When e-activities meet spatial accessibility: A theoretical framework and empirical space-time thresholds for simulated spatial settings
Bibliographic record
Abstract
The high penetration of e-activities (e-working, e-shopping, e-leisure) has empowered people to overcome space-time constraints in daily routines, and this trend is growing. Accordingly, new knowledge is sorely needed to incorporate e-activities into accessibility planning and to define new conceptualizations, methods, and quantifiers that recognize digital and in-person accessibility in real life. This paper introduces the framework of “augmented accessibility” and identifies space-time thresholds in which e-activities are more competitive than in-person activities. By being aware of these thresholds, specific policies could be adopted for encouraging people to save travel time and allocate it to reach other in-person destinations, thereby increasing their overall spatial accessibility. At methodological level, time geography concepts and elasticity analysis are combined, estimating space-time thresholds for six simulated spatial settings: from polycentric and compact cases to sprawled and monocentric cases. The results indicate that e-activities are more competitive in sprawled settings and emphasize the relevance of travel directionality (from opportunity hubs to low-density places) for retaining high spatial accessibility levels and increased travel distances. The social and policy implications of space-time thresholds are discussed for each simulated spatial setting by estimating and comparing critical travel distances for different transport modes. The paper closes by discussing aspects related to practical operationalization of the proposed framework, its communicability, interpretability, and usability.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".