is a limited understanding of the role of land use with detailed categories in energy-related CEs is still absent. This paper aims to narrow the knowledge gap using multi-dimension metrics, including land use scale, mixture, and intensity. These metrics were derived from three years of sequential POI data. A GWR analysis was carried out to examine the associations between land use change and energy-related CEs. Our results show that (1) the scale of most land use types exerted a bidirectional effect on CEs, demonstrating apparent spatiotemporal heterogeneity; (2) land use mixture of mature city agglomerations had a significant suppressive effect on CEs, suggesting mixed land use be advocated in the urbanization process; (3) Land use intensity had a bi-directional association with CEs in most cities, but its adverse effect gradually spread from the west to the northeast. Therefore, systematically regulating land transaction to control land scale, appropriately interplanting biofuel plants, and utilizing renewable energy are encouraged to reduce energy footprints and mitigate CEs in China. The findings and conclusions of this paper enhance our knowledge on the relationship between land use and CEs and present the scientific basis for policy-making in building low-carbon cities in the context of rapidly urbanizing China.

Using mobile phone big data to discover the spatial patterns of rural migrant workers' return to work in China's three urban agglomerations in the post-COVID-19 era.

Knowing how workers return to work is a key policymaking issue for economic recovery in the post-COVID-19 era. This paper uses country-wide time-series mobile phone big data (comparing monthly and annual figures), obtained between February 2019 and October 2019 and between February 2020 and October 2020, to discover the spatial patterns of rural migrant workers' (RMWs') return to work in China's three urban agglomerations (UAs): the Beijing-Tianjin-Hebei Region, the Yangtze River Delta and the Pearl River Delta. Spatial patterns of RMWs' return to work and how these patterns vary with location, city level and human attribute were investigated using the fine-scale social sensing related to post-pandemic human mobility. The results confirmed the multidimensional spatiotemporal differentiations, interaction effects between variable pairs and effects of the actual situation on the changing patterns of RMWs' return to work. The spatial patterns of RMWs' return to work in China's major three UAs can be regarded as a comprehensive and complex interaction result accompanying the nationwide population redistribution, which was affected by various hidden factors. Our findings provide crucial implications and suggestions for data-informed policy decisions for a harmonious society in the post-COVID-19 era.