Potential Path Areas and Activity Spaces in Application: A Review
Bibliographic record
Abstract
The potential path area (PPA) and activity space (AS) concepts play a central role in the substantial amount of applied research focusing on the quantitative analysis and description of people's spatial behaviour. Given this large literature, and the surprising lack of a formal review of the research, the time is ripe for a systematic review. This paper examines how the key concepts of PPAs and ASs have evolved, how they have been applied, what issues need to be resolved, and potential areas for future research. The review begins with the main theoretical developments influencing the applied use of these methods, and continues with a categorization of the literature across three dimensions — research domain, methods of calculation and application purpose. We find that the methods have been used not only in the core originating fields of travel behaviour and transport geography, but also in health, criminology and demography, and are growing fastest in health. The methods have been applied to a number of purposes with applications to accessibility the most common and the fastest growing. Demonstrated interest in these methods, along with the technologies and data to facilitate them, suggests a bright future for the use of PPAs and ASs in the social sciences.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".