Estimating park visitation in Canadian national parks using volunteered geographic information (VGI)
Bibliographic record
Abstract
Understanding human ecological impact in natural areas is important for ensuring overall environmental and human health. Managing the human ecological impact in protected areas like national parks can be laboursome and expensive with traditional visitation monitoring methods due to their size and complexity of use. Volunteered geographic information from digital platforms can provide researchers and park managers with unique insights into visitation that would otherwise be difficult or impossible to obtain. In this study, we test whether data collected from the photo-sharing and storage platform Flickr, the outdoor recreation tracking platform AllTrails, and the exercise tracking platform Strava, can be used to estimate official visitation data in Canadian national parks through simple and multiple linear regression. Our hypothesis is that these data sources can be used to estimate visitation patterns of outdoor recreation in Canadian national parks. We identified that all three sources of volunteered geographic data are significantly correlated with official visitation data obtained from Parks Canada. Our models were strengthened when the data sources were used together in a multiple regression rather than individually in bivariate models. This study demonstrates this relationship for the first time with the Strava Global Heatmap and AllTrails proxy data and creates a basis for using volunteered geographic information from different platforms to estimate visitation and patterns of outdoor recreation in Canadian national parks. Further, the ubiquitous spatial nature of these datasets suggests they may be used as a reliable proxy in other areas where official visitor counts are unavailable.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 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".