Long-Term Expansion Plan of Intermediate Cities Using GIS: The Case of Sinjar City, Iraq
Bibliographic record
Abstract
According to UIA-CIMES Declaration, several governments have included a group of intermediate cities in their program for sustainable urban development. In third world countries, many of these urban areas still suffer from neglect and shortcomings in the production process of well-thought-out urban expansion plans, regardless of their capacity and potential for urbanization and transformation. This research aims to take advantage of the digital capabilities of GIS software to study the most effective factors in formulating urban expansion plans necessary to develop appropriate policies for intermediate cities from the perspective of UIA-CIMES. Its methodology focuses on identifying a range of factors affecting the development of the master plan for these cities, including identifying many aspects related to sustainable urban development such as cultural, physical, economic, and environmental aspects, with their details and land uses, besides determining several urban factors and indicators approved for differentiation and classification of expansion areas. This is followed by an analytical study of the Idrisi program on the city of Sinjar in northern Iraq as a case study to provide a comprehensive view of the urbanization of the city in a long-term GIS plan. Idrisi is characterized by its ability to deal with raster images more than vector graphics, and the ease of working and training. After extracting the results, planning trends are analyzed and identified to show the appropriate land use for each of the mentioned expansion areas. These results are available to the decision-maker in formulating the scenario of the new master plan for Sinjar city 2040. The analysis of the potential expansion areas utilizing Idrisi has revealed the existence of two types of regions in Sinjar: basic expansion zones and non-basic expansion zones, and the study of its future expansion must be based on the principles of sustainable urban development according to long-term planning.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".