CRISys is a data-driven framework that measures community resilience during disasters using census data and machine learning. It shows how socio-economic factors shape recovery in Jefferson County, TX.
Community resilience is defined as an ability of an ordinary community to plan, prepare, absorb and recover from a natural or manmade disasters. Construct a comprehensive framework that includes all variables which influenced by a disaster and depict variation in them under this such event is vital. To achieve these kind of frameworks researchers and scholars introduce various frameworks. These frameworks suffered from many weaknesses and limitation that was the key motivator of our study to develop a custom framework based on the needs and limitations of Jefferson County, TX that we named it CRISys- Community Resilience Indicator System-. To develop it we looked community resilience in data-driven approach and utilized data analysis techniques. In this research we applied Principal Component Analysis as an unsupervised machine learning to reduced dimensionality and weigh to 22 variables that we explored for socio-economic status as a main dimension in CRISys framework. Data collection was done based on the reliable sources such as the United States Census Bureau that reflects the variable’s values for seven consecutive years from 2015 to 2021. The results show CRISys framework can illustrate socio-economic status of Jefferson County, TX under a disaster event and complies to mathematical representation of community resilience.
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