Use of Integrated Urban Models to Assess Urban Sustainability and Health Impacts of Future Growth Policies in Canadian Cities
Bibliographic record
Abstract
Background/Objectives: Public health and quality of life have been increasingly impacted by changes in land use and transportation. Integrated Urban Models (IUMs) support urban planning by simulating the impacts of such changes. To promote healthier cities, we need planning tools that consider sustainability and health outcomes. This study expanded the SmartPlans IUM simulation platform to produce a variety of environmental, social and economic indicators that could be used to evaluate the efficacy of alternate urban growth scenarios. SmartPlans was also expanded to evaluate the health benefits of simulated scenarios.Methods: A broad array of sustainability and health indicators was identified for incorporation in SmartPlans including 18 acute and chronic health endpoints. We obtained feedback from 128 stakeholders from academia, all levels of government organizations, and regional health authorities across Canada to support indicator selection. SmartPlans was also updated to produce concentrations of pollutants using Land Use Regression models. These concentrations, which are used in the calculations of the 18 health endpoints to evaluate health benefit outcomes, rely on the outputs of the land use and transportation modules of SmartPlans.Results: Input from stakeholders across Canada was incorporated into the final indicator selection and weighting. Simulation tests representing various land use and transportation scenarios with SmartPlans for Halifax, Nova Scotia and London, Ontario highlight the strengths of using SmartPlans to assess progress towards sustainability and health benefits. Simulations tests show the benefits of urban intensification over suburbanization.Discussion/Conclusions: This study demonstrates the utility of extending IUMs like SmartPlans to include sustainability and health indicators. SmartPlans can be used to support prioritization of environmental and health outcomes in urban decision-making and environmental 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".