Content uploaded by William L. Rice
Author content
All content in this area was uploaded by William L. Rice on Apr 05, 2023
Content may be subject to copyright.
45
a Parks, Tourism, and Recreation Management Program, University of Montana, Missoula, MT
b Department of Society and Conservation, University of Montana, Missoula, MT
Please send correspondence to William L. Rice, william.rice@umontana.edu
Regular Paper
Exclusionary Effects of Campsite Allocation through
Reservations in U.S. National Parks: Evidence from
Mobile Device Location Data
William L. Rice,a,b Jaclyn R. Rushing,b Jennifer M. Thomsen,a,b and
Peter Whitneya,b
Executive Summary
Campsites represent highly sought aer 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 in-
creasingly dicult 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 allocat-
ing 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 anity for camping in national parks. Given national park camping’s
history of exclusivity, this exploratory study seeks to explore how online reserva-
tion systems may impact the demographics of national park campers. Using mo-
bile device location data, estimated demographics were calculated for campers in
ve national park campgrounds in the U.S. that each contained some sites requir-
ing reservations and some sites available on a rst-come, rst-served basis. We
detail results from analyses of variance between campsites requiring reservations
and those that are available on a rst-come, rst-served basis. Results suggest that
for each of the ve campgrounds, those campers camping in sites that require res-
ervations came from areas with higher median household incomes, on average. In
three of the ve campgrounds, this dierence was signicant. 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 reser-
vation 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 per-
haps non-White, would-be campers—a conclusion drawn from the results of this
exploratory study. is discussion includes an analysis of the distributive justice of
online reservation systems.
https://doi.org/10.18666/JPRA-2022-11392
Volume 40, Issue 4, Winter 2022, pp. 45–65
Journal of Park and Recreation Administration
Rice et al.
46
Keywords
Campgrounds, equity, allocation, reservations, exclusion, mobile device data
Introduction
e national parks of the United States are a great source of national pride and
identity for many Americans; some have even likened U.S. national parks to “American
covenants” (Soukoup & Machlis, 2021, p. 585). Despite this, U.S. national parks do not
serve all Americans equally. Compared to U.S. residents in the 2010 census, national
park visitors are wealthier (i.e., 6% earn less than $25,000 compared to 24% of U.S.
residents), more educated (i.e., 32% have a graduate degree compared 16% of U.S. resi-
dents), and vast majority white (i.e., 95% compared to 72% of U.S. residents) (Vaske &
Lyon, 2014). Demographics of national park visitors compared to the U.S. population
have changed since 2010; for instance, it is now estimated that 80% of visitors are White
(Hicks et al., 2021); however, visitor demographics remain glaringly unrepresentative
of the U.S. population.
In an eort to make U.S. national parks relevant, diverse, and inclusive (NPS,
2021), the National Park Service (NPS) needs to ask itself some dicult questions re-
garding privilege such as “what agency practices reinforce inequities?” (Roberts, 2021,
p. 443). Camping in national parks is one practice that historically reinforced inequities
(Young, 2009). Yet, there is very limited contemporary research examining the demo-
graphics of campers in national parks and how they compare to the U.S. population.
is research examines the use of online-based reservations systems in frontcountry
camping in U.S. national park campgrounds, and explores how researchers can use
mobile device data as a means to understand who protected areas, such as national
parks, serve and how fairly that service is distributed.
Camping in the United States and National Parks
Camping is dened by the NPS as “erecting of a tent or shelter of natural or syn-
thetic material, preparing a sleeping bag or other bedding material for use, parking of
a motor vehicle, motor home or trailer, or mooring of a vessel for the apparent purpose
of overnight occupancy” (Parks, Forests, and Public Property, 2020). Most camping
in national parks is considered frontcountry camping, “where visitors drive to an es-
tablished campground… that typically consists of camping loops (roads shaped in an
actual loop), and each loop has numerous camping sites established to accommodate
tents, and in some cases, towed campers and RVs [recreational vehicles]” (NPS, 2018a,
para. 1).
Since the late 19th century, camping has been a primary means of outdoor recre-
ation in the U.S. (Young, 2017). ough originally conceptualized as a means of leisure
to escape urban stresses in an increasingly industrialized society, the primary moti-
vations for camping soon expanded to include aordable and/or novel accommoda-
tions while traveling or vacationing in—or proximate to—parks and protected areas
(Newcombe, 2016; Young, 2021). Camping thus became a means to tourism for many
residents in the United States (i.e., a place to stay), as opposed to a means of leisure or
recreation (i.e., a way to experience leisure) (Young, 2021). is shi led to two concur-
Exclusionary Campsite Allocation
47
rent trends in modern camping: a) a countereort by the leisure class to reappropriate
camping as a leisure activity utilized largely by the wealthy (Young, 2021), and b) the
signicant long-term growth within the camping industry (Young, 2021). Both trends,
and their historical impacts, are experienced by campers today (Hogue, 2011; Young,
2021).
Historically, exclusivity in camping is noted in national parks, where post-World
War II campground designs oered a “striking visual foreshadowing of a suburban hous-
ing development” that included “evocative street names, curvilinear road system[s],
[and] more clearly demarcated site boundaries” (Young, 2021, p. 189) that emulated
suburban hedges and fences. rough designing campgrounds that mirrored White
spaces and emphasized ownership through reservations, Young (2021) concludes that
the NPS drew strong connections between campsite design and homeownership and
therefore contributed to a post-war social contract that disenfranchised less auent
and non-White Americans—in camping and more broadly—“as both homeownership
and outdoor recreation continued to contain mechanisms of discrimination” (p. 191).
Today, campers remain largely White in the U.S. (78%; e Outdoor Foundation, 2017)
and relatively wealthy—from 2014 to 2016 U.S. national park campers had an annual
median household income $4,000 higher, on average, than the larger U.S. population
(Walls et al., 2018). Because of these demographic discrepancies, U.S. national parks
and other camping areas are oen conceptualized as exclusionary spaces (Finney, 2010,
2014; More, 2002; Scott & Lee, 2018; Weber & Sultana, 2012).
Camping now generates $166 billion in economic activity annually within the U.S.
(e Outdoor Industry Association, 2017). Demand for campsites within frontcountry
campgrounds in U.S. national parks increased signicantly during the previous decade
(Rice et al., 2019), accelerating at the onset of the COVID-19 pandemic (Ma et al.,
2021; Michelson, 2021). Increasing demand has led many national park campsite ad-
ministrators to move to online reservation systems, primarily Recreation.gov (Michel-
son, 2021; Rice et al., 2019). is online reservation platform allows users to search for
campsites by location using advanced ltering tools and book them up to six months
in advance. Online reservation systems such as Recreation.gov allow for improved trip
planning for campers and ecient allocation of campsites for managers. However, high
demand for some campsites, paired with the ability for users to book remotely, has led
to a market for campsites where supply regularly fails to meet demand. As reported by
the administrators of Recreation.gov (2021), “A popular campground with 57 camp-
sites can see close to 19,000 people all trying to reserve the same campsites for the same
dates immediately aer they’re released for reservation” (para. 8). Due to the incredibly
high demand for campsite reservations, obtaining a campsite ahead of time is likewise
very competitive and requires the ability to plan up to six months in advance, access
to highspeed Internet, and institutional knowledge related to the park and Recreation.
gov. us, issues of equity have been raised concerning the allocation of U.S. national
park campsite reservations (Rice & Park, 2021).
