Measuring space–time accessibility to urban opportunities: a study on demand for and supply of activities performed by university teachers and students of Khulna City
Bibliographic record
Abstract
People participate in various activity places leading their livelihood pattern (raising family, working, shopping, recreation, socializing, etc.) to have services/opportunities in their reach. Activity in urban opportunity places requires space and time, which in turn are subject to accessibility of those opportunity places. An activity-based approach within time geographical framework can explain how accessible the opportunity places are, considering space (travel barrier) and time (limited time) constraints. This study uses two checks to measure the accessibility of an opportunity: fi rst, whether the opportunity place is within the Daily Potential Path Area (DPPA) of a participant; and second, whether the participant’s activity reach time is within the opportunity opening hours. Potential Path Area (PPA) delimits a geographical area containing all feasible routes and urban oppor-tunities given the space time constraints determined by the particular pair of fi xed out of home activities, and then DPPA is prepared with the aggregation of all individual PPAs in a day. This study presents accessibility level of 10 major urban opportunity places, which are selected based on the frequency of participation performed by the uni-versity teachers and students of Khulna. This paper concludes by explaining the usefulness of the activity-based approach used in this study in accessibility studies over conventional accessibility measuring approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".