Patterns of human activity paired with census data for the largest city parks in Toronto, Canada
Bibliographic record
Abstract
This dataset contains spatial and temporal data on urban parks and their usage in Toronto, Canada. It was used to examine how anonymized mobility data from Mapbox can help identify and reduce inequality in the availability and use of green spaces. The dataset consists of four files:Toronto 2021 Census.shp: A shapefile that contains census data for the park catchments in Toronto, including variables such as housing density and car ownership.greenSpaceActivityWithWeather.csv: A spreadsheet that contains the daily Mapbox activity value for each park, as well as the average temperature and total precipitation from local weather stations.Simplified Large Parks.shp: A shapefile that contains the polygons of the target parks used in the study, which are larger than 10 hectares and have more than 1000 visits per year.Park amenities.csv: A spreadsheet that contains the amenities available in each park, such as sports fields, transportation options, gardens, and playgrounds.The dataset supports a manuscript published in People and Nature titled: “Using anonymized mobility data to reduce inequality in the availability and use of urban parks”. The manuscript presents the methods and results of the analysis, as well as the implications and recommendations for urban planning and policy.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".