Disasters such as heat waves are on the rise due to global warming-induced climate change, leading to increased rates of heat-related casualties. Hence, researchers are actively seeking methods to tackle heat waves, especially identifying vulnerable areas. However, the data are often aggregated at the administrative district level in most studies, which limits the relevance of the findings to the actual population at risk. Therefore, this study focused on Daegu Metropolitan City in Korea, and converted all data to a grid-level format to identify heat-vulnerable areas in detail and analyze the suitable site of heat wave reduction facilities. Raster, vector, and statistical data were scaled to uniform grid units through resampling and interpolation. The evaluation indicators were then categorized as exposure, sensitivity, or adaptive capacity, which are key factors in assessing heat wave vulnerability, as per IPCC (Intergovernmental Panel on Climate Change) guidelines. Subsequently, the obtained datasets were subjected to fuzzy theory-based overlap analysis, followed by spatial autocorrelation and hotspot analyses to study the spatial distribution patterns. The resulting heat wave vulnerability map identified key vulnerable areas by comparing them with the spatial distribution of heat-related diseases. Based on the analyzed vulnerable areas, an analysis of suitable sites for heat wave reduction facilities was conducted. The results of this study can help to accurately identify heat wave vulnerable areas at a more detailed level beyond administrative districts and to mitigate heat wave-related casualties through an analysis of suitable sites for heat wave reduction facilities, and can serve as reference material for additional heat wave reduction facilities.