A framework of comparative urban trajectory analysis
Bibliographic record
Abstract
The increasing availability of urban trajectory data from the GPS-enabled devices has provided scholars with opportunities to study urban dynamics at a finer spatiotemporal scale. Yet given the multi-dimensionality of urban trajectory dynamics, current research faces challenges of systematically uncovering spatiotemporal and societal implications of human movement patterns. Particularly, a data-driven policy-making process may need to use data from various sources with varying resolutions, analyze data at different levels, and compare the results with different scenarios. As such, a synthesis of varying spatiotemporal and network methods is needed to provide researchers and planning specialists a foundation for studying complex social and spatial processes. In this paper, we propose a framework that combines various spatiotemporal and network analysis units. By customizing the combination of analysis units, the researcher can employ trajectory data to evaluate urban built environment dynamically and comparatively. Two case studies of Chinese cities are carried out to evaluate the usefulness of proposed conceptual framework. Our results suggest that the proposed framework can comprehensively quantify the variation of urban trajectory across various scales and dimensions.
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 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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".