Adapting to change: Visitor patterns in national parks across the pandemic timeline
Bibliographic record
Abstract
• COVID-19 reshaped forest-based visitor behaviour, creating lasting new normal trend. • Remote forest-immersive activities surged while popular routes saw less traffic. • Seasonal and spatial patterns decentralized, with more between-park movement. • Health crises highlight trees and forests role in supporting people’s health. • We offer insights for park management amid COVID-19′s new normal and future crises. The COVID-19 pandemic has substantially impacted visitor behaviour and forest tourism management, introducing new visitor patterns that persist in the post-COVID-19 period. As critical components of national parks, forests and tree-dominated natural environments have gained renewed attention for their role in promoting mental and physical health during public health crises. This study analysed pandemic-induced shifts in visitor activity and movement patterns from a temporal-spatial perspective in Banff, Jasper, Yoho and Kootenay National Parks using social media big data from pre, peri , and post COVID-19. Temporal analysis of social media posts aligned with official park attendance trends (2019–2023), validating big data as a reliable indicator. Results show a long-term behaviour shift toward nature-immersive activities in remote and forested wilderness areas, reduced traffics on historically popular routes, and emerging between-park connectivity. Seasonal and spatial visitation patterns became less centralised, increasing conservation pressures in ecologically sensitive forested areas and necessitating proactive infrastructure, zoning, and transit management. This research fills the knowledge gap on pandemic-driven visitation trends using big data, offering the implications extend beyond the current pandemic for effective and prompt park resources and tourism management, balancing conservation and public well-being.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".