Determination of catchment areas to be used in the site selection in segmented geographies

Models to be established in large geographies or for multiple location selections should be as dynamic as the urban area itself. As much as the feature selection of the variables to be used for site selection models, the accuracy of the catchment areas will make important differences in the right location decisions. With the study, methods to adjust the catchment area by using the features of the facility to be positioned and the differences of the geography will investigate. In general, facility location models are static models that evaluate demand and environmental conditions in a specific period. However, cities are dynamic and the demand and environmental conditions that will occur in cities are changing. Therefore, it is not efficient or applicable to use access areas determined by the literature in each site selection study. In addition, as the demographic variables focused on each sector and geography may change, the catchment area should be redefined according to the geography and sector. The study will focus on a location selection model that can be applied in a vast geography for a function that requires many locations to be determined. There are two questions that the study is designed to answer. First, how can the correct reach or area of attraction be determined for a large geography? Second, is it possible to produce dynamic site selection models by adjusting the catchment areas determined in the literature according to the characteristics of the geography? The method will be one of the most critical decisions in the study. There are two prescribed methods for determining the correct distance. First, according to the details of the fieldwork, a variable related to distance will be generated and it is assumed that this variable will pass through feature selection. Another method can be to perform a consistency test with segments produced for different reach distances. The findings of this study will be supported by further analysis for example case studies with different dependent variable data such as the number of users and/or sales data. In addition, the outputs of this study may support researchers to develop new adjustment methods for facilities that are not studied before.