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ASSESSING GROWTH PREFERENCES ON THE RURAL-URBAN FRINGE
USING A DISCRETE CHOICE MODEL AND SPATIAL ANALYSIS METHODS
BY
LORRAINE R. STAMBERGER
THESIS
Submitted in partial fulfillment of the requirements
for the degree of Master of Science in Natural Resources and Environmental Sciences
in the Graduate College of the
University of Illinois at Urbana-Champaign, 2018
Urbana, Illinois
Master’s Committee:
Assistant Professor Carena J. van Riper
Professor William Stewart
Professor Amy Ando
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ABSTRACT
Landscapes on the rural-urban fringe are experiencing change and diversification, yet the
preferences of local residents for how these landscapes should develop is largely overlooked. My
thesis contributes to a growing body of research that explores residents’ preferences for
landscape change through two specific aims: (1) understand how preferences for growth are
influenced by multiple landscape-scale attributes, and (2) explore how these preferences are
distributed across spatial scales. My research takes place in two case study sites–Will County,
Illinois and Jasper County, Iowa–characterized as mixed-use landscapes with strong agrarian
roots, close proximity to large metropolitan centers, and conservation practices. I draw from
residential survey data to first determine how the impacts of land use and economic conditions
influence respondent choices for future growth scenarios using a discrete choice model that
includes six attributes: residential growth, protected grasslands, distance to recreation,
agriculture, bison presence, and unemployment. Next, using only the data collected from
respondents in Will County, I conducted posterior spatial tests to examine the spatial dependence
of preferences for these attributes at the individual household level. Global and local spatial
methods were applied together to understand overarching trends in spatial dependence and
regional clustering of preferences. My results showed that preferences varied within the sample
and across spatial scales. My research on the rural-urban fringe extends the geographic scope of
the choice modeling literature, and I look beyond preferences for a particular project or policy by
assessing landscape-scale attributes with implications for planning at a broader regional scale. At
the rural-urban fringe where change is inevitable, I help to pave the way for greater
understanding of stakeholder preferences and a more democratized planning process.
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ACKNOWLEDGEMENTS
This project would not be possible without funding provided by USDA National Institute
of Food and Agriculture (NIFA) and support from local partners involved in the project. A
special thanks goes out to the interviewees, focus group participants, and survey respondents in
both Will and Jasper Counties. Thank you for your participation and for sharing thoughtful
insights on the needs and visions for your local communities. I also acknowledge the many folks
that helped refine our survey instrument.
At this university, I have been fortunate to have worked with many wonderful people.
These include past and present graduate students – Eric, El, Kaitlyn, Katie, Vijay, Clint, Ben,
Nate, Dana, Elizabeth, Nikki, Doug, and John – and other faculty and staff – Max, Pushpendra,
and Sarah. Thank you for the laughs, mentoring, and encouragement along the way.
My committee members, Amy Ando, Bill Stewart, and Carena van Riper, have been
instrumental in arriving at this point. Amy has been a valuable sounding board for my analysis
ideas throughout the process. Bill’s boundless passion for the field has allowed many productive
and enjoyable conversations to ensue. Carena, my advisor, has provided me with many
wonderful opportunities – teaching several courses, winning a conference quiz bowl, and
spending a field season in Denali National Park just to name a few. But more importantly,
Carena has been a genuine and motivating mentor. I have learned so much from her, and am
grateful for her investment in my success throughout this program. I would also like to thank my
additional co-authors, Paul Gobster and Len Hunt, for their helpful reviews on previous
manuscript drafts.
Lastly, thank you to my other half, Rob, and our wild pup, Forrest, for your continued
moral support (Rob) and endless entertainment (Forrest).
