Un modelo de simulación computacional integrado a SIG para explorar la dinámica de crecimiento de la Zona Metropolitana de la Ciudad de México (1998-2008) / A Computer Simulation Model Integrated into SIG to Explore the Dynamics of Growth of the Metropoli
Bibliographic record
Abstract
En este trabajo se explora la dinámica de crecimiento económico de los municipios y delegaciones de la Zona Metropolitana de la Ciudad de México (ZMCM), mediante simulaciones de un modelo dinámico de contagio tipo Autómata Celular integrado a Sistemas de Información Geográfica. Los resultados del modelo indican que en condiciones de intensa interacción local de los municipios o delegaciones, la zmcm tiende a obtener una tasa de crecimiento promedio del PIB inferior a la que se ha observado en los últimos años. Además se muestra que la tasa de crecimiento en equilibrio de la ZMCM no se ve afectada por el carácter monocéntrico o policéntrico que pudiera presentar la metrópoli, pero sí por la cercanía o lejanía de los municipios o delegaciones con el centro o los subcentros económicos de la región. Finalmente, la investigación demuestra que es posible alcanzar tasas de crecimiento del PIB superiores a las observadas bajo ciertas condiciones de coordinación en la estructura urbana sin que necesariamente haya interacción global entre los municipios o delegaciones. AbstractThis paper explores the dynamics of economic growth in the municipalities and delegations of the Metropolitan Area of Mexico City (MAMC) through simulations of a dynamic model of Cellular Automata contagion integrated into Geographic Information Systems. Model results indicate that under conditions of intense local interaction by municipalities and delegations, the MCMA tends to achieve a lower average growth rate of GDP than has been observed in recent years. They also show that the equilibrium growth rate of the MCMA is not affected by the monocentric or polycentric nature of the metropolis, but rather by the proximity or distance of the municipalities and delegations in regard to the center or economic sub-centers of the region. Lastly, the research shows that it is possible to achieve higher GDP growth rates than those observed under certain conditions of coordination in the urban structure without necessarily being global interaction between municipalities and delegations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".