Current studies on measuring the accessibility of medical services for the elderly (AMSE) have ignored the potential competition among supply and demand and the distance decay laws. Hence, an enhanced two-step floating catchment area (E2SFCA) method (i.e., the road network-based Gaussian 2SFCA method) is proposed to calculate AMSE scores after considering different types of roads, including urban rail transit, freeways, major roads, minor roads and rural roads. Based on the first National Geographic Conditions Monitoring (NGCM) data, this study took Wuhan, China, as a case study and assessed the variation of AMSE using two different threshold times (i.e., Platinum Ten and Golden Hour). Next, global (i.e., sensitivity and hot spot analysis) and local analyses (i.e., three regional area internal comparisons) of AMSE scores were conducted to accurately identify details in the variation of spatial accessibility. It was observed that the E2SFCA method could be easily applied to measure AMSE. The results showed that 48.63% of the elderly population in Wuhan had a higher or the highest level of medical accessibility in “Platinum Ten”, while 72.97% had a higher or the highest level in the “Golden Hour”, and hot spots of AMSE scores were located in central urban areas and presented an enclosure structure using both threshold travel times, which could provide guidance to governments or planners on issues of spatial planning and identifying elderly medical services shortage areas.

The pollution problem caused by urban black and odorous waters has received much attention from the Chinese government. Our research aims at systematically identifying the characteristics and the influential factors of spatial differentiation of urban black and odorous waters across China. The research, based on the data of 2100 black and odorous waters in China, was conducted with the spatial analysis tool of ArcGIS. We found that the amount of Chinese urban black and odorous waters varied in spatial distribution, which was an agglomerated type with significant agglomeration. The kernel density was characterized by independent single kernel centers with ribbon-like and sporadic distributions of subcenters. The cold and hot spots showed a gradient distribution pattern of cold in the southwest and hot in the central east. These spatial distribution characteristics could be attributed to the following core factors, total wastewater discharge, length of urban drainage pipelines, municipal solid waste collection, daily urban sewage treatment capacity, and investment in urban pollution treatment of wastewater. The findings reveal the current geospatial distribution of black and odorous waters pollution and provide reference for the Chinese government to treat the pollution from several key points. Lastly, it is suggested that the Chinese government should establish joint control, joint prevention, and joint treatment mechanisms in the black and odorous waters areas and improve the safety standards of the whole water environment, so as to promote the treatment and elimination of urban black and odorous waters.

Basic education resources are basic urban and rural social public security resources, and their spatial distribution is an important issue related to people’s livelihoods and social justice. Taking Wuhan as a case study, this paper analyzed the spatial distribution characteristics of rural basic education resources based on the methods of the average nearest neighbor index, imbalance index, kernel density analysis and two-step floating catchment area and then used geographic detector analysis to detect its influencing factors. The following findings were obtained: (1) Rural kindergartens and elementary schools in Wuhan City’s new urban districts showed a clustered distribution pattern, while secondary schools showed a uniform distribution trend. The spatial distribution of rural basic education resources is poorly balanced, with a tendency to cluster in Huangpi District, Xinzhou District and Caidian District; the overall spatial distribution density of rural basic education resources showed the distribution characteristics of “block-like clustering and multicenter development”. (2) The spatial accessibility of kindergartens showed a spatial pattern of “large dispersion and small clustering”, with multiple high-value clustering areas; and the accessibility of elementary and secondary schools showed a spatial pattern of high in the south and low in the north. (3) The population, economy and education development level are the main factors affecting the spatial distribution of rural basic education resources, while the influence of infrastructure construction is weak. The core influencing factors of the spatial distribution of each type of basic education resource are both consistent and different. According to the interaction factor detection, the spatial distribution of rural basic education resources in Wuhan City’s new urban districts is the result of the combined effect of multiple factors.

Market services industries are closely related to residents’ lives, and its spatial distribution has an important impact on satisfying residents’ consumption needs and promoting economic development. In recent years, with the rapid development of urban–rural integration and the gradual implementation of a rural revitalization strategy, rural areas around metropolises have become the “frontier” of urban geographic expansion, causing the rural market services industries to specialize, commercialize, and modernize. Taking Wuhan as a case study, the spatial distribution characteristics and influencing factors of the market services industries were measured using the average nearest neighbor index, the kernel density estimate, the spatial correlation analysis, and the Geodetector method. The results are as follows. (1) The market services industries in Wuhan’s new urban districts, as a whole and individually, showed characteristics of agglomeration. The market services industries overall formed two high-density areas and multiple agglomeration areas, and the high-density areas of different types of market services industries showed characteristics of partial spatial overlap. (2) There was a significant positive spatial correlation in market services industries, as a whole and individually. Among them, the life services industry had the strongest spatial correlation, while the accommodation services industry had the weakest. (3) Market demand and traffic conditions were the core factors influencing the distribution of the market services industries in Wuhan’s new urban districts, followed by urbanization and economic levels; and tourism conditions had a lower impact. Each detector had a different impact on the spatial distribution of different market services industries, and the interaction research showed that the spatial distribution of the market services industries was the result of a combination of multiple factors. This research provides a future development direction for market service industries in rural areas.

This study takes 1597 key villages and towns part of rural tourism in China as research objects and uses the ArcGIS spatial analysis method, combined with the geodetector and the multiscale geographically weighted regression model, to analyze the intensity and spatial differentiation of factors influencing the spatial distribution of these villages and towns. (1) The key villages and towns of rural tourism exhibit clustering distribution patterns with more locations in the east than in the west. The center of gravity of this distribution shifts to the northwest, displaying a “belt–point” trend with scattered hot spots in Beijing, Tianjin, Hebei, and the Yangtze River Delta, while cold spots are primarily concentrated in the northwest. (2) Spatial variation in the key villages and towns of rural tourism is due to multiple factors, of which population density, resident consumption expenditure, and per capita GDP display the highest explanatory powers for the spatial distribution of tourism. (3) The primary influencing factors are spatial differences in their roles and substantial local imbalances. Positive and negative correlation analysis units exhibit the aggregation characteristics of being blocked and banded. These results can provide valuable guidance for the development of rural tourism, promoting its sustainable development and contributing to the revitalization of rural areas.