As an important factor in urban planning and urban design, blocks exhibit complex and diverse thermal environment characteristics due to the thermal properties of the underlayment materials and the non-uniformity of the spatial distribution of the buildings. Previous research has predominantly concentrated on the urban-scale thermal environment and its underlying drivers. Yet, there remains a notable inadequacy in the precise identification of core urban heat island patches and critical nodes, the scientific rigor applied in selecting geographical units and research methodologies, as well as the depth of exploration concerning improvement strategies for the thermal environment at the block scale. To address this gap, this study uses a typical urban neighborhood in Jinan as a case study. It employs geographic information system (GIS), spatial statistics, and analysis methods, grounded in the spatial heterogeneity of different geographical units, to explore the spatial distribution characteristics and heterogeneity mechanisms of the thermal environment at the neighborhood scale. The results indicate that: (1) in the study area, the core area and the edge area account for the largest proportion of the heat island landscape, and the accumulation, diffusion, and radiation of the two areas lead to the increasing degree of aggregation among the heat island patches, which has an important impact on the adjustment balance of the heat environment inside the block and the spatial distribution pattern of the heat island. (2) There are significant differences in the correlation and explanatory power between urban form indicators and the land surface temperature (LST) among different geographical units. Local climate zoning (LCZ) can preserve the complete urban landscape type and has strong explanatory power for local thermal environmental effects, making it highly suitable for the block-scale analysis of thermal environmental spatial feature correlations. (3) The HRE (height of roughness elements), BEI (building evenness index), and SVF (sky view factor) are the indicators that have the greatest impact on the LST. Building height, evenness, and openness have a significant impact on the spatial distribution pattern of heat islands. Urban planners should fully consider the impact mechanisms of the indicator factors to minimize the LST. We believe that these findings can offer new theoretical foundations and practical pathways for the precise governance of urban heat island effects and the intelligent regulation of urban climates.