A 3D agent-based model for simulating urban densification in Toronto, Canada
Bibliographic record
Abstract
The use of land parcel data, 3D visualisation and urban theories offers a significant opportunity for advancing simulations of urban densification. This paper presents a 3D agent-based model (ABM) to explore future urban densification dynamics in Toronto based on stakeholder behaviour and interactions, the impact of zoning regulations, and profit expectations. The ABM establishes residents, developers, landowners, and the local zoning authority as primary actors involved in urban densification. This model replicates the Toronto urban development process through a structured framework of submodels which represent different stages of this process, based on the literature and gentrification theories. Three different scenarios are developed which show the city is projected to experience between 46 and 98 new developments by the year 2040. Average building height could increase by 17% to 56%, and the city could have 10,238 to 25,070 new units to meet future population demand. These simulations characterise Toronto’s future capacity for urban densification, realise the levels of densification required to meet Toronto’s growing population, and ultimately provide a more comprehensive understanding of the city’s future transformation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".