Unintended Impacts of Campsite Reservation Systems on Distributive
Justice
In U.S. national parks, extremely high demand for a limited number of campsites
has led to concerns about the impacts of reservation systems on distributive justice
(Rice & Park, 2021). In the context of recreation and tourism, Park et al. (2007) dene
distributive justice as being “concerned with a gain-to-loss ratio, or the exchange of
compensation in terms of input-output consistence with social position” (p. 90). More
Rice et al.
48
directly, Manning and Lime (2000) dene it as a management principle “whereby indi-
viduals obtain what they ‘ought’ to have based on criteria of fairness” (p. 38). Because
fairness is a multidimensional concept, Shelby et al. (1989) recommend the analysis
of four—sometimes competing—tenets when making decisions about the allocation
for recreation resources (e.g., campsites): equality, equity, need, and eciency. With
these tenets in mind, Shelby et al. (1989) note that reservation systems seek to maxi-
mize equality—assuming “everyone has an equal chance to plan ahead” (p. 63)—while
generally failing to adequately address goals related to need (e.g., improving or ensur-
ing access to shaded campsites for individuals with low heat tolerance or underlying
medical conditions), equity (e.g., improving or ensuring access for locals with limited
nancial resources for travelling elsewhere), or eciency (e.g., “no show” reservation
holders causing underutilization of the campsites).
Although reservation systems are based on equality, obtaining campsites through
online systems such as Recreation.gov may be associated with various constraining fac-
tors that could cater to higher socioeconomic groups (Floyd & Stodolska, 2014; Taylor
et al., 2011), which are oen White (Bowser, 2007; Stodolska & Shinew, 2014; U.S.
Bureau of Labor Statistics, 2011). Reserving a national park campsite online requires
a) institutional knowledge (including campground knowledge and website navigation
knowledge), b) ability to plan up to six months in advance, and c) ability to access
the Internet for reservation system websites, all of which have been identied as con-
straints for participation in various forms of outdoor recreation.
Skills such as eectively navigating competitive online reservation systems require
experience and/or mentorship that have cultural ties and equity implications. Previous
research has identied the exclusionary nature of parks and outdoor recreation ac-
tivities coupled with socioeconomic factors (i.e., place of residence and poverty) have
created an environment in which many ethnic and racial groups have less access to
institutional knowledge and skills related to outdoor recreation (e.g., Bixler et al., 2011;
Edmonds, 2019; Scott & Lee, 2018). In the context of camping, campers with previous
experience and greater expertise pay signicantly more attention to the availability of
locations when selecting a campsite (Gursoy & Chen, 2012). erefore, successfully
reserving a popular campsite oen requires a reasonably high level of institutional
knowledge—thus leading to the possibility of exclusion of less experienced or knowl-
edgeable campers (Rice & Park, 2021).
Previous research refutes the assumption that all campers have equal ability to
plan ahead. Early research of campsite reservation systems in 1973 found that only
34% of campers in California had jobs that allowed them to plan their trips 12 weeks in
advance (Magill, 1976). A more recent study on online reservations found that for most
national park campsites, 50% of reservations are made more than one week in advance
(Supak et al., 2017). Furthering this issue of exclusion, at least two proprietary services
have emerged to alert customers—for a fee—when a campsite becomes available for
their preferred time and place (Michelson, 2021), thus potentially giving those able to
pay an unfair advantage when attempting to reserve campsites.
Campsite reservations are most commonly made online through sites such as
Recreation.gov, which brings up potential issues of equity in terms of access to in-
ternet. Despite the pervasive role of the Internet and smart devices in today’s culture,
access to Internet devices (e.g., smart devices, tablets, and desktop or laptop comput-
ers) vary among racial groups and are associated with disadvantages (Atske & Perrin,
2021; Winter et al., 2019). Atske and Perrin at the Pew Research Center (2021) found
Exclusionary Campsite Allocation
49
that Black/African American and LatinX adults in the U.S. “remain less likely than
White adults to say they own a traditional computer or have high-speed Internet at
home” (para. 1). Especially in a highly competitive market, such as that for popular
campgrounds, Internet access and access to high speeds can be crucial for ensuring a
successful reservation.
us, there is a need to understand if online campsite reservation systems are ex-
clusionary toward specic groups. Demographic research of campers conrms that the
group remains mostly White and skews wealthier than the greater U.S. population (e
Outdoor Foundation, 2017; Walls et al., 2018). However, dierences in the ethnic di-
versity and level of wealth among campers utilizing campsites that require reservations
and those utilizing rst-come, rst-served campsites have not been assessed to date.
is gap in the research may be due to the diculty of gaining a robust sample of the
two types of campers across multiple campgrounds. e advent of gaining basic demo-
graphic information about campers’ home locales through mobile device location data
oers a means of overcoming this potential barrier (Lawson, 2021).
Using Mobile Device Data to Estimate Demographics in Parks
Location data gathered from personal mobile devices is an emerging means of
monitoring and measuring tourism and visitor use in parks and protected areas (Law-
son, 2021). In recent years, a small—albeit rapidly growing—body of research has
emerged to this end (e.g., Creany et al., 2021; Kim et al., 2020; Kubo et al., 2020; Liang
et al., 2021; Merrill et al., 2020; Monz et al., 2019; 2021). Mobile device data provides a
potentially more cost-eective means of measuring managerially important variables
in park spaces (i.e., visitor travel and use patterns, activity styles, and demographics)
compared to traditional surveying methods (Monz et al., 2021). is data may be pur-
chased or otherwise obtained from an array of vendors (e.g., AirSage, Near, SafeGraph,
and Streetlight) that aggregate and anonymize location data from cell phones with GPS
capabilities (Lawson, 2021). ese vendors gather data from “a sample of about 30%
of U.S. cell phone users” (Lawson, 2021, p. 30). Given this large sample size, reputable
vendors can provide estimates for visitor use and visitor demographics with very high
levels of condence. Concerning income, Near (formally UberMedia, or UM)—the
mobile location data vendor used in the following analysis—reports that “the Pearson’s
correlation between the (inferred) number of UM device users per income bracket
and the number of census respondents per income bracket is r = 0.994, which is both
very high and highly signicant (p<0.01)” (UberMedia, 2021b, p. 4). Further, concern-
ing ethnicity, “the Pearson’s correlation between population counts and device counts
across ethnicity is 0.999, which is both very high and highly signicant (p < 0.01)”
(UberMedia, 2021b, p. 4). Lawson (2021) notes that in parks and protected areas these
estimates are likely most accurate in more densely used areas. Additionally, given that
mobile location data vendors typically retain archival mobile device location data,
researchers are able to use this accurate archival data to study previous park visita-
tion and trends analysis—a practice usually not possible in traditional survey research
(Monz et al., 2019).
To date, two studies have used aggregated mobile device data to estimate demo-
graphics of park visitors (Liang et al., 2021; Monz et al., 2021). Both of these previous
studies focused on assessing and validating the representativeness of visitor demo-
graphics estimates based on data purchased or provided by mobile location data ven-
dors. When comparing demographic estimates between mobile device data provided
Rice et al.
50
by the vendor StreetLight and survey data, Monz et al. (2021) found visitor race/eth-
nicity distributions and income levels estimated via mobile device data “were, for the
most part, consistent” (p. 128) with previous survey-based research. When comparing
demographic estimates between mobile device data provided by the vendor SafeGraph
and survey data, Liang et al. (2021) found signicant dierences in the estimated
proportional distributions of four of seven income groups and signicant dierences
among the estimated proportional distributions of one of three racial/ethnic groups.
