A GIS-based multi-criteria decision analysis approach for public school site selection in Surabaya, Indonesia
Bibliographic record
Abstract
Surabaya is one of the old cities of Indonesia and has been inhabited since the Colonial era. It has been continuously growing until today leading to expansion of its area to the south, east, and west. Unfortunately, it has not been supported by the addition of new public schools, particularly at the secondary and high school levels. This research aimed to help the government by determining the suitability level of the whole area of the city for locating a new school and for evaluating current school locations. This research proposed six spatial factors: administration, population, transportation, land-use, student flow, and public preferences. Each factor was represented as raster file built from primary and secondary tabular and spatial data. Each factor then was weighted from the multi-criteria decision analysis step using the analytical hierarchy process method. The results show recommended and non-recommended areas in Surabaya for locating a new school building. This research integrated GIS analysis, web-GIS application, public participation, and MCDA to identify the best solution for this case.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".