Multimodal spatial availability: A singly-constrained measure of accessibility considering multiple modes
Bibliographic record
Abstract
Place-based accessibility measures communicate the potential interaction with opportunities at a zone that populations can access. Recent research has explored the implications of how opportunities are counted by different accessibility methods. In conventional measures, opportunities are multiply counted if more than one zone offers access to the same opportunity. This multi-count of opportunities leads to values of accessibility that are difficult to interpret. A possible solution to enhance the meaning-making of accessibility results is by constraining the calculations to match a known quantity. This ensures all zonal values sum up to a predetermined quantity (i.e., the total number of opportunities). In this way, each value can be meaningfully related to this total. A recent effort that implements this solution is spatial availability, a singly-constrained accessibility measure. In this paper, we extend spatial availability for use in the case of multiple modes or more generally, heterogeneous population segments with distinct travel behaviors. After deriving a multimodal version of spatial availability, we proceed to illustrate its features using a synthetic example. We then apply it to an empirical example of low emission zones in Madrid, Spain. We conclude with suggestions for future research and its use in evaluating policy interventions.
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.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".