PRIME: A CyberGIS Platform for Resilience Inference Measurement and
  Enhancement
Debayan Mandal Dr. Lei Zou Rohan Singh Wilkho Joynal Abedin Bing Zhou Dr. Heng Cai Dr. Furqan Baig Dr. Nasir Gharaibeh Dr. Nina Lam
In an era of increased climatic disasters, there is an urgent need to develop
reliable frameworks and tools for evaluating and improving community resilience
to climatic hazards at multiple geographical and temporal scales. Defining and
quantifying resilience in the social domain is relatively subjective due to the
intricate interplay of socioeconomic factors with disaster resilience.
Meanwhile, there is a lack of computationally rigorous, user-friendly tools
that can support customized resilience assessment considering local conditions.
This study aims to address these gaps through the power of CyberGIS with three
objectives: 1) To develop an empirically validated disaster resilience model -
Customized Resilience Inference Measurement designed for multi-scale community
resilience assessment and influential socioeconomic factors identification, 2)
To implement a Platform for Resilience Inference Measurement and Enhancement
module in the CyberGISX platform backed by high-performance computing, 3) To
demonstrate the utility of PRIME through a representative study. CRIM generates
vulnerability, adaptability, and overall resilience scores derived from
empirical hazard parameters. Computationally intensive Machine Learning methods
are employed to explain the intricate relationships between these scores and
socioeconomic driving factors. PRIME provides a web-based notebook interface
guiding users to select study areas, configure parameters, calculate and
geo-visualize resilience scores, and interpret socioeconomic factors shaping
resilience capacities. A representative study showcases the efficiency of the
platform while explaining how the visual results obtained may be interpreted.
The essence of this work lies in its comprehensive architecture that
encapsulates the requisite data, analytical and geo-visualization functions,
and ML models for resilience assessment.