Where Do Tourists Go? Visualizing and Analysing the Spatial Distribution of Geotagged Photography
Bibliographic record
Abstract
Visualizing the geographical positions of photographs taken by tourists is a promising method to measure tourist activity in urban spaces. Most photo-sharing sites on the Internet offer the possibility of “geotagging” photos, resulting in geographical information retrievable by databases using the application programming interface (API) of such sites. These data sets can be visualized on maps or digital globes, which make the correlations of the photo density and the geographical objects of a given area expressive. It is possible to differentiate pictures taken by locals from those of visitors by examining the temporal distribution of a specific user's photos. Resulting maps revealed interesting correlations between the tourist attractions of the area and the number of photos taken there. In the case study of Budapest tourists took photos only in the areas around main tourist attractions. In contrast, locals also photographed recreational spaces or interesting sights not advertised for tourists. The development of recreational infrastructure is also visible in the case of the most recent projects.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".