Authors: Eunjung Yang*, University of Florida, Jinwon Kim, University of Florida
Topics: Tourism Geography
Keywords: sustainable community management, community resilience, Resilience Inference Measurement (RIM) model, community health, Local Indicators of Spatial Association
Session Type: Paper
Start / End Time: 3:05 PM / 4:45 PM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded
Increased and intensified natural hazards have negatively affected community health, well-being, and sustainability. To minimize the negative impacts of natural hazards on communities, quantifying community resilience to natural hazards is a major issue for researchers and policymakers. Previous studies have attempted to quantify community resilience with regard to specific natural hazards such as hurricanes and earthquakes. However, each area has potential risks from multiple natural hazards, so that the community resilience to natural hazards should be more comprehensively measured reflecting the multiple natural hazards simultaneously. The purpose of this study is to measure the changing community resilience to all kinds of natural hazards for 67 counties in Florida between 2000 and 2017, and then to identify which counties have weak resilience to natural hazards and which natural hazards significantly influence community resilience. To achieve this purpose, this study will apply the resilience inference measurement (RIM) approach, which offers a manner for measuring community resilience and validating the variable selection internally and externally. GIS-based spatial statistical techniques (i.e., local indicators of spatial association & K-means clustering analysis) in combination with discriminant analysis will be employed to quantify community resilience. The expected findings suggest that community resilience has continuously changed over time according to natural disasters. In addition, it is also affected by the key indicators of the community. Findings from this study can help local agencies or policymakers understand the spatio-temporal dynamics of community resilience to natural hazards, ultimately providing geographical insights into location-based community disaster management planning, policy, and strategy.