Algorithmic optimization of sampling for the National Visitor Use Monitoring program

Abstract

The National Visitor Use Monitoring (NVUM) program provides the U.S. Department of Agriculture, Forest Service and the U.S. Congress with statistically sound estimates of recreation use in the National Forest System and the economic contribution of those recreation visits to rural communities. After 25 years and five rounds of NVUM sampling, the program has assembled long-term monitoring datasets to characterize forest visitors and understand spatial and temporal patterns of use. However, because the visitor-intercept sampling methodology for NVUM surveys is cost-intensive, program managers are exploring approaches to reduce data-collection costs while maintaining the quality and utility of NVUM data. Here we describe an algorithmic optimization approach that uses historical NVUM data to generate sampling scenarios that systematically reduce the number of site-days (sampling days) while minimizing the impact on the data quality and its applications and end uses.

Citation

Creany, Noah; White, Eric M.; Cline, Sarah. 2026. Algorithmic optimization of sampling for the National Visitor Use Monitoring program. Res. Note. PNW-RN-584. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 21 p. https://doi.org/10.2737/pnw-rn-584.