However, these signicant dierences between the SafeGraph and survey data may be
due to poor cell phone service coverage in their study location—Yellowstone National
Park (NPS, 2020b). In addition to these studies specic to park settings, numerous
other studies have utilized mobile device data to estimate visitor home locations (also
referred to as the common evening locations of their mobile devices) in tourism (Cal-
abrese et al., 2010; Ma & Kirilenko, 2021; Park & Pan, 2018).
Study Purpose
Given the legacy of ethnic and economic exclusion in camping, the issues of dis-
tributive justice inherent to reservation systems, and the growing popularity of online
reservation systems in U.S. national park campgrounds, this study seeks to quantify
potential demographic dierences of campers in campsites requiring reservations and
those note requiring reservations. At present, the lack of research to this end leaves
national park campground managers without vital data to guide their decision-making
when considering the implementation of online reservation systems. U.S. national park
campgrounds were selected as the research setting due to availability of data concern-
ing their reservation statuses and the noted high demand for their campsites (Rice
et al., 2019). is research represents a rst, exploratory attempt to examine demo-
graphic dierences among reservation-holding and rst come, rst served campers,
and provide subsequent management implications. e following two research ques-
tions guide this research:
R1: In the selected NPS-managed campgrounds, do U.S. campers in camp-
sites requiring reservations come from locales with higher median annual
household incomes than those in campsites not accepting reservations?
R2: In the selected NPS-managed campgrounds, do U.S. campers in camp-
sites requiring reservations come from locales with higher portions of White
residency than those in campsites not accepting reservations?
Methods
Study Site
Study sites were selected using the following criteria: a) NPS-managed campground
with at least one campground loop requiring reservations in 2019 and at least one loop
not accepting reservations in 2019, and b) having mobile device LTE data coverage
provided by at least three major cell phone service providers (e.g., Verizon, AT&T,
Sprint, T-Mobile) according to Federal Communication Commission (FCC) 2018 data
(FCC, 2020). Using the NPS “Find a Campground” explorer tool (NPS, 2020a), ve
campgrounds were identied that met the dened criteria: Buckhorn Campground
in Chickasaw National Recreation Area (Oklahoma), Green River Campground in
Colorado National Monument (Colorado), Lo Mountain Campground in Shenan-
Exclusionary Campsite Allocation
51
doah National Park (Virginia), Oak Ridge Campground in Prince William Forest Park
(Virginia), and Saddlehorn Campground in Dinosaur National Monument (Utah; see
Figure 1). Given the exploratory nature of this research, it is acknowledged that these
sites may not be fully representative of U.S. national park campgrounds; however, all
ve campgrounds follow the traditional NPS design (Young, 2018) comprising a series
of one-way driving loops branching from a common drive, each loop containing a
number of campsites. Additionally, all ve campgrounds require reservations in cer-
tain loops via Recreation.gov during “peak season” (generally April through October).
Importantly, neither price nor access to amenities (e.g., picnic tables, campre rings,
access to electricity) were dependent on reservation status in these campgrounds, as
discovered through a review of NPS.gov (e.g., NPS, 2017; 2018b; 2019a; 2019b; 2019c).
In Buckhorn (Chickasaw National Recreation Area) and Lo Mountain (Shenandoah
National Park) Campgrounds, price was directly correlated with access to electricity;
however, sites with and without electricity (and therefore at higher and lower pric-
es) were available via both reservation and rst-come, rst-served status (NPS, 2017;
2019a). Electricity access was not available at any of the sites in Green River (Dino-
saur National Monument), Oak Ridge (Prince William Forest Park), and Saddlehorn
(Colorado National Monument) Campgrounds; therefore, all campsites were of equal
price (NPS, 2018b; 2019b; 2019c). A full listing of campground attributes is contained
in Table 1.
EXCLUSIONARY CAMPSITE ALLOCATION 39
Figures
Figure 1. Campgrounds included in the study
Figure 1
Campgrounds Included in this Study
Rice et al.
52
Data Collection
Using ArcGis Pro and referencing ocial NPS maps, polygons were delineated
around each of the loops within each campground. Using these polygons, data were
then exported from aggregated mobile device location data provided by Near (formally
UberMedia), for only U.S.-based mobile devices. Location data provided by Near is
captured by applications (apps) in mobile devices that have location services enabled,
which report coordinates from the operating system of individual GPS-enabled mobile
device (Near, 2021a). Raw data is then aggregated, screened for accuracy and quality,
and organized to the study’s requested parameters in a data export.
Data is gathered by proprietary Soware Development Kits (SDKs) embedded
into device applications (Near, 2021a). SDKs, provided by Near or other location-gath-
ering vendors, are embedded into the operating soware of mobile-device applications
by app and web developers. From pop-up ads to apps like Pokémon Go, raw data from
over 100,000 applications contribute to the location dataset (Near, 2021a). e Near
dataset used for this study included four data sources; ~50% of data was “second-party”
data (gathered by other location-data providers and shared with Near), ~48% of data
was “bid stream data” (collected through soware embedded into banner and video
advertisements), ~1% of the aggregated data was provided by “rst-party” apps (those
developed with publishers that have a direct relationship with Near), and ~1% gathered
through apps created by Near (UberMedia, 2021c). Given the volume and variability
inherent to mobile device location data, Near applies several layers of data screening
to its long-term dataset. Basic screening removes faulty data reporting from individual
devices, “power law” screening removes implausibly high levels of device requests or
device density, fraudulent data created by “bad actor” devices is removed. Additional
levels of screening include audit-based data testing and other report-based screening
methods (Near 2021a).
Data were exported for the entire 2019 “peak season” dened for each camp-
ground when reservations are required for certain loops (as dened by the NPS, 2017;
2018b; 2019a; 2019b; 2019c). Further, to reduce the impact of individuals and vehi-
cles passing through the campground loops en route to another loop or exploring the
campground, location data were only exported from 20:00 to 5:00 local time and any
devices traveling at a speed greater than three miles per hour for their entire dura-
Running head: EXCLUSIONARY CAMPSITE ALLOCATION 36
Tables
Table 1
Campground Attributes
Campground
Total # of
campsites
Total # of
reservable
campsites
Loops
requiring
reservation
during peak
season
Loops not
accepting
reservation
during peak
season
2019
peak
season
1
Nearest Metropolitan
Statistical Area (population)
Miles to
nearest
Metropolitan
Statistical Area
Buckhorn
134
43
C
A, B, & D
5/25 –
9/9
Oklahoma City, OK
(646,244)
78
Green River
80
34
B
A & C
5/15/ -
9/21
Salt Lake City, UT (600,730)
141
Loft
Mountain
207
55
F, G, &
Upper north
A, B, C, D,
E, Lower, &
Upper south
5/14 –
10/27
Richmond, VA (633,765)
83
Oak Ridge
100
58
B & C
A
4/1 –
10/31
Washington-Arlington-
Alexandria, DC-VA-MD-
WV (3,249,197)
29
Saddlehorn
80
20
B
A & C
4/1 –
10/31
Salt Lake City, UT (600,730)
203
1(NPS, 2017; 2018b; 2019a; 2019b; 2019c)
Table 1
Campground Attributes
Exclusionary Campsite Allocation
53
tion within a loop were excluded. e subsequent mobile location data exports were
comprised of spreadsheets—respective to each campground loop—listing U.S. Census
block groups containing the “common evening location” of at least one visitor’s mobile
device and the number of visitor mobile devices falling within each block group (See
Table 2). As dened by Near, common evening location is “estimated by determining
where a device most frequently appears during the ‘non-work’ hours” (UberMedia,
2021a, p. 2). “Non-work hours” are dened as between 18:00 and 08:00 on Mondays
through Fridays and all day on Saturdays and Sundays (UberMedia, 2021a). e de-
ned common evening location is then “jittered in 50 m [meters] a random direction”
to “help maintain the de-identication of device-level data” (UberMedia, 2021a). e
exported spreadsheets also contained demographic information for each U.S. Census
block group containing the common evening location of at least one visitor’s mobile
device. is demographic information was queried from the U.S. Census Bureau’s 2016
American Community Survey (UberMedia, 2021b).