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TABLE OF CONTENTS
CHAPTER 1: INTRODUCTION ................................................................................................... 1
1.1 Dynamics on the Rural-Urban Fringe ................................................................................... 1
1.2 Discrete Choice Experiments: Theory and design ................................................................ 3
1.3 Spatial Applications of Discrete Choice Experiments .......................................................... 5
1.4 Research Questions ............................................................................................................... 7
CHAPTER 2: ASSESSING PREFERENCES FOR GROWTH ON THE RURAL-URBAN
FRINGE USING A STATED CHOICE ANALYSIS .................................................................... 9
2.1 Abstract ................................................................................................................................. 9
2.2 Introduction ......................................................................................................................... 10
2.3 Background ......................................................................................................................... 12
2.4 Methods............................................................................................................................... 16
2.5 Results ................................................................................................................................. 25
2.6 Discussion ........................................................................................................................... 29
2.7 Conclusion .......................................................................................................................... 33
CHAPTER 3: ASSESSING SPATIAL PREFERENCE HETEROGENEITY IN A MIXED-USE
LANDSCAPE ............................................................................................................................... 35
3.1 Abstract ............................................................................................................................... 35
3.2 Introduction ......................................................................................................................... 36
3.3 Methods............................................................................................................................... 40
3.4 Results ................................................................................................................................. 48
3.5 Discussion ........................................................................................................................... 54
3.6 Conclusion .......................................................................................................................... 57
CHAPTER 4: CONCLUSION ..................................................................................................... 58
REFERENCES ............................................................................................................................. 61
APPENDIX A: WILL COUNTY SURVEY QUESTIONNAIRE ............................................... 77
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CHAPTER 1: INTRODUCTION
1.1 Dynamics on the Rural-Urban Fringe
The rural-urban fringe, also referred to as ‘exurbia’ or the ‘urban hinterland,’ is the place
where rural and urban landscapes meet. An early definition of this region, provided by Pryor
(1968), asserted that “the rural-urban fringe is the zone of transition...lying between the
continuously built-up urban and suburban areas of the central city and the rural hinterland” (p.
206). Researchers have since attempted to delineate this region in a variety of ways using
location, population density, zoning regulations, socio-demographics, and other variables to set
thresholds for defining the region (Butler & Beale, 1990; Lichter & Brown, 2011; Tali &
Nusrath, 2014). However, this area is difficult to define because it is a dynamic and fluid
landscape that does not exist in isolation and is part of a larger network (Albrecht, 2007; Iaquinta
& Drescher, 2000; Tali & Nusrath, 2014). Research in the context of these fringe locations has
overwhelmingly centered around growth and its impacts on rural areas (Sonya Salamon, 2003;
Smith & Sharp, 2005; Soini, Vaarala, & Pouta, 2012; Taylor, 2011; von Wirth, Gret-Ragamey,
Moser, & Stauffacher, 2016), but less is known of how residents perceive this growth and prefer
these regions to change and develop in the future (Slemp et al., 2012). Because the rural-urban
fringe is continuously in a state of flux and adjustment (Masuda & Garvin, 2008), researchers
should prioritize studying the dynamics of people and environments in this setting.
Growth on the rural-urban fringe is occurring at a rapid pace, bringing with it many
changes to the landscape and human communities. This trend has been evidenced in both the
United States (Slemp et al., 2012; Walker & Ryan, 2008) and other countries (Haregeweyn,
Fikadu, Tsunekawa, Tsubo, & Meshesha, 2012; Valencia-Sandoval, Flanders, & Kozak, 2010),
and continues to occur as metropolitan centers push outward and residents seek desirable
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locations to live. The movement of people is partly based on a locale’s natural, cultural, and
social amenities, known as “amenity migration” in the literature (Gosnell & Abrams, 2011). The
rural-urban fringe is sought after by prospective residents as a desirable location due to its
proximity to urban amenities and jobs while upholding a quasi-rural lifestyle (Smith & Krannich,
2000). As such, the fringes of many urban centers have witnessed unprecedented population
spikes. The American West, in particular, is one of the fastest growing regions in the U.S where
the region’s natural amenities, quaint downtowns, and proximity to larger metropolitan centers
have all been cited as reasons people migrate to these locations (Gosnell & Abrams, 2011;
Graber, 1974; Krannich, Petrzelka, & Brehm, 2006). While some evidence suggests a back-to-
the city movement in recent years (Hyra, 2015; Smith, 2005), growth on rural-urban fringe still
persists (Kasarda, Appold, Sweeney, & Sieff, 1997; Smith et al., 2017).
