A GIS approach to analyzing the spatial pattern of baseline resilience indicators for community (BRIC)

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.