Table 2
Example Cleaned Output of Mobile Device Data
Census Block # of Devices Median Household Portion of
Group ID Income White Residency
60133230001 1 $110,417 0.6937
60170309011 1 $76,553 0.9594
60190064034 1 $80,259 0.7566
60230011011 1 $63,333 0.8379
60610213222 2 $103,365 0.6206
As mobile device location data is derived from an opt-in anonymous identier,
demographic data cannot be directly associated with individual device locations. In-
stead, the established proxy for determining users’ demographic characteristics is the
census block group of the device user’s common evening location (UberMedia, 2021b).
In the study data, U.S.-based devices with established common evening locations were
associated to their census block group’s median household income and racial distri-
bution. For both of these measures, demographic representativeness is measured by
reporting the Pearson’s correlation between the inferred number of device users and
the number of census correspondents. Each measure is found to be both very high and
highly signicant (p<0.01) (UberMedia, 2021b, p. 5)
Assessment of Mobile Device Location Data Representativeness
To understand if the common evening locations of campers—derived from the
mobile device location data—in each campground suciently represented the geo-
graphic distribution of home locations among the population of campers in each
campground, we compared a) the zip codes of campers’ common evening locations
among our data—derived through Near mobile device location data—to b) the zip
codes collected by the NPS—through the reservation website Recreation.gov—for
campers making reservations in the study’s campgrounds for the same 2019 dates listed
in Table 1. All reservations made through Recreation.gov are archived on the publicly-
available Recreation Information Database (Supak et al., 2017). Importantly, we only
Rice et al.
54
used the common evening locations of campers in campground loops requiring reser-
vations in this analysis—to ensure we were comparing the correct datasets (i.e., exclud-
ing campers camping in rst come, rst served campsites, not available for reservation
on Recreation.gov). Using zip code centroid point data of both a) the common evening
location zip codes of campers in our mobile device location dataset and b) the zip
codes recorded from all reservation transactions on Recreation.gov, we assessed spatial
correlation among the point densities of both datasets across the United States using
the band collection statistics tool in ArcGIS Pro (e.g., Ghalambordezfooli & Hosseini,
2019; Sajid Mehmood et al., 2021) which outputs a correlation matrix for determining
the degree of correlation between the spatial coverages of the two datasets.
Analysis
Dierences in demographics in campground loops requiring reservations and
those not accepting reservations (rst come, rst served) were analyzed via aggregated
datasets for each campground—for example, common evening locations of campers
in Lo Mountain (Shenandoah National Park) Campground’s Loops F, G, and Up-
per north (requiring a reservation) and Loops A, B, C, D, E, Lower, and Upper south
(not accepting reservations) were aggregated, respectively, prior to analysis. Follow-
ing the dened research questions, the median annual household income and portion
of White residency were analyzed for the home locales (U.S. Census block groups)
for campers in campground loops requiring reservations and campground loops not
accepting reservations. One-way analyses of variance (ANOVAs) were carried out to
compare dierences in the average median annual household income and portion of
White residency for campground loops requiring and not accepting reservations (rst-
come, rst-serve). Averages and portions were weighted according to the number
of devices within common evening locations coming from within each block group.
ANOVAs are useful in determining dierences in the averages (or means) for continu-
ous variables across groups (Vaske, 2008). Following Huberty and Morris (1989), two
one-way ANOVAs were selected over a single MANOVA due to the small number of
dependent variables (median annual household income and portion of white residen-
cy) and the exploratory nature of the study. Levene’s F test was used to assess if equality
of variance could be assumed for each dependent variable (Vaske, 2008). When equal-
ity of variance could not be assumed for the dependent variable, Welch’s test of Equality
of Means was used to correct the signicance level of the omnibus test.
Results
Common evening locations from approximately 3,250 mobile devices, represent-
ing campers’ home locales, were exported from the Near data explorer. e spatial
distribution of common evening location zip codes derived from the mobile device
location data and the zip codes derived from reservations made through Recreation.
gov ranged from highly correlated to nearly identical across the ve campgrounds in
the study (see Table 3), with negligible dierences likely resulting from campers hail-
ing from dierent home locales than their friends or family members who made the
campsite reservation. us, based on these universally high levels of correlation, we
determined that the mobile device location data presented a reliable sample of camp-
ers from which conclusions concerning the demographics of their home locales (i.e.,
census block groups) could be drawn. Descriptive and ANOVA results are listed in
Table 3. Dierences in the total samples (number of mobile devices) used for each
Exclusionary Campsite Allocation
55
of the two ANOVAs (median annual household income and portion of White resi-
dency) within each campground result from unequal availability of census data for
block groups (e.g., 591 census block groups which contained common evening loca-
tions for Green River Campground campers had available racial residency data vs. 581
census block groups had available median household income data). In all ve camp-
grounds, the mean median annual household income for campers’ home locales was
higher in loops requiring reservations than those not accepting reservations. For three
of the ve campgrounds—Buckhorn (Chickasaw National Recreation Area), Green
River (Dinosaur National Monument), and Lo Mountain (Shenandoah National
Park) Campgrounds—the average (mean) median annual household income was sig-
nicantly higher in loops requiring reservations at a minimum 95% condence inter-
val. Concerning the portion of white residency in campers’ home locales, one of ve
campgrounds—Oak Ridge (Prince William Forest Park) Campground—contained a
signicant dierence between loops requiring reservations and those not accepting
reservations. Oak Ridge (Prince William Forest Park) Campground contained a dier-
ence of 6.86% in the portion of White residency between reservation statuses.
Discussion
Institutional Barriers to Campsite Use in NPS
Based on our ndings from this exploratory research, the allocation of national
park campsites through reservation systems can prove exclusionary toward lower in-
come and non-White individuals in the United States. is suggests that reservation
systems act as institutional barriers to campsite use in U.S. national parks. is nding
juxtaposes the democratic nature of the national park idea as described by journalist
and early national park advocate Robert Sterling Yard (1922):
Already the national parks are benecently aecting the national mind…Of
great importance is their strong tendency to redemocratize in a period which
needs it. Nowhere else do people from all the states mingle in quite the same
spirit as they do in their national parks…Here the social dierences so in-
sisted on at home just don’t exist. (p. 583)
Yet, national parks were historically managed as White spaces—largely o limits to
people of color. is is exemplied through the policies discouraging African Ameri-
can visitation (O’Brien & Wairimu Ngaruiya, 2012), the exclusion of African Ameri-
cans from parks in the South (Byrne & Wolch, 2009; Scott, 2014), and designing
parks—and the campgrounds therein—for the preferences of White visitors (Le, 2012;
Young, 2021). Krymkowski and colleagues (2014) hypothesize that these historical
policies may have resulted in people of color, especially African Americans, feeling like
national parks do not belong to them.
Despite overwhelming evidence to the contrary (Davis, 2019; Erickson et al., 2009;
Scott & Lee, 2018; Weber & Sultana, 2012; Young, 2017), national parks are still largely
romanticized for their role in furthering democratization within American culture
(Grebowicz, 2015), as popularized through Ken Burns’ (2009) lm, e National Parks:
America’s Best Idea:
Rice et al.
