Introducing spatial availability, a singly-constrained competitive-access accessibility measure
Bibliographic record
Abstract
Accessibility measures are widely used to summarize the ease of reaching potential destinations. As such, they combine, into a single summary measure, properties of the land use system, on the one hand, and the transportation system and travel behavior on the other. Defined as the weighted sum of the opportunities that can be reached given the cost of movement, accessibility is used in transportation planning, health planning, economic analysis, etc. This workshop introduces spatial availability. Much like accessibility, spatial availability measures the ease of reaching potential destinations. However, unlike accessibility, it makes opportunities available uniquely to members of the population. For example, a job, once it is available to someone, it is no longer available to somebody else. In effect, spatial availability is a singly-constrained accessibility measure that preserves the number of opportunities. In this workshop, we explain the intuitions behind spatial availability and describe the mechanisms to implement it. A key to this is the idea of proportional allocation, and the use of proportional allocation factors. The use of proportional allocation factors as a mechanism for constraining the spatial availability means that the results are easier to interpret than those obtained from accessibility analysis, and they are more intuitive as well. One exercise is provided, meant to be solved by hand. The workshop finishes with a practical example of implementation in R. Data from a real survey in the Greater Toronto and Hamilton Area and use of package {accessibility} give hands-on practice that can serve as a launching pad for your own experiments and applications.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".