Authors Dan McCole Elizabeth E. Perry John Noyes Jungho Suh Tatiana A. Iretskaia
License CC-BY-4.0
land
Article
Park Design Informed by Stated Preference Choice: Integrating
User Perspectives into the Development of an Off-Road Vehicle
Park in Michigan
Dan McCole 1, *, Tatiana A. Iretskaia 1 , Elizabeth E. Perry 1 , Jungho Suh 2 and John Noyes 3
1 Department of Community Sustainability, Michigan State University, 480 Wilson Rd.,
East Lansing, MI 48824, USA
2 Department of Management, School of Business, The George Washington University,
Washington, DC 20052, USA
3 Oakland County Parks and Recreation, Waterford, MI 48328, USA
* Correspondence: mccoleda@msu.edu; Tel.: +1-517-802-7011
Abstract: At a time when many public park and recreational programs are required by local govern-
ments to be financially self-sustaining, it is critical for planners to design a new development with the
end-user in mind. Feasibility studies often either do not examine user preferences or use Likert-type
surveys to investigate features in isolation without evaluating trade-offs from financial and finite
space limitations. This study used stated preference choice method (SPCM) to inform the initial design
of an off-road vehicle (ORV) park. The park was developed near Detroit, Michigan, a metropolitan
area with many registered ORVs, but few places to legally use them. The SPCM examined trade-
offs among desired features and helped planners ensure publicly funded investments resulted in
a successful park. Researchers mailed a survey with choice sets to 3935 registered ORV users and
2083 completed surveys were retuned (53%). Additional survey items also allowed researchers to
Citation: McCole, D.; Iretskaia, T.A.;
create preference models for specific segments of users (i.e., serious ORV enthusiasts/casual users;
Perry, E.E.; Suh, J.; Noyes, J. Park
residents/visitors; or users of different ORV types). The findings informed the design of the park
Design Informed by Stated
Preference Choice: Integrating User
by revealing preferences for segments, allowing planners to design the park for specific markets.
Perspectives into the Development of The park’s initial success suggests a study in the design stage of development offers utility, though
an Off-Road Vehicle Park in park managers have noticed unanticipated user segments that influence preferences for park features.
Michigan. Land 2022, 11, 1950. The findings based on segments also suggest planners should be cautious when designing to an
https://doi.org/10.3390/ average user. Implications of this study are helpful to planners of any capital-intensive land-use
land11111950 project, especially in the public sector.
Academic Editor: Zhonghua Gou
Keywords: stated preference choice method; Detroit; park planning; ORV; trade-offs; preferences;
Received: 25 September 2022 motorized recreation
Accepted: 28 October 2022
Published: 1 November 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in 1. Introduction
published maps and institutional affil- Outdoor recreation is an important contributor to people’s well-being and quality
iations. of life [1–3]. In heavily populated areas, municipal parks frequently host outdoor recre-
ation [4,5]. As recreational trends evolve, new uses and new demands for use can put stress
on municipal parks that were not planned for these uses and use levels. Park administra-
tors are challenged to mitigate these stresses by providing adequate space, infrastructure,
Copyright: © 2022 by the authors.
and services to meet nearby residents’ recreation preferences [6]. They must do this in an
Licensee MDPI, Basel, Switzerland.
environment that demands fiscal responsibility of local politicians and civic entities. This
This article is an open access article
paper describes how researchers and administrators successfully navigated this terrain to
distributed under the terms and
develop an activity-specific park, through recreationist-focused inquiry on tradeoffs among
conditions of the Creative Commons
proposed park amenities.
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Land 2022, 11, 1950. https://doi.org/10.3390/land11111950 https://www.mdpi.com/journal/land
Land 2022, 11, 1950 2 of 18
1.1. Park and Recreation Benefits
Recreation programs can enhance the social, environmental, and economic benefits
of parks on communities, such as improving community members’ physical and mental
health or improving a community’s natural environment (e.g., [7–10]). For policy makers, the
economic impacts of parks and recreation are of high importance, as they consider allocating
tax dollars to pay for parks [11]. Many policy makers want to know that capital expenditures
for parks and public recreation facilities will offer a positive return on investment (ROI). Many
researchers have correspondingly investigated this topic, to assist in policy decision-making.
Notable findings include parks delivering positive economic benefits via park user spending
(e.g., [12]), tourism attraction (e.g., [13]), amenity migration [14], and increased property values
(e.g., [15]). For local governments in the US, property value increases are particularly relevant
to the ROI: property taxes support 74% of their general funds [16].
1.2. Park planning for New Recreation Uses
Parks support dynamic recreation activities. Over time, broad categories of activities
differentiate into multiple types [17] and individuals change preferences and levels of spe-
cialization [6,18]. For example, general off-road vehicle (ORV) recreation has differentiated
into all-terrain vehicle (ATV), motorcycle, full sized, and side by sides. Committed ORV
recreationists may develop interests and expertise within one of these. Increased visibility
of a park can also shift recreation-specific uses and users, highlighting the need to provide
broad appeal across audiences (e.g., locals, visitors) and specific interest points within
each audience. Therefore, planners must ensure investments made to parks and recreation
match these evolving diversifications, preferences, and audiences.
Though public parks and recreation facilities can have positive economic impacts,
they must still match user preferences or risk going unused. Thus, policy makers scrutinize
each investment, understanding that its success or failure could influence the next election
or millage vote. For local politicians, planners, and park administrators, significant pres-
sure exists to use taxpayer funds efficiently and economically when planning new park
projects [19]. Planning a new park therefore requires considerations such as its features,
amenities, and policies [20]. Poorly planned parks may not generate as much activity as
those designed with end-users in mind.
Therefore, parks often conduct a feasibility study before developing a new attrac-
tion [21]. These generally assess whether a project can succeed in the current and predicted
economic climate and specifically in meeting residents’ needs [22]. Many focus on whether
the project can be successful, without attending to specific features or potential users’
preferences. Where preferences have been examined, research design often limits the find-
ings’ utility. Planners typically do not study user preferences with the necessary scientific
rigor [2] and even rigorous research requires recreationists to disjointedly reveal their
preferences using Likert scales and item-by-item approaches [23]. Such studies offer limited
management implications, as recreationists’ ratings for favorable/preferred items often
exceed planners’ budget and space constraints. These studies neglect measures forcing the
kinds of trade-offs and prioritizations that projects require [23].
Additionally, many studies of preferences focus on existing facilities (e.g., [24]) or
general assessments for new projects, rather than potential audience segments’ preferences
for new facilities’ specific features, amenities, and policies (e.g., [25]). Once developed, it
may often be difficult, impractical, or cost-prohibitive to make changes to better appeal to
a potential audience if the initial design missed the mark. When a study evaluates user
preferences for potential features and amenities of a new facility, it typically includes the
aforementioned Likert items that present managerial challenges [23].
1.3. Off-Road Vehicle Park Planning in Michigan
This paper presents research related to the initial planning of an ORV county park in
Michigan, US. ORV use in the US, and associated managerial challenges, have increased
dramatically in recent decades [26]. Smith et al. [27] address the impacts of this rapid
Land 2022, 11, 1950 3 of 18
growth: “As OHV use continues to grow and diversify, recreation resource managers will
experience more acute difficulties in providing opportunities for these users to achieve
desired outcomes while simultaneously minimizing potential impacts.”
The Bureau of Land Management [28] provides several reasons for the increase in ORV
popularity, including population growth, aging population, technological advancements,
and a rising public interest in unconfined outdoor recreation. Michigan exemplifies this
growth trend and is an established ORV destination. In 2020, for example, Michigan sold
204,000 ORV licenses [29], and offered over 10,000 miles of ORV trails. In the most recent
study of ORV use in Michigan, visitors made over 200,000 distinct trips to use their ORVs
in Michigan [30].
The largest percentage of registered ORV owners reside in the densely populated south-
east Michigan, but most of the trails available for ORV use are located hours away, in rural
northern Michigan. The large distance between this concentration of ORV users and planned
ORV use landscapes has resulted in sites of illegal ORV use around southeast Michigan [31].
