A National Model for Estimating United States Public Land Visitation
Citation
Merrill, N. H., Winder, S. G., Hanson, D. R., Wood, S. A., & White, E. M. (2025). A national model for estimating United States public land visitation. Scientific Reports, 15(1), 42764. doi.org/10.1038/s41598-025-26926-w
Public land agencies need reliable counts of how many people visit parks and natural areas. These numbers help land managers protect the environment and understand the benefits that nature provides to the public.
In this study, researchers created and tested models that estimate visitor numbers using several kinds of digital mobility data, such as geotagged social media posts, community science observations, and anonymous mobile phone locations. They compared these digital estimates to visitation numbers from the National Park Service, the U.S. Forest Service, and the U.S. Fish and Wildlife Service.
Researchers found that the models work best when at least some on-site visitor counts are included. These on-site numbers help account for important differences in how visitation relates to the digital data across different places and agencies. They also found that some digital data sources are more useful than others, and that combining multiple data sources leads to better predictions than using only one—even when that one source is mobile device data.
The paper also discusses what these results mean for land managers and how future work can further improve estimates of how many people visit public lands.
Abstract
Public land management relies on accurate visitor counts in order to understand and mitigate environmental impacts and to quantify the value of ecosystem services provided by natural areas. We build and test predictive visitation models suitable for publicly-managed parks, open space, and other protected lands based on multiple sources of digital mobility data including geotagged posts to social media platforms, community science observations, and a mobile device location dataset from a commercial vendor. Using observational visitation data series from the United States’ National Park Service, Forest Service, and Fish and Wildlife Service, we quantify the accuracy of statistical models to predict on-the-ground visitation using individual and combined sources of mobility data alongside other covariates. We find the predictive models performed best in settings where some on-site visitation data can be integrated into the models. On-site visitation data helps to account for meaningful differences in modeled relationships both within and across the three agencies considered. We find variation in the usefulness of the digital mobility data sources, with models combining multiple data sources outperforming those using a single source, including those based solely on mobile device locations. We discuss the practical implications of these findings as well as paths forward to improve visitation estimation on public lands.