A GIS enhanced data analytics approach for predicting nursing home hurricane evacuation response
Nazmus Sakib1, Kathryn Hyer2, Debra Dobbs2
1Department of Industrial and Management Systems Engineering, University of South Florida, Tampa, FL 33620 USA.
Health Information Science and Systems
|September 19, 2022
Summary
Predicting nursing home (NH) evacuation decisions during hurricanes is crucial for emergency planning. This study introduces a GIS-enhanced predictive model, fusing environmental and NH data for improved accuracy in forecasting NH evacuation responses.
Area of Science:
- Disaster Management
- Gerontology
- Geographic Information Systems (GIS)
Background:
- Nursing homes (NHs) house vulnerable older adults susceptible to natural disasters like hurricanes.
- NH evacuation decisions (evacuate vs. shelter-in-place) are complex due to environmental and facility-specific factors.
- Accurate prediction of NH evacuation demand is vital for emergency resource allocation.
Purpose of the Study:
- To develop an analytics-based method for forecasting NH evacuation responses.
- To improve prediction accuracy by fusing multi-source data, including GIS information.
- To enhance emergency management planning for NH evacuations.
Main Methods:
- Proposed a Geographic Information System (GIS) data-enhanced predictive analytics approach.
- Fused multi-source data: storm conditions, geographical information, NH characteristics, staffing, and resident health.
- Extracted GIS features: distance to storm trajectory, wind speed, storm surge, and NH elevation.
Main Results:
- The GIS-enhanced approach demonstrated superior prediction performance compared to methods without GIS information.
- Successfully forecasted NH evacuation responses using fused multi-source data.
- Validated the model with a case study of NH evacuations during Hurricane Irma (2017).
Conclusions:
- GIS data integration significantly improves the accuracy of NH evacuation response prediction.
- The proposed method offers a valuable tool for emergency management agencies.
- Enhanced prediction facilitates proactive planning and resource allocation for NHs during hurricanes.
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