Multi-criteria site selection for fire services: the interaction with analytic hierarchy process and geographic information systems

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Erden T., COSKUN M. Z.

NATURAL HAZARDS AND EARTH SYSTEM SCIENCES, vol.10, no.10, pp.2127-2134, 2010 (SCI-Expanded) identifier identifier


This study combines AHP and GIS to provide decision makers with a model to ensure optimal site location(s) for fire stations selected. The roles of AHP and GIS in determining optimal locations are explained, criteria for site selection are outlined, and case study results for finding the optimal fire station locations in Istanbul, Turkey are included. The city of Istanbul has about 13 million residents and is the largest and most populated city in Turkey. The rapid and constant growth of Istanbul has resulted in the increased number of fire related cases. Fire incidents tend to increase year by year in parallel with city expansion, population and hazardous material facilities. Istanbul has seen a rise in reported fire incidents from 12 769 in 1994 to 30 089 in 2009 according to the interim report of Istanbul Metropolitan Municipality Department of Fire Brigade. The average response time was approximately 7 min 3 s in 2009. The goal of this study is to propose optimal sites for new fire station creation to allow the Fire Brigade in Istanbul to reduce the average response time to 5 min or less. After determining the necessity of suggesting additional fire stations, the following steps are taken into account: six criteria are considered in this analysis. They are: High Population Density (HPD); Proximity to Main Roads (PMR); Distance from Existing Fire Stations (DEF); Distance from Hazardous Material Facilities (DHM); Wooden Building Density (WBD); and Distance from the Areas Subjected to Earthquake Risk (DER). DHM criterion, with the weight of 40%, is the most important criterion in this analysis. The remaining criteria have a weight range from 9% to 16%. Moreover, the following steps are performed: representation of criterion map layers in GIS environment; classification of raster datasets; calculating the result raster map (suitability map for potential fire stations); and offering a model that supports decision makers in selecting fire station sites. The existing 35 fire stations are used and 17 fire stations are newly suggested in the study area.