The Michigan Department of Natural Resources (DNR) has been working to develop
legal, public ORV use opportunities in southeast Michigan. In 2012, the DNR and Oakland
County Parks and Recreation (OCPR) began co-developing an ORV park on an old gravel
mining operation—the state would fund the land purchase, and the county would develop,
own, and operate the park. Oakland County is adjacent to Detroit and has a population of
1.3 million (about 13% of Michigan’s population), making it second only to Wayne County
(home of Detroit). It also has the most registered ORV users out of Michigan’s 83 counties.
The county’s extensive park system is nationally recognized, with an annual operating
budget of USD 34 million [32].
The proposed park, while serving the needs of the many ORV users, was not supported
by all county residents. Many were concerned about the sound and dust an ORV park
would generate. The county’s government officials and policy makers were interested in
the potential economic benefits, but were also concerned about the potential for a large and
contentious development to become financially burdensome. Oakland County governance
pressured OCPR administrators to ensure that all parks were financially self-sustaining.
This pressure caused park administrators to enquire about research to inform the park’s
design, ensuring it would generate high use levels by multiple audiences. Park planners
also wanted to know whether specific user groups (i.e., serious ORV enthusiasts/casual
users; residents/visitors; or those who use ATVs, full-sized vehicles, or off-road motorcy-
cles) differed in their park design preferences. If they did, then OCPR, DNR, and Oakland
County could internally discuss which groups to prioritize in design decisions.
1.4. Stated Preference Choice Method
This study used the stated preference choice method (SPCM) to investigate user group
preferences for features, amenities, and policies that would inform the park’s initial design.
SPCM is commonly used to understand relationships between contextual characteristics
and consumer purchase behavior and evaluate user preferences for destinations and at-
tractions [33,34]. SPCM is rooted in random utility maximization theory: individuals make
choices to maximize utility [35] and, given a set of choices, consumers will select the one
with maximum perceived utility [36].
In SPCM, respondents make several choices between hypothetical combinations of
attribute levels for a product. These relate the relative importance of those attribute levels
and trade-offs in consumers’ decision making [23]. SPCM identifies users’ preferences for
trade-offs collectively [23] and is considered a major improvement in understanding the
multi-attribute preferences of site users and recreationists [23,35].
SPCM is a long-recognized tool to inform park infrastructure needs (e.g., [33,37]). For
example, Campagnaro et al. [38] explored perceived safety preferences for green spaces
in Padua, Italy, by showing respondents modified images of different green scenarios.
Another typical use of SPCM is to examine a site’s social carrying capacity overall and by
user group, to correspondingly inform managerial decisions [39,40].
trade-offs collectively [23] and is considered a major improvement in understanding the
multi-attribute preferences of site users and recreationists [23,35].
SPCM is a long-recognized tool to inform park infrastructure needs (e.g., [33,37]). For
example, Campagnaro et al. [38] explored perceived safety preferences for green spaces
Land 2022, 11, 1950 in Padua, Italy, by showing respondents modified images of different green scenarios. 4 of 18
Another typical use of SPCM is to examine a site’s social carrying capacity overall and by
user group, to correspondingly inform managerial decisions [39,40].
Applications of SPCM to user preferences have occurred mostly in the context of ex-
Applications of SPCM to user preferences have occurred mostly in the context of
isting places (e.g., [23,41]). These studies are important, but existing investments, budgets,
existing places (e.g., [23,41]). These studies are important, but existing investments, budgets,
and expectations of repeat visitors may limit managers’ ability to implement major
and expectations of repeat visitors may limit managers’ ability to implement major changes
changes recommended.
recommended. Employing Employing
SPCM toSPCM
informtopotential
inform potential development
development could
could aid aid in
in design
design decisions matching use-goals and user group preferences, though apparently
decisions matching use-goals and user group preferences, though apparently few studies few
studies have used it in this planning context. This study addresses this gap, using
have used it in this planning context. This study addresses this gap, using an ORV park an ORV
park and
and its its users
users as a setting.
as a setting.
2. Methods
Methods
2.1. Location of ORV Park and Landscape Setting
The study ORV park is in in Holly
Holly (Oakland
(Oakland County),
County), Michigan,
Michigan, 52 52 miles
miles (84
(84km)
km)north-
north-
west from downtown Detroit (Figure 1). It It was
was in
in the
the planning
planning stage
stage at
at the
the time
time of the
research and waswas subsequently
subsequentlybuilt
builton
onland
landofofmixed
mixeduse and
use and landscapes:
landscapes: legacy andand
legacy ac-
tive sand
active and
sand gravel
and mining
gravel miningoperations, wooded
operations, woodedareas, andand
areas, openopenexpanses. ThisThis
expanses. landscape
land-
diversity
scape is bothisappealing
diversity to and to
both appealing accommodating of all types
and accommodating of alloftypes
ORVs,ofincluding full-size
ORVs, including
vehicles, side-by-sides, all-terrain vehicles, and motorcycles.
full-size vehicles, side-by-sides, all-terrain vehicles, and motorcycles.
Figure 1. Location of the Holly Oaks off-road vehicle park—Michigan, USA.
Figure 1. Location of the Holly Oaks off-road vehicle park—Michigan, USA.
2.2.
2.2. Focus
Focus Groups
Groups
Researchers
Researchers first
first conducted
conducted three
three focus
focus group
group interviews
interviews with
with 19
19 people
people over
over aa six-
six-
week
week period.
period. Focus
Focusgroup
groupparticipants
participantsincluded
includedORVORVusers
usersand
andrepresentatives
representativesofofORV-
ORV-
related
related businesses,
businesses, residents
residents and
and non-residents of Oakland
non-residents of Oakland County, and users
County, and users of
of different
different
types of ORVs. The focus groups discussions informed the study design encompassing
potential features and amenities, ORV-specific lingo, and wording for the invitation for survey
participation. Focus group participants also provided survey instrument critique, validation,
and piloting and review of associated communications (e.g., invitation, completion reminders).
Land 2022, 11, 1950 5 of 18
2.3. Study Sample
The study population was people who purchased Michigan ORV permits during the
previous year. The DNR provided a full list of this population and researchers drew a stratified
random sample from this list, based on four geographic strata: residents of Oakland County,
adjacent Michigan counties, non-adjacent Michigan counties within a three-hour drive, and
non-adjacent non-Michigan (but still US) counties within a three-hour drive. Each stratum
included 1008 people, for a total sample size of 4032. OCPR previously determined that
most park users would be locals (i.e., county residents and residents of the seven adjacent
counties) and visitors from beyond these counties but within a three-hour drive. These groups’
preferences were vital in OCPR’s planning, to ensure the park met locals’ preferences and
enticed visitors to travel to and spend money in the park and its surrounding community. The
sampling design thus reflected these definitions and priorities.
The sample was contacted using a modified Dillman method [42] for contacting via
postal mail: a pre-notice, invitation, reminder, and final contact.
Survey respondents were largely male (92.3%), white (97.9%), tended to have less
education than a four-year degree (75.0%), and were most commonly in the USD 40-
80,000 income bracket (38.6%). The age of respondents ranged from 18–86 with a mean
age of 48.2 years (SD = 12.97). The majority indicated they live in small towns or rural
locales, while a little over one third live in suburbs. Respondents indicated that they had
used ORVs recreationally for as many as 55 years (mean = 16.2). Regarding recreation
specialization, almost half of respondents (47.4%) indicated that they are “active” ORV
users, 35.3% “casual,” and 17.3% “committed.” Oakland County residents accounted for
28.7% of respondents.
2.4. Survey Design and Identification of Attributes
The survey included items on user demographics, ORV experience and use (e.g., types
of vehicles used, years of experience, frequency of use, trips taken), and stated preference
choice sets. The attributes and levels for the choice sets were developed from managerial
considerations, focus group discussions, and SPCM literature review.
