A GIS-FAHP Framework for Prioritizing Sustainable Urban Infrastructure: Evidence from Medan, Indonesia
Bibliographic record
Abstract
This study presents a GIS-FAHP integrated framework for prioritizing sustainable urban infrastructure, with empirical evidence from Medan City, Indonesia-a rapidly urbanizing area facing spatial inequality, congested road networks, and uneven distribution of public facilities.The framework combines GIS spatial analysis with FAHP-based multi-criteria weighting to enhance objectivity and manage uncertainty in infrastructure planning.The model integrates three key criteria-building density, road infrastructure, and public facilities-selected for their relevance to spatial accessibility and service provision.Expert-based pairwise comparisons, adjusted through fuzzification, reveal that building density exerts the strongest influence (60.4%), followed by road infrastructure (26.4%) and public facilities (13.2%).A GIS-based weighted overlay produces spatial suitability maps, categorizing Medan into five infrastructure development priority zones.Notably, only 4.26% of the city area is identified as highly suitable for infrastructure investment, while over 54% remains unsuitable due to accessibility constraints and infrastructure gaps.Spatial autocorrelation analysis using Moran's I confirms significant clustering patterns, validating the robustness of the model.This integrated GIS-FAHP approach contributes to the advancement of sustainability-focused urban planning by offering a replicable, scalable, and evidence-based tool for prioritizing infrastructure development.The findings support policy decisions aimed at reducing spatial disparities and promoting equitable, sustainable urban growth in Medan and comparable Southeast Asian cities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".