Urban Distortion in Classifying Urban Areas Through Residential Zone Redistricting and Land Management Challenges in Najaf City: Diagnosis and Treatment Using GIS to Achieve Sustainability
Bibliographic record
Abstract
This study investigated the impact of residential land redistribution on urban growth.It also examined population density management in Najaf by comparing Al-Furat, Al-Amir, and Al-Shurta areas.The study lasted from 2000 to 2023.Re-subdivision methods were examined to determine how they addressed population growth demands and enhanced residential land use distribution.It also explored the planning, social, and economic issues that changed these areas' urban landscapes.Based on the geographical analysis of maps from the investigated years, resubdivision improved land usage and public services, notably in the Al-Furat and Amir areas.Significant re-subdivision rates were 29.63% and 31.58%.Meanwhile, Al-Shurta had little change.Its unique location and residential purpose raised it to 2.75%.The data also showed a definite urban tendency to convert open areas into residential and commercial units, and to alter land use patterns to meet population demands.Spatial analysis was performed using Geographic Information Systems (GIS) and the statistical software SPSS 29.After conducting T-test and ANOVA analysis, significant differences were found for most uses, including residential, which is the focus of our research.The analysis revealed the percentage of urban distortion resulting from residential redevelopment, pressure on services, and the housing ratio.These percentages were achieved at levels less than 0.05, reaching 0.001 and 0.009.They were graded from 2000 to 2023, the periods used for comparison and analysis.This highlights the importance of re-subdividing in urban management and enhancing quality of life.The research concluded that re-subdivision can improve urban fabric if accompanied by a balanced planning framework that considers environmental conservation and provides adequate infrastructure to meet future population growth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".