Spatio-Temporal Analysis to Dynamically Map and Audit Urban Growth in Peri-Urban Areas
Bibliographic record
Abstract
Villages located today in the peri-urban area, are in continuous agglomeration. The lack of proper guidelines and monitoring systems to define, promote, regulate and manage the pattern of development in these areas, causes massive deteriorations to the environment and quality of life. In Egypt, very few states have a dedicated legal framework that addresses the need for planned development in the peri-urban areas. A Spatio-temporal model framework consisting of a set of geospatial indicators is required to regulate and direct the growth and development of these areas and prevent further spill across administrative boundaries. This article responds to the lack of a geo-spatial quantifiable criterion as a key to detect, analyze, and better govern spatial and temporal patterns of urban growth in peri-urban areas, where the results are to be represented dynamically in forms of spatial patterns evolving in time. The research aims to develop a process-wise, contextual mapping of peri-urban dynamics using GIS to detect and analyze spatial and temporal patterns of urban growth. It has been applied to one of the peri-urban areas in Egypt, in Meet Assas Village, chosen as a case study. Miscellaneous primary and secondary data sources together with the methods used for monitoring: UN Criteria (Goal 11) targets, thematic headlines, village’s problems and geo-spatial indicators were the main constitutions of the model frame work. The results show that the village is characterized by a dispersed pattern of development with the absence of basic infrastructure and services. Besides that, the study reveals, through temporal patterns, that the future and unplanned growth will continue to intensify; posing numerous threats on the environment and the quality of life. A harmonized standardized measurement framework for planning, development, and management is crucial rather than demolishing these areas.
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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 | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".