Figure 2 lists the attributes and their levels, which can be generally categorized as park
elements, amenities, staffing, and price. Park elements include “Trail usage,” or whether a
trail is open to all ORVs or dedicated to certain types, and “Variety of park features,” or
the park’s selection of specialized terrain (e.g., rocks, mud pits, hills, off-camber terrain).
Amenity attributes included the presence/absence of a vehicle wash station with high
pressure hose and types of restrooms ranging in level from basic pit toilets to facilities
with showers and flush toilets. Though the focus groups emphasized that users would
self-enforce rules and thus limit the need for park staffing to regulate use and sanction
users, OCPR requested broader input on this and thus it was included as a staffing attribute.
This staffing, combined with the considerations of the park elements’ and amenities’ levels,
would certainly impact the price of admission and users’ willingness to pay. “Daily entry
fee” is correspondingly included as the price attribute.
2.5. Recreation Specialization
Recreation specialization is a multidimensional construct of activity involvement
based upon an individual’s behavior, knowledge, skill development, and commitment to
the activity [43]. It has been used as a collective measure to understand the motivation
and behavior of recreationists in a variety of settings (see [44], for comprehensive lists). To
account for the multidimensionality of recreation specialization, researchers have often
taken a multiple indicator approach: several survey items are grouped to categorize
recreationists as casual, active, or committed [45]. This method of measurement consumes
valuable survey space, as up to 24 items are used to determine specialization [46]. From this
space constraint and respondent burden realization came the development and refinement
of a single-item specialization classification approach. In this, respondents read descriptions
Land 2022, 11, 1950 6 of 18
Land 2022, 11, x FOR PEER REVIEW 6 of 19
of recreationists at each specialization level and select the one that best describes them
(e.g., [45,47,48]). This study used this single-item approach to classify ORV users (Figure 3).
Figure 2. Attributes and levels used in the stated preference choice models.
2.5. Recreation Specialization
Recreation specialization is a multidimensional construct of activity involvement
based upon an individual’s behavior, knowledge, skill development, and commitment to
the activity [43]. It has been used as a collective measure to understand the motivation
and behavior of recreationists in a variety of settings (see [44], for comprehensive lists).
To account for the multidimensionality of recreation specialization, researchers have often
taken a multiple indicator approach: several survey items are grouped to categorize rec-
reationists as casual, active, or committed [45]. This method of measurement consumes
valuable survey space, as up to 24 items are used to determine specialization [46]. From
this space constraint and respondent burden realization came the development and re-
finement of a single-item specialization classification approach. In this, respondents read
descriptions of recreationists at each specialization level and select the one that best de-
scribes them (e.g., [45,47,48]). This study used this single-item approach to classify ORV
Figure 2.2.Attributes
Attributes
and levels used in the stated preference choice models.
Figure
users (Figure 3). and levels used in the stated preference choice models.
2.5. Recreation Specialization
Recreation specialization is a multidimensional construct of activity involvement
based upon an individual’s behavior, knowledge, skill development, and commitment to
the activity [43]. It has been used as a collective measure to understand the motivation
and behavior of recreationists in a variety of settings (see [44], for comprehensive lists).
To account for the multidimensionality of recreation specialization, researchers have often
taken a multiple indicator approach: several survey items are grouped to categorize rec-
reationists as casual, active, or committed [45]. This method of measurement consumes
valuable survey space, as up to 24 items are used to determine specialization [46]. From
this space constraint and respondent burden realization came the development and re-
finement of a single-item specialization classification approach. In this, respondents read
Figure
Figure3.3.Recreation
descriptions specialization
of recreationists
Recreation atlevels
specialization levelsof
each ofORV
ORVusers
usersutilized,
specialization with
level and
utilized, withdescriptions
select presented
the one
descriptions totode-
that best
presented
respondents for self-selection of single specialization level.
scribes themfor
respondents (e.g., [45,47,48]).
self-selection Thisspecialization
of single study used level.
this single-item approach to classify ORV
users (Figure 3).
2.6.Experimental
2.6. ExperimentalDesign
Design
AAsequential
sequentialorthogonal
orthogonal factorial
factorial design
design was
wasused
usedtotogenerate
generatechoice
choicesets forfor
sets a con-
a con-
venience sample pilot study of 42 ORV users in the study area. This pilot study
venience sample pilot study of 42 ORV users in the study area. This pilot study providedprovided
parameterestimates
parameter estimates then
then used
used toto generate
generatechoice
choicesets
setswith
withananefficient
efficientfractional factorial
fractional factorial
design using Ngene software. Researchers generated 36 choice sets, which
design using Ngene software. Researchers generated 36 choice sets, which were were divided
divided
among nine otherwise identical surveys (each with four of the choice sets). Each choice
set presented respondents with two hypothetical parks offering different combinations
of attributes and attribute levels (Figure 4). For each of the four choice sets presented,
respondents chose either one of the two parks or neither.
Figure 3. Recreation specialization levels of ORV users utilized, with descriptions presented to
respondents for self-selection of single specialization level.
2.6. Experimental Design
A sequential orthogonal factorial design was used to generate choice sets for a con-
venience sample pilot study of 42 ORV users in the study area. This pilot study provided
parameter estimates then used to generate choice sets with an efficient fractional factorial
among nine otherwise identical surveys (each with four of the choice sets). Each choice set
presented respondents with two hypothetical parks offering different combinations of at-
Land 2022, 11, 1950 7 of 18
tributes and attribute levels (Figure 4). For each of the four choice sets presented, respond-
ents chose either one of the two parks or neither.
Figure 4.
Figure 4. Sample
Sample choice
choice set.
set.
2.7. Model
SPCM is based on the assumption that individuals taking into account the relative
importance
importance of of various
various factors
factors are
are more
more likely
likely to
to choose
choose thethe option
option that
that maximizes
maximizes these
these
factors’ personal utility. Using SPCM, researchers can ask
factors’ personal utility. Using SPCM, researchers can ask individuals toindividuals to make
make choices
choices
about
about hypothetical park designs
hypothetical park designs with
with different
different levels
levels of
of attributes. According to
attributes. According to random
random
utility
utility theory, the utility function can be decomposed into the deterministic and random
theory, the utility function can be decomposed into the deterministic and random
error components[35].
error components [35].Utility
Utility cannot
cannot be be estimated
estimated directly,
directly, due due
to thetorandom
the random
error error
com-
component encapsulating the effect of unobserved factors on an individual’s
ponent encapsulating the effect of unobserved factors on an individual’s choice. Thus, the choice. Thus,
the probability
probability of choice
of choice results
results is used.
is used. TheThe indirectutility
indirect utilityfunction
functionofofan anORV
ORV user
user across
across
the choice of park “j” can be presented
the choice of park “j” can be presented as: as:
U𝑼 𝒋 =V𝑽
j = (𝑨)
j (𝒋A ) ++εj𝜺𝒋==Aβ
𝑨𝛽++εj𝜺𝒋
where where Uj is
Uj is the the utility
utility of choosing
of choosing ORV parkORV park
j, Vj is aj,deterministic
Vj is a deterministic
component component
of utility, of
A
utility, A is a vector of observed variables relating to alternative j, 𝛽 is a vector
is a vector of observed variables relating to alternative j, β is a vector of random coefficients,of random
coefficients,
and εj reflectsand
an εj reflects an unobservable
unobservable error component errorofcomponent
utility. of utility.
The deterministic utility (V) is assumed to be observed by the
The deterministic utility (V) is assumed to be observed by the researchers, while the
researchers, while the
random error component (ε) indicates the utility explained by attributes
random error component (ε) indicates the utility explained by attributes unobserved. Thus, unobserved.
