Multi-Criteria Spatial Assessment of Urban Open Spaces for Promoting Physical Activity and Spatial Justice

This dataset accompanies the study “Multi-Criteria Spatial Assessment of Urban Open Spaces for Promoting Physical Activity and Spatial Justice: A Case Study of Tehran Metropolitan”. It includes both the input and output data used in the spatial multi-criteria decision analysis (SMCDA) of urban open spaces in Tehran, as well as the full set of codes, scripts, and processing workflows employed in the study.

The dataset comprises:

1. Processed spatial layers: Normalized environmental and accessibility/infrastructure criteria (20 layers in total), representing the factors used in evaluating urban open space quality.

2. Weighting results: Criterion weights derived using a hybrid approach combining the Best-Worst Method (BWM) and Criteria Importance Through Intercriteria Correlation (CRITIC).

3. Output maps and spatial analyses:

   • Weighted Linear Combination (WLC) integration results of environmental and infrastructure criteria.

   • Hot Spot Analysis (Getis-Ord Gi*) and Local Moran’s I clusters.

   • Spatial justice measures including Lorenz curves and Gini coefficients.

4. Codes and workflows: Three fully annotated MATLAB scripts covering:

   • Criteria weighting (subjective, objective, and hybrid methods).

   • WLC integration for environmental and infrastructure criteria.

   • Spatial equity and clustering analyses (Hot Spot Analysis, Local Moran’s I, Lorenz Curve, and Gini Coefficient).

Each script includes detailed English comments, and a comprehensive README file guides users step-by-step through the implementation process.

These datasets and workflows enable researchers and practitioners to replicate the study, test alternative criteria weights, or adapt the framework to other urban contexts. They provide a robust basis for assessing the spatial distribution of urban open space quality in Tehran and for examining inequalities in access to spaces that support physical activity.