GIS-Based Multi-Criteria Decision Analysis for Post-Conflict Reconstruction Prioritization in Ukraine
De Lange, Kevin (2026)
2026
Master's Programme in Sustainable Societies and Digitalisation
Informaatioteknologian ja viestinnän tiedekunta - Faculty of Information Technology and Communication Sciences
Hyväksymispäivämäärä
2026-06-09
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202606036898
https://urn.fi/URN:NBN:fi:tuni-202606036898
Tiivistelmä
This thesis develops a spatial multi-criteria decision analysis (MCDA) model to support reconstruction prioritization in post-conflict Ukraine. With reconstruction costs estimated at hundreds of billions of dollars and damage unevenly distributed across regions, transparent and spatially explicit methods are urgently needed to guide resource allocation. The study integrates Geographic Information Systems (GIS) with the TOPSIS method to construct a reconstruction priority index from nine open-access criteria spanning building damage, population density, social vulnerability, infrastructure access, land use, and terrain, all processed at 1 km resolution across inhabited Ukraine.
Results show that eastern oblasts, particularly Donetsk, Luhansk, and Kharkiv, consistently emerge as the highest-priority regions across multiple weighting scenarios. Sensitivity analysis confirms these findings are robust to variations in criteria weights, and a comparison with a Weighted Linear Combination model yields a Spearman rank correlation of r = 0.984, indicating that the spatial conclusions reflect the underlying data rather than any specific algorithmic choice. The multi-criteria framework also surfaces patterns that a damage-only approach would miss: wartime displacement suppresses priority scores in Zaporizhia and Kherson below what their physical destruction warrants, while western destination oblasts show elevated need driven by population pressure.
The study demonstrates that open-source GIS tools and publicly available data can be combined to produce a transparent and reproducible spatial prioritization framework applicable at national scale. The resulting index provides a methodologically grounded starting point for reconstruction planning and contributes to the literature by bridging the gap between projectlevel MCDA and area-level spatial prioritization in post-conflict contexts.
Results show that eastern oblasts, particularly Donetsk, Luhansk, and Kharkiv, consistently emerge as the highest-priority regions across multiple weighting scenarios. Sensitivity analysis confirms these findings are robust to variations in criteria weights, and a comparison with a Weighted Linear Combination model yields a Spearman rank correlation of r = 0.984, indicating that the spatial conclusions reflect the underlying data rather than any specific algorithmic choice. The multi-criteria framework also surfaces patterns that a damage-only approach would miss: wartime displacement suppresses priority scores in Zaporizhia and Kherson below what their physical destruction warrants, while western destination oblasts show elevated need driven by population pressure.
The study demonstrates that open-source GIS tools and publicly available data can be combined to produce a transparent and reproducible spatial prioritization framework applicable at national scale. The resulting index provides a methodologically grounded starting point for reconstruction planning and contributes to the literature by bridging the gap between projectlevel MCDA and area-level spatial prioritization in post-conflict contexts.