Thus,todue
due the to the presence
presence of theof the random
random error error component,
component, the probability
the probability thatORV
that an an ORV
user
user chooses
chooses park park
j overj over alternative
alternative i inchoice
i in the the choice
set Mset M is used:
is used:
P(i|i ∈ M ) = P Vi (A) − Vj (A) > εj − εi
This probability depends on the assumption that the error terms (εj − εi) are inde-
pendently and identically distributed (IID) Type 1 extreme values, (the Gumbel distribu-
Land 2022, 11, 1950 8 of 18
tion; [36]). The probability of choosing ORV park i is given by the following conditional
logit model:
exp (Vi )
P(i|i ∈ M ) =
∑j=M exp Vj
where M is the set of all park design scenarios included. This model exhibits the indepen-
dence of irrelevant alternatives (IIA) property, which requires that for a specific individual,
the ratio of the probabilities only depends on the two alternatives not being compared on
any of the other alternatives [49]. To assess the effectiveness of the proposals based on
the reflected changes in the attribute levels of park features and amenities, implicit prices
can be calculated once the model is estimated. Therefore, the willingness to pay to use an
ORV park given the specific attribute level for each option can be measured to inform the
benefits gain or loss. Hanemann [50] suggested that the computation of implicit prices is
given by the following:
1
(V0 − V1 )
β fee
V0 denotes the utility from the initial condition of an ORV park, and V1 indicates
the utility from the new scenario with the adjusted levels of attributes. Moreover, the
attributes can be altered to reflect possible preferences for park design scenarios. Each
scenario’s probability of selection was estimated in accordance with Bateman et al. and
Blamey et al. [51,52].
3. Results
Of the 4032 surveys mailed to registered ORV users, 97 were returned as undeliverable
and 2083 were completed for a 52.9% response rate. Incomplete choice model responses
rendered a small number (n = 48, 2.3%) of surveys as unusable, but all other items could
tolerate variable response rates and retain inclusion (e.g., skipped demographics). Re-
spondents, were largely male (92.3%), white (97.9%), tended to have less education than a
four-year degree (75.0%), and were most commonly in the USD 40-80,000 income bracket
(38.6%). The average age of respondents was 48.2 years and the majority indicated they live
in small towns or rural locales, while a little over one third live in suburbs. Respondents
indicated that they had used ORVs recreationally for as many as 55 years (mean = 16.2).
Regarding recreation specialization, almost half of respondents (47.4%) indicated that they
are “active” ORV users, 35.3% “casual,” and 17.3% “committed.” Oakland County residents
accounted for 28.7% of respondents.
The conditional logit model was used to create several preference models for the
ORV user groups important to park planners. In these, coefficients and implicit prices for
conditional logit model are listed and an alternative specific constant (ASC) was added to
represent the unobserved attributes that were not part of the model [53]. For the qualitative
attributes, dummy variables were used to help with interpretation of the coefficients.
In the pooled model of all respondents (Table 1), all main coefficients were significant
at α = 0.05 except for “restrooms with showers,” which was not significant in any of
the models. Although the implicit prices vary among the different models, the signs of
the significant coefficients (positive and negative) are identical for each attribute level in
all of the models. This suggests that different ORV user groups do not have conflicting
preferences for the examined attributes. However, the differences in implicit prices show
that there is variation in the magnitude of those preferences among user groups.
The signs of the coefficients for attributes in the models indicate the direction of user
preferences. Respondents prefer trails that are dedicated to their specific type of vehicle
(negative coefficient for mixed trail use), while trails that can be used by all vehicle types
are considerably less preferred (positive coefficient for dedicated use trails). Similarly,
coefficients indicate that respondents prefer a large variety of park features and place less
value on a park with little variety of features. Respondents are willing to pay more for a
Land 2022, 11, 1950 9 of 18
park with enough staff to enforce the rules and for a park with a vehicle wash station but
are willing to pay less for a park that has only porta-potties for restroom facilities.
Table 1. Summary of choice model findings for all respondents (n = 2035).
Implicit Value
Attribute Level Coefficients Range
($)
−3.2716
ASC
(0.166)
−0.2590 *
Mixed Motorized −2.34
Trails (0.034) 4.15
0.2009 *
Dedicated Use 1.81
(0.033)
0.2427 *
Large Variety 2.19
Park Features (0.031) 2.83
−0.0715 *
Little Variety −0.64
(0.030)
0.1594 *
Staff to Enforce Rules Yes 1.44 2.88
(0.027)
0.1897 *
Vehicle Wash Station Yes 1.71 3.42
(0.031)
−0.2984 *
Porta-potties −2.69
Restrooms (0.033) 5.07
Restrooms w/showers 0.2634 2.38
−0.1107 *
Daily Fee
(0.011)
* Indicates statistical significance at the 0.05 level.; (Standard error).
Next, Table 2 details coefficients and implicit prices by recreation specialization (casual,
active, and committed ORV users). The main coefficients are significant for all three models
with the exception of “restrooms with showers” for all models and “porta-potties” for the
committed ORV users. The sign of the significant coefficients for all three models are the
same, which shows some consistency of preference among the three groups. However, the
implicit prices show differences in the strength of those preferences regarding trail usage and
park feature variety. The value of the preferred attribute levels and the devaluing of the less
preferred attribute levels are more extreme for the committed users, showing stronger levels
of preference for the most specialized ORV users. For example, the range between the implicit
prices for dedicated-use trails and a trail shared by all vehicle types is USD 8.07 for committed
users, whereas it is only USD 4.28 for casual users and USD 3.25 for active users.
Participants were asked to identify the type of ORV they most commonly use. Most
respondents indicated they primarily use either ATV, off-road motorcycle, side-by-side, or
full-sized vehicles. Some respondents indicated another primary vehicle type, but these
were too few to create additional vehicle type categories in the model. Table 3 shows the
coefficients and implicit prices for the four conditional logit models created based on vehicle
type. With the exception of “restroom with showers,” which again was not significant in
any model, all of the main coefficients were significant for the ATV and full-sized vehicle
users. The attribute “staff to enforce rules” was not significant for motorcycle users. Only
one attribute (large variety of park features) was significant in the side-by-side model.
The signs of all significant coefficients again show consistency of preference among users
of different vehicle types and the implicit prices show differences in magnitude of those
preferences. The implicit prices for ATV users were closest to those of all respondents,
which is expected as the largest respondent group (55%). Full-sized vehicle users were
willing to pay more than others for a large variety of features and a wash station. The
implicit prices for motorcycle users show stronger levels of preference for trail usage. They
indicate a willingness to pay USD $6.01 more for a park with dedicated-use trails but
would pay USD 4.89 less for a park with shared trails. This represents a range of USD 10.90,
compared to ranges of USD 4.34 for ATV users and USD 2.99 for full-sized vehicle users.
Land 2022, 11, 1950 10 of 18
Table 2. Summary of choice model findings by recreation specialization level.
Casual Active Committed
(n = 655) (n = 878) (n = 321)
Implicit Value ($)
Implicit Value ($)
Implicit Value ($)
Coefficients
Coefficients
Coefficients
Attribute Level
Range
Range
Range
−2.87231 −3.5671 −3.2477
ASC
(0.283) (0.242) (0.433)
−0.2245 * −0.2501 * −0.3811 *
Mixed Motorized −2.11 −2.11 −4.17
(0.059) 4.28 (0.050) 3.25 (0.093) 8.07
Trails
0.2308 * 0.1353 * 0.3565 *
Dedicated Use 2.17 1.14 3.90
(0.057) (0.048) (0.088)
0.1393 * 0.2771 * 0.3723 *
Large Variety 1.31 2.34 4.07
(0.054) 2.39 (0.046) 3.95 (0.085) 6.88
Park Features
−0.1351 * −0.1678 * −0.3483 *
Little Variety −1.08 −1.61 −2.81
(0.049) (0.040) (0.047)
0.2225 * 0.1639 * 0.0000 *
Staff to Enforce Rules Yes 2.09 4.18 1.38 2.76 0.00 0.00
(0.047) (0.040) (0.052)
0.1216 * 0.2457 * 0.1914 *
Vehicle Wash Station Yes 1.14 2.28 2.08 4.1 1.68 3.36
(0.054) (.046) (0.046)
−0.2798 * −0.3369 * −0.1420
Porta-potties −2.63 −2.85 −1.55
Restrooms (0.056) 5.53 (0.048) 6.04 (0.087) 3.51
Restrooms w/showers 0.3086 2.90 0.3777 3.19 0.1790 1.96
−0.1065 * −0.1184 * −0.0915 *
Daily Fee
(0.019) (0.016) (0.029)
* Indicates statistical significance at the 0.05 level.; (Standard error).
