Predicting Urban Growth of the Greater Toronto Area - Coupling a Markov Cellular Automata with Document Meta-Analysis
Bibliographic record
Abstract
Toronto’s Census Metropolitan Area (CMA) has faced on-going challenges concerning its demographic shifts in the urban and rural fringe tending to become a megacity over the coming decades, due to rapid population increase and urban amalgamation. For this research we examine past urban land use transitions in Toronto’s CMA based on collected remote sensing data between 1973 and 2010. A Markov Cellular Automata approach is used deriving the CMA urban future based on the existing and planned strategies for Ontario. This is done by a combination of multi-criteria evaluation processes originating transition probabilities that allow a better understanding of the regions urban future by 2030. While the transition probabilities are incorporated from the traditional Markov Chain process, the variables for suitability are measured through a text mining approach, by incorporating several planning documents. The result offers a more integrative vision of policymaker’s preference of future planning instruments, allowing for the creation of a better integration of propensity of future growth indicators. The northern part of Toronto is expected to register continuous growth in the coming decades, while agricultural land will continue to decrease. Urban areas after 2020 tend to become more clustered suggesting an importance of planning of green spaces within the Toronto.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".