Leveraging human mobility data to assess recreational activity across the United States’ most visited National Forest

Abstract

Increasing participation in outdoor recreation is driving elevated levels of human presence in wildlands. If properly managed, outdoor recreation can benefit conservation goals while providing the public with numerous benefits. However, balancing increased demand for recreation opportunities and maintaining ecological integrity can pose difficult tradeoffs for managers. As such, there is a need for additional data-driven resources to help inform land management decisions. Traditional approaches of measuring recreational use, such as trail counters, are costly, labor intensive, and lack the ability to efficiently scale up across large areas. Human mobility data (HMD) offers a detailed, dynamic representation of the human footprint and can provide essential information for the management of human activity at a fine resolution across broad scales. HMD is relatively low-cost and can be analyzed to discern different types of recreation behaviors (e.g., snowmobiling, hiking). Our objective was to explore how HMD can be used to better understand recreation patterns and behaviors within the White River National Forest in Colorado, the nation's most visited National Forest. We leveraged 233 million smartphone locations over 4 years to 1) discern spatial visitor use patterns by creating interactive maps of human use intensity across the WRNF, 2) classify recreation behaviors and summarize user types, 3) quantify overlap between recreation and wildlife habitat.