Locating temporary shelter sites after the earthquake using developed geographically weighted regression (District 22 of Tehran city)

The purpose of this research is to select temporary accommodation centers after the earthquake crisis to meet the needs of the victims. Therefore, this research has tried to identify temporary accommodation sites in District 22 of Tehran with the help of effective criteria. The required data has been obtained from the Tehran Disaster Mitigation and Management Organization and the results of the general population and housing census of 2017. The novelty of this research is to present a new combination approach to determine the effective criteria for locating temporary shelter sites. In this regard, the combination of geographically weighted regression (Gaussian and tri-cube kernels) with a binary particle swarm optimization algorithm was used. The recommended combination method is suitable for spatial regression problems because it is compatible with two unique properties of spatial data, i.e. spatial autocorrelation and spatial non-stationarity. The best value of the fitness function (1-R2) for Gaussian and tri-cube kernels was obtained at 0.04616 and 0.0097, respectively, which indicates the high compatibility of the tri-cube kernel with effective criteria. According to the obtained maps, Chitgar Park and Azadi Sports Complex are some of the widest and most suitable areas for the construction of temporary shelter sites after the earthquake crisis in the case study. By identifying temporary shelters, relevant organizations can provide the appropriate infrastructure for these selected centers so that there is no need to spend time to provide these services in the event of an earthquake.