Multiple economic, social, and environmental changes stem from growth on the rural-
urban fringe. For example, these locations have witnessed economic restructuring in which new
industries (i.e., service and technology) have replaced old (i.e., farming and mining; Gosnell &
Abrams, 2011). The impacts of economic restructuring have been mixed as it can diversify and
strengthen the economic base (Strauser et al., 2018) but has potential to degrade local livelihoods
(Salamon, 2007). Gentrification processes have also occurred on the rural-urban fringe with land
prices increasing to the point of displacing long-term residents (Green, Marcouiller, Deller, &
Erkkila, 1996; Lichter & Brown, 2011). Concerning the social implications of growth on the
rural-urban fringe, it can mask unique, local identities but also has potential to bolster social
institutions, increase social learning through greater flow of ideas and cultures, and diversify the
demographic makeup of typically aging populations (Krannich et al., 2006; Salamon, 2007).
Lastly, growth can put pressure on important environmental resources (Haregeweyn et al., 2012).
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For example, Strauser et al. (2018) found that focus group participants viewed growth as a threat
to existing open spaces and shared that grasslands, in particular, would be increasingly important
for providing ecosystem services such as storm water management in the future. Given the
implications of growth on the rural-urban fringe, there is a strong need for understanding how
residents respond and react to growth in these rapidly changing regions.
1.2 Discrete Choice Experiments: Theory and design
Discrete choice experiments have been used to analyze individual preferences in a
diverse range of contexts and fields of study. McFadden's (1974) early application of the method
examined people’s choices for different modes of transportation. Later Louviere & Woodworth,
(1983) began estimating choice models from hypothetical and intended (stated) instead of
reported (revealed) behaviors. Since then, researchers in other fields, such as recreation and
leisure (Reichhart & Arnberger, 2010; Van Riper, Manning, Monz, & Goonan, 2011),
environmental science (L. M. Hunt, 2005; Ivanova & Rolfe, 2011), and planning (Audirac, 1999;
Rambonilaza & Dachary-Bernard, 2007; Sayadi, Gonzalez-Rosa, & Calatrava-Requena, 2009),
have used discrete choice experiments for understanding preferences for specific policies,
conditions, and environments. For example, recent applications have assessed ‘willingness-to-
pay’ for grassland restoration (Dissanayake & Ando, 2014), understood residential location
choices (Liao, Farber, & Ewing, 2015), and analyzed recreational trail preferences (Reichhart &
Arnberger, 2010), thus demonstrating the wide-ranging utility of discrete choice experiments.
Further, this method has been useful in its ability to predict support for goods or services that do
not yet exist which has important implications for policy-makers and managers, such as
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assessing the feasibility of future policies and projects (Audirac, 1999; Louviere, Hensher, &
Swait, 2000).
This well-established method captures preferences by instructing survey respondents to
choose between competing options or alternatives (Louviere et al., 2000). These alternatives are
defined by characteristics (or attributes) held at various levels, both specified by the researcher.
The choice behavior of respondents is understood through the theory of utility maximization
which posits that individuals make choices to maximize their utility (i.e., well-being; Ben-Akiva
& Lerman, 1985). Further, individuals are known to gain utility from the characteristics of the
alternatives rather than the alternatives themselves (Lancaster, 1966). These sets of alternatives
have been presented in survey instruments using a variety of formats. Researchers often use
tables that list each attribute level (Birol, Smale, & Gyovai, 2006; De Valck et al., 2014;
Espinosa-Goded, Barreiro-Hurlé, & Ruto, 2010), but have also represented alternatives using
visual images (Lizin, Brouwer, Liekens, & Broeckx, 2016) and written narratives (Hoehn, Lupi,
& Kaplowitz, 2010). Researchers have used a suite of logit models to analyze choice data,
operating under random utility theory which acknowledges researchers’ uncertainty in
understanding, measuring, and estimating utility for each individual (Manski, 1977; Thurstone,
1927). The ‘workhorse’ of choice models, the multinomial logit (MNL) model, has been
implemented widely by researchers (Louviere et al., 2000), but in recent decades, there has been
a shift to using more flexible models to analyze individual preferences (Hensher & Greene,
2003; Johnston et al., 2017; Train, 1998; Tu, Abildtrup, & Garcia, 2016).
