Data-Driven Municipal Infrastructure Planning for Healthy Communities
Bibliographic record
Abstract
Infrastructure systems are a crucial component of North American communities, supporting quality of life, providing basic necessities, and allowing for social development, and economic growth. According to the UN, 55% of the world population, or roughly 4.3 billion people, live in urban areas worldwide, with an anticipated increase as rural populations continue to move into urbanized areas. With increasing urbanized populations comes increasing healthcare expenditures for municipalities. In 2022, the healthcare expenditure per Canadian was up to $8,563 dollars, or roughly $331 billion dollars total. Literature suggests there is a close link between built municipal infrastructure, and the health of the community. This study aims to examine and expand upon the correlation between municipal infrastructure and community health, using a combination of Bayesian Belief Networks and Machine Learning Approaches. The findings are then used to determine which infrastructure systems have the greatest impact on community health. These findings give insight into how to improve community health through infrastructure. The machine learning model is able to accurately predict health indices for cities, making it a valuable tool for predicting health indices in Canada. Based on the findings, infrastructure has an impact on the health and wellbeing of the community. Relationships were found between community health and air quality, drinking water quality, the amount of greenspace and parks, the amount of walking or biking paths, and the number of recreational facilities. This provides insight into which municipal infrastructure assets provide the greatest health benefits to community members.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".