56
Running head: EXCLUSIONARY CAMPSITE ALLOCATION 37
Sample Size of Recreation.gov
data and Correlations
n (# of
devices)
Mean
Std.
Deviation
Mean
Difference
F-value/
Welch
Statistic
p-
value
Levine
statistic
n (# of
reservations)
Correlation
with
common
evening
location zip
codes
Buckhorn (Chickasaw National
Recreation Area) Campground
1,032
0.860
Median Annual Household Income
626
$5,940
10.322
0.001a
16.227d
Requiring Reservations
285
$59,735
$25,491
No Reservations
341
$53,796
$19,700
Portion of White Residency
632
0.0023
0.025
0.875
0.076e
Requiring Reservations
288
0.7182
0.1860
No Reservations
344
0.7159
0.1922
Green River (Colorado National
Monument) Campground
1,344
0.890
Median Annual Household Income
581
$5,084
3.919
0.048c
1.24e
Requiring Reservations
302
$74,364
$32,548
No Reservations
279
$69,280
$29,066
Portion of White Residency
591
0.0108
0.450
0.503
0.738e
Requiring Reservations
307
0.7797
0.1911
No Reservations
284
0.7689
0.1988
Loft Mountain (Shenandoah National
Park) Campground
1,439
0.995
Median Annual Household Income
1289
$6,369
6.484
0.011b
11.641d
Requiring Reservations
417
$81,825
$43,863
No Reservations
872
$75,455
$37,854
Portion of White Residency
1313
-0.0106
0.677
0.411
1.057e
Requiring Reservations
426
.7266
.2138
No Reservations
887
.7372
.2218
Table 3
ANOVA Results and Mobile Device/Reservation Zip Code Correlations
Exclusionary Campsite Allocation
57
Table 3 (cont.)
EXCLUSIONARY CAMPSITE ALLOCATION 38
Oak Ridge (Prince William Forest Park)
Campground
Median Annual Household Income
307
$1,991
0.163
0.687
0.003e
Requiring Reservations
187
$97,627
$43,115
No Reservations
120
$95,636
$40,570
Portion of White Residency
310
0.0686
6.142
0.014b
0.027e
Requiring Reservations
188
.6486
.2387
No Reservations
122
.5800
.2271
Saddlehorn (Dinosaur National
Monument) Campground
1,746
0.943
Median Annual Household Income
722
$3,899
2.800
0.095
1.221e
Requiring Reservations
341
$71,113
$32,367
No Reservations
381
$67,214
$30,234
Portion of White Residency
732
-0.0133
0.874
0.350
0.723e
Requiring Reservations
343
.7710
.1966
No Reservations
389
.7843
.1883
Note: Median Annual Household Income and Portion of White Residency are calculated at the U.S. Census Block Group level
aDifference in means significant at a 99% confidence interval
bDifference in means significant at a 98% confidence interval
cDifference in means significant at a 95% confidence interval
dEquality of variances cannot be assumed.
e
Equality of variances can be assumed.
Running head: EXCLUSIONARY CAMPSITE ALLOCATION 37
Sample Size of Recreation.gov
data and Correlations
n (# of
devices)
Mean
Std.
Deviation
Mean
Difference
F-value/
Welch
Statistic
p-
value
Levine
statistic
n (# of
reservations)
Correlation
with
common
evening
location zip
codes
Buckhorn (Chickasaw National
Recreation Area) Campground
1,032
0.860
Median Annual Household Income
626
$5,940
10.322
0.001a
16.227d
Requiring Reservations
285
$59,735
$25,491
No Reservations
341
$53,796
$19,700
Portion of White Residency
632
0.0023
0.025
0.875
0.076e
Requiring Reservations
288
0.7182
0.1860
No Reservations
344
0.7159
0.1922
Green River (Colorado National
Monument) Campground
1,344
0.890
Median Annual Household Income
581
$5,084
3.919
0.048c
1.24e
Requiring Reservations
302
$74,364
$32,548
No Reservations
279
$69,280
$29,066
Portion of White Residency
591
0.0108
0.450
0.503
0.738e
Requiring Reservations
307
0.7797
0.1911
No Reservations
284
0.7689
0.1988
Loft Mountain (Shenandoah National
Park) Campground
1,439
0.995
Median Annual Household Income
1289
$6,369
6.484
0.011b
11.641d
Requiring Reservations
417
$81,825
$43,863
No Reservations
872
$75,455
$37,854
Portion of White Residency
1313
-0.0106
0.677
0.411
1.057e
Requiring Reservations
426
.7266
.2138
No Reservations
887
.7372
.2218
Rice et al.
58
At the heart of the park idea is the notion that by virtue of being an American,
whether your ancestors came over on the Mayower or whether they just ar-
rived, whether you’re from a big city or from a rural setting, whether your
daddy owns the factory or your mother is a maid….they [the national parks]
belong to you. (00:6:20)
In reality, as seen through this addition to a growing body of research, national parks
are exclusive places where public ownership does not guarantee equitable access for
the diverse public. Further, as demand increases for limited amenities (e.g., campsites,
trails, parking) and reservation systems are implemented to manage supply, this exclu-
sion is only likely to increase. ough this study revealed campsites requiring reserva-
tions to have signicantly higher portions of White residency in just one of ve camp-
grounds, signicantly higher average median annual household incomes was revealed
among campsites in three of the ve campgrounds.
In an instance, as reported by Recreation.gov (2021), “A popular campground with
57 campsites can see close to 19,000 people all trying to reserve the same campsites
for the same dates immediately aer they’re released for reservation” (para. 8), only
0.3% of would-be campers are able to negotiate the constraints involved with getting
a campsite through the highly competitive online reservation system. Constraints for
obtaining a NPS campsite reservation and for visiting a national park are manifold and
span intrapersonal (e.g., fear, anxiety, perceived self-skills), interpersonal (e.g., family
obligations, cultural expectations), and structural constraints (e.g., access to highspeed
internet, ability to plan in advance). Some of the potential constraints for obtaining an
advanced reservation through Recreation.gov include: the ability to take a vacation to
a national park, access to camping equipment, ability to plan up to six months in ad-
vance, internet access for obtaining a reservation, exibility of work schedules to make
reservations when they come available, the ability to pay for an external service for
monitoring campsite availabilities (e.g., Campnab), and the institutional knowledge of
when and how to obtain a reservation through Recreation.gov.
ere have been substantial eorts to enhance diversity, equity, and inclusion
(DEI) in the NPS, and in recreation and tourism more broadly (akur et al., 2021).
For example, Schultz et al. (2019) found a total of 1,359 relevancy, diversity, and inclu-
sion programs were reported across 161 park units from 2005-2016 with 12% of pro-
grams focused on ethnicity, 12% on race, and 10% on economic status. However, our
research illuminates the ongoing constraints within the NPS and, in particular, camp-
site reservation systems that may further exacerbate inequities across socioeconomic
groups. Similarly, Schultz et al. (2019) concluded their review of NPS DEI programs
by emphasizing the disparity in representing dierent forms of diversity, the need to
strengthen relationships between the NPS and external partners in communities, and
the importance of sustaining programs over time to achieve DEI outcomes.
