Activity Patterns in Space and Time: Calculating Representative Hagerstrand Trajectories
Bibliographic record
Abstract
Daily travel diaries can be recorded as sequences of characters representing events and their contexts as they unfold during the day. Dynamic programming algorithms as used in bioinformatics have been used by a number of researchers to measure the similarities and differences between travel patterns on the basis of temporal sequencing of events, activity transition, and total activity time. The resultant similarity matrices have been shown to be more effective in classifying sequential patterns than classifications based on alternative similarity indices. The basic algorithms can be amended to include Euclidean distance by specifying the geographic coordinates of events. This permits quantitative classification of Hagerstrand-type activity trajectories. The approach allows grouping and classification of trajectories on the basis of activity and spatial similarity. Such groups can be used to identify representative trajectories that are analogous to means or modes in univariate statistics, giving more concrete meaning to the concept of the activity pattern than any other method now available. The paper illustrates the effect of considering both events and locations in the classification of daily activity patterns using activity diary data gathered in the town of Reading. The amendment has been implemented in the Clustal TXY alignment software package.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".