Krista Heinrich

In recent years, governing bodies have turned their attention to providing publicly available measures of wellbeing within their communities. These measures provide citizens and decision-makers alike with the opportunity to not only observe, but in some cases to define wellbeing in their own terms. Recently, the City of Toronto has developed an online interactive information system demonstrating an accessible and modifiable approach to measuring neighbourhood wellbeing through multi-criteria analysis. This research utilizes the Wellbeing Toronto data and application as a case study to examine two aspects of spatial multi-criteria analysis, normalization and standardization, through the exploration of two research objectives. Firstly, to determine which type of data normalization (by population or by area) method is most correlated to raw untransformed variables and how this normalization affects neighbourhood rankings resulting from multi-criteria analysis. Secondly, to determine how similar neighbourhood rankings are when using either the score range transformation or Z score standardization for composite indices generated for multiple points in time. Results of the normalization analysis indicated inconsistencies in the behaviour of variables across treatment types while the standardization analysis revealed slight variances in neighbourhood rank on close inspection with less pronounced differences when examined in a more general quintile format.