GIS-Based Public Services Analysis Based on Municipal Election Areas: A Methodological Approach for
Bibliographic record
Abstract
Copyright © 2013 Gomaa M. Dawod et al. This is an open access article distributed under the Creative Commons Attribution Li-cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The spatial fairness of public services is a major aspect in offering a healthy and cheerful living environment in a city. The Geographic Information System (GIS) technology has been applied to investigate the spatial distribution of some selected public services over the municipal election areas within Makkah city, Saudi Arabia. These services include education, health, security, religious, commercial, and sports services. The attained results show that except for reli-gious services, other public services are not quantitatively proportional with the population variations in Makkah. It has been found that the third election area posses almost one third of five public services and two thirds of the sport services. That might be attributed to the fact that the third election area possesses almost a quarter of the city population. But, although the first election area has a closer percentage of the total population, it does not include a comparable percent-age of public services. On a spatial basis, results of GIS spatial analysis (particularly the Euclidian distance, the mean distance band to a neighbor, the standard distance, the directional distribution ellipse, and the average nearest neighbor ratio tools) have concluded that there is inequity in the services distribution over municipal election areas in Makkah city. Consequently, it is recommended that local planners and decision makers should take the obtained results into
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".