Simulating multiple urban land use changes by integrating transportation accessibility and a vector-based cellular automata: a case study on city of Toronto
Bibliographic record
Abstract
The accessibility provided by the transportation system plays an essential role in driving urban growth and urban functional land use changes. Conventional studies on land use simulation usually simplified the accessibility as proximities and adopted the grid-based simulation strategy, leading to the insufficiencies of characterizing spatial geometry of land parcels and simulating subtle land use changes among urban functional types. To overcome these limitations, an Accessibility-interacted Vector-based Cellular Automata (A-VCA) model was proposed for the better simulation of realistic land use change among different urban functional types. The accessibility at both local and zonal scales derived from actual travel time data was considered as a key driver of fine-scale urban land use changes and was integrated into the vector-based CA simulation process. The proposed A-VCA model was tested through the simulation of urban land use changes in the City of Toronto, Canada, during 2012–2016. A vector-based CA without considering the driving factor of accessibility (VCA) and a popular grid-based CA model (Future Land Use Simulation, FLUS) were also implemented for comparisons. The simulation results reveal that the proposed A-VCA model is capable of simulating fine-scale urban land use changes with satisfactory accuracy and good morphological feature (kappa = 0.907, figure of merit = 0.283, and cumulative producer’s accuracy = 72.83% ± 1.535%). The comparison also shows significant outperformance of the A-VCA model against the VCA and FLUS models, suggesting the effectiveness of the accessibility-interactive mechanism and vector-based simulation strategy. The proposed model provides new tools for a better simulation of fine-scale land use changes and can be used in assisting the formulation of urban and transportation planning.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".