An approach for understanding human activity patterns with the motivations behind
Bibliographic record
Abstract
A mechanistic understanding of human activity patterns lays a foundation for many applications. The majority of the current research aims to outline human activity patterns mainly from spatiotemporal perspectives (i.e., modeling human mobility patterns), lacking of understanding of the motivations behind behaviors. The aim of this study is to model and understand human activity patterns within urban areas using both spatiotemporal and cognitive psychology methods to measure both human behavior patterns and the underlying motivations . We first propose a framework that enables us to analyze the spatiotemporal patterns of urban human activities, infer the associated semantic patterns that represent the motivations driving human mobility choices and behaviors, and measure the similarity between human activities. We then construct a human activity network based on the similarity to depict human activity patterns. The framework is applied to a case study of Toronto, Canada, where geotagged tweets are used as a proxy for human activities to explore activity patterns. The analysis of the human activity network shows that 61% of tweeter users follow similar activity patterns. Our work provides a new tool for better understanding the way individuals interact with urban environments that could be applied to a variety of urban applications.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".