Measuring the Exposure and Vulnerability of Transit Riders to Cold Temperatures in London, Ontario
Bibliographic record
Abstract
Due to the expansion of suburban areas in many cities around the world, accessibility to public transit through walking has become a new challenge for riders. Considering the impacts of climate change in creating more extreme weather, such as extreme cold, heat, or flooding, transit riders may thus be at a higher risk of exposure for various weather-related illnesses (Fraser & Chester, 2016).\nUsing ArcGIS Pro and ArcMap software, and data collected from City of London’s Open Portal, the United States Geological Survey, Open Mobility Data, and Statistics Canada, the exposure and vulnerability of transit riders to cold temperatures in London were calculated and visualized. This was done by measuring the walking distance from the center of postal codes to the three closest bus stops, and thereafter calculating the average land surface temperature along the respective route. The analysis excluded areas where the average distance to the closest stop was beyond 800 meters, since it be would unlikely for an individual to walk longer distances to access public transit (Fraser & Chester, 2016). The information was then amalgamated and averaged to the dissemination area scale in order to standardize the neighbourhoods to match census tracts.\nThe cold exposure index was then calculated by multiplying the average walking time to the closest bus stop with the average absolute value of the land surface temperature of the respective route. A walking speed of 4.7 km/h, consistent with the average human walking speeds, was selected for the analysis.\nLastly, the median total income and visible minority population data within private households in each neighborhood in London were collected and visualized using the 2016 national census to discern any equity implications related to the vulnerability of riders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".