Aggregation and spatial analysis of walking activity in an urban area: results from the Halifax space-time activity survey
Bibliographic record
Abstract
This study examines neighborhood characteristics affecting the incidence of walking trips in urban and suburban areas of Halifax, Canada. We employ data from the Space-Time Activity Research (STAR) survey, conducted in 2007-8. Primary respondents completed a two- day time-diary survey, and their movements were tracked using a GPS data logger. Primary respondents logged a total of 5,005 walking trips, specified by 781,205 individual GPS points. Redundant and erroneous points, such as those with zero or excessive speed, were removed. Data points were then imported into ArcGIS, converted from points to linear features, visually inspected for data quality, and cleaned appropriately. From mapped walking tracks we developed hypotheses regarding variations in walking density. To test these, walking distances were aggregated by census tracts (CTs), and expressed as walking densities (per resident, per metre of road, and per developed area). We employed multivariate regression to examine which neighborhood (CT) variables are most useful as estimators of walking densities. Contrary to much of the planning literature, built-environment measures of road connectivity and dwelling density were found to have little estimating power. Office and institutional land uses are more useful estimators, as are the income and age characteristics of the resident population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".