Changes in visitor behaviour in Canadian National Parks due to COVID-19: focusing on visitor spatial location and the distance between park facilities and visitors
Bibliographic record
Abstract
• COVID-19 significantly reduced visitor numbers in Canadian National Parks. • Visitor spatial distribution shifted toward remote, less-populated park areas. • Distance to natural features outweighed proximity to hotels post-COVID-19. • Flickr geotagged data revealed long-term shifts in visitor behaviour. • Policy should support diversified tourism products adapted to new preferences. COVID-19 has significantly altered visitor behaviour in Canadian National Parks (NPs), which are largely composed of forested landscapes that support vital human–nature interactions. To investigate these changes, this study selected six NPs and examined shifts in spatial visitation patterns and their relationship with proximity to park facilities before and during the pandemic. Based on 48,041 geotagged Flickr photos from 2018 to 2023, we employed the Seasonal Concentration Index (SCI), Geographic Concentration Index (GCI), Kernel Density Estimation (KDE), and the Optimal Parameter-based Geographic Detector (OPGD). Temporally, all six NPs experienced a pronounced drop in visitor numbers in 2020–2021, with only partial recovery by 2023. Spatially, visitor distribution remained highly uneven, with hotspots clustered around Banff, Jasper, and Lake Louise. However, a gradual dispersion of visitor flow toward less-developed and lower-density areas was observed in recent years. Importantly, COVID-19 shifted visitors’ spatial preferences: proximity to natural features such as rivers became more influential than the traditional pull of accommodations and services. These findings suggest a pandemic-induced reconfiguration of tourism behaviour, favouring more dispersed and nature-oriented experiences, though not necessarily closer to forest interiors. The study underscores the need for adaptive tourism planning that aligns with changing recreational demands and the ecological character of NPs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".