The process of urbanization has accelerated economic growth while also presenting social challenges. Urban renewal is crucial for achieving sustainable urban development, especially by preserving traditional villages as cultural heritage sites within cities. This study employs Python algorithm programming and visual analysis functions to conduct a bibliometric analysis of 408 research papers on the preservation of traditional village cultural heritage in urban renewal from 1999 to 2023 in the Web of Science core database. The objective is to examine the historical background, current status, and future trends in this area. The analysis explores cooperation networks, co-citation relationships, co-occurrence patterns, and emerging characteristics of research on traditional village cultural heritage protection in urban renewal. It focuses on various aspects, such as authors, institutions, countries, journals, documents, and keywords. The results indicate that the study of traditional village cultural heritage protection in urban renewal can be divided into three developmental stages. “Sustainable development”, “cultural heritage”, “historic urban landscapes”, and “rural revitalization” are the research hotspots and future trends in this field. The results of this study provide a comprehensive overview of the evolution of research hotspots in this field and can help researchers willing to work in this research area quickly understand the research frontiers and the general situation.

Under rapid urbanization, street crime poses growing challenges to urban safety. Existing studies often treat gray and green spaces as independent variables, limiting the understanding of nonlinear crime patterns and spatiotemporal heterogeneity. Using day–night street crime data from Shanghai between 2010 and 2020, this study applies an interpretable machine learning framework combining CatBoost and SHAP to examine how the coupling of gray–green spatial structures influences street crime. Gray–green spatial morphology is quantified using both MSPA- and Fragstats-based indicators, which are integrated into composite coupling indices. The results indicate that gray–green structural coupling exhibits significant nonlinear and threshold-dependent effects on street crime. Compared with conventional Fragstats metrics, MSPA-based structural indicators demonstrate stronger explanatory power. Theft-specific analysis further indicates that gray-space core–edge structures exhibit higher crime risk at night, with this effect becoming more pronounced in the later period. Across both study periods and day–night contexts, green branch areas (G_BRANCH) consistently show stable inhibitory effects, with the strongest suppression occurring when G_BRANCH values range between 0 and 1.6 and interact with gray core–edge structures (B_CORE and B_EDGE). These findings provide quantitative evidence that gray–green spatial structures function through coupled, nonlinear interactions and offer targeted spatial planning implications for crime prevention in high-density cities.