Using a Gis-Based Genetic Algorithm to Identify Optimal Installation Locations for Aeds in Urban Convenience Stores through the Use of Spatial and Temporal Weighting Model

Immediate treatment with an automated external defibrillator increases out-ofhospital cardiac arrest patient survival potential. While considerable attention has been given to determining optimal public AED locations, spatial and temporal factors such as time of day and distance from emergency medical services are understudied. During the proposed two-year investigation we will develop a geocomputational genetic algorithm with a new stirring operator that considers spatial and temporal cardiac arrest occurrence factors when assessing the feasibility of using convenience stores in urban area as installation locations for AEDs. In the second year of the project Our model will be based on multiple AED conveyance modes, walking, running and driving, involving service distances of 100-700 meters, respectively. Our results will suggest different AED allocation strategies involving convenience stores in urban settings. In commercial areas, such installations can compensate for temporal gaps in EMS locations when responding to nighttime OHCA incidents. In residential areas, store installations can compensate for long distances from fire stations, where AEDs are currently held in urban area.

Project ID:PC10907-0890
External Project ID:MOST109-2314-B182-037