Rapid urbanization and land-use changes may affect the intensity of urban heat islands (UHIs). However, research on the eastern Chinese city of Yangzhou is lacking. Using land cover data and the InVest Urban Cooling model, this study evaluated the spatiotemporal heterogeneity of the UHI effect from 1990 to 2020 and its socioecological drivers in Yangzhou City. Landscape pattern indices such as patch area (CA), percentage of landscape (PLAND), number of patches, patch density, and aggregation index were created using Fragstats 4.2 software. Several social indicators, such as gross domestic product (GDP), night-light index, and population density, were considered to explore their correlation with UHI indicators. During the past three decades, rapid urbanization in Yangzhou has intensified the UHI effect, with the cooling capacity (cc park) and heat mitigation index (HMI) decreasing by ~9.6%; however, the mixed air temperature (T air) has increased by 0.14 °C. The main heat island areas are concentrated in southern Yangzhou, including the Hanjiang and Guangling districts, and have expanded over time. T air was positively correlated with GDP, night-light index, and population density. Moreover, for the impervious land use type, cc park and HMI were negatively correlated with CA and PLAND (p < 0.01). This study contributes to a deeper understanding of the dynamics of UHIs and provides valuable insights for policymakers, urban planners, and researchers striving to create sustainable and climate-resilient cities in Yangzhou.

Leisure agriculture has become an essential driver of rural revitalization in China, yet most existing studies focus on provincial or municipal levels and rely on single-method approaches, leaving a gap in understanding spatial distribution patterns and driving mechanisms in highly urbanized metropolitan regions. This study addresses this gap by constructing a comprehensive leisure agriculture database for southern Jiangsu using multi-source data, including POI (Point of Interest), statistical yearbooks, and GIS datasets. Kernel density estimation, nearest neighbor index (NNI), geographic concentration index (GCI), and ordinary least squares (OLS) regression with VIF testing were applied to analyze spatial clustering and influencing factors. Results reveal that leisure agriculture resources exhibit significant clustering with a clear “core–periphery” pattern, concentrated in urban–rural transition zones. Agricultural output value and the number of A-level scenic spots significantly promote clustering, whereas GDP, population, and transportation density show weaker explanatory power. Theoretically, this study integrates multiple spatial statistical methods into a comprehensive analytical framework, enriching the understanding of leisure agriculture evolution under metropolitanization. Practically, it provides empirical evidence to support the optimization of leisure agriculture resource allocation, inform rural revitalization policies, and guide coordinated urban–rural planning in developed regions.