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Variable Screening and Rank-Stability Diagnostics for GIS-Based Pre-Transformation Valuation Support in an Istanbul Urban Renewal Area
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DOI：10.3390/land15081445.png)
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Abstract
En 中文
Prioritising properties for early review in dense urban renewal areas is a spatial multi-criteria decision problem in which physical condition, accessibility, environmental quality, and hazard exposure must be weighed before formal appraisal. This study develops a reproducible GIS-based multi-criteria decision analysis workflow for pre-transformation valuation support in Fatih Neighborhood, Esenler District, Istanbul, covering 54 independent property units. A 311-indicator geodatabase was screened for local discrimination, leaving 284 active sub-criteria under 10 retained main criteria. Expert pairwise judgments were converted into weights by six procedures compared under a common log-ratio objective, and the resulting TOPSIS rankings were tested for redundancy, benchmark divergence, judgment uncertainty, and structural robustness. The retained variables carry substantial overlapping spatial information, so the matrix does not provide hundreds of independent evidence signals, and equal within-group allocation concentrates influence in single-variable criteria. The leading priority tier nevertheless remains stable under calibrated weight variation and realistic judgment noise, whereas the wider rank order is sensitive to grouping, weighting, normalisation, and scoring choices. The workflow therefore separates robust priority signals from model-dependent rankings without interpreting the scores as market-value estimates, so that the intensity of field verification and appraisal can follow the reported stability of each unit.
Keywords:
GIS-MCDA
urban transformation
pre-transformation valuation
variable screening
rank stability
AHP
TOPSIS
Best-Worst Method
Monte Carlo
Istanbul
Journal
IF:
3.2
Papers:
1.3W
Citations:
2.5W
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