FutureScapes

A design thinking approach to blending computational models and scenario narratives for urban futures

Journal Article (2026)
Author(s)

Supriya Krishnan (TU Delft - Technology, Policy and Management)

Hedwig van Delden (Research Institute for Knowledge Systems)

Nazli Yonca Aydin (TU Delft - Technology, Policy and Management)

Tina Comes (TU Delft - Technology, Policy and Management)

Research Group
System Engineering
DOI related publication
https://doi.org/10.1177/23998083261438856 Final published version
More Info
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Publication Year
2026
Language
English
Research Group
System Engineering
Journal title
Environment and Planning B: Urban Analytics and City Science
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23
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Abstract

Accelerating urbanization and the inherent uncertainty in urban planning are increasing the demand for approaches that meaningfully integrate qualitative insights with quantitative analysis. While scenarios are widely used to explore multiple urban futures, existing methods that combine narrative storylines with computational models face persistent challenges: narrative assumptions are often oversimplified during translation; model structures frequently lack transparency regarding their underlying assumptions; and integrative processes tend to prioritize consensus, often sidelining the specialized insights of practitioners essential for urbanization strategies. Design Thinking (DT) offers a promising framework to address these limitations through its iterative, non-linear structure that bridges creative and analytical reasoning. Yet, a systematic, reproducible workflow that operationalizes DT for urban scenario development remains underdeveloped. This paper introduces FutureScapes (FS), a stepwise Design Thinking methodology for blending computational models and scenario narratives that embeds expert feedback into the modelling process. FS centers the spatial reasoning of expert stakeholders and introduces semi-quantitative boundary objects—in the form of scenario design maps—to break the traditionally linear sequence from story to simulation. This enables a reflexive process where model outputs actively reshape qualitative scenario assumptions to inform policy-relevant outcomes. The study contributes a generalizable methodology that enhances the contextual relevance, transparency, and strategic utility of computational scenario modelling for metropolitan planning.