Sensing global tourism numbers with millions of publicly shared online photographs
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
In our increasingly connected world, individuals produce continuous streams of data through their constant interactions with the Internet. This data is opening up opportunities to measure human behaviour that was previously time consuming or expensive to capture. Here, we explore whether data from online photographs can be used to estimate travel statistics on a global scale. We draw on the locations attached to 69 million publicly shared photographs to infer the global travel patterns of almost half a million users of the photo-sharing platform Flickr. We find that our photo-based estimates of tourist arrival statistics for the G7 countries Canada, France, Germany, Italy, Japan, the United Kingdom and the United States correlate with the corresponding official statistics released by those countries. Our results highlight the potential for vast volumes of online data to inform the generation of timely, low-cost indicators of the state of society. We discuss practical considerations that remain before this methodology could be used in the production of official statistics.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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