Achieving the Efficient Distribution of Police Stations and Rescue Police Points in Duhok City/ Iraq by Using (GIS)
Bibliographic record
Abstract
Duhok city in Kurdistan of Iraq have witnessed a great urban expansion and population growth since 2003. Many areas are built up and population has increased because of the migration from other parts outside the governorate to Duhok city. This research intends the investigation and evaluation of existing situations and distribution of rescue police points and police stations in Duhok city. The study tries to find out whether the existing rescue police points are fairly distributed or not, and to which extent they can control the city and reach at crime position efficiently. The research investigates the rescue police and police stations and compares the police stations services with local and regional criteria regarding population and quarter’s area. In spite of the hardships and difficulties faced by the researchers in collecting necessary data, the researchers have obtained data about RP and PSs by field surveys and interviews. Data about RP staff, equipment and cars have been collected. The study has used new techniques such as; Global Position System (GPS) for determining the points, locations, and Geographic Information System (GIS) for producing maps and other needed issues. The research followed local and regional planning criteria such as; crime rate, population density, roads, main roads, roads intersections, and important public utilities, with field study. The research has reached some recommendations and suggestions regarding the efficient distribution of rescue police and police stations, in the whole Duhok city. The recommendations also include methods and solutions for the problems that are facing the RP and PSs in order to strengthen and better operate their duties in reducing crime rates. Keywords: Police Stations (PS), Rescue Police Points (RPP), Geographic Information System (GIS).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".