Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress

Lorena Torres Lahoz, Carlos Lima Azevedo, Leonardo Ancora, Paulo Morgado, Zenia Kotval, Bruno Miranda, Francisco Camara Pereira

TL;DR

The study addresses planning under deep uncertainty for stress-reducing urban policies by applying Scenario Discovery within a Robust Decision Making framework. It combines a Lisbon-based neurorubanism-inspired stress predictor with Copenhagen street-data to derive path-specific vegetation thresholds that reduce stress, revealing how density, crowding, and high extraversion can undermine vegetation benefits. The work introduces adaptive sampling variants (Adaptive PRIM and Adaptive PRIM borders) and an active-learning approach to cut simulation runs while preserving discovery quality, achieving $R^2=0.53$ on Lisbon data and vegetation thresholds spanning roughly $7\%$ to $28\%$ across paths. The resulting, replicable workflow offers decision-ready guidance for designing resilient, vegetation-rich cities under uncertainty.

Abstract

Urban environments significantly influence mental health outcomes, yet the role of an effective framework for decision-making under deep uncertainty (DMDU) for optimizing urban policies for stress reduction remains underexplored. While existing research has demonstrated the effects of urban design on mental health, there is a lack of systematic scenario-based analysis to guide urban planning decisions. This study addresses this gap by applying Scenario Discovery (SD) in urban planning to evaluate the effectiveness of urban vegetation interventions in stress reduction across different urban environments using a predictive model based on emotional responses collected from a neuroscience-based outdoor experiment in Lisbon. Combining these insights with detailed urban data from Copenhagen, we identify key intervention thresholds where vegetation-based solutions succeed or fail in mitigating stress responses. Our findings reveal that while increased vegetation generally correlates with lower stress levels, high-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce its effectiveness. This work showcases our Scenario Discovery framework as a systematic approach for identifying robust policy pathways in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical.

Scenario Discovery for Urban Planning: The Case of Green Urbanism and the Impact on Stress

TL;DR

The study addresses planning under deep uncertainty for stress-reducing urban policies by applying Scenario Discovery within a Robust Decision Making framework. It combines a Lisbon-based neurorubanism-inspired stress predictor with Copenhagen street-data to derive path-specific vegetation thresholds that reduce stress, revealing how density, crowding, and high extraversion can undermine vegetation benefits. The work introduces adaptive sampling variants (Adaptive PRIM and Adaptive PRIM borders) and an active-learning approach to cut simulation runs while preserving discovery quality, achieving on Lisbon data and vegetation thresholds spanning roughly to across paths. The resulting, replicable workflow offers decision-ready guidance for designing resilient, vegetation-rich cities under uncertainty.

Abstract

Urban environments significantly influence mental health outcomes, yet the role of an effective framework for decision-making under deep uncertainty (DMDU) for optimizing urban policies for stress reduction remains underexplored. While existing research has demonstrated the effects of urban design on mental health, there is a lack of systematic scenario-based analysis to guide urban planning decisions. This study addresses this gap by applying Scenario Discovery (SD) in urban planning to evaluate the effectiveness of urban vegetation interventions in stress reduction across different urban environments using a predictive model based on emotional responses collected from a neuroscience-based outdoor experiment in Lisbon. Combining these insights with detailed urban data from Copenhagen, we identify key intervention thresholds where vegetation-based solutions succeed or fail in mitigating stress responses. Our findings reveal that while increased vegetation generally correlates with lower stress levels, high-density urban environments, crowding, and individual psychological traits (e.g., extraversion) can reduce its effectiveness. This work showcases our Scenario Discovery framework as a systematic approach for identifying robust policy pathways in urban planning, opening the door for its exploration in other urban decision-making contexts where uncertainty and design resiliency are critical.

Paper Structure

This paper contains 25 sections, 2 equations, 21 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Handeling uncertainty in Urban Planning
  4. Scenario Discovery
  5. Algoritims for Scenario Discovery
  6. Methodology
  7. Scenario Discovery Framework
  8. Perfomed algorithms
  9. Baseline
  10. Adaptative PRIM
  11. Adaptative PRIM borders
  12. Case study: Reducing stress in Greater Copenhagen
  13. Lisbon data collection
  14. Stress model
  15. Copenhagen data collection
  16. ...and 10 more sections

Figures (21)

  • Figure 1: Example of coverage/density trade-off curves using 1,2,3, or any number of restricted parameters SD
  • Figure 2: An example of a sequence of the peeling processes performed by the PRIM algorithm on a given dataset SD
  • Figure 3: Proposed Scenario Discovery framework
  • Figure 4: Design overview of the Case Study
  • Figure 5: A modified version of the affective slider 12 for valence and arousal evaluation
  • ...and 16 more figures