This study proposes the methodology to identify heatwave hotspots in Seoul, the metropolis of Korea, using high-resolution data. Resident credit data, population mobility data, and temperature observation data are analyzed to determine vulnerable regions to heatwaves. Potentially vulnerable regions are derived in two ways: static vulnerable regions (SVRs) and dynamic vulnerable regions (DVRs), depending on their characteristics. SVRs are determined by lowincome (lower 20% income quantile) residential areas fixed on time. In contrast, DVRs vary with the time and day of the week. DVRs are defined by the place less responsive to heatwaves, where are with low population variability and low correlation with temperature. The final vulnerable regions, so-called hotspots, are determined by the high temperature predicted area where the SVRs and DVRs intersect. We examine how to remove commuting-related displacement signals, which are represented as noise when analyzing population mobility. An example of the hotspots identification result is also shown using temperature hindcast data generated by the Korean Meteorological Administration short-range forecast system. Applying the vulnerability information can improve the quality of disaster planning and decision-making by highlighting the time and area of need for resources in the implementation of short-and long-term disaster response.