Identifying the Optimal Locations of New Urban Centres for Population in Kuwait Using GIS
Bibliographic record
Abstract
The economic and cultural core of many cities around the world is typically close to its geographical centre. Whilst this was a sensible urban form during the first few waves of urbanisation, it is also associated with various issues stemming from the high built density and concentration of services, including traffic congestion, high land and development prices, and stretched infrastructure. This article explores the notion that Kuwait City may benefit from the development of new urban centres. Firstly, multiple candidate locations were identified via a public survey. Then, the availability of services and amenities in each location, as well as the population coverage were modelled. Finally, using a set of three criteria each location was assigned with a score in order to identify the optimal candidates for the new urban centres. The modelling process relied heavily on spatial optimisation algorithms, such as the location-allocation method in GIS. It was concluded that the three most optimal locations for urban centres were in Khaitan, Jahra and Eqaila. These three centres, if developed, would be sufficient to cover demand for services and alleviate pressure from the centre of Kuwait.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".