The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness.

Today, unpredictable damage can result from extreme weather such as heat waves and floods. This damage makes communities that cannot respond quickly to disasters more vulnerable than cities. Thus, people living in such communities can easily become isolated, which can cause unavoidable loss of life or property. In the meantime, many disaster management studies have been conducted, but studies on effective disaster response for areas surrounded by mountains or with weak transportation infrastructure are very rare. To fill the gap, this research aimed at developing an automated analysis tool that can be directly used for disaster response and recovery by identifying in real time the communities at risk of isolation using a web-based geographic information system (GIS) application. We first developed an algorithm to automatically detect communities at risk of isolation due to disaster. Next, we developed an analytics module to identify buildings and populations within the communities and efficiently place at-risk residents in shelters. In sum, the analysis tool developed in this study can be used to support disaster response decisions regarding, for example, rescue activities and supply of materials by accurately detecting isolated areas when a disaster occurs in a mountainous area where communication and transportation infrastructure is lacking.