Acil durum servislerinin yer seçimi: Analitik Hiyerarşi Yöntemi ve CBS entegrasyonu

In the early 1980s Geographical Information Systems (GIS) software emerged as a new information processing technology offering unique capabilities of automating, managing, and analysing a variety of spatial data. Many applications of GIS developed over the last decade provided information necessary for the decision-making in diverse areas including natural resource management, regional planning, and disaster management. Two perspectives on developing better decision support capabilities of GIS can be identified, one based on analytical problem solving as a centrepiece of Spatial Decision Support Systems (SDSS) and another based on integration of GIS and specialized analytical models. According to first perspective, SDSS should offer modelling, optimization, and simulation functions required to generate, evaluate, recommend, and test the sensitivity or problem solution strategies. These capabilities are essential to solving semi-structured spatial decision-making problems. The second perspective on improving the decision support capabilities focuses on the expansion of GIS descriptive, prescriptive, and predictive capabilities by integrating GIS software with other statistical software and analytical models. According to this view, mapping, query, and spatial modelling functions of GIS can provide data display at different scales, preprocessing, and data input for environmental and statistical models. The general objective of Multi-criteria Decision Making (MCDM) is to assist the decision-maker (DM) in selecting the best alternative from the number of feasible choice-alternatives under the presence of multiple choice criteria and diverse criterion priorities. The problem of multicriterion choice in decision making is the paramount challenge faced by individiuals, public and private corporations. The nature of challenge is two-fold: How to identify choice alternatives satisfying the objectives of parties involved in the decision-making process? How to order the set of feasible choice alternatives to identify the most preferred alternative? The challenge of multicriterion choice can be attributed to many spatial decision-making problems involving search and location/allocation of resources. These problems, often analysed in (GIS), include location/site selection for: service facilities, retail outlets, critical areas for specific resource management, and emergency service locations where are key locations for effective emergency management. In this study, the criteria and its priorities/weights that should be considered for finding optimal locations of fire stations are determined; and multicriteria site analysis is conducted based on mentioned criteria weights in (GIS) environment. Moreover, in order to test the sensitivity and robustness of the model developed, a sensitivity analysis is performed based on the combination of the criterion weights by using (GIS) capabilities. With these analyses performed, it is focused on the creating themodel that supports decision makers in decisionmaking for finding theoptimal locations of fire stations. In this study, these steps are followed: Definition of the problem/objective (determining the optimal locations of fire stations); determining the potential criteria in finding the optimal locations of fire stations; data collection and preparation and transfer to (GIS) environment; creation of raster data sets representing the regionalised criteria; classification of raster data sets; establishment of preference matrix, assigning preference values to the relevant criteria by using the pairwise comparison feature of Analytic Hiyerarchy Process (AHP); determination of criteria weights by calculating eigenvalues and eigenvectors of the preference matrix which evaluated by two decision maker group; determining the criteria priorities/weights values by using the synthesis of priorities and calculating the overall composite weights; calculating the result raster (suitability map for potential fire stations) as a weighted summation of all criteria raster data sets; conducting the sensitivity analyses in (GIS) environment in order to test the sensitiveness and robustness of the model developed; offering a system that supports decision makers in determining the optimal locations of fire stations. The integration of the (AHP) and (GIS) combines decision support methodology with powerful visualisation and analysing capabilities which should considerably facilitate finding optimal locations of fire stations and this process improves the decision making in emergency management.