Using Planning Data to Monitor the Health of Communities - The Healthy Development: Monitoring and Mapping Project
Bibliographic record
Abstract
IntroductionThe impact of the built environment on health and chronic disease outcomes is increasingly being recognized. As Public Health develops interventions to transform the health-promoting potential of built environments, effective monitoring and evaluation will require the creation and baseline measurement of key health-promoting urban elements. Objectives and ApproachThe Healthy Development Monitoring Project aims to assess health-promoting aspects of the existing built environment across the Region of Peel, a large region of 1.382 million people in Southern Ontario comprised of three local municipalities (the Cities of Mississauga, Brampton and Town of Caledon). Project objectives include: Produce evidence-informed indicators to measure health-promoting built form elements at a neighbourhood-scale across the region Produce a GIS-based visualization that incorporates these indicators into a single model to measure their combined impact on the built form Reproduce these indicators over time to monitor for changes in Peel’s built form ResultsThe resulting Healthy Development Monitoring Map (HDMM) is an interactive online mapping tool that includes twenty built form indicators characterizing the region’s built environment, including: density, service proximity, land use mix, street connectivity, streetscape characteristics and efficient parking. These indicators were created through extensive cross-sectoral collaboration with regional and municipal staff in land-use and policy planning, transportation, internal data centers and academic institutions. This collaborative approach enabled the linking of data sets from land-use planning, urban design and transportation to allow the health-promoting potential of existing built environment conditions to be objectively described. The HDMM demonstrates considerable progress in producing precise, neighbourhood-level built environment indicators at a regional scale by integrating census and local data into a comprehensive set of empirically-derived measures. Conclusion/ImplicationsThe HDMM is a novel approach to quantifying a social determinant of health through collaborative data acquisition and analysis. The HDMM benefits public health, planning and non-governmental decision-makers by creating a holistic presentation of key infrastructure and design elements that contribute to healthier urban environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".