Using fuzzy suitability analysis to determine retail locations in the metropolitan counties in MN
Abstract
GIS has been used to solve location problems such as finding the best location for retail, emergency services or warehouses (Church 2002). In retail, demographics of existing and ideal costumers are also considered when determining the next retail location (Trubint et al. 2006). Costco, a warehouse style store, has nine locations in the seven metropolitan counties of Minnesota. This study aims to determine suitable locations for a new Costco location based on social demographics, accessibility, and proximity to competing business and existing Costco locations. The results show that the peripheral tracts have the highest suitability score when considering all the demographic and proximity factors. There are also a few tracts within the city that appear to have relatively high suitability score. This is suspected to be caused by high scores in a demographic parameter like income and accessibility to roads since these are located near downtown areas. This model can be improved by incorporating more detailed demographic information, where users are coming from, networks for time cost considerations, and resources available at each existing location.
Results
The fuzzy suitability score calculated using the fuzzy overlay tool ranged from 0-0.8342 (fig 5). In comparison the score calculated from the weighted sum ranged from 5.04-13.91. The majority of the fuzzy overlay results had a value of 0, but the areas that had higher scores showed it was areas near the edges (fig 5). Only areas that corresponded with the planned land use for business and commercial were given a score other than zero. When the land-use was not considered, the fuzzy overlay results showed more suitable areas near the periphery of the metropolitan counties area (fig 6). Overall the areas with the most suitable scores are located at the edges of the metropolitan counties area after consideration of all factors.
The weighted sum tool showed different results as well based on if the factors were equally weighted or weighted differently. In the unweighted, the same pattern of higher suitability scores near the periphery were visible with the lower suitability being smoothly dispersed from the center to the edges (fig 8). When the weights were considered, the scores were of course higher, but more areas had a higher suitability score (fig 97. Taking the averages of the calculated weighted sum scores for each tract, the higher suitability scores are actually present in the center of the Minneapolis and St. Paul area, as well as the outer tracts of the metro counties (fig 9).
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