Dynamic Modelling as A Support Tool for Forestall Uncertainty in Complex Urban Systems: Edmonton and Metro Vancouver Case Study
Bibliographic record
Abstract
Planning, in essence, is dealing with uncertainty. Planners forecast future population, employment, dwelling units, and land uses for the following 20, 50, or even 100 years ahead. The further we go into the future; the higher the uncertainty. To deal with uncertainty, planners produce scenarios. Scenarios, in the context of planning, are development options that could be based on different assumptions for future growth. More scenarios means testing out a larger suit of options and their implications on a set of relevant criteria. In Edmonton, Canada, we developed an Urban growth model for the city where we dealt with uncertainty at the local scale. Three scenarios were developed to test alternative development perspectives: ‘Strong Central City’, ‘Node City,’ and ‘Corridor City’. Each scenario has a narrative story that expressed the scenario’s high-level goal, scenario’s approach to residential and commercial activities, transportation vision, and employment, and environmental principles. Dealing with uncertainty at the planning level within cities, is quite different from dealing with regional development of larger areas, where the focus shifts from assessing planning alternatives to a more holistic understanding of the complexities involved in future developments. In the Metro Vancouver Region, we therefore apply a different approach with more attention for inter- and intraregional migration patterns, the diversities in the economy, and the physical limitations for growth, including those likely to be imposed on the region by climate change. Metro Vancouver has 23 member jurisdictions and mandates to coordinate the development across the region. The governance structure is complex, and the future vision has multiple competing interests. This shift in focus has also resulted in a different, more complex modelling approach and a different interaction process with decision makers. The presentation will focus on the lessons learnt in Edmonton and the ongoing process in Vancouver.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".