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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. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic significantly affected human mobility. This study examines the changes in people's activity-travel behavior over 23 months (from Jan 2020 to Nov 2021) and how these changes are associated with the socio-economic status (SES) at the block group level in North Carolina. We identified 5 pandemic stages with different restriction regimes: the pre-pandemic, lockdown, reopening stage, restriction, and complete opening stage. Using the block-group mobility data from SafeGraph, we quantify visits to 8 types of destinations during the 5 stages. We construct regression models with interaction terms between SES and stages and find that visit patterns during the pandemic vary for different types of destinations and SES areas. Specifically, we show that visits to retail stores have a slight decrease for low and medium SES areas, and visits to retail stores and restaurants and bars bounced back immediately after the lockdown for all SES areas. The results suggest that people in low SES areas continued traveling during the pandemic. Transportation planners and policymakers should carefully design the transportation system to satisfy travel needs of those residents. Furthermore, the results also highlight the importance of designing mitigation policies that recognize the immediate recovery of visits to retail locations, restaurants, and bars. • Examined impacts of COVID-19 on travel to 8 destination types using linear mixed models. • Found visits to retail stores, restaurants, and bars bounced back immediately after the lockdown. • Demonstrated visits to retail stores were steady during the pandemic for low and medium SES areas. • Identified smaller decreases in travel during the lockdown in low SES areas. [ABSTRACT FROM AUTHOR]

Objectives: The outbreak of atypical pneumonia caused by the novel coronavirus (COVID-19) has currently become a global concern. The generations of the epidemic spread are not well known, yet these are critical parameters to facilitate an understanding of the epidemic. A seafood wholesale market and Wuhan city, China, were recognized as the primary and secondary epidemic sources. Human movements nationwide from the two epidemic sources revealed the characteristics of the first-generation and second-generation spreads of the COVID-19 epidemic, as well as the potential third-generation spread. Methods: We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient,r, and spatial stratified heterogeneity, q, statistics. Results: Two categories of device trajectories had generally significant correlations and determinant powers of the epidemic spread. Bothr and q statistics decreased with distance from the epidemic sources and their associations changed with time. At the beginning of the epidemic, the mixed first-generation and second-generation spreads appeared in most cities with confirmed cases. They strongly interacted to enhance the epidemic in Hubei province and the trend was also significant in the provinces adjacent to Hubei. The third-generation spread started in Wuhan from January 17–20, 2020, and in Hubei from January 23–24. No obvious third-generation spread was detected outside Hubei. Conclusions: The findings provide important foundations to quantify the effect of human movement on epidemic spread and inform ongoing control strategies. The spatiotemporal association between the epidemic spread and human movements from the primary and secondary epidemic sources indicates a transfer from second to third generations of the infection. Urgent control measures include preventing the potential third-generation spread in mainland China, eliminating it in Hubei, and reducing the interaction influence of first-generation and second-generation spreads.

Monitoring visitor use in parks and protected areas (PPAs) provides essential information for managers of PPAs to evaluate aspects of the visitor experience and balance the ecological disturbance that use creates. Traditional methods for quantifying visitation and spatial use of PPAs are resource intensive and thus are conducted infrequently or at cost-effective intervals which may fail to capture the dynamic nature of modern visitor use trends. This paper provides an addition to a growing literature using mobile-device data to quantify visitation and spatial density of use of urban-proximate PPAs in Orange County, California, USA using the analysis platform Streetlight, Inc. The results of our analysis compared favorably with well-established automatic trail counting and GPS-based monitoring methods, and illustrate several advantages of mobile device data to inform the management of PPAs. Mobile device data provide reliable estimates of visitation and spatial density of use and can augment and compliment existing social and resource monitoring for PPA management and planning.

The systematic gathering of data on visitors to parks and protected areas (PPAs) is an important aspect of adaptive management, but also presents an ongoing challenge to conduct. Visitor questionnaires, often administered at entrance facilities, trailheads and parking areas, are the most common approach, but require significant staff time for field work and analysis and may not be inclusive of all visitors. In this paper, we present a novel, mobile device-based assessment strategy that can determine basic visitor demographic attributes without the need for field-based assessments. We compared results from this strategy to a common, trailhead questionnaire approach in three urban-proximate park locations and generally found similar results, except in a situation where a significant number of visitors may not have entered via the main location, and thus were missed by the survey. Overall, the mobile device strategy is likely more accurate in situations where visitors enter parks from multiple, sometimes informal entry locations, but is also somewhat limited in the types of data available. [ABSTRACT FROM AUTHOR]

