2025 / Public land

Leveraging Digital Mobility Data to Estimate Visitation in National Wildlife Refuges

Citation

Winder, S. G., Wood, S. A., Brownlee, M. T., & Lia, E. H. (2025). Leveraging digital mobility data to estimate visitation in National Wildlife Refuges. Journal of Environmental Management, 373, 123417. doi.org/10.1016/j.jenvman.2024.123417

Link to file

The U.S. Fish and Wildlife Service takes care of over 500 National Wildlife Refuges and dozens National Fish Hatcheries across the country. Knowing how many people visit these places is important for planning, protecting nature, and meeting public needs. But figuring out accurate visitor numbers is difficult across such a large system.

This study built upon existing research and tested new ways to estimate how many people visit these areas by using data from mobile devices, social media, and a community science platform. Researchers found that social media and mobile device data by themselves did not reliably show how many people visit, because the connection between these data and actual visitor numbers changes depending on the source, the location, and the time. Researchers found that combining data from several sources gave better results than using just one. They also found that each refuge is different, so adding details about each site helps make the predictions more accurate. This new approach could help staff get better visitor numbers more easily and plan more effectively for the future.

Abstract

Figure 6. Predictive power (R2) of nine different visitation models. The first (Mobility + Refuge) is the combined model that uses all the digital mobility data and a fixed effect for refuge.
Figure 6. Predictive power (R2) of nine different visitation models. The first (Mobility + Refuge) is the combined model that uses all the digital mobility data and a fixed effect for refuge. The second (Mobility) uses all the digital mobility data but does not include an effect for refuge. Each bar shows the amount of variability that is explained by each predictor in that model, calculated as the General Dominance. The other columns are the total R2 values of simple linear regression models which regressed a single predictor against observed visits. They are named for the model predictor.

The US Fish and Wildlife Service manages over 500 National Wildlife Refuges and dozens of National Fish Hatcheries across the United States. Accurately estimating visitor numbers to these areas is essential for understanding current recreation demand, planning for future use, and ensuring the ongoing protection of the ecosystems that refuges safeguard. However, accurately estimating visitation across the entire refuge system presents significant challenges. Building on previous research conducted on other federal lands, this study evaluates methods to overcome constraints in estimating visitation levels using statistical models and digital mobility data. We develop and test a visitation modeling approach using multiple linear regression, incorporating predictors from eight mobility data sources, including four social media platforms, one community science platform, and three mobile device location datasets from two commercial vendors. We find that the total number of observed visitors to refuges correlates with the volume of data from each mobility data source. However, neither social media nor mobile device location data alone provide reliable proxies for visitation due to inconsistent relationships with observed visitation; these relationships vary by data source, refuge, and time. Our results demonstrate that a visitation model integrating multiple mobility datasets accounts for this variability and outperforms models based on individual mobility datasets. We find that a refuge-level effect is the single most important predictor, suggesting that including site characteristics in future models will make them more generalizable. We conclude that statistical models which incorporate digital mobility data have the potential to improve the accuracy of visitor estimates, standardize data collection methods, and simplify the estimation process for agency staff.