Modeling Urban Morphogenesis at a Macro-Scale Based on Cellular Automata: The Case of the Casbah of Algiers
Bibliographic record
Abstract
Urban morphogenesis refers to the dynamic process creating urban form. This study develops a cellular automata model (CA) of the Casbah of Algiers, a historic Islamic urban fabric. The model integrates topographic data, elevation, slope, and street network, and combines two predictive approaches: artificial neural networks (ANN) and logistic regression (LR). Calibrated with historical land use data, it estimates spatial transition potentials and simulates the city's evolution over time. The results demonstrate the ability of cellular automata to reproduce the complex structure of traditional Islamic cities determined by morphological constraints. Based on complexity theories, the research clarifies the decision-making systems and underlying logic behind irregular and ambiguous urban forms such as the Casbah. However, the model is limited by the availability and resolution of historical spatial data, and it does not incorporate detailed micro-scale spatial dynamics that influence urban transformations. Future research can explore multi-scalar interactions, integrate socioeconomic, cultural, and religious dimensions, and apply the methodology to other traditional urban fabrics for comparative analysis. This work contributes to a better understanding of traditional Islamic cities and proposes a flexible modeling framework to enrich current approaches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".