The random parameter logit (RPL) model has been an important innovation for the
advancement of choice modeling research. The RPL model is superior to traditional choice
models in several ways. First, it allows for more complex error structures. Relaxing the
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independent and identically distributed (IID) assumption, the error component of RPL models
can be correlated with alternatives and multiple iterations (Greiner, Bliemer, & Ballweg, 2014;
Hensher & Greene, 2003). Second, the RPL model accounts for heterogeneity in preferences,
contrasting previous models that assumed fixed utility of coefficients across a sample (Bliemer
& Rose, 2013; Cadavid & Ando, 2013; Greiner et al., 2014). Because individuals are known to
possess differences in their stated choices, the model parameters are specified as random rather
than fixed, thus, exhibiting a mean and standard deviation. Third, specifying the parameters as
random allows for estimates to be drawn from each individual in the sample (Hensher, Rose, &
Greene, 2005; Train, 2009). These ‘individual-specific parameters’ are conditional on
individuals’ known choices and can be used as variables in further analyses (Vollmer, Ryffel,
Djaja, & Gret-Ragamey, 2016). While the RPL model identifies heterogeneity in a sample, it
does not point to the sources of heterogeneity ( Hunt, 2005; Mieno, Shoji, Aikoh, & Arnberger,
2016). Thus, other techniques are needed in combination with basic RPL models to explore both
how and why preferences differ.
1.3 Spatial Applications of Discrete Choice Experiments
Understanding preferences as they differ across space is a prominent and growing area in
the choice literature. Bockstael's (1996) instrumental work spurred choice modelers to focus
greater attention on accounting for spatial heterogeneity in choice experiments as a way of
understanding how the local landscape influences people’s preferences. Subsequently,
researchers have incorporated space in choice experiments at a variety of geographic scales,
ranging from analyzing differences between large regions (Martin-Ortega, Brouwer, Ojea, &
Berbel, 2012) to assessing differences among neighboring households (Campbell, Scarpa, &
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Hutchinson, 2008). To analyze preference heterogeneity at a broader geographic scale,
techniques such as segmentation by site (Liao et al., 2015; Martin-Ortega et al., 2012; Van Riper
et al., 2011) and interactions of site-specific variables have been used (Brouwer, Martin-Ortega,
& Berbel, 2010; Sayadi et al., 2009). In segmenting a sample, separate logit models are used to
better represent preferences of distinct regions. For example, Liao et al. (2015) used spatial
segmentation by county to analyze preferences for compact development on the Wasatch front in
Utah. Other studies have used interactions terms in one encompassing model to assess relative
preferences between sites. Brouwer et al. (2010) interacted specific regions of a river basin in
Spain with preference for water quality and found that local residents placed higher value on
water quality levels for their sub-basin than did non-residents.
Preferences have also been analyzed at the individual household level to better
understand localized patterns (Czajkowski, Budziński, Campbell, Giergiczny, & Hanley, 2017;
Johnston, Ramachandran, Schultz, Segerson, & Besedin, 2011; Meyerhoff, 2013; Yao et al.,
2014). To this end, researchers have connected individual-specific parameters to respondents’
address locations and applied this data using a variety of techniques (Johnston, Holland, & Yao,
2016). Vollmer et al. (2016), for example, analyzed how preferences for a river rehabilitation
project in Jakarta, Indonesia differed based on household location by plotting willingness-to-pay
(WTP) against a household’s distance from the river. Other researchers have used household-
level preferences as the dependent variable in regression models to understand how spatial
covariates can explain individual preferences (Abildtrup, Garcia, Boye Olsen, & Stenger, 2013;
Czajkowski et al., 2017; Yao et al., 2014). Another collection of studies implemented spatial
autocorrelation methods to examine local clustering of preferences within a study area (Johnston,
et al., 2011; Johnston, Jarvis, Wallmo, & Lew, 2015; Meyerhoff, 2013). Meyerhoff (2013), for
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example, explored how preferences for wind power varied across the southeast region of
Germany and found that clusters of low WTP for wind alternatives existed within the city of
Leipzig, indicating that urban dwellers were less resistant to wind power than their rural
counterparts. These studies improve our understanding of spatial heterogeneity in preferences,
which is especially relevant for landscapes that vary across space. However, no known study has
applied these methods to the rural-urban fringe where diverse landscapes and preferences are
well-documented. The core of my thesis aims to address this intellectual and empirical gap.
