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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".