Using data derived from cellular phone locations to estimate visitation to natural areas: An application to water recreation in New England, USA

Leveraging digital mobility data to estimate visitation in National Wildlife Refuges

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.

Leveraging big data for outdoor recreation management: A case study from the York river in Virginia

Outdoor recreation is important for improving quality of life, well-being, and local economies, but quantifying its value without direct monetary transactions can be challenging. This study explores combining non-market valuation techniques with emerging big data sources to estimate the value of recreation for the York River and surrounding parks in Virginia. By applying the travel cost method to anonymous human mobility data, we gain deeper insights into the significance of recreational experiences for visitors and the local economy. Results of a zero-inflated Negative Binomial model show a mean consumer surplus value of $26.91 per trip, totaling $15.5 million across nearly 600,000 trips observed in 2022. Further, weekends, holidays, and the summer and fall months are found to be peak visitation times, whereas those with young children and who are Hispanic or over 64 years old are less likely to visit. These findings shed light on various factors influencing visitation patterns and recreation values, including temporal effects and socio-demographics, revealing disparities that warrant targeted efforts for inclusivity and accessibility. Policymakers can use these insights to make informed and sustainable choices in outdoor recreation management, fostering the preservation of natural resources for the benefit of both visitors and the environment.

Using cellular device location data to estimate visitation to public lands: Comparing device location data to U.S. National Park Service's visitor use statistics

Understanding human use of public lands is essential for management of natural and cultural resources. However, compiling consistently reliable visitation data across large spatial and temporal scales and across different land managing entities is challenging. Cellular device locations have been demonstrated as a source to map human activity patterns and may offer a viable solution to overcome some of the challenges that traditional on-the-ground visitation counts face on public lands. Yet, large-scale applicability of human mobility data derived from cell phone device locations for estimating visitation counts to public lands remains unclear. This study aims to address this knowledge gap by examining the efficacy and limitations of using commercially available cellular data to estimate visitation to public lands. We used the United States' National Park Service's (NPS) 2018 and 2019 monthly visitor use counts as a ground-truth and developed visitation models using cellular device location-derived monthly visitor counts as a predictor variable. Other covariates, including park unit type, porousness, and park setting (i.e., urban vs. non-urban, iconic vs. local), were included in the model to examine the impact of park attributes on the relationship between NPS and cell phone-derived counts. We applied Pearson's correlation and generalized linear mixed model with adjustment of month and accounting for potential clustering by the individual park units to evaluate the reliability of using cell data to estimate visitation counts. Of the 38 parks in our study, 20 parks had a correlation of greater than 0.8 between monthly NPS and cell data counts and 8 parks had a correlation of less than 0.5. Regression modeling showed that the cell data could explain a great amount of the variability (conditional R-squared = 0.96) of NPS counts. However, these relationships varied across parks, with better associations generally observed for iconic parks. While our study increased our confidence in using cell phone data to estimate visitation, we also became aware of some of the limitations and challenges which we present in the Discussion.

Exclusionary Effects of Campsite Allocation through Reservations in U.S. National Parks: Evidence from Mobile Device Location Data

Campsites represent highly sought after recreational amenities in the national parks of the United States. Equitable allocation of scarce recreational resources has long been a key management issue in U.S. national parks, but has become increasingly difficult in an era of increasing demand. At present, a growing number of national park campsites are allocated through an online reservation system well in advance of a camper's arrival at a park. Compounding the challenge of allocating these campsites is a long history of exclusivity within national park camping-institutionalized through campground design and predicated on a legacy of the leisure class's affinity for camping in national parks. Given national park camping's history of exclusivity, this exploratory study seeks to explore how online reservation systems may impact the demographics of national park campers. Using mobile device location data, estimated demographics were calculated for campers in five national park campgrounds in the U.S. that each contained some sites requiring reservations and some sites available on a first-come, first-served basis. We detail results from analyses of variance between campsites requiring reservations and those that are available on a first-come, first-served basis. Results suggest that for each of the five campgrounds, those campers camping in sites that require reservations came from areas with higher median household incomes, on average. In three of the five campgrounds, this difference was significant. Additionally, in an urban-proximate setting, those camping in sites requiring reservations came from areas with a higher portion of White residency than those campers in campsites not requiring reservations, on average. We conclude with discussion that includes management implications concerning the growing prominence of online reservation systems for outdoor recreation amenities, and a brief research agenda for diversity, equity, and inclusion (DEI) as they relate to campgrounds. Principally, the former group of implications includes the realization that online reservation systems present the unintended consequence of excluding low-income, and perhaps non-White, would-be campers-a conclusion drawn from the results of this exploratory study. This discussion includes an analysis of the distributive justice of online reservation systems.