Research Priorities for Campgrounds and DEI
Despite the growing body of research on DEI and public lands and outdoor recre-
ation (e.g., Flores et al., 2018; Lee et al., 2020; Winter at al., 2020), there remain major
gaps specic to frontcountry camping, particularly in NPS settings, that can inform
priorities for future research. Frontcountry camping is the h most popular outdoor
recreation activity among all U.S. residents, is among the top four most popular out-
door recreation activities among African American, Asian, and Hispanic U.S. resi-
dents, has the second highest level of interest among low-income U.S. residents not yet
Exclusionary Campsite Allocation
59
participating in outdoor recreation, and is the third most popular outdoor recreation
activity among U.S. residents ages 6 to 17 years old (Outdoor Foundation, 2020). Yet,
this activity appears to receive very little research interest (beyond the annual KOA
North American Camping Report), compared to other activities (e.g., hiking—which
is less popular among African Americans, Asians, and Hispanics and U.S. residents
ages 6 to 17; Outdoor Foundation, 2020). e lack of research in this area stands at
odds with its growing interest among an increasingly diverse U.S. population.
Additionally, this research addresses permitting and reservation equity, which
has received little attention in the literature. We were only able to nd one study to
this end—from decades ago (i.e., Magill, 1976)—and NPS reservation systems and the
constraints people face have changed in many ways since then. We recommend future
research to focus on the dierent types of intrapersonal, interpersonal, and structural
constraints, dierent types of campground reservation systems (e.g., in-person, online,
etc.) and dierent types of campgrounds (e.g., frontcountry, backcountry, RV, etc.).
Several of the campgrounds studied here have transitioned a signicant portion of
their rst come, rst served sites to reservation-only since 2019 (i.e., Lo Mountain
and Saddlehorn) or are now completely reservation-only (i.e., Oak Ridge)—thus, high-
lighting the importance of this line of research. Additionally, a large focus of previous
research has been on people who were able to obtain a permit or get a campsite versus
the people who were unsuccessful (e.g., those not successful in securing a campground
are not present for surveying). When studying constraints of online reservation sys-
tems, it is particularly important to have a representative sample. Social media, mobile
device data, and surveys outside NPS sites (e.g., Barros et al., 2020; Liang et al., 2020;
McCreary et al., 2020) can be particularly important to reaching populations who
are not successful in getting the campsites or may not have any interest in getting the
campsites due to various constraints or disconnect of these populations with NPS sites.
Management Considerations for Implementing a Reservation System
As seen through this exploratory study, NPS campsite allocation systems requiring
reservations favor wealthier individuals and, in the case of the urban-proximate Prince
William Forest Park, White individuals. As the agency moves more campsites onto
Recreation.gov and out of rst-come, rst-served systems, national park camping will
likely become an even more exclusive activity. We recommend that the NPS and other
land management agencies consider distributive justice in their decision-making con-
cerning campsite allocation. First, consider who is currently using the campgrounds,
how this population has changed over time in comparison with census and local demo-
graphics changes. Additionally, think of who is not currently using the campgrounds
and visiting NPS sites and how does this population compare to the various aspects
and dimensions of diversity.
Second, consider how reservations are made for campgrounds and other permits
and how information is communicated on working with these systems to break down
barriers and constraints. Recent trailed strategies to this end—which could be used to
inform how reservations are made—include Yosemite National Park’s 2022 reservation
access lottery for campsites in the popular North Pines Campground, through which
hopeful campers enter a lottery for an equal chance to reserve a campsite during peak
summer season, with the intention of oering “a new method for reserving campsites
at this high-demand location for a more equitable experience” and addressing “percep-
tions of an unfair reservation process” (NPS, 2022, para. 2). Viewed through a dis-
Rice et al.
60
tributive justice lens, such a program strives for equity while also seeking to minimize
unintended negative impacts toward equality and eciency (Shelby et al., 1989). Addi-
tionally, this research agenda must address how changes in the reservation and permit
system reect have changed who is using the sites.
ird, when were these changes made and is equity an issue for the timing and
access of reservations? Lastly, where are the campgrounds, facilities, resources that re-
quire reservations and permits? What is the proximity to urban areas and how many
are frontcountry versus backcountry or wilderness sites? When considering these dif-
ferent aspects, managers can transition from decision-making based on specic crowd-
ing or demand metrics to decision-making that meaningfully integrates aspects of DEI
to support a more just process.
Conclusion and Limitations
is exploratory study used an innovative approach to examine the use of on-
line-based reservations systems in frontcountry camping in U.S. national park camp-
grounds, and explores how researchers can use mobile device data as a means to un-
derstand who national park campgrounds serve and the equitability of that service. e
ndings illuminate the trends in online-based reservation systems that may exacerbate
the issue of exclusion of BIPOC (Black, Indigenous, and people of color) populations
from national parks and campgrounds. Considering the growing use of online-based
reservation systems, ticketed entry, and other required permits through online sys-
tems, this topic requires more research to inform decisions by management and agency
decisions to use these approaches.
While mobile device location data represents a powerful tool for monitoring and
measuring tourism and visitor use in parks and protected areas, there are important
limitations to the application of this data that should be considered. In computing
demographic information about individual device users in the United States, Near
analyses census data at the census block group level. Data are tested for bias between
census block groups, but dierences within individual blocks are not visible. erefore,
reported demographic information is based on the census block group in which one
resides, rather than the actual demographic background of the individual. Given this
limitation, bias is easier to detect and remove in areas that have “highly typied neigh-
borhoods, such as one with many ethnic or economic enclaves” and more dicult to
detect in an area that has a “well-integrated population with few ethnic or economic
enclaves” (UberMedia, 2021b). Another consideration when interpreting mobile de-
vice location data is in the sample selection. By virtue of the method of data collec-
tion, the sample can only include campground visitors that had a mobile device with
location services activated while onsite. Other users, those who do not have a mobile
device or do not have an application with location services activated, are not captured.
erefore, there is no way to ensure a truly random sample of campground visitors.
e changing sociodemographic landscape of the U.S. and other countries oers
opportunities to enhance the relevancy, diversity, and inclusion in national parks and
protected areas. However, the increasing demand for visitation to these places has cre-
ated a tension for managers on how to control crowding and sustain resources while
not creating exclusionary practices such as online reservation systems and ticketed
entry. e lack of research on this topic further limits the ability to inform decisions
based on sound science. We hope this exploratory study catalyzes meaningful discus-
sion on these management systems through the lens of relevancy, diversity, and in-
Exclusionary Campsite Allocation
61
clusion and can enhance the equity and access to campgrounds, national parks, and
protected areas.
Disclosure Statement: e authors have no disclosures or competing interests to declare.
Funding: Data collection used for this publication was supported by the National Institute of
General Medical Sciences of the National Institutes of Health (P20GM130418).
References
Atske, S., & Perrin, A. (2021). Home broadband adoption, computer ownership vary
by race, ethnicity in the U.S. Pew Research Center. https://www.pewresearch.org/
fact-tank/2021/07/16/home-broadband-adoption-computer-ownership-vary-by-
race-ethnicity-in-the-u-s/.
Barros, C., Moya-Gómez, B., & Gutiérrez, J. (2020). Using geotagged photographs and
GPS tracks from social networks to analyse visitor behaviour in national parks.
Current Issues in Tourism, 23(10), 1291–1310.
Bixler, R. D., James, J. J., & Vadala, C. E. (2011). Environmental socialization incidents
with implications for the expanded role of interpretive naturalists in providing
natural history experiences. Journal of Interpretation Research, 16(1), 35–64.
Bowser, B. P. (2007). e Black middle class. Lynne Rienner.
Burns, K. (Director). (2009). Going home [Documentary Episode]. In K. Burns, & D.
Duncan (Producers), e national parks: America's best idea. Public Broadcasting
Service.
