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 black and odorous waters from 2100 Chinese cities, 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.
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.
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.
The spatial distribution pattern of the economic development among counties is an important external representation of a balanced and sustainable regional development in China. With the rapid development of globalization and localization, spatial pattern of economic growth is increasingly obvious. The mechanisms of regional economic growth in China are also gradually gaining attention. However, there is still a lack of research at the province and county levels. As a result, based on the per capita GDP of each county in Hubei province from 2005 to 2020 as the research index, the spatial autocorrelation and the spatial variation function are used to analyze the spatial pattern evolution and the county economy mechanism in Hubei province. The results show that 1) there is a remarkable phenomenon of county-level economic spatial agglomeration in Hubei province. The urban area of Wuhan and its surrounding counties are high–high (HH-type) county agglomeration areas. The low–low (LL-type) counties are mainly distributed in the western parts of Hubei province and scattered in the northeastern and southern parts of Hubei province; 2) the county economy of Hubei province presents a spatial distribution pattern of “high in the east and low in the west.” The hot areas of the county economy are primarily located in the urban area of Wuhan and its surrounding areas. In the process of development, the hot spot areas tend to shift to Yichang, Jingmen, and Xiangyang. The cold spot areas are located on the edges of the western, northeastern, and southeastern areas of Hubei province; 3) the spatial continuity and self-organization of the county economic development are strengthened. The structural differentiation trend caused by spatial autocorrelation is also strengthened. The county economy is relatively balanced from the southeast to the northwest, and the spatial difference in economic development in other directions is increasing; and 4) the spatial evolution of county economic development in Hubei province is the result of the comprehensive effects of historical and cultural background, economic development, traffic location, and policy system, and the A-shaped point-axis structure is a reliable spatial structure for regional development in Hubei province.
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