Assessing the validity of mobile device data for estimating visitor demographics and visitation patterns in Yellowstone National Park

Monitoring visitor demographics and temporal visitation patterns can help national park managers understand their visitors and allocate resources more effectively. Traditional approaches, such as visitor surveys or vehicle counts, are limited by time, space, labor, and financial resources. More recently, mobile device data have been adopted for monitoring visitors in park-related or tourism research. However, few studies validated mobile device data with traditional visitor surveys or count data. Combining mobile device data with the American Community Survey (ACS), this study assessed mobile device data's validity in a national park context with three approaches: Points of Interest (POIs), visitor demographics, and temporal visitation patterns. The results revealed that only half of the POIs inside Yellowstone National Park are valid. Compared to traditional visitor surveys, mobile device data are limited due to platform bias and the exclusion of international visitors, resulting in discrepancies in visitor demographics, such as education and income levels. Conversely, mobile device data have strong correlations with count data regarding monthly and daily visitation patterns. The results suggest that with careful consideration, mobile device data can serve as an additional and complementary source of information to traditional survey data for understanding visitor demographics and temporal visitation patterns.

Capacity development challenges and solutions for Natura 2000: an approach through blended learning

Capacity development is essential for the effective management of protected areas and for achieving successful biodiversity conservation. European Natura 2000 sites form an extensive network of protected areas and developing the capacity of staff at all levels is a priority that will positively influence the appropriate implementation of conservation actions. In this study we identify the main challenges and potential solutions to developing the skills, knowledge and tools required for effective Natura 2000 site management. Our findings are based on a case study of the European project LIFE e-Natura2000.edu, which focuses on capacity development in practical biodiversity conservation and management through integrated and blended learning experiences (i.e. a combination of face-to-face and virtual teaching). We illustrate the main elements for successfully building capacity within a variety of knowledge and experience backgrounds and operating levels related to the management of Natura 2000 sites. Multifaceted, blended learning approaches are key to tackling the various needs of Natura 2000 managers in terms of skills, knowledge and tools.

Coastal Recreation in Southern New England: Results from a Regional Survey

This paper presents a summary of coastal recreation of New England residents from a survey conducted in the summer of 2018. The management of New England's coasts benefits from understanding the value of coastal recreation and the factors influencing recreational behavior. To address this need, the survey collected the geographic location and trip details for both day and overnight visits to any type of location on the New England coast for a range of water recreation activities, providing a comprehensive view of coastal recreation in the region. This paper summarizes participation in various types of water recreation activities, including beachgoing, swimming, fishing, wildlife viewing, boating, and other coastal recreation activities. We quantify demand for coastal recreation using participation and effort models that disaggregate the dimensions of recreational behavior over space and census demographics. This provides insights on the scale and location of beneficiaries of this important human use of the natural environment. We found that 71% of people in the surveyed region participate in coastal recreation and engage in a wide range of coastal recreation activities at varied locations from open-ocean-facing coastal beaches to sheltered, estuarine ways to water. On average, people in the region take 37 trips to recreate on the coast of New England in a year, spending 167 hours per year visiting recreation sites and 66 hours traveling. This adds up to nearly 170.5 million trips from our sample region, 772.4 million hours of recreation time, and 304.6 million hours of travel time. Distance to the coast, demographics, and recreational activities affect how often people go and how much time they spend on coastal recreation.

