Heatwaves, along with their affiliated illnesses and mortalities, are increasing in frequency and severity under climate change. Spatial analyses at the level of census output areas can produce detailed maps of heatwave risk factors and potential correlated damages, thus contributing to practical policies to reduce the risk of heatwave illnesses. This study analyzed the 2018 summer heatwave in Gurye and Sunchang counties in South Korea. To compare damages and analyze the detailed causes of heatwave vulnerability, spatial autocorrelation analyses were conducted, incorporating weather, environmental, personal, and disease factors. Gurye and Sunchang, although similar in demographics and region, exhibited large differences in heatwave damage specifically in the number of heat-related illness cases. In addition, exposure data were constructed at the census output area level by calculating the shadow pattern, sky view factor, and mean radiant temperature, revealing a higher risk in Sunchang. Spatial autocorrelation analyses revealed that the factors most highly correlated with heatwave damage were hazard factors, in the case of Gurye, and vulnerability factors, in the case of Sunchang. Accordingly, it was concluded that regional vulnerability factors were better distinguished at the finer scale of the census output area and when detailed and diversified weather factors were incorporated.