Calabrese, F., Pereira, F. C., Di Lorenzo, G., Liu, L., & Ratti, C. (2010). e geography of
taste: Analyzing cell-phone mobility and social events. In P. Floréen, A. Krüger, &
M. Spasojevic (Eds.), Pervasive computing: Vol. 6030 LNCS (pp. 22–37). Springer.
Craig, C. A., & Karabas, I. (2021). Glamping aer the coronavirus pandemic. Tourism
and Hospitality Research, 21(2), 251–256.
Creany, N. E., Monz, C. A., D’Antonio, A., Sisneros-Kidd, A., Wilkins, E. J., Nesbitt, J.,
& Mitrovich, M. (2021). Estimating trail use and visitor spatial distribution using
mobile device data: An example from the nature reserve of orange county, Califor-
nia USA. Environmental Challenges, 4, 100171.
Davis, J. (2019). Black faces, Black spaces: Rethinking African American underrepre-
sentation in wildland spaces and outdoor recreation. Environment and Planning E:
Nature and Space, 2(1), 89–109.
Edmonds, D. A. (2019). Perceptions of African-American outdoor experiences. [Master’s
esis, North Carolina State University].
Erickson, B., Johnson, C. W., & Kivel, B. D. (2009). Rocky Mountain National Park:
History and culture as factors in African-American park visitation. Journal of Lei-
sure Research, 41(4), 529–545.
Federal Communication Commission. (2020, February 20). LTE coverage by number
of providers–YE 2018. Reports & research. https://www.fcc.gov/reports-research/
maps/lte-coverage-number-providers-ye-2018/
Finney, C. (2010). is land is your land, this land is my land: People and public lands
redux. e George Wright Forum, 27, 247–254.
Finney, C. (2014). Black faces, White spaces: Reimagining the relationship of African
Americans to the great outdoors. e University of North Carolina Press.
Rice et al.
62
Floyd, M. F., & Stodolska, M. (2014). eoretical frameworks in leisure research on
race and ethnicity. In M. Stodolska, K. J. Shinew, M. F. Floyd, & G. J. Walker (Eds.),
Race, ethnicity, and leisure (pp. 9–20). Human Kinetics.
Ghalambordezfooli, R., & Hosseini, F. (2019). e spatial correlation between social
capital and crime: A case study of the new town of Pardis, Iran. Environmental and
Socio-Economic Studies, 7(4), 62–68.
Grebowicz, M. (2015). e national park to come. Stanford University Press.
Gross, R. (2018). From buckskin to Gore-Tex: Consumption as a path to mastery in
twentieth-century American wilderness recreation. Enterprise and Society, 19(4),
826–835.
Gursoy, D., & Chen, B. T. (2012). Factors inuencing camping behavior: e case of
Taiwan. Journal of Hospitality Marketing and Management, 21(6), 659–678.
Hicks, A. S., Malone, Z., Moore, M. A., Powell, R., ompson, A., Whitener, P. A., &
Williams, R. (2021). Green inequities: Examining the dimensions of socioenviron-
mental injustice in marginalized communities. Parks Stewardship Forum, 37(3),
534–540.
Hogue, M. (2011). A short history of the campsite. Places Journal, 5.
Huberty, C. J., & Morris, J. D. (1989). Multivariate analysis versus multiple univariate
analyses. Psychological Bulletin, 105(2), 302–308.
Kim, Y. J., Lee, D. K., & Kim, C. K. (2020). Spatial tradeo between biodiversity and
nature-based tourism: Considering mobile phone-driven visitation pattern. Glob-
al Ecology and Conservation, 21, e00899. https://doi.org/10.1016/J.GECCO.2019.
E00899
Kubo, T., Uryu, S., Yamano, H., Tsuge, T., Yamakita, T., & Shirayama, Y. (2020). Mo-
bile phone network data reveal nationwide economic value of coastal tourism un-
der climate change. Tourism Management, 77, 104010. https://doi.org/10.1016/J.
TOURMAN.2019.104010
Lawson, M. (2021). Innovative new ways to count outdoor recreation: Using data from
cell phones, tness trackers, social media, and other novel data. Headwaters Eco-
nomics. https://headwaterseconomics.org/outdoor-recreation/counting-outdoor-
recreation/
Liang, Y., Kirilenko, A. P., Stepchenkova, S. O., & Ma, S. (2020). Using social media
to discover unwanted behaviours displayed by visitors to nature parks: Compari-
sons of nationally and privately owned parks in the Greater Kruger National Park,
South Africa. Tourism Recreation Research, 45(2), 271–276.
Liang, Y., Yin, J., Pan, B., Lin, M., & Chi, G. (2021). Assessing the validity of SafeGraph
data for visitor monitoring in Yellowstone National Park. In B. Pan, S. O. Step-
chenkova, & S. Elliot (Eds.), Collection of TTRA 2021 Research Papers and Ab-
stracts - 51st Annual Travel and Tourism Research Association International Confer-
ence. Fort Worth, TX: Travel and Tourism Research Association.
Ma, S., Craig, C. A., Feng, S., & Liu, C. (2021). Climate resources at United States na-
tional parks: A tourism climate index approach. Tourism Recreation Research,
1–15. https://doi.org/10.1080/02508281.2021.1946652
Ma, S., & Kirilenko, A. (2021). How reliable is social media data? Validation of TripAd-
visor tourism visitations using independent data sources. In W. Wörndl, C. Koo,
& J. L. Stienmetz (Eds.), Information and communication technologies in tourism
(pp. 286–293). Springer.
Exclusionary Campsite Allocation
63
Magill, A. W. (1976). Campsite reservation systems: e camper’s viewpoint. USDA For-
est Service Research Paper PSW-121. U.S. Department of Agriculture, U.S. Forest
Service, Pacic Southwest Forest and Range Experiment Station.
Manning, R. E., & Lime, D. W. (2000). Dening and managing the quality of wilderness
recreation experiences. In D. N. Cole, S. F. McCool, W. T. Borrie, & J. O’Loughlin
(Eds.), Wilderness Science in a Time of Change Conference-Volume 4: Wilderness
visitors, experiences, and visitor management; 1999 May 23-27; Missoula, Montana
(Vol. 4, pp. 13–52). U.S. Department of Agriculture, Forest Service, Rocky Moun-
tain Research Station.
McCreary, A., Seekamp, E., Davenport, M., & Smith, J. W. (2020). Exploring qualitative
applications of social media data for place-based assessments in destination plan-
ning. Current Issues in Tourism, 23(1), 82–98.
Merrill, N. H., Atkinson, S. F., Mulvaney, K. K., Mazzotta Id, M. J., & Bousquin, J.
(2020). Using data derived from cellular phone locations to estimate visitation
to natural areas: An application to water recreation in New England, USA. PLoS
ONE, 15(4), e0231863.
Michelson, M. (2021, May 7). How to snag a campsite this summer. Outside Magazine.
https://www.outsideonline.com/adventure-travel/destinations/north-america/
book-campsite-summer/
Monz, C., Creany, N., Nesbitt, J., & Mitrovich, M. (2021). Mobile device data analysis
to determine the demographics of park visitors. Journal of Park and Recreation
Administration, 39(1), 123–130.