Toward privacy-aware federated analytics of cohorts for smart mobility

Location-based Behavioral Analytics (LBA) holds a great potential for improving the services available in smart cities. Naively implemented, such an approach would track the movements of every citizen and share their location traces with the various smart service providers—similar to today's Web analytics systems that track visitors across the web sites they visit. This study presents a novel privacy-aware approach to location-based federated analytics that removes the need for individuals to share their location traces with a central server. The general approach is to model the behavior of cohorts instead of modeling specific users. Using a federated approach, location data is processed locally on user devices and only shared in anonymized fashion with a server. The server aggregates the data using Secure Multiparty Computation (SMPC) into service-defined cohorts, whose data is then used to provide cohort analytics (e.g., demographics) for the various smart service providers. The approach was evaluated on three real-life datasets with varying dropout rates, i.e., clients not being able to participate in the SMPC rounds. The results show that our approach can privately estimate various cohort demographics (e.g., percentages of male and female visitors) with an error between 0 and 8 percentage points relative to the actual cohort percentages. Furthermore, we experimented with predictive models for estimating these cohort percentages 1-week ahead. Across all three datasets, the best-performing predictive model achieved a Pearson's correlation coefficient above 0.8 (strong correlation), and a Mean Absolute Error (MAE) between 0 and 10 (0 is the minimum and 100 is the maximum). We conclude that privacy-aware LBA can be achieved using existing mobile technologies and federated analytics.

Big data from a popular app reveals that fishing creates superhighways for aquatic invaders

Human activities are the leading cause of biological invasions that cause ecologic and economic damage around the world. Aquatic invasive species (AIS) are often spread by recreational anglers who visit two or more bodies of water within a short time frame. Movement data from anglers are, therefore, critical to predicting, preventing, and monitoring the spread of AIS. However, the lack of broad-scale movement data has restricted efforts to large and popular lakes or small geographic extents. Here, we show that recreational fishing apps are an abundant, convenient, and relatively comprehensive source of "big" movement data across the contiguous United States. Our analyses revealed a dense network of angler movements that was dramatically more interconnected and extensive than the network that is formed naturally by rivers and streams. Short-distanced movements by anglers combined to form invasion superhighways that spanned the contiguous United States. We also identified possible invasion fronts and invaded hub lakes that may be superspreaders for two relatively common aquatic invaders. Our results provide unique insight into the national network through which AIS may be spread, increase opportunities for interjurisdictional coordination that is essential to addressing the problem of AIS, and highlight the important role that anglers can play in providing accurate data and preventing invasions. The advantages of mobile devices as both sources of data and a means of engaging the public in their shared responsibility to prevent invasions are probably general to all forms of tourism and recreation that contribute to the spread of invasive species.

Variation of population density on a beach: A simple analytical formulation

Since the novel coronavirus was reported (December 2019), the virus spread rapidly breaking through frontiers and impacting almost all countries of the world. Tourism is one of the sectors that has been heavily affected by the crisis. Even though there are several protocols for tourism activities that were written for the austral summer season, at the present, physical distancing is considered an effective way to reduce the spread of the virus. Clear and simple public actions should be rapidly implemented by the authorities to minimize the number of people on the beach. For instance, increasing the number of accesses to the beaches, building parking lots adjacent to the farthest beaches, or opening coastal roads on the outskirts of town to expand usable beaches, would be some simple and direct measures to reduce the beach population density. A simple analytical formulation for assessing the percentage decrease of the static population density respect to the absolute maximum population density on a beach is described in this paper. The variables of this simple analytical tool are the instantaneous sea-level (tide), air temperature, and beach expansion. Beach expansion is the single and manageable variable of the formulation, and refers to the inclusion of some near or adjacent extension of beach. It is suggested that the expansion of beaches would be very useful not only for pandemic time but also for the new normality. An application of this methodology is presented in the municipality of La Costa, Argentina.