Characterisation of Accessibility in Halifax, Canada: Developing a Composite Network-distance-based Accessibility Measure
Bibliographic record
Abstract
Composite accessibility analyses provide significant insight for land-use and transportation policies that promote sustainable transportation planning. This paper contributes to the limited research on multi- modal, multi-destination accessibility measures by employing a unique, Composite Network-distance- based Accessibility Measure (CNAM). The CNAM estimates accessibility at a finer-grained disaggregate level, which can be scaled to any aggregate spatial unit of interest. This study defines a five-interval Likert accessibility scale informed by planning, engineering, and public health professionals. Experts suggest that accessibility thresholds vary for different service destinations and travel modes. They selected shorter distance ranges to represent very high accessibility thresholds for food stores and child day cares and larger distances to define very high accessibility to government services and recreation destinations. The CNAM signifies the density of service destinations that are proximate to a parcel. A higher CNAM reveals greater proximity to a higher number of destinations. The results of the CNAM highlight that there are considerable spatial differences in accessibility across the Halifax region, particularly within the Regional Centre. Halifax is most accessible to eating places, followed by offices and clinics of physicians. The scores for walking and biking were highest for the Regional Centre and poor for the Rural Commutershed. The parcel-level estimation yields finer-grained results, but accessibility at the dissemination area (DA) level was also estimated, revealing that the majority of DAs have a relatively low accessibility to service destinations, particularly by active travel modes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".