Introducing spatial availability, a singly-constrained competitive-access accessibility measure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.297 | 0.048 |
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 it