Document Type
Article
Journal/Book Title/Conference
Journal of Environmental Management
Volume
243
Publisher
Academic Press
Publication Date
5-10-2019
First Page
1
Last Page
27
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Abstract
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 to 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.
Recommended Citation
Clark, M., Wilkins, E. J., Dagan, D. T., Powell, R., Sharp, R. L., & Hillis, V. “Bringing forecasting into the future: Using Google to model national park visitation.” Journal of Environmental Management. https://doi.org/10.1016/j.jenvman.2019.05.006