<p>Disasters can lead to bridge collapses, road flooding, and mudslide blockages, disrupting the road network and hindering victims' movement and rescue operations. After a disaster, it is crucial to quickly assess the damage and identify access routes to affected areas to transport relief supplies and conduct rescue operations. Recent advances in digital transformation (DX) technology have the potential to revolutionize disaster management planning. Uncrewed Aerial Vehicles (UAVs) are being utilized to assess damage, while GPS data from mobile phones is helping to visualize evacuation behavior. The goal of this research is to enhance community resilience, minimize damage and enable rapid recovery by improving emergency response after a disaster. We propose a method for efficiently observing road damage after a disaster using population flow data derived from UAV and GPS data. The method focuses on assessing concentrated population areas, touring from one evacuation center to another based on the road network. We employ the Traveling Salesman Problem (TSP) and route search methods to calculate distances between points. The proposed model was applied to the Mabi city, Kurashiki City, Okayama Prefecture, which suffered significant damage during the July 2018 rainstorm. Results indicate that the proposed method can effectively gather data on road blockages in areas with higher population densities than static census data. Additionally, it found that investigating the flooding situation of roads at that time through land routes, such as vehicles, proved challenging because many routes connecting densely populated areas to evacuation centers were continuously flooded.</p>