Table 3. Summary of choice model findings by vehicle type.
ATV Motorcycle Full-Sized Side x Side
(n = 1124) (n = 328) (n = 258) (n = 151)
Implicit Value ($)
Implicit Value ($)
Implicit Value ($)
Implicit Value ($)
Coefficients
Coefficients
Coefficients
Coefficients
Attribute Level
Range
Range
Range
Range
−3.2106 −2.6987 −0.2485 −2.2303
ASC
(0.245) (0.424) (0.483) (0.667)
Mixed −0.2784 * −0.4497 * 0.0938 * −0.195
−2.35 −4.89 −2.17 −5.58
Motorized (0.051) (0.086) (0.104) (0.135)
Trails 0.2355 * 4.34 0.5529 * 10.90 0.1547 2.99 0.0581 7.24
Dedicated Use 1.99 6.01 0.82 1.66
(0.048) (0.085) (0.096) (0.133)
0.2134 * 0.2156 * −0.2786 * 0.3246 *
Park Large Variety 1.80 2.34 4.41 9.28
(0.046) (0.079) (0.095) (0.121)
Features −0.0877 * 2.54 −0.2557 * 5.12 −0.2280 * 6.84 0.0623 7.50
Little Variety −0.74 −2.78 −2.43 1.78
(0.044) (0.075) (0.088) (0.119)
Staff to 0.1668 * 0.0558 0.2372 * 0.1446
Yes 1.41 2.82 0.61 1.22 2.07 4.14 4.13 8.26
Enforce Rules (0.040) (0.069) (0.080) (0.109)
Vehicle 0.1989 * 0.0002 * −0.2931 *
Yes 1.68 3.36 0.00 0.00 2.55 5.10 0.2046 5.85 11.70
Wash Station (0.047) (0.081) (0.093)
−0.3513 * −0.2255 * −0.0766 * −0.1889
Porta-potties −2.96 −2.45 −2.45 −5.40
(0.048) (0.082) (0.098) (0.129)
Restrooms Restrooms 5.59 2.90 5.56 4.35
0.3115 2.63 0.0486 0.45 −0.1147 3.11 −0.0368 −1.05
w/showers
−0.1186 * −0.0919 * −0.2485 * −0.0349
Daily Fee
(0.016) (0.028) (0.032) (0.044)
* Indicates statistical significance at the 0.05 level.; (Standard error).
Land 2022, 11, 1950 11 of 18
Table 4 shows the coefficients and implicit prices for two models based on county
residency: Oakland County or other counties. OCPR was interested in knowing if residents,
who would have less distance to travel and may likely use the park more often, have
different preferences than those who may travel further to the park less frequently, but
provide the economic benefit of visitor spending at Oakland County businesses. All of
the main coefficients were significant in both models. Although the implicit prices are
similar for both groups, residents showed a greater preference for a variety of features than
non-residents, perhaps reflecting that they might use the park more frequently and would
therefore appreciate a greater variety of features.
Table 4. Summary of choice model findings for residents and non-residents.
Residents Non-Residents
(n = 585) (n = 1450)
Implicit Value ($)
Implicit Value ($)
Coefficients
Coefficients
Attribute Level
Range
Range
−3.2346
ASC 3.294641
(0.231)
−0.2493 * −0.2645 *
Trails Mixed Motorized −2.39 −2.27
(0.049) 4.00 (0.049) 4.28
0.1677 * 0.2345 *
Dedicated Use 1.61 2.01
(0.046) (0.048)
0.2682 * 0.2149 *
Large Variety 2.58 1.85
Park (0.044) (0.046)
4.63 2.94
Features −0.2132 * −0.1274 *
Little Variety −2.05 −1.09
(0.0416) (0.043)
0.1968 * 0.1203 *
Staff to Enforce Rules Yes 1.89 3.78 1.03 2.06
(0.038) (0.040)
0.1832 * 0.1931 *
Vehicle Wash Station Yes 1.76 3.52 1.66 3.32
(0.045) (0.046)
−0.2720 * −0.3253 *
Porta-potties −2.61 −2.79
Restrooms (0.046) 5.39 (0.047) 6.34
Restrooms w/showers 0.2893 2.78 0.4130 3.55
−0.1041161 * −0.1165 *
Daily Fee
(0.016) (0.016)
* Indicates statistical significance at the 0.05 level.; (Standard error).
4. Discussion
This study was conducted at the request of planners from a county park system that
was in the early phases of developing a new ORV park. Planners wanted data about
potential users to inform decisions about the design of this park. Although there were
many supporters of a new ORV park, many others were strongly opposed. Adding to the
political nature of this park were pressures from county government leaders to ensure it
would be financially self-sustaining. These factors put pressure on park planners to ensure
the park would sustain enough interest and demand at prices that would at least cover
operating expenses. Additionally, park planners wanted data segmented by user groups, to
explore differences in design preferences and take a more thoughtful approach to designing
the park with specific user groups in mind.
Financial constraints and finite space at the proposed park site forced design limits on
the new park’s development. These limits required a research design examining user prefer-
ences, while still accounting for inherent trade-offs: investment in some features/amenities
would necessitate denial of others. The SPCM is appropriate for this context as it examines
Land 2022, 11, 1950 12 of 18
subjects’ valuation of various features and amenities of a hypothetical park, while forcing
trade-offs between attribute levels.
Our approach and findings highlight why detailed information at this stage of park
planning is crucial to both sophisticated research methods and on-the-ground practicality.
This discussion focuses on three key, interrelated points stemming from the data presented
and the subsequent use of these data by OCPR. First, our advice to park planning to include
specific, diverse features appears to have resulted in initial success in the park development
and early years of operation. Second, the segmented approach to examining findings
allowed us to consider the preferences of specific groups and compare those results to
the average. There are no average ORV recreationists, and we need robust methods and
analyses to parse differences that averages obscure and that focus/advisory groups alone
may not surface. Third, in the time since this study was conducted, park planners and
managers have noticed unanticipated user segmentations whose preferences have a greater
impact on park design. The following elaborates on each of these three main points.
4.1. Informing Park Design
Figure 5 summarizes the range of implicit values between the lowest and highest
levels of each attribute for all respondents and by user group segmentation. Plotting the
differences between these levels for each attribute depicts the perceived attractiveness of
enhanced versus lower levels of attributes. It also allows for a relative comparison of where
there is more or less congruence among perceptions by different user groups. Looking
across the attributes and user groups highlights descriptive patterns for particular user
group delineations, specific user groups, and attribute comparisons. A few key observations
about these data are noted below.
Overall, all users prefer trails in the park to be of mostly dedicated use, but motor-
cyclists and committed users especially value this feature (at least twice as much as the
other user types), while the full-size vehicle users have the least pronounced preference
(i.e., most ambivalence).
As for the variety of park features, all users overall prefer a large variety, with the
importance of this feature (and a willingness to pay for it) most clearly pronounced by
side-by-side users, followed then by committed users, full-size users, and motorcyclists. It
is of least importance to casual and non-resident users.
The vehicle wash option is particularly important to side-by-side users (who are
willing to pay almost three times more than the average respondent for a park equipped
with such a feature), followed by full-size users. Motorcyclists, conversely, do not place any
value in this feature, and casual users place the second least. Overall, all users feel strongly
about having modern restroom facilities with flush toilets. This feature is most valued by
non-resident visitors and the active users.
Lastly, the presence of staff to enforce rules is most valued by side-by-side users,
followed by full-size users and casual users. It is of lesser value to motorcyclists and
non-residents and not of any value to committed users. The overall preference for staff
to enforce rules was an especially informative finding for the park planners, as the focus
group participants had emphasized that the park system could save operating costs on
staffing because ORV riders have a strong culture of rule self-enforcement. This result
underscores the importance of using multiple methods to obtain user preferences when
informing design.