• LBS-requesting mobile device data is helpful to identify the generations of the early COVID-19 epidemic spread. • A third-generation epidemic spread had appeared in Wuhan from January 17–20, 2020 and in Hubei from January 23–24. • The mixed first and second-generation spreads strongly interacted to enhance the epidemic in Hubei excluding Wuhan. • The trend of the interactions between the first and second-generation spreads was evident in Hubei's adjacent provinces. • The mixed first and second-generation spreads appeared in most cities at the beginning of the outbreak. The outbreak of atypical pneumonia caused by the novel coronavirus (COVID-19) has currently become a global concern. The generations of the epidemic spread are not well known, yet these are critical parameters to facilitate an understanding of the epidemic. A seafood wholesale market and Wuhan city, China, were recognized as the primary and secondary epidemic sources. Human movements nationwide from the two epidemic sources revealed the characteristics of the first-generation and second-generation spreads of the COVID-19 epidemic, as well as the potential third-generation spread. We used spatiotemporal data of COVID-19 cases in mainland China and two categories of location-based service (LBS) data of mobile devices from the primary and secondary epidemic sources to calculate Pearson correlation coefficient, r , and spatial stratified heterogeneity, q , statistics. Two categories of device trajectories had generally significant correlations and determinant powers of the epidemic spread. Both r and q statistics decreased with distance from the epidemic sources and their associations changed with time. At the beginning of the epidemic, the mixed first-generation and second-generation spreads appeared in most cities with confirmed cases. They strongly interacted to enhance the epidemic in Hubei province and the trend was also significant in the provinces adjacent to Hubei. The third-generation spread started in Wuhan from January 17–20, 2020, and in Hubei from January 23–24. No obvious third-generation spread was detected outside Hubei. The findings provide important foundations to quantify the effect of human movement on epidemic spread and inform ongoing control strategies. The spatiotemporal association between the epidemic spread and human movements from the primary and secondary epidemic sources indicates a transfer from second to third generations of the infection. Urgent control measures include preventing the potential third-generation spread in mainland China, eliminating it in Hubei, and reducing the interaction influence of first-generation and second-generation spreads. [ABSTRACT FROM AUTHOR]

This paper extends demographers' traditional approaches to estimating local populations using symptomatic data. We augmented those approaches in order to track one community's de facto population—both its permanent residents ("Census population") and other sojourners—and assorted others in residence for shorter spells of time ("impermanent residents"). We illustrate how a new type of mobility data—the anonymous "pings" emitted by people's personal mobile devices—can unveil the presence and mobility patterns of de facto populations within a community by month, week, and day. We use these data to gauge the seasonal ebb and flow of population on Nantucket Island, MA, a seasonal resort community whose effective population far outnumbers its "Census population." We distinguish the following factors: (1) Permanent Residents, for whom Nantucket is their "usual place of residence" and where one votes and files one's tax return; (2) Commuting Workers, who reside off-island and regularly commute to jobs on-island via high-speed ferry or air taxi; and (3) Sojourners of three types: (a) Seasonal residents, most occupying a second home they either own or rent; (b) Seasonal workers, present for several months to fill many hospitality, landscaping, and other temporary jobs from April through September; and (c) Visitors, present for shorter stays, as vacationers or on business. For each segment, we highlight the estimation methodologies we devised and evaluate their strengths and limitations. Our research exemplifies the evolution of traditional demographic methodologies to address practical concerns at local community scales using "Big Data." Resort communities and winter "snowbird" destinations in Sunbelt locales experience regular annual influxes of visitors and/or seasonal residents in particular months. Just as daytime urban populations strain downtown infrastructure and transportation, such impermanent residents—however, brief or lengthy their stay—impose seasonal strains on local infrastructure and public services. [ABSTRACT FROM AUTHOR]

In 1998, the National Research Council published People and Pixels: Linking Remote Sensing and Social Science. The volume focused on emerging research linking changes in human populations and land use/land cover to shed light on issues of sustainability, human livelihoods, and conservation, and led to practical innovations in agricultural planning, hazard impact analysis, and drought monitoring. Since then, new research opportunities have emerged thanks to the growing variety of remotely sensed data sources, an increasing array of georeferenced social science data, including data from mobile devices, and access to powerful computation cyberinfrastructure. In this article, we outline the key extensions of the People and Pixels foundation since 1998 and highlight several breakthroughs in research on human–environment interactions. We also identify pressing research problems—disaster, famine, drought, war, poverty, climate change—and explore how interdisciplinary approaches integrating people and pixels are being used to address them. [ABSTRACT FROM AUTHOR]

