To evaluate the final maps and address the strengths and weaknesses of the MCE and overlay analysis models.
While an MCE is a useful tool in planning and decision-making processes (Jankowski et al., 2001), there are some limitations associated with the implementation of an analytical hierarchy process. This project followed the pairwise comparison method to allocate weights to each criterion. This method is easy to use as it allows for flexibility and adaptability within the MCE; however, it also imposes a degree of subjectivity within the MCE. Because weights are allocated to each criterion based on the user’s perspective and opinions, the MCE output may be influenced by any biases the user may have. Furthermore, it is difficult to allocate a definitive weight to criteria that have multiple dimensions (environmental and socioeconomic), as different people will place value on different aspects of each criterion, and thus the overall criterion, based on their own needs. Once the final output maps were produced, the identified suitable sites were evaluated in order to ensure that the model’s results truly reflected a suitable site. While an MCE is important in decision-making, it is only a support tool for the user to implement as desired. Aquifer location data could not be retrieved within the Special Areas of Alberta, possibly due to the rural location of this municipality. The inclusion of aquifer data would have decreased the amount suitable area even more, potentially resulting in no suitable area at all. Contrarily, it may have minimally decreased the amount of suitable area since aquifers are typically located near water bodies, which were already included in the analyses.
An overlay analysis of EDP Renewable’s site map with the final MCE output consisting of both environmental and socioeconomic factors was conducted to determine the ideal location for the Sharp Hills Wind Farm. An overlay is a GIS operation that binds together both the features and attributes of multiple data sets to identify potential relationships between them (ArcGIS, 2016). Overlay analyses can either be implemented using raster or vector data, yet all joined data must be of the same type (ArcGIS, 2016). Raster data modelling is more ideally suited for quantitative analysis and data downloaded in vector format should be converted into raster data (ArcGIS, 2016). A limitation to overlay analysis is the reduction in accuracy and resolution when converting vector data to raster data (ArcGIS, 2016). When converting to raster data, one must consider cell size and computation time where computation time increases with decreasing cell size. The most prominent limitation of the overlay tool is the lack of efficiency when working with large, complex data. When combining large datasets that include a high degree of feature overlap, this tool is susceptible to geoprocessing tool failure and poor performance (Esri, 2012).