Land 2022, 11,
Land 2022, 11, 1950
x FOR PEER REVIEW 1413of
of 19
18
Figure 5. Ranges of implicit values between the lowest and highest level of each attribute for all
Figure 5. Ranges of implicit values between the lowest and highest level of each attribute for all
respondents and by specialization categories, vehicle types, and Oakland County residency. Spe-
respondents
cific and
values for thebyranges
specialization categories,
plotted are listed invehicle types, and
the preceding Oakland County residency. Specific
tables.
values for the ranges plotted are listed in the preceding tables.
4.2.
4.2. Potential
Potential Issues
Issues with
with Designing
Designing to to the
the Average
Average
This
This study—and the resulting planning and
study—and the resulting planning and design
design ofof the
the ORV
ORV park—speaks
park—speaks to to the
the
importance
importance of of matching
matching research
research design
design to to the
the complexity
complexity of of park
park planning.
planning. InIn this
this work,
work,
we
we used
used aamore
moresensitive
sensitiveand andintegrated
integrated method
method (SPCM,
(SPCM, with attributes
with examined
attributes examined in tan-
in
dem) than the single-item Likert scale responses (attributes examined
tandem) than the single-item Likert scale responses (attributes examined in isolation), in isolation), at-
tempting
attempting totocapture
capturethe thereality
realityofofpark
parkplanning
planningchoices.
choices.Similarly,
Similarly, we
we have
have presented
presented
information not just on the average responses to the attributes
information not just on the average responses to the attributes in the SPCM, in the SPCM, but but
alsoalso
de-
tailing
detailingthethe
preferences
preferences of specific segments
of specific segmentsof the
of likely useruser
the likely groups. Capturing
groups. thesethese
Capturing sub-
tleties of recreation
subtleties of recreationpreferences
preferences at the parkpark
at the planning
planningstage provides
stage nuanced
provides nuancedutility for
utility
park managers,
for park managers, particularly in deciding
particularly in decidingwhich features,
which and and
features, diversity of features,
diversity to pri-
of features, to
oritize. Examining these trade-offs in both a multi-attribute way
prioritize. Examining these trade-offs in both a multi-attribute way and by specific and by specific user
groups provides greater opportunity
opportunity to to create
create a suite
suite ofof ORV park features that meet the
preferences
preferences of of actual
actualdiverse
diverseusers
usersrather
ratherthan
thana ahypothetical
hypotheticalaverage
average ORVORV recreationist.
recreationist. In
In
thisthis way,
way, thethe planning
planning process
process hashas a greater
a greater chance
chance of success
of success financially
financially andand socially.
socially.
As depicted in Figure 5, the preferences of all respondents combined (i.e., the sample
average)
average) appear
appear nearnear the middle of each attribute column, column, but some user segments
segments varyvary
significantly from
from these
thesemidpoints.
midpoints.AApark parkdesigned
designedfor forthe
the“average
“average user”
user” might
might result
result in
a park
in a parkthat is less
that appealing
is less appealing to all
to users. Identifying
all users. Identifying differences among
differences useruser
among segments
segmentscan
can help planners to consider, and make informed decisions about, groups to whom the
Land 2022, 11, 1950 14 of 18
help planners to consider, and make informed decisions about, groups to whom the park
should be targeted. In this case, however, the user group that responded in the greatest
number, ATV users (n = 1124 or 60.4%), are very close to the average, and might therefore
be satisfied with a park designed to the average user’s preferences. However, here too,
caution must be taken when interpreting such results.
Naturally, the vehicle types with the highest number of responses will be closer to
the overall average, because of their influence on the average. However, we do not know
whether these respondents are truly the largest user group in the likely user population or if
some other factor could be responsible for the relatively high number of responses from this
segment. For instance, researchers were pleasantly surprised by the overall response rate of
52.9%, which was higher than expected for a relatively long and unsolicited questionnaire
sent to a random sample by mail.
When researchers reported study findings to the OCPR and DNR, a number of focus
group participants were present, as they are also part of a DNR ORV advisory group. When
we reported the high survey response rate, we learned that the various ORV clubs in the
state encouraged members to complete the survey if they received one. Many ORV users
belong to clubs that align with their vehicle type (e.g., clubs for motorcycle riders, jeeps,
or ATV users). These clubs were aware of the potential ORV park and therefore wanted
to influence the design of the park. It is possible that some of these clubs have higher
membership numbers, did a better job communicating about the survey to their members,
or more effectively harnessed related issues and enthusiasm into strong participation. If
so, these factors might explain higher numbers of respondents for certain ORV types. It
is equally possible that response rates per vehicle type segment match perfectly with the
population, but the caution in interpreting the results is similar to that of the focus groups.
Planning studies are an important tool in designing a park that will be enjoyed and used by
residents and visitors. However, if a particular vehicle user group is overrepresented in the
survey, results might skew toward that group, potentially influencing a park design that
favors a minority of users. This work spotlighted that planning consideration in nuanced
and actionable ways.
4.3. Unanticipated User Segmentations
Researchers decided to examine segments of ORV users based on vehicle type after the
focus groups revealed that preferences for the studied features and amenities vary by vehicle
type. This segmentation approach is further supported by the literature (e.g., [54,55]). As
results of the present study show, there were indeed preference differences based on vehicle
type. For example, motorcyclists had much stronger preferences for dedicated use trails and
side-by-side users showed stronger preferences for a high variety of park features.
Although the park has only been operational for a short time, and that entire time
within the COVID-19 pandemic, park managers have noted that the actual differences
in preferences seem to be based less on vehicle type and more on riding styles. For
example, some park users prefer sections that allow for unidirectional travel and higher
speed, whereas others prefer obstacles and off-camber sections that require a slower more
technical approach. Reports from park managers suggest these styles are not correlated to
vehicle type.
Park administrators also report that feedback from park users through focus groups,
evaluations, letters, and conversations have indicated that park users appreciate that
the park was designed for users who have different riding/driving styles. Many ORV
users visit the park with others (e.g., family members, friends, or ORV club members).
However, not all members of these groups have the same riding/driving style. In fact,
the park’s proximity to the Detroit metropolitan region, with its high population density,
almost ensures that groups visiting the park will have diversity of styles, abilities, and
commitment. Therefore, a park that accommodates different riding styles can allow group
members to use the park in the way that they most prefer while still socializing with a
diversity of others. So impactful was this observation, that when OCPR developed a new
Land 2022, 11, 1950 15 of 18
section of the park, it intentionally designed it so that users who have different styles of
riding/driving would have a better chance of encountering each other at various times
during their visit. Future studies that segment ORV users might consider doing so based
on riding/driving style preferences. Obviously, understanding more about user riding
style preferences would provide valuable insights to the initial design of an ORV park.
Similar to riding style, OCPR has realized the importance of the segmentation by
recreation specialization. It is common for experienced and committed participants in any
recreational activities, particularly ones in which club membership is common as with ORV
use, to invite less committed people into their activity. Therefore, if the park planners are
informed about preferences of different specialization levels and allow these preferences to
inform park design, groups visiting the park with diverse levels of recreation specialization
will find something for everyone. Again, positive feedback to OCPR from users indicates
that in addition to different riding styles, groups of people of different ability levels can all
find something to challenge themselves at this park.
4.4. Limitations
Although this study offers important contributions to park design (e.g., eliciting park
preferences from potential users, doing so before the design stage, gathering information
about specific attributes, and forcing tradeoffs among those attributes), it is important
to understand that the stated preference method is inherently a hypothetical experiment.
Participants do not have to demonstrate their valuation of park features (e.g., make actual
purchase decision), but merely choose from mixes of park attributes at certain prices
without financial consequences.
Because the study was related to the development of a potential park that would likely
be developed based on their responses, it is possible that responses were influenced by
strategic behavior. In fact, the relatively high response rate could be an indication that users
encouraged other like-minded users to complete the survey if they were sampled.
Additionally, despite using a segmentation approach to better understand differ-
ent groups of users (e.g., locals, ATV users, committed users), the study method treats
each group of respondents in those categories as a single population with homogeneous
preferences. However, users within each group likely have different preferences.
