Chuang Tian

With rapid urbanization, improving school quality in compulsory education is critical for optimal educational resource allocation. This study integrates a random forest machine learning model, GIS spatial analysis, and a spatial econometric model to examine the spatiotemporal differentiation of school quality in Dalian, China, in 2016 and 2020, as well as its relationships with the construction land development cycle, population density, and housing prices. The findings reveal a core–periphery structure, with overall school quality on the rise and basic facility configuration exerting a stronger impact than teacher strength. Among internal resources, per capita sports venue area (PCSFA) and per capita teaching equipment value (PCTRE) contribute most significantly to school quality, while high-quality clusters in traditional educational hubs, university-covered areas, and transitional zones spur improvements in surrounding schools. The population density, housing prices, and the construction land development cycle all positively correlate with school quality, highlighting the need for coordinated action among urban planners, education authorities, and housing regulators to ensure that land development, housing affordability, and school facility investments advance equitable access to quality education. These results provide a novel perspective on compulsory education quality assessment and offer a valuable foundation for guiding education policies and urban development strategies.

Fishing ports are important fishery production platforms, and the transformation of these has had a profound impact on the fishing industry. A reasonable classification solution is crucial for scientific understanding, development, and management of fishing ports. Current research on the use of spatial distributional characterization and the construction of a classification system for fishing ports to improve their management is limited. Therefore, in this study, a fishing port classification system was constructed using remote sensing images, with fishing port boundaries accurately extracted for classification. Using graphical summarization, fishing ports were classified into five types, including inland, estuarine, shoreline, gulf, and islands. A port type identification system was also constructed based on distance from the shoreline, water area, and circulation. Finally, fishing port characteristics and differences were investigated based on spatial superposition. The results demonstrate that shoreline and gulf types are the most prevalent fishing port types along the Chinese coast, accounting for 43% and 26% of the total number of fishing ports, respectively. This provides a strong foundation for China’s fishery production, processing, and trade. Through the establishment of a comprehensive scientific classification system, fishing port management can be modernized, supporting the sustainable development and utilization of coastal zones.