Objective 2 - To establish an MCE model to determine the most suitable location for incoming dispensaries in approved municipalities.
A GIS provides multiple avenues to generate a model which identifies the most suitable space for a new project. However, a weighted multi-criteria evaluation (MCE) is the best form of analysis to determine the most suitable location for a cannabis storefront in the City of Guelph. MCEs have been identified in GIS literature as being especially capable of including multiple socioeconomic and environmental considerations when siting projects where conflict surrounding the site location exists (Zhang, 2012). Literature regarding the siting of other politically sensitive sites must be analyzed as there is no literature detailing a spatial model used to site recreational marijuana stores.
Multi criteria models are designed to account for factors and constraints imposed on the siting of new developments (Romano, 2015). Factors or criteria are commonly measured by either continuous or discontinuous numerical scales to enhance or reduce the land suitability for the target objective, while constraints are impediments that do not allow development (Romano, 2015). The MCE model uses standardized ranking derived from a pairwise comparison where the criteria are weighted based on their importance. A pairwise comparison is a process of comparing multiple entities (factors, criteria, ect.) against one another to determine which is preferred or posseses a greater arbitrary weight compared to its counterparts (Barzilai, 1990). As the model is based in the vector data format, the criterion are compared using the field calculator in an attribute table with the weights assigned using the pairwise matrix described in Objective 3. As the model only takes into account those areas leftover after constraints are applied (further described in Objective 3), it is not necessary to include these constraints in the final equation as a raster MCE might. The MCE is based on the following formula where W=Weight, and F= factors: