Hybrid spatial data mining methods for site selection of emergency response centers

Traditional methods for site selection fail to give reasonable weight to the potential risk grade of different locations. Consequently, site selection does not highlight locations with higher risk grades. Thus, a hybrid analytical method for the site selection of emergency response centers is proposed. First, spatial predicates are incorporated into emergency event analysis. These spatial predicates describe the spatial relationships between emergency locations and surrounding objects. A spatial data association mining method is then developed to identify the correlation rules that contain emergency information and geographical factors. Such rules act as the weight adding mechanism on different urban buildings, such that every spatial object is assigned a risk grade based on these rules. Furthermore, a simulated annealing algorithm is developed by incorporating the weight adding method to locate the optimal sites for emergency centers. A series of experiments is conducted, and the results demonstrate the efficiency of the proposed hybrid method.