A National Model for US Public Land Visitation

Nathaniel Merrill, Samantha G. Winder, Dieta Hanson, Spencer A Wood and Eric White
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Samantha G. Winder: University of Washington
Spencer A Wood: University of Washington

No avjue, SocArXiv from Center for Open Science

Abstract: 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 posts to social media, recreation report platforms, and a cellular 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 locational data. 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 cellular device locations. We discuss the practical implications of these findings as well as paths forward to improve visitation estimation on public lands by incorporating digital mobility data.

Date: 2024-12-20
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:avjue

DOI: 10.31219/osf.io/avjue

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