1.4 Research Questions
Preferences for landscapes are diverse and warrant a research approach that allows for
preferences to differ across individuals. There is a particular need to better understand how
preferences vary based on the location where individuals reside. Using discrete choice modeling,
this gap can be analyzed at a broad geographic scale by using interaction terms to distinguish
study site differences, or at a finer scale, by examining preferences at the individual household
level. I analyze and compare growth preferences of residents in two distinct study sites (Chapter
2), spatially examine the patterns of these preferences (Chapter 3), and then draw conclusions
from the results to guide future research and policy (Chapter 4). My thesis addresses the
following two research questions and corresponding objectives:
1. How do changing landscape and economic conditions in Will and Jasper Counties
influence residents’ choices for future growth scenarios?
a. Determine the effects of the six attributes–residential growth, protected
grasslands, recreation, agriculture, bison presence, and unemployment rates–on
preferences for future growth.
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b. Compare growth preferences between Jasper and Will County residents.
2. How do individual growth preferences vary across spatial scales in Will County?
a. Estimate resident’s preferences for the study attributes.
b. Assess the spatial dependence of individual preferences.
c. Analyze and map the local spatial patterns of preferences for the model attributes.
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CHAPTER 2: ASSESSING PREFERENCES FOR GROWTH ON THE RURAL-URBAN
FRINGE USING A STATED CHOICE ANALYSIS1
2.1 Abstract
Increasing the capacity of communities on the rural-urban fringe to accommodate
sustainable growth is a key concern among resource management agencies. Decisions about the
future of these landscapes involve difficult tradeoffs that underscore the importance of
incorporating diverse stakeholder values and preferences into planning efforts. We assessed
residents’ preferences for exurban growth alternatives in two Midwestern counties–Jasper
County, IA and Will County, IL–that have strong agrarian roots and lie at the fringe of rapidly
expanding metropolitan areas. A random parameters logit model was employed to better
understand how residents responded to different growth scenarios. Specifically, we identified
how six landscape characteristics influenced respondents’ stated choices for growth scenarios.
Informed by previous research, focus groups, and pilot testing, our final model evaluated
preferences for residential growth, protected grasslands, recreation, agriculture, bison
reintroduction, and unemployment. Results from a county-wide survey mailed to 3,000 residents
indicated that five of the six landscape-scale attributes significantly influenced residents’
choices. Residential growth, increases in protected grasslands and agriculture, and greater access
to recreation positively predicted choices for hypothetical growth scenarios while residents
preferred future scenarios with low levels of unemployment. Further, the strength of preferences
for these land use and economic conditions differed between Jasper and Will County residents.
The study findings aid decision makers who face growth and urbanization pressures and provide
insight on how to integrate preferences of current residents into planning decisions at a regional
scale.
1 Formatted for potential publication in Landscape and Urban Planning.
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Keywords: choice modeling; growth preferences; regional planning; stakeholder engagement;
exurbia, random parameters logit
2.2 Introduction
Growth in historically rural areas can take different forms but often happens in a rapid
and unplanned fashion. Areas along the fringes of large metropolitan centers, known as exurban
or peri-urban areas, are especially ripe for unplanned development due to their close proximity to
both urban amenities (i.e., city parks and public transit) and rural landscapes (Slemp et al., 2012).
While exurban growth can counter the decline of small towns that previously relied on farming
and other extractive industries (Krannich et al., 2006), it has potential to diminish natural
resource amenities (Albrecht, 2007), force long-term residents out through gentrification
(Gosnell & Abrams, 2011), and erase local symbols and identities (Tunnell, 2006). A variety of
public policy efforts at local, regional, and state scales have been implemented to manage
sprawling development patterns in the United States and other countries (Bengston, Fletcher, &
Nelson, 2004; Siedentop, Fina, & Krehl, 2016) and have been most successful when stakeholders
are involved in the planning process (Burby, 2003). Stakeholder participation for growth
management and open space protection increases the likelihood that policies will reflect local
values and conditions, facilitate a sense of ownership for community members, and minimize
social and land use conflicts (Burby, 2003). A stronger understanding of residential values and
preferences is especially critical for the rural-urban interface where landscape change is likely to
occur (Soini et al., 2012).
