Measuring Toronto's vital signs – Comparing global and local ideal point analysis in an urban equity case study

Multi-Criteria Decision Analysis (MCDA) offers a unique analytical lens for examining socio-economic indicators within cities. Competing, interrelated criteria are combined to produce a single weighted score for each location or spatial unit. The Toronto Foundation's Vital Signs report provides an annual snapshot of Toronto's quality of life, using ten indicator categories. While the report is published at a city scale, a spatially explicit approach could offer a deeper interpretation of the results. While global MCDA methods can conceal geographic variation, novel local techniques account for spatial heterogeneity in relevant characteristics. In this paper, a localized ideal point method, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), was applied to the Vital Signs report. The results show more variation and less spatial autocorrelation than the global approach and a simplified representation of the report. GIS researchers are increasingly exploring local MCDA approaches using vector data, but the more complex techniques such as TOPSIS are relatively underdeveloped. This case study aims to fill this conceptual gap and illustrate the applicability of local ideal point analysis in an urban equity context.