From the perspective of understanding ORV users, another major limitation of this
study is that is asked users specifically about their preferences for an ORV park. Since there
are few ORV parks, especially in the region where the study was conducted, users might
not have truly known their preferences if their experience has been limited to using ORVs
on farms, on the vast network of trails throughout northern Michigan, or on public lands
in the western US. Finally, as our sample was limited to people who had registered their
ORVs in Michigan, results of this study cannot be generalized to other regions.
5. Conclusions
In general, preemptively identifying preferences of potential users can help ensure a
more successful outcome for creating a park or any attraction—be it in Midwest region of
the US, or elsewhere. A “consumer-informed” approach to design is more likely to result
in a park that is used and reused, thereby reducing the risk to investors, planners, and host
communities of possible park locations.
This study can serve as a research model for other landscape and urban attractions
that are in the planning stages and offers insights to interpreting any research of consumers.
Using the stated preference choice model to inform the design of public capital projects
increases the likelihood of the researchers capturing the realistic preference trade-offs
inherent in such projects. Taking a segmentation approach to analysis can provide planners
with information to help them design an attraction to a thoughtfully targeted group, while
offsetting the risk of designing an attraction that is not fully embraced by any segment
because it was designed to a non-existent average user. Designing an attraction that
attracts and retains users will consequently lead to a more successful project, decreasing
Land 2022, 11, 1950 16 of 18
the demand on tax revenues to subsidize operating costs while increasing the likelihood of
support for future worthwhile projects.
Author Contributions: Conceptualization, D.M. and J.N.; funding acquisition, D.M. and J.N.; method-
ology, D.M. and J.S.; software, J.S. and D.M.; validation, J.S. and D.M.; formal analysis, J.S. and D.M.;
investigation, D.M. and J.S.; data curation, J.S.; writing—original draft preparation, D.M., T.A.I.,
E.E.P., J.S. and J.N.; writing—review and editing, D.M., E.E.P., T.A.I. and J.S.; project administration,
D.M.; All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by Oakland County Grant Number RC069750.
Institutional Review Board Statement: All subjects gave their informed consent for inclusion before
they participated in the study. The study was conducted in accordance with the Declaration of
Helsinki, and the protocol was approved by the Ethics Committee of Michigan State University
(Project identification code: IRB#X12-1176).
Data Availability Statement: Not applicable.
Acknowledgments: The authors thank Oakland Country Parks and Recreation and the Michigan
Department of Natural Resources for supporting this work and the thousands of ORV riders in
Michigan and the upper Midwest who completed the survey informing the eventual park design.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Buchecker, M. Withdrawal from the local public place: Understanding the process of spatial alienation. Landsc. Res. 2009,
34, 279–297. [CrossRef]
2. Komossa, F.; Wartmann, F.M.; Verburg, P.H. Expanding the toolbox: Assessing methods for local outdoor recreation planning.
Landsc. Urban Plan. 2021, 212, 104105. [CrossRef]
3. Plieninger, T.; Bieling, C.; Fagerholm, N.; Byg, A.; Hartel, T.; Hurley, P.; López-Santiago, C.A.; Nagabhatla, N.; Oteros-Rozas, E.;
Raymond, C.M.; et al. The role of cultural ecosystem services in landscape management and planning. Curr. Opin. Environ.
Sustain. 2015, 14, 28–33. [CrossRef]
4. Cecchini, M.; Zambon, I.; Pontrandolfi, A.; Turco, R.; Colantoni, A.; Mavrakis, A.; Salvati, L. Urban sprawl and the ‘olive’
landscape: Sustainable land management for ‘crisis’ cities. GeoJournal 2018, 84, 237–255. [CrossRef]
5. Colléony, A.; Prévot, A.C.; Jalme, M.S.; Clayton, S. What kind of landscape management can counteract the extinction of
experience? Landsc. Urban Plan. 2017, 159, 23–31. [CrossRef]
6. Rice, W.L.; Mateer, T.J.; Reigner, N.; Newman, P.; Lawhon, B.; Taff, B.D. Changes in recreational behaviors of outdoor enthusiasts
during the COVID-19 pandemic: Analysis across urban and rural communities. J. Urban Ecol. 2020, 6, juaa020. [CrossRef]
7. Powell, K.E.; Martin, L.M.; Chowdhury, P.P. Places to walk: Convenience and regular physical activity. Am. J. Public Health 2003,
93, 1519–1521. [CrossRef]
8. Samuelsson, K.; Barthel, S.; Colding, J.; Macassa, G.; Giusti, M. Urban Nature as a Source of Resilience during Social Distancing
Amidst the Coronavirus Pandemic. 2020. Available online: https://www.stockholmresilience.org/publications/publications/20
20-05-10-urban-nature-as-a-source-of-resilience-during-social-distancing-amidst-the-coronavirus-pandemic.html (accessed on
13 October 2021). [CrossRef]
9. Crompton, J.L. Evolution of the “parks as lungs” metaphor: Is it still relevant? World Leis. J. 2017, 59, 105–123. [CrossRef]
10. Dudley, N.; Stolton, S. Running Pure: The Importance of Forest Protected Areas to Drinking Water. World Bank/WWF Alliance for
Forest Conservation and Sustainable Use. 2003. Available online: https://openknowledge.worldbank.org/handle/10986/15006
(accessed on 13 October 2021).
11. Harnik, P.; Crompton, J.L. Measuring the total economic value of a park system to a community. Manag. Leis. 2014, 19, 188–211.
[CrossRef]
12. Crompton, J.L.; Nicholls, S. An assessment of tax revenues generated by homes proximate to a greenway. J. Park Recreat. Adm.
2006, 24, 103–108.
13. Jeong, J.Y.; Crompton, J.L.; Dudensing, R.M. The potential influence of researchers’ “hidden” procedure decisions on estimates of
visitor spending and economic impact. J. Travel Res. 2016, 55, 874–888. [CrossRef]
14. Poudyala, N.C.; Hodgesa, D.G.; Cordell, H.K. The role of natural resource amenities in attracting retirees: Implications for
economic growth policy. Ecol. Econ. 2007, 68, 240–248. [CrossRef]
15. Crompton, J.L.; Nicholls, S. The impact on property prices of the proportion of park-like space in the proximate area. World Leis. J.
2021, 63, 201–215. [CrossRef]
16. U.S. Census Bureau. Local Property Taxes as a Percentage of Total Local Tax Revenue. In Statistical Abstract of the United States;
U.S. Census Bureau, Ed.; Department of Commerce: Washington, DC, USA, 2016.
Land 2022, 11, 1950 17 of 18
17. Manning, R. Studies in Outdoor Recreation: Search and Research for Satisfaction, 3rd ed.; Oregon State University Press: Corvallis, OR,
USA, 2011.
18. Valck, J.D.; Broekx, S.; Liekens, I.; Nocker, L.D.; Orshoven, J.V.; Vranken, L. Contrasting collective preferences for outdoor
recreation and substitutability of nature areas using hot spot mapping. Landsc. Urban Plan. 2016, 151, 64–78. [CrossRef]
19. Slyke, D.M.V.; Hammonds, C.A. The privatization decision: Do public managers make a difference? Am. Rev. Public Adm. 2003,
33, 146–163. [CrossRef]
20. Lockwood, M.; Worboys, G.K.; Kothari, A. Managing Protected Areas: A Global Guide. IUCN World Commission on Protected Areas;
Earthscan: London, UK, 2006.
21. Brockhouse, J.W.; Wadsworth, J.J. Vital steps: A Cooperative Feasibility Study Guide, Service Report 58. Washington, DC:
U.S. Department of Agriculture Rural Business-Cooperative Service. 2010. Available online: https://www.rd.usda.gov/
publicationforcooperatives/sr-58-vital-steps-cooperative-feasibility-study-guide (accessed on 4 June 2018).
22. Liu, H.L.; Wu, I.C.; Caneday, L. Using a Feasibility Study as a Management Tool: A Case Study of Oklahoma State Park Lodges.
