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.

As a driving force for regional development, innovation holds an increasing position in regional competitiveness, and a reasonable and coordinated innovation network structure can promote high-quality regional development. Utilizing the modified gravity model and social network analysis method, an innovation network composed of 27 cities in the Yangtze River Delta urban agglomeration from 2010 to 2021 was studied. The following conclusions were founded: (1) The innovation development level in the Yangtze River Delta urban agglomeration was constantly improving, and the innovation development level generally showed a spatial pattern of high in the southeast and low in the northwest. (2) The intensity and density of innovation network correlations in urban agglomerations were increasing, and the centrality of network nodes had an obvious hierarchical characteristic. The innovation network had a significant core–periphery spatial structure, with core cities that had higher centrality, such as Shanghai, Nanjing, and Hangzhou, playing the role of “intermediaries” and “bridges”, while cities with lower centrality, such as Anhui and cities in northern Jiangsu, generally played the role of “periphery actors” in the network. (3) The spatial correlation network of innovation of the Yangtze River Delta urban agglomeration could be divided into four blocks, namely, main benefit, broker, two-way spillover, and net spillover, and the spillover effect among them had obvious gradient characteristics of hierarchy.

Education infrastructure is a critical public service facility in rural areas. The evaluation of rural education infrastructure could have important implications for the spatial distribution optimization of public educational services in countryside regions. Based on the elementary education data and survey data of Liji Town in 2022, this paper built an index evaluation system for Liji Township to explore the optimized spatial distribution mode of rural education infrastructure using models of MCR and field intensity. This system consisted of the potential energy of rural basic education infrastructure, as well as district and service thresholds. The results show the following: (1) The resistance of the rural education infrastructure in Liji Township was lower in the eastern and western parts of the township, as well as along the northern–southern county highway; however, the resistance was higher in the southern and northeastern areas. (2) There were significant differences in the potential energy component of education field intensity, showing a gradual decreasing tendency from the central villages to the peripheral villages. The spatial distribution of the central villages’ potential energy component was consistent with the component of the service threshold. However, the components of both district and service thresholds showed higher values for the suburban villages and lower values for the peripheral villages. (3) The rural basic education infrastructure can be divided into three types, and the corresponding development path is proposed in combination with different types. The optimization result is easy to explain and has potential applications in public education evaluation and facility layout planning. These modes can facilitate the allocation and spatial distribution optimization of basic education infrastructure in rural regions of metropolitan areas.