Previous research has underlined the importance of including stakeholders in the
decision-making process (Burby, 2003; Williams, Stewart, & Kruger, 2013). Stakeholders such
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as residents and business owners have been engaged using a variety of techniques, including
participatory mapping (Brown & Raymond, 2007; van Riper, Kyle, Sutton, Barnes, & Sherrouse,
2012), semi-structured interviews (Slemp et al., 2012; Valencia-Sandoval et al., 2010), public
forums and workshops (Burby, 2003), and stakeholder surveys (Dillman, Smyth, & Christian,
2014). Within survey research traditions, stated choice modeling has been used to understand
individual preferences for specific choice alternatives (Hensher, Rose, & Greene, 2005; Johnston
et al., 2017; Louviere et al., 2000). In a stated choice experiment, an individual is asked to
choose an alternative from set of alternatives that are described by relevant characteristics or
‘attributes.’ While a large body of work is dedicated to using choice data in tandem with
planning efforts (Audirac, 1999; Cadavid & Ando, 2013; Sayadi et al., 2009), there is a strong
need to understand tradeoffs among stakeholder preferences for future growth in the context of
urbanization, particularly in changing landscapes on the rural-urban fringe. Choice modeling, a
rapidly advancing field, shows promise for quantifying growth preferences across a diverse
sample of stakeholders in rural contexts.
This study assessed residents’ preferences for future growth scenarios in two Midwestern
U.S. counties–Jasper County, IA and Will County, IL–both of which face urbanization pressures
from rapidly expanding adjacent metropolitan areas while working to preserve their strong
agrarian roots. As both counties are historically situated within a prairie ecosystem, conserving
and restoring native grasslands is crucial for their ecological vitality. Grasslands provide a
multitude of ecosystem services (Dissanayake & Ando, 2014) and counter the ecological impacts
of development (Slemp et al., 2012). Similar to previous work (Dissanayake & Ando, 2014;
Greiner, Bliemer, & Ballweg, 2014; Nassauer, Dowdell, & Wang, 2011; Rambonilaza &
Dachary-Bernard, 2007; van Riper, Manning, Monz, & Goonan, 2011), we gauged preferences
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for conservation in future scenarios but distinguished between two relevant components of
Midwest prairie conservation–protected grasslands and bison reintroduction–as bison are integral
to the prairie ecosystem and are being reintroduced into the American Midwestern landscape.
Diverging from most planning-based stated choice experiments (see Arnberger & Eder, 2011 and
Bockstael, 1996 for exceptions), this study assessed landscape-scale preferences through the use
of attributes that reflected regional variation in land use and economic conditions. We also
transcended municipal boundaries to combat ‘leapfrog development’ and support landscape scale
decision-making (Bengston et al., 2004; Slemp et al., 2012). Our research on the desirability of
land uses and tradeoffs made by residents in two rural Midwestern counties contributes to an
increasingly important conversation on regional planning and growth in the face of change.
2.3 Background
Rural-Urban Landscape Trends
For the better part of the past century, many countries around the world have experienced
rapid and expansive urbanization in lands surrounding large metropolitan centers. In the U.S.,
most of the land-use conversion fueling this urbanizing trend is in the form of suburban
landscapes. Suburbs are markedly different than more established urban areas and their residents
are often characterized as car-dependent, affluent, and homogenous or ‘placeless’ (Jackson,
1985; Salamon, 2007). In these areas, residents benefit from natural amenities in the hinterland
while simultaneously maintaining employment in the city (Gosnell & Abrams, 2011). Given
improvements in transportation technologies and infrastructure, suburbs are able to expand at
rates that may be unsustainable for the region as a whole (Albrecht, 2007). Thus, as suburbs
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continue to experience growth in population and capital, cities and rural communities alike suffer
from out-migration and loss of unique identities.