J. Park Recreat. Adm. 2018, 36, 174–190. [CrossRef]
23. Oh, C.O.; Ditton, R.B.; Gentner, B.; Riechers, R. A stated preference choice approach to understanding angler preferences for
management options. Hum. Dimens. Wildl. 2005, 10, 173–186. [CrossRef]
24. Schneller, A.J.; Binzen, G.L.; Cameron, C.; Vogel, S.T.; Bardin, I. Managing recreation in New York’s Adirondack Park: A case
study of public perceptions and preferences for reducing user impacts to the High Peaks Wilderness Complex. J. Park Recreat.
Adm. 2021, 39, 63–80. [CrossRef]
25. Jansson, M.; Persson, B. Playground planning and management: An evaluation of standard-influenced provision through user
needs. Urban For. Urban Green. 2010, 9, 33–42. [CrossRef]
26. Borden, A. Outdoor Recreation in the Northern United States and Projected Outlook to 2060; Nova Science Publishers, Inc.: New York,
NY, USA, 2014.
27. Smith, J.W.; Miller, A.B.; Lamborn, C.C.; Spernbauer, B.S.; Creany, N.; Richards, J.C.; Meyer, C.; Nesbitt, J.; Rempel, W.;
Wilkins, E.J.; et al. Motivation and spatial behavior of OHV recreationists: A case study from central Utah. J. Outdoor Recreat. Tour.
2021, 36, 100426. [CrossRef]
28. Bureau of Land Management. National Management Strategy for Motorized Off-Highway Vehicle Use on Public Lands. U.S.
Department of the Interior, Bureau of Land Management. 2001. Available online: https://www.ntc.blm.gov/krc/uploads/320/
National%20OHV%20Strategy.pdf (accessed on 7 January 2022).
29. Department of Natural Resources. Licenses and Permits Fee Report. Report prepared for Natural Resources Commission
Meeting. 2020. Available online: https://www.michigan.gov/documents/dnr/LicenseSalesReport_696421_7.pdf (accessed on
7 January 2022).
30. Nelson, C.M.; Stynes, D.J.; Wu, C.; McCarty, E.; Hughes, N. Michigan Licensed ORV Use and Users-2010; Technical Report for
Michigan Department of Natural Resources and Environment: East Lansing, MI, USA, 2010.
31. Brooker, J. Big land: How large land parcels create significant recreational opportunities for Michiganders. Model D. 2020.
Available online: https://www.modeldmedia.com/features/large-parcels-mnrtf-series-11.aspx (accessed on 10 January 2022).
32. Cavitt, M. Commissioners Approve $34 Million Budget for Oakland County Parks System; The Oakland Press: Troy, MI, USA, 2021;
Available online: https://www.theoaklandpress.com/2021/10/01/commissioners-approved-34-million-budget-for-oakland-
county-parks-system/ (accessed on 10 January 2022).
33. Hearne, R.R.; Salinas, Z.M. The use of choice experiments in the analysis of tourist preferences for ecotourism development in
Costa Rica. J. Environ. Manag. 2002, 65, 153–163. [CrossRef] [PubMed]
34. Oh, C.O.; Draper, J.; Dixon, A.W. Comparing resident and tourist preferences for public beach access and related amenities. Ocean
Coast. Manag. 2010, 53, 245–251. [CrossRef]
35. Louviere, J.J.; Hensher, D.A.; Swait, J.D. Stated Choice Methods: Analysis and Applications; Cambridge University Press: Cambridge,
UK, 2000.
36. McFadden, D. Conditional Logit Analysis of Qualitative Choice Behavior; University of California at Berkeley: Berkeley,
CA, USA, 1974.
37. Schroeder, H.W.; Louviere, J. Stated choice models for predicting the impact of user fees at public recreation sites. J. Leis. Res.
1999, 31, 300–324. [CrossRef]
38. Campagnaro, T.; Vecchiato, D.; Arnberger, A.; Celegato, R.; Re, R.D.; Rizzetto, R.; Semenzato, P.; Sitzia, T.; Tempesta, T.;
Cattaneo, D. General, stress relief and perceived safety preferences for green spaces in the historic city of Padua (Italy). Urban For.
Urban Green. 2020, 52, 126695. [CrossRef]
39. Burns, R.C.; Arnberger, A.; von Ruschkowski, E. Social carrying capacity challenges in parks, forests, and protected areas. Int. J.
Sociol. 2010, 40, 30–50. [CrossRef]
40. Arnberger, A.; Eder, R. The influence of age on recreational trail preferences of urban green-space visitors: A discrete choice
experiment with digitally calibrated images. J. Environ. Plan. Manag. 2011, 54, 891–908. [CrossRef]
41. Chen, C.F.; Chen, P.C. Exploring stated preferences of tourists for heritage tourism services: The case of Tainan City, Taiwan. Tour.
Econ. 2012, 18, 457–464. [CrossRef]
42. Dillman, D.A.; Smyth, J.D.; Christian, L.M. Internet, Mail and Mixed-Mode Surveys: The Tailored Design Method, 3rd ed.; John Wiley:
Hoboken, NJ, USA, 2009.
Land 2022, 11, 1950 18 of 18
43. Smith, J.W.; Burr, S.W.; Reiter, D.K. Specialization among off-highway vehicle owners and its relationship to environmental
worldviews and motivations. J. Park Recreat. Adm. 2010, 28, 57–73.
44. Scott, D.; Shafer, C.S. Recreational specialization: A critical look at the construct. J. Leis. Res. 2001, 33, 319–343. [CrossRef]
45. Sorice, M.G.; Oh, C.O.; Ditton, R.B. Exploring level of support for management restrictions using a self-classification measure of
recreation specialization. Leis. Sci. 2009, 31, 107–123. [CrossRef]
46. Kim, H.; Song, H. Measuring hiking specialization and identification of latent profiles of hikers. Landsc. Ecol. Eng. 2017, 13, 59–68.
[CrossRef]
47. Kerins, A.J.; Scott, D.; Shafer, C.S. Evaluating the efficacy of a self-classification measure of recreation specialization in the context
of ultimate frisbee. J. Park Recreat. Adm. 2007, 25, 1–22.
48. Needham, M.D.; Sprouse, L.J.; Grimm, K.E. Testing a self-classification measure of recreation specialization among anglers. Hum.
Dimens. Wildl. 2009, 14, 448–455. [CrossRef]
49. Street, D.J.; Burgess, L. The Construction of Optimal Stated Choice Experiments: Theory and Methods; Wiley-Interscience: Hoboken, NJ,
USA, 2007.
50. Hanemann, W.M. Welfare evaluations in contingent valuation experiments with discrete responses. Am. J. Agric. Econ. 1984,
66, 332–341. [CrossRef]
51. Bateman, I.J.; Carson, R.T.; Day, B.; Hanemann, M.; Hanley, N.; Hett, T.; Jones-Lee, M.; Loomes, G.; Mourato, S.; Ozdemiroglu, E.;
et al. Economic Valuation with Stated Preference Techniques: A Manual; Edward Elgar: Northampton, UK, 2002.
52. Blamey, R.; Gordon, J.; Chapman, R. Choice modelling: Assessing the environmental values of water supply options. Aust. J.
Agric. Resour. Econ. 1999, 43, 337–357. [CrossRef]
53. Bennett, J.; Adamowicz, V. Some Fundamentals of Environmental Choice Modeling. In The Choice Modeling Approach to Environ-
mental Valuation; Bennett, J., Blamey, R., Eds.; Edward Elgar: Northampton, MA, USA, 2001; pp. 37–69.
54. Bruyere, B.L.; Rodriguez, D.A.; Vaske, J.J. Enhancing importance-performance analysis through segmentation. J. Travel Tour. Mark.
2002, 12, 81–95. [CrossRef]
55. Ferguson, M.D.; Burns, R.C. Innovations in outdoor recreation visitor use management: Applying market segmentation at the
Timberline Lodge Recreation Complex. Int. Leis. Rev. 2018, 7, 108–131.