GIS-Based Public Services Analysis Based on Municipal Election Areas: A Methodological Approach for the City of Makkah, Saudi Arabia
Bibliographic record
Abstract
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 religious 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 percentage 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 consideration to achieve fair and better distribution of public services in the city.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".