We explore the baseline resilience to natural hazards through the Baseline Resilience Indicators for Community (BRIC) in northeastern Taiwan. Based on the specific situation of our study site, we slightly modified the BRIC. Due to the correlation between some of the subcomponents, we apply principal component analysis (PCA) to solve this issue. Therefore, we slightly changed the classification of subcomponents. We aggregated economic resilience, social resilience, and community capital resilience into socioeconomic community resilience. The result of geographically weighted regression (GWR) shows that even though we modified the indicator, the BRIC we built is still valid. Through spatial autocorrelation analysis, it reveals that the urban region in plain areas is the cluster of high resilience areas. On the other hand, almost all the entire mountain areas are the cluster of low resilience areas. The topography is the most important factor to cause this distribution. Plain areas have favorable characteristics to trigger development and create high socioeconomic community resilience. Mountain areas, on the other hand, do not have these advantages. The distribution of institutional and infrastructure subcomponents shows no particular pattern. That is to say, institutional and infrastructure subcomponents do not influence the distribution of BRIC. The difference in socioeconomic community resilience causes the uneven distribution of baseline resilience to natural hazards. Nevertheless, the distribution of institutional and infrastructure resources is also a crucial issue. In our case, although the distribution of institutional and infrastructure follows the “distributive justice” approach and distribution randomly, whether it is an appropriate approach is still under debate.

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This research aims to explore the spatial pattern of vulnerability and resilience to natural hazards in northeastern Taiwan. We apply the spatially explicit resilience-vulnerability model (SERV) to quantify the vulnerability and resilience to natural hazards, including flood and debris flow events, which are the most common natural hazards in our case study area due to the topography and precipitation features. In order to provide a concise result, we apply the principal component analysis (PCA) to aggregate the correlated variables. Moreover, we use the spatial autocorrelation analysis to analyze the spatial pattern and spatial difference. We also adopt the geographically weighted regression (GWR) to validate the effectiveness of SERV. The result of GWR shows that SERV is valid and unbiased. Moreover, the result of spatial autocorrelation analysis shows that the mountain areas are extremely vulnerable and lack enough resilience. In contrast, the urban regions in plain areas show low vulnerability and high resilience. The spatial difference between the mountain and plain areas is significant. The topography is the most significant factor for the spatial difference. The high elevation and steep slopes in mountain areas are significant obstacles for socioeconomic development. This situation causes consequences of high vulnerability and low resilience. The other regions, the urban regions in the plain areas, have favorable topography for socioeconomic development. Eventually, it forms a scenario of low vulnerability and high resilience.