Growth in historically rural areas brings with it numerous social, economic, and
environmental implications. While growth in rural areas can enhance human capital, boost local
organizations, and increase household incomes (see Lichter & Brown, 2011), it can strain
existing institutions and transform held cultures and traditions (Krannich et al., 2006). Referred
to as the rural ‘growth machine,’ a new and growing amenity-based economy is viewed as
inherently good by local leaders (Green et al., 1996; Kunstler, 1994). However, economic
consequences of the rural growth machine model include diminishing agricultural livelihoods,
increasing vulnerability to national business cycles, increasing lower wage service-based jobs,
and displacing long-term residents through gentrification (Krannich et al., 2006; Lichter &
Brown, 2011). Rapid land conversion processes also have adverse effects on environmental
conditions such as increased storm-water runoff, habitat fragmentation, and air pollution
(Haregeweyn et al., 2012). Concerted planning at a regional scale has potential to address the
range of challenges along the rural-urban fringe (Davis, Nelson, & Dueker, 1994), particularly if
coupled with environmental social science research, grassroots community forums, and other
mechanisms for eliciting stakeholder input to make decisions about preferences for the future.
Choice Modeling
Individual preferences can be evaluated using a statistical technique referred to as “choice
modeling.” Choice models were first developed to address transportation-related problems
involving individual preferences for private and public modes of travel (McFadden, 1974).
Typically, a stated choice model presents pairs of hypothetical alternatives and asks respondents
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to choose the most preferable alternative (Louviere et al., 2000). The alternatives alone do not
drive decisions, but rather the characteristics (or attributes) of the alternatives drive choices
(Lancaster, 1966). Attributes are often arranged in a series of levels that encompass a realistic
range of conditions found across a given study area. The researcher then assembles the attribute
levels into paired comparisons of alternatives (i.e., choice sets) using an experimental design.
These paired comparisons come in many different forms such as narratives describing the
alternatives (Hoehn et al., 2010), tables that list each attribute level (Cadavid & Ando, 2013), or
visual images illustrating different conditions (van Riper et al., 2011). To model choice data,
variations of the multinomial logit (MNL) regression model are most commonly used (Hensher
et al., 2005; McFadden, 1986). In particular, the random parameters logit (RPL) model has
gained traction in the stated choice literature due to its ability to account for heterogeneity in
preferences (Greiner et al., 2014; Hensher & Greene, 2003).
Choice Modeling and Planning
Stated preference models are commonly used in economics and marketing-based
applications, but they have also been incorporated into community planning research (Audirac,
1999; Dissanayake & Ando, 2014; Hunt & McMillan, 1994; Johnston, Swallow, & Bauer, 2002).
Researchers have investigated the types of future growth scenarios that residents desire. Audirac
(1999), for example, examined whether residents of Florida would be willing to trade off the
presence of a large yard for access to shared neighborhood amenities, while Bockstael (1996)
analyzed preferences in terms of access to city centers and desirable landscape features (i.e.,
waterfronts). Johnston et al. (2002) focused on specific types of land-uses by including protected
open spaces, residential development, and recreational facilities in an assessment of scenarios for
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future growth in rural Rhode Island. Results from this study indicated that residents favored
larger areas of preserved open space and smaller areas of developed land with lower housing
densities. Recreational facilities were favored by some but were also seen to impact preserved
natural areas (Johnston et al., 2002). Other researchers have focused on evaluating preferences
for specific planning efforts, such as municipal storm water management (Cadavid & Ando,
2013), wetland valuation (Mahan, Polasky, & Adams, 2000), and prairie restoration
(Dissanayake & Ando, 2014). This body of past research has indicated stated choice experiments
carry relevance for landscape and urban planning and can be useful tools for informing decisions
about growth and development.
A stated choice model was developed for this study to evaluate residents’ preferences for
changing landscape and economic conditions of Midwestern U.S. areas on the rural-urban fringe.
Specifically, we employed a stated choice experiment in a county-wide survey sent to residents
of Jasper County, Iowa and Will County, Illinois in Spring 2018. We were guided by two
objectives: 1) determine the effects of the study attributes–residential growth, protected
grasslands, recreation, agriculture, bison reintroduction, and unemployment–on preferences for
future growth; and 2) compare growth preferences between Jasper and Will County residents.
This study provides insight on stakeholder preferences for planning at the regional level, which
is rare in the stated choice literature. Given that planning decisions are often dominated by
elected leaders and developers (Green et al., 1996), understanding the growth preferences of
diverse stakeholders, including long-term residents and people in minority groups, will represent
less powerful voices and democratize planning at the intersection of rural and urban life.
