Understanding Human Mobility Patterns in a Developing Country Using Mobile Phone Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study demonstrates the use of mobile phone data to derive country-wide mobility patterns. We identified significant locations of users such as home, work, and other based on a combined measure of frequency, duration, time, and day of mobile phone interactions. Consecutive mobile phone records of users are used to identify stay and pass-by locations. A stay location is where users spend a significant amount of their time measured through their mobile phone usage. Trips are constructed for each user between two consecutive stay locations in a day and then categorized by purpose and time of the day. Three measures of entropy are used to further understand the regularity of user’s spatiotemporal mobility patterns. The results show that user’s in a high entropy cluster has high percentage of non-home based trips (77%), and user’s in a low entropy cluster has high percentage of commuting trips (49%), indicating high regularity. A set of doubly constrained trip distribution models is estimated. To measure travel cost, the concept of a centroid point that assumes the origins and destinations of all trips are concentrated at an arbitrary location such as the centroid of a zone is replaced by multiple origins and destinations represented by cell tower locations. Note that a cell tower location can only be used as trips origin/destination location when a stay is detected. The travel cost measured between cell tower locations has resulted in shorter trip distances and the model estimation shows less sensitivity to the distance-decay effect.
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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 | 0.015 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it