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.

Under rural revitalization and rapid construction in China, the mismatch between contemporary rural communities and villagers’ space behavior habits has attracted widespread attention. This study proposes and practices a design methodology for a newly built rural community based on spatial elements and their relationship with the behavior of local ancient villages. We explore the relationship between the two by applying drone-tech aerial photos, GPS, and ArcGIS. The results indicate that: (1) Ancient villages have abundant external spatial elements different from urban communities. Each space element of the ancient village has a specific function corresponding to the villagers’ daily life needs. (2) Village space elements are outcomes of the sociocultural process, and their topological structure is not random but follows their use based on hours of the day. (3) About 94.6% of space use behavior in the case of ancient village residents is associated with five space elements. (4) Updating the external spatial elements of ancient villages under the demands of contemporary life makes the application of spatial elements not only continue the villagers’ natural life process, but also satisfy the demands of the contemporary way of life on the external space. This study proposes the characteristics of the relationship between ancient village spatial behavior and external spatial elements through scientific analysis methods, which can provide references for contemporary rural planning and help to solve the mismatch problem between contemporary rural communities and villagers’ behavior habits.