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2.4 Methods
Study Context
Our two case study sites, Will County in Illinois and Jasper County in Iowa, are situated
near Midwestern U.S. metropolitan centers (see Figure 2.1). While both counties exhibit
similarities in terms of urbanization pressures, the two diverge in population dynamics and
economic conditions. Will County, located in the far southern part of the Chicago metropolitan
region, is the fourth most populous county in the state of Illinois (Data USA, 2018b). Its 700,000
residents are located primarily in the northern part of the county, which is characterized by
growing suburban and exurban landscapes. From 2000-2010, Will County experienced a 35%
population increase (U.S. Census Bureau, 2010). Several years ago, the county led the country in
the highest population shift, indicating the county experienced the biggest swing in the number
of people moving in and out of the area (Podmolik, 2015). Due to these episodes of rapid growth,
providing ample transportation infrastructure and amenities such as schools and emergency
services has been a challenge for Will County and local units of government. Economically, the
CenterPoint Intermodal Center has remained a regional hub, employing a segment of the county
in the transportation sector (4.3%) (Data USA, 2018b; “Will County Profile,” 2018).
Jasper County is located in central Iowa and home to 36,700 residents (Data USA,
2018a). While growth and landscape change in Jasper County has been less evident than Will
County, western portions of the county are experiencing urbanization pressures as the Des
Moines metropolitan area expands outward. In 2016, the Des Moines Metro was the fastest
growing area in the Midwest with a growth rate of 2% across a 12-month time period, outpacing
Fargo, ND (1.9%), Sioux Falls, SD (1.5%), and Madison, WI (1.3%) (Aschbrenner, 2017).
Recently, the economy in Jasper County has shifted as it recovers from a major industry,
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Maytag, pulling out of the area (Margolis, 2017). At the time this research was conducted,
employment was primarily driven by the manufacturing sector but also supported by occupations
in farming, fishing, and forestry (Data USA, 2018a).
Figure 2.1. Map of Jasper County, IA and Will County, IL in the context of urban sprawl
A better understanding of regional preferences for future growth is strongly needed in
landscapes spanning rural and urban contexts where land use change is widespread. Particularly
in Will and Jasper Counties, there are uneven growth patterns outside of adjacent metropolitan
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centers, and agricultural lands are rapidly being converted to new uses. Amidst land use change,
both counties have prioritized the protection of large tracts of land for conservation. Further,
federal properties within Jasper and Will Counties have initiated bison reintroduction in 1996
and 2015, respectively. Given changing socio-cultural, economic, and environmental conditions,
the future direction of Will and Jasper Counties could benefit from greater knowledge of their
residents’ preferences to promote growth that aligns with current interests. This context
motivated the present study to engage with county- and city-level planning to generate insights
on the preferences and tradeoffs residents were willing to make when considering their futures.
Survey and Choice Model Design
We developed an experimental design for a stated choice model. Mixed methods were
employed to engage stakeholders early on in the research process and build from qualitative and
quantitative data (Dissanayake & Ando, 2014; Greiner, Bliemer, & Ballweg, 2014; Johnston et
al., 2017; Ryan, Gerard, & Amaya-Amaya, 2008). The attributes of the choice model were
conceptualized through informal interviews (n = 20) and focus groups (two groups with eight
participants each) with community leaders, including planners, journalists, farmers,
conservationists, tourism professionals, and economic development representatives to identify
recent changes, key issues, and projected shifts in the region (Strauser et al., 2018). All
qualitative data were transcribed verbatim, thematically analyzed, and checked for inter-rater
reliability. This process maintained relevancy for local residents and ensured realistic attributes
were used in the experimental design (Johnston et al., 2002; Greiner et al., 2014). The focus
groups and past work (Bockstael, 1996; Johnston, Swallow, & Bauer, 2002; Nassauer, Dowdell,
& Wang, 2011) aided in formalizing six attributes–residential growth, protected grasslands,
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bison presence, recreation, agriculture, and unemployment–that characterized how growth might
occur in Will and Jasper Counties (see Table 2.1). Each attribute was assigned between three and
five levels that encompassed a realistic range of conditions. Illustrative icons were then created
to reflect the attributes and levels (Dissanayake & Ando, 2014; Johnston & Ramachandran,
2014) using Adobe Illustrator CC 2017 software.
Table 2.1. Choice model attributes and levels for the survey instrument
Attribute Description Levels
1. Residential growth