Assessment of the spatial distribution and accessibility of traditional villages is closely related to their development. However, the impacts of spatial heterogeneity on the accessibility of traditional villages remain largely unknown. A total of 644 national-level traditional villages in the Jiangnan region were used to explore the spatial heterogeneity impact with a geographically weighted regression (GWR) model. We found: (1) spatially, the villages exhibit a predominant aggregation distribution pattern with significant local spatial disparities (R < 1, p < 0.01), predominantly originating from the Song and Ming dynasties (59.21%). Their clustering shifts from northeast to southwest, with over 70% of the villages located on slopes ranging from 0° to 20° and within 1 km of water. (2) The accessibility of these villages is generally low, with 85.66% being accessible within 200 ~ 300 min; it decreases concentrically outwards from Changzhou and exhibits clustering autocorrelation characteristics (Moran’s I > 0, Z > 2.58). (3) Road network density, elevation, and slope are significantly correlated with accessibility (p < 0.05), demonstrating pronounced spatial heterogeneity in their effects, with these factors collectively explaining approximately 85% of the accessibility levels. These findings provide a basis for comprehensive planning and categorized strategies for traditional villages.

Air pollution, particularly fine particulate matter (PM_2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM_2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM_2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM_2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM_2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R² value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM_2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM_2.5, revealing effective approaches for addressing urban environmental issues.