The accurate delineation of urban agglomeration boundary is conductive to not only the better understanding of the development relationship between cities in urban agglomeration but also to the guidance of regional functions as well as the formulation of regional management policies. At the same time, the fusion of land relations and urban internal relations can greatly improve the accuracy of the delineation of urban agglomeration boundary. Still, for all that, previous studies delineated the boundary only from the perspective of land relations. In this study, firstly, wavelet transform is used to fuse Night-time Light data (NTL), POI (Point of Interest) data and Tencent Migration data, respectively. Then, the image is segmented by multiresolution segmentation to delineate the urban agglomeration boundary of GBA. Finally, the results are verified. The results show that the accuracy of urban agglomeration boundary delineated by NTL data is 85.57%, with the Kappa value as 0.6256, respectively. While, after fusing POI data, the accuracy is 88.97%, with the Kappa value as 0.7011, respectively. What is more, the accuracy of delineating urban agglomeration boundary by continuous fusion of population movement data reaches 93.60%, and that of Kappa value as 0.8155. Therefore, it can be concluded that compared with delineating the boundary of urban agglomeration only based on land relations, the fusion of population movement data of urban agglomerations by wavelet transform strengthens the interconnection between cities in urban agglomeration and contributes to the accurate division of urban agglomeration boundaries. What is more, such accurate delineation not only has important practical value for optimizing the spatial structure of urban agglomerations, but also assists in the formulation of regional management and development planning policies.

The built environment refers to the objective material environment built by humans in cities for living and production activities. Existing studies have proven that the built environment plays a significant role in human health, but little attention is paid to the elderly in this regard. At the same time, existing studies are mainly concentrated in Western developed countries, and there are few empirical studies in developing countries such as China. Based on POI (point of interest) data and 882 questionnaires collected from 20 neighborhoods in Guangzhou, we employ multilevel linear regression modeling, mediating effect modeling, to explore the path and mechanism of the impact of the built environment on elderly individuals’ physical health, especially the mediating effects of physical and social interaction activity. The results show that the number of POIs, the distance to the nearest park and square, and the number of parks and squares are significantly positively correlated with the physical health of the elderly, while the number of bus and subway stations and the distance to the nearest station are significantly negatively correlated. Secondly, physical activity and social networks play a separate role in mediating the effect of the built environment on elderly individuals’ physical health. The results enrich the research on the built environment and elderly individuals’ health in the context of high-density cities in China and provide some reference basis for actively promoting spatial intervention and cultivating a healthy aging society.

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

Hive Box is a company that operates a network of express unattended collection and delivery points (UCDPs) in China. Hive Box distribution enhances community-based end-to-end delivery services and low-carbon city logistics. It is argued that UCDPs compared with attended collection and delivery points (ACDPs) should be considered for further investigation. Therefore, the present study employed kernel density estimation, spatial autocorrelation analysis, and geographically weighted regression to investigate the spatial heterogeneity of Hive Box distribution across Guangzhou. Hive Box location data were collected from smartphone apps. The results were as follows: (1) the kernel density declined from the city center toward the outskirts, and showed point-like spatial agglomerations in the city center; (2) the Moran’s I index analysis showed that Hive Box distribution exhibited spatial agglomeration from a global perspective and geographic variations in locality in space; the heterogeneity of urban–rural differences implies the uneven development of Hive Box distribution in Guangzhou; and (3) the factors influencing Hive Box distribution were multilevel, and their effects were complex and varied across regions. These results shed light on the agglomeration and heterogeneity characteristics of the spatial distribution and influencing factors of Hive Boxes. For an enhanced community-based end-to-end delivery service, this study suggested the identification of the geographic variations of Hive Box distribution and the combined effects of multiple factors in intensifying the infrastructure of unattended locker points.

With the accommodative policy and rapid urbanization in China, large-scale migration of rural-to-urban peasants seeking nonagricultural employment has occurred. This has led to farmland rental, which is considered an effective means of land arrangement. Multiple variables were selected to examine the influencing factors of land rental for rural–urban peasants in China by using survey data collected in six core cities of the Pearl River Delta and a logistic regression model. This study revealed that benefits, household members, and urban living conditions and urban integration are factors that affect land rental. According to the results, improvements in working conditions, urban social insurance and urban integration, annual gross household income, and secure land ownership can promote land rental, whereas stronger hometown connections and parenting inhibit land rental. Women and youth excluded from China’s previous land allocation hold complex attitudes toward land rental, with age and sex statistically significant variables affecting land rental. We underscore the influence of family members and urban living conditions for land rental, which were ignored in earlier studies, to provide suggestions for future policy development, with an emphasis on the land rental market and redistribution of idle land.