Measuring and monitoring use levels in parks and protected areas (PPAs) remains an ongoing challenge for managers worldwide. Understanding visitation levels is particularly important as contemporary use trends in PPAs have become increasingly dynamic due to many factors that influence demand, including for example, the popularizing of locations via social media. In this paper, we present a novel, mobile device data analysis approach for understanding use levels in PPAs, measured as vehicle arrivals to formal and informal park entrances. We initiated this research in effort to develop an alternative use estimation technique, particularly in situations where visitors may enter a PPA in a more diffuse manner, via informal entrance locations that are difficult to monitor by conventional direct counting methods. Our approach uses a readily available mobile data analysis platform, Streetlight InSight®, developed for transportation planners that is capable of accessing and processing a vast resource of mobile device data. We tested this approach in a network of urban-proximate PPAs in Orange County, California, via both currently available data processing procedures, and sampling and calibration techniques we developed. Our results compare favorably to available use estimates in the study area that employ standard counting techniques. For example, we statistically compared monthly estimates calculated via the Streetlight model and direct counts at a popular entrance location and found no significant difference. We also examined a time period of a known park closure due to a forest fire event to determine if erroneous data were being collected and estimated use at or near zero. Although our method verification relies mainly on face validity due to limited availability of other use estimates in our study location, our results suggest that acceptable use estimates can be obtained in a wide range of PPA applications. This approach has several substantial advantages to PPA management. First, since mobile data are available, managers can obtain current use level estimates in PPAs with a significantly reduced need for field devices and field staff time and effort. Second, retrospective data back to 2014 are available, making it possible to examine trends over the last several years even if no on-site counts were ever obtained. Last, parks with multiple, diffuse entry locations can be assessed more comprehensively, since locations can be identified and use estimated via a digital mapbased interface. [ABSTRACT FROM AUTHOR]

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. • Mobile data are validated from three aspects: POIs, visitor demographics, and temporal visitation patterns. • Mobile data are unable to accurately estimate certain visitor demographics, such as income levels. • Mobile data have strong correlations with monthly and daily visitor counts, representing more nuanced visitation patterns. [ABSTRACT FROM AUTHOR]

Transportation disruptions caused by COVID-19 have exacerbated difficulties in health care delivery and access, which may lead to changes in health care use. This study uses mobile device data from SafeGraph to explore the temporal patterns of visits to health care points of interest (POIs) during 2020 and examines how these patterns are associated with socio-demographic and spatial characteristics at the Census Block Group level in North Carolina. Specifically, using the K-medoid time-series clustering method, we identify three distinct types of temporal patterns of visits to health care facilities. Furthermore, by estimating multinomial logit models, we find that Census Block Groups with higher percentages of elderly persons, minorities, low-income individuals, and people without vehicle access are areas most at-risk for decreased health care access during the pandemic and exhibit lower health care access prior to the pandemic. The results suggest that the ability to conduct in-person medical visits during the pandemic has been unequally distributed, which highlights the importance of tailoring policy strategies for specific socio-demographic groups to ensure equitable health care access and delivery. [ABSTRACT FROM AUTHOR]

We use mobile device data to construct empirical interpersonal physical contact networks in the city of Portland, Oregon, both before and after social distancing measures were enacted during the COVID-19 pandemic. These networks reveal how social distancing measures and the public's reaction to the incipient pandemic affected the connectivity patterns within the city. We find that as the pandemic developed there was a substantial decrease in the number of individuals with many contacts. We further study the impact of these different network topologies on the spread of COVID-19 by simulating an SEIR epidemic model over these networks and find that the reduced connectivity greatly suppressed the epidemic. We then investigate how the epidemic responds when part of the population is vaccinated, and we compare two vaccination distribution strategies, both with and without social distancing. Our main result is that the heavy-tailed degree distribution of the contact networks causes a targeted vaccination strategy that prioritizes high-contact individuals to reduce the number of cases far more effectively than a strategy that vaccinates individuals at random. Combining both targeted vaccination and social distancing leads to the greatest reduction in cases, and we also find that the marginal benefit of a targeted strategy as compared to a random strategy exceeds the marginal benefit of social distancing for reducing the number of cases. These results have important implications for ongoing vaccine distribution efforts worldwide., (© The authors 2021. Published by Oxford University Press. All rights reserved.)

Effective quantification of visitation is important for understanding many impacts of the COVID-19 pandemic on national parks and other protected areas. In this study, we mapped and analyzed the spatiotemporal patterns of visitation for six national parks in the western U.S., taking advantage of large mobility records sampled from mobile devices and released by SafeGraph as part of their Social Distancing Metric dataset. Based on comparisons with visitation statistics released by the U.S. National Park Service, our results confirmed that mobility records from digital devices can effectively capture park visitation patterns but with much finer spatiotemporal granularity. In general, triggers of visitation changes corresponded well with the parks' management responses to COVID-19, with all six parks showing dramatic decreases in the number of visitors (compared to 2019) beginning in March 2020 and continuing through April and May. As restrictions were eased to promote access to the parks and the benefits associated with outdoor recreation, visitation in 2020 approached or even passed that from 2019 by late summer or early autumn at most of the parks. The results also revealed that parks initially saw the greatest increases in visitation after reopening originating from nearby states, with visitorship coming from a broader range of states as time passed. Our study highlights the capability of mobility data for providing spatiotemporally explicit knowledge of place visitation. [ABSTRACT FROM AUTHOR]