Enhancing Urban Sustainability by Integrating MCDM and Model of GIS Spatial Analysis in Al-Nasiriyah Heritage Center Development
Bibliographic record
Abstract
Various countries of the world endeavor to achieve a new paradigm of sustainability by preserving their heritage and history and avoid the risk of old process preservation.Therefore, this article adopts several concepts for investigating heritage areas and then builds a model based on spatial analysis using GIS.So, the main problem this article addresses is that planning does not rely on appropriate mechanisms for preserving the heritage area of Al-Nasiriyah in Dhi-Qar Governorate, Iraq, which has resulted in erosion and destruction of that area.Consequently, the article aims to determine the optimal process for the rehabilitation to the heritage city center, using spatial analysis mechanisms and to achieve urban sustainability.To do this, the article first discusses various countries' experiences in the field of heritage conservation to understand successful principles for implementing the new sustainable paradigm.Subsequently, the Analytical Hierarchy Process (AHP) is used to calibrate and assign weights to the analysis mechanism.This process is based on the most prominent elements that depend on land uses and necessary services in the spatial analysis.After analyzing eight indicators, a consistency ratio of 0.47 was obtained, which is the acceptable excitation range in AHP standards.Finally, the study built a model in GIS to determine the most suitable area for the development of any city with the same condition.Therefore, the spatial analysis and model process identified Zones 2 and 3 as the most suitable for development, with Zone 4 being the optimal choice according to the MCDM analysis to be selected as the first area for development.Thus, this can limit the process of erosion of the urban heritage fabric due to the encroachment of commercial use and the rise in the value of achieving cultural sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".