Monz, C., Mitrovich, M., D’Antonio, A., & Sisneros-Kidd, A. (2019). Using mobile de-
vice data to estimate visitation in parks and protected areas: An example from
the nature reserve of Orange County, California. Journal of Park and Recreation
Administration, 37(4), 92–109. https://doi.org/10.18666/jpra-2019-9899
More, T. A. (2002). “e parks are being loved to death” and other frauds and deceits
in recreation management. Journal of Leisure Research, 34(1), 52–78. https://doi.or
g/10.1080/00222216.2002.11949960
National Park Service. (2017, June 28). Campgrounds. Chickasaw National Recreation
Area. https://web.archive.org/web/20190612052419/https://www.nps.gov/chic/
planyourvisit/campgrounds.htm
National Park Service. (2018a, July 25). Frontcountry camping (car camping). Camping.
https://www.nps.gov/subjects/camping/frontcountry-camping.htm
National Park Service. (2018b, December 11). Green River campground. Dinosaur Na-
tional Monument. https://web.archive.org/web/20190611141738/https://www.
nps.gov/dino/planyourvisit/greenrivercampground.htm
National Park Service. (2019a, February 1). Lo Mountain campground. Shenandoah
National Park. https://web.archive.org/web/20190706051533/https://www.nps.
gov/shen/planyourvisit/upload/LoMountain_CampgroundMap.pdf
National Park Service. (2019b, February 4). Saddlehorn campground. Colorado Na-
tional Monument. https://web.archive.org/web/20190706024059/https://www.
nps.gov/colm/planyourvisit/saddlehorn-campground.htm
National Park Service. (2019c, March 6). Oak Ridge campground. Prince William For-
est Park. https://web.archive.org/web/20190705063001/https://www.nps.gov/
prwi/planyourvisit/oakridge.htm
National Park Service. (2020a, March 23). Find a campground. Where can I camp?
https://www.nps.gov/subjects/camping/campground.htm
Rice et al.
64
National Park Service. (2020b, July 7). Cellular and Wi-Fi. Goods and services. https://
www.nps.gov/yell/planyourvisit/goodsandservices.htm
National Park Service. (2021, March 11). Oce of Relevancy, Diversity, and Inclusion.
https://www.nps.gov/orgs/1244/index.htm
National Park Service. (2022, February 10). North Pines Campground early access lot-
tery pilot. Yosemite National Park. https://www.nps.gov/yose/planyourvisit/lot-
tery-pilot.htm
Near. (2021a). Understanding mobile location data. Near.
Newcombe, E. (2016). Camping, climbing, and consumption: e Bean Boot, 1912-
1945. Material Culture, 48(1), 10–27.
Park, J., Ellis, G. D., Kim, S. S., Ruddell, E. J., & Agrusa, J. (2007). Predictors of social
equity and price acceptability judgments of user fees. Journal of Travel & Tourism
Marketing, 21(2–3), 89–103.
Park, S., & Pan, B. (2018). Identifying the next non-stop ying market with a big data
approach. Tourism Management, 66, 411–421.
Parks, Forests, and Public Property, 36 CFR § 1.4 (2020).
Recreation.gov. (2021, May). Increased demand for popular activities. e Story
Board: May 2021 [Newsletter]. https://recreationgov.activehosted.com/index.
php?action=social&c=564&m=48049
Rice, W. L., & Park, S. (2021). Big data spatial analysis of campers’ landscape prefer-
ences: Examining demand for amenities. Journal of Environmental Management,
292, 112773.
Rice, W. L., Park, S., Pan, B., & Newman, P. (2019). Forecasting campground demand
in US national parks. Annals of Tourism Research, 75, 424–438.
Roberts, N. S. (2021). Does systemic racism and structural power activate increase ac-
cess? Parks Stewardship Forum, 37(3), 442–447.
Sajid Mehmood, M., Li, G., Jin, A., Rehman, A., Wijeratne, V. P. I. S., Zafar, Z., Khan,
A. R., & Khan, F. A. (2021). e spatial coupling eect between urban street
network’s centrality and collection ad delivery points: A spatial design network
analysis-based study. PLoS ONE, 16(5), e0251093. https://doi.org/10.1371/journal.
pone.0251093
Scott, D., & Lee, K. J. (2018). People of color and their constraints to national parks
visitation. e George Wright Forum, 35(1), 82.
Shelby, B., Whittaker, D., & Danley, M. (1989). Idealism versus pragmatism in user
evaluations of allocation systems. Leisure Sciences, 11(1), 61–70. https://doi.
org/10.10k80/01490408909512205
Soukup M. A., & Machlis, G. E. (2021). National parks as American covenants. Parks
Stewardship Forum, 37(3), 585–591.
Stodolska, M., & Shinew, K. J. (2014). Leisure among Latino Americans. In M. Stodol-
ska, K. J. Shinew, M. F. Floyd, & G. J. Walker (Eds.), Race, ethnicity, and leisure (pp.
75–96). Human Kinetics.
Supak, S., Brothers, G., Ghahramani, L., & Van Berkel, D. (2017). Geospatial analytics
for park and protected land visitor reservation data. In Z. Xiang & D. R. Fesen-
maier (Eds.), Analytics in smart tourism design (pp. 81–109). Springer.
akur, P., Khoo, C., & Pyar, W. Y. K. (2021). Diversity training: Where are we, and
where should we be heading? A systematic literature review. Tourism Recreation
Research, 1–24. https://doi.org/10.1080/02508281.2021.1965369
Exclusionary Campsite Allocation
65
e Outdoor Foundation. (2017). 2017 American camper report. e Outdoor Industry
Association.
e Outdoor Industry Association. (2017). Outdoor recreation economy report. e
Outdoor Industry Association.
UberMedia. (2021a). Derivation of common evening and common daytime locations.
UberMedia.
UberMedia. (2021b). Demographic representation of the UM device graph. UberMedia.
UberMedia. (2021c). Location measurement data and attribution methodology. Uber-
Media.
U. S. Bureau of Labor Statistics. (2011). Labor force characteristics by race and ethnicity
2010. Report no. 1032. http://www. Bls.gov/cps/cpsrace2010.pdf.
U.S. Census Bureau. (n.d.) Tallies. Census reference les. https://www.census.gov/ge-
ographies/reference-les/time-series/geo/tallies.html
Vaske, J. (2008). Survey research and analysis: Applications in parks, recreation and hu-
man dimensions. Venture Publishing.
Vaske, J. J., & Lyon, K. M. (2014). Linking the 2010 census to national park visitors:
Natural Resource Technical Report NPS/WASO/NRTR—2014/880. National Park
Service.
Walls, M., Wichman, C., & Ankney, K. (2018). Nature-based recreation: Understanding
campsite reservations in national parks. Resources for the Future.
Weber, J., & Sultana, S. (2012). Why do so few minority people visit national parks?
Visitation and the accessibility of “America’s best idea.” Annals of the Association of
American Geographers, 103(3), 437–464.
Winter, P., Crano, W. D., Basáñez, T., & Lamb, C. S. (2020). Equity in access to outdoor
recreation—Informing a sustainable future. Sustainability, 12(1), 124.
Xu, J., Wang, X., & Xiao, H. (2020). How environmental bureaucrats inuence funding
legislation: An information processing perspective. Environmental Politics, 29(7),
1284–1305. https://doi.org/10.1080/09644016.2020.1742551
Yard, R. S. (1922). e people and the national parks. e Survey, 48(13), 547–553, 583.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.).
SAGE Publications, Inc.
Young, P. S. K. (2021). Camping grounds: Public nature in American life from the Civil
War to the Occupy Movement. Oxford University Press.
Young, T. (2009). “A contradiction in democratic government”: W. J. Trent, Jr., and the
struggle to desegregate national park campgrounds. Environmental History, 14(4),
651–682.
Young, T. (2017). Heading out: A history of American camping. Cornell University Press.
Young, T. (2018). E. P. Meinecke and the development of the modern auto campground.
IdeAs, 12, 1–22. https://doi.org/10.4000/ideas.3502
