Urban heat island (UHI), a significant environmental issue caused by urbanization, is a pressing challenge in modern society. To mitigate it, urban thermal policies have been implemented globally. However, despite differences in topographical and environmental characteristics between cities and within the same city, these policies are largely uniform and fail to reflect contexts, creating notable drawbacks. This study analyzed three cities in Korea with high land surface temperatures (LSTs) to identify factors influencing LST by applying Extreme Gradient Boosting (XGBoost) with Shapley Additive explanations (SHAP) and Geographically Weighted Regression (GWR). Each variable was derived by calculating the average values from May to September 2020. LST was the dependent variable, and the independent variables were chosen based on previous studies: Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), ALBEDO, Population Density (POP_D), Digital Elevation Model (DEM), and SLOPE. XGBoost-SHAP was used to derive the relative importance of the variables, followed by GWR to assess spatial variation in effects. The results indicate that NDBI, reflecting building density, is the primary factor influencing the thermal environment in all three cities. However, the second most influential factor differed by city: SLOPE had a strong effect in Daegu, characterized by surrounding mountains; POP_D had greater influence in Incheon, where population distribution varies due to clustered islands; and DEM was more influential in Seoul, which contains a mix of plains, mountains, and river landscapes. Furthermore, while NDBI and ALBEDO consistently contributed to LST increases across all regions, the effects of the remaining variables were spatially heterogeneous. These findings highlight that urban areas are not homogeneous and that variations in land use, development patterns, and morphology significantly shape heat environments. Therefore, UHI mitigation strategies should prioritize improving urban form while incorporating localized planning tailored to each region’s physical and socio-environmental characteristics. The results can serve as a foundation for developing strategies and policy decisions to mitigate UHI effects.

Rural plans incorporating regional identity are vital for fostering regional revitalization and offering viable policy alternatives. The need for a systematic approach that recognizes both the diversity and shared characteristics of rural areas has become increasingly clear. Although numerous studies have explored rural classification, research examining specific regional characteristics remains limited. Hence, this study aimed to establish a comprehensive standard for developing effective rural plans. To this end, a study was conducted to classify the characteristics of rural areas using topic modeling, which is a text-mining algorithm. An analysis of publications on rural revitalization projects in Korea over the past decade revealed five common factors of success themes across each region. The five success factors, “local cultural experience”, “environment and landscape utilization”, “community activation”, “regional infrastructure development”, and “local economic activation”, should be considered in rural areas with different characteristics when establishing rural plans and policies. This classification of success factors serves as the foundation for establishing rural plans and policies. By applying different weights to the five success factors according to the unique characteristics and conditions of each region, it would open a great number of possibilities to establish more precise and effective customized plans. Future research is required to provide more empirical and broadly applicable results based on the classification framework proposed in this study.