From doom to zoom!

Cities don’t just shape our lives — they shape our well-being. From air quality to population density, the urban environment influences how healthy, comfortable, and livable a city truly is. Yet assessing Urban Environmental Quality (UEQ) is far from straightforward, especially when different planners and decision-makers may view the same conditions through pessimistic or optimistic lenses. This makes flexible and transparent tools essential for understanding how environmental and infrastructural factors interact across a city. The Landscape Ecology Group (Dagmar Haase) introduced a scenario-driven spatial decision support framework designed to optimize UEQ zoning. The method brings together a structured geodatabase, standardized criteria, multi-criteria weighting, and scenario building using Ordered Weighted Averaging (OWA). By comparing pessimistic, neutral, and optimistic decision-making scenarios, we show how key factors — particularly air pollution and population density — shape UEQ outcomes in Isfahan. The results highlight how different planning outlooks can substantially shift the proportion of areas classified as environmentally poor. Overall, the proposed framework provides a flexible and robust way to evaluate urban conditions under a range of perspectives, supporting more informed and adaptable urban management. Definitely check out their Land Article!

Abstract

Urban managers and decision-makers may approach Urban Environmental Quality (UEQ) assessment with perspectives that range from highly pessimistic to highly optimistic scenarios. The objective of this study was to introduce a scenario-driven spatial decision support system framework for optimizing UEQ zoning. The proposed framework includes six steps: (1) building a geodatabase of criteria, (2) standardizing criteria using minimum and maximum methods, (3) determining criteria weights using the Analytic Hierarchy Process (AHP) method, (4) combining criteria and creating scenarios using the OWA method, (5) analyzing UEQ maps with statistical analyses, and (6) examining variability through histogram analysis of UEQ values across scenarios. The results indicate that, among environmental and infrastructural criteria, air pollution and population density had the most significant impact on UEQ zoning in Isfahan city. In the five decision-making scenarios (highly pessimistic, pessimistic, neutral, optimistic, and highly optimistic), 8% (19), 12% (15), 16% (12), 21% (8), and 25% (5) of Isfahan’s area were classified as poor, respectively. Additionally, the percentage of the population in poor classes across the scenarios was 5% (14), 10% (11), 13% (7), 17% (5), and 20% (3), respectively. The findings demonstrate that the proposed framework offers high flexibility and capability for assessing UEQ across different decision-making scenarios.