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MEASURING NATURE-BASED RECREATION USING HUMAN MOBILITY DATA
Citation:
Tsai, W., N. Merrill, M. Grupper, AND A. Neale. MEASURING NATURE-BASED RECREATION USING HUMAN MOBILITY DATA. A Community on Ecosystem Services, Washington DC, DC, December 12 - 15, 2022.
Impact/Purpose:
This work explores the usage of human mobility data for estimating visitation counts and visitor profile in national parks. Findings from this study provide an alternative approach to understand visitro counts and visitors' demographic characteristics for better management in public lands and addressing social inequity of recreational access. This work could support conservation initiatives, such as America the Beautiful.
Description:
Parks provide cultural ecosystem services; participating in nature-based recreation improves human health and well-being, increases social cohesion, and supports local economies. However, nature-based recreation is not always equitably distributed. To better manage recreation on public lands and address social inequity to support conservation iniatives, such as America the Beautiful, collecting visitor data is fundamental. However, compiling these data across time and space is often challenging. This presentation will introduce a method to measure nature-based recreation consistently. Visitor counts and profiles were estimated based on cellular device locations for the top 50 most visited US National Park Service (NPS) units in 2018 and 2019. We examined the correlation between NPS data (NPS STATS) and anonymized and aggregated human mobility data purchased from Airsage Inc. We also applied a mixed effect model to account for month and clustering by park unit. Visitor origin information based on home Census Block Group was used to estimate travel distance and demographic characteristics. Cellular device-based visitation counts were generally welll-correlated with the NPS STATS counts, with a correlation greater than 0.8 for most parks. Regression results showed that park attributes, such as population center (urban vs. non-urban), recognition from non-local communities (iconic vs. local), and porousness level of park boundary (high vs. low), played a role in the relationships between cellular device and NPS counts. Human mobility data tended to better predict NPS STATS’ measures for non-urban iconic parks and large parks with low porosity. Origin information based on Census demographics indicated that only 9% of park visitors were African American while they make up around 13% of the general population. Demographics varied by park attributes. Human mobility data provide an alternative and reliable approach to collect visitor information across large temporal and spatial scales. Visitors profile information can be used to promote equitable access. This method could be considered for recreation accounting for the United Nations System of Integrated Environmental Accounting – Experimental Ecosystem Accounts (SEEA EA) framework. Future research should consider taking advantages of this alternative approach to understand visitation and address social inequity of recreational access.