In recent years, visitation to U.S. National Parks has been increasing, with the majority of this increase occurring in a subset of parks. As a result, managers in these parks must respond quickly to increasing visitor-related challenges. Improved visitation forecasting would allow managers to more proactively plan for such increases. In this study, we leverage internet search data that is freely available through Google Trends to create a forecasting model. We compare this Google Trends model to a traditional autoregressive forecasting model. Overall, our Google Trends model accurately predicted 97% of the total visitation variation to all parks one year in advance from 2013-2017 and outperformed the autoregressive model by all metrics. While our Google Trends model performs better overall, this was not the case for each park unit individually; the accuracy of this model varied significantly from park to park. We hypothesized that park attributes related to trip planning would correlate with the accuracy of our Google Trends model, but none of the variables tested produced overly compelling results. Future research can continue exploring the utility of Google Trends to forecast visitor use in protected areas, or use methods demonstrated in this paper to explore alternative data sources to improve visitation forecasting in U.S. National Parks.
This is an author-produced, peer-reviewed version of this article. © 2019, Elsevier. Licensed under the Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 International license. The final, definitive version of this document can be found online at Journal of Environmental Management, doi: 10.1016/j.jenvman.2019.05.006
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Clark, Matt; Wilkins, Emily J.; Dagan, Dani T.; Powell, Robert; Sharp, Ryan L.; and Hillis, Vicken. (2019). "Bringing Forecasting into the Future: Using Google to Predict Visitation in U.S. National Parks". Journal of Environmental Management, 243, 88-94. https://dx.doi.org/10.1016/j.jenvman.2019.05.006
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