Urbanization in the Mexico City Metropolitan Area 1900–2020: Urban Dynamics and Driving Factors
Bibliographic record
Abstract
The variability of urban growth in the Mexico City conurbation reflects the complexity of changes and uncertainty experienced in many urban areas. The goal of this project is to identify how the urban expansion of the Mexico City conurbation reflects the changes in socioeconomic variables of the region over recent decades. The authors first spatially quantified the rate of urban growth from 1990 to 2020 using Landsat data and then identified the socioeconomic variables associated with this urban expansion. Results showed a progressive loss in vegetated land and an increase in urban land. The population’s access to roads and the marginalization index had the highest positive correlation with the observed urban growth. Although these variables were highly correlated with each other, access to roads was not found to be a variable of importance for projecting urban growth. Finally, two distinct zones of urban growth were determined using cluster statistics; the first showed no growth, which corresponded with more established, older municipalities closer to the city centre, and the second zone had significant growth, which corresponded to municipalities away from the centre, coinciding with urban decentralization and new lifestyle patterns. The identification of urban growth zones and socioeconomic variables associated with that growth will assist with effective planning, infrastructure development, and resource management.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".