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.

多标准决策分析（MCDA）为检查城市内的社会经济指标提供了独特的分析视角。相互关联的相互竞争的标准组合在一起，为每个位置或空间单位生成了一个加权分数。多伦多基金会的生命体征报告使用十种指标类别，提供了多伦多生活质量的年度快照。虽然该报告以城市规模发布，但空间明确的方法可以提供对结果的更深入的解释。尽管全局MCDA方法可以掩盖地理变化，但新颖的本地技术可解决相关特征中的空间异质性。在本文中，将局部理想点方法，即“通过与理想解决方案的相似性进行优先顺序偏好技术”（TOPSIS）应用于“生命体征”报告。结果表明，与全局方法相比，差异更大，空间自相关性也更小，并且简化了报告的表示。GIS研究人员正在越来越多地使用矢量数据探索本地MCDA方法，但是相对较复杂的技术（例如TOPSIS）却相对欠发达。本案例研究旨在填补这一概念空白，并说明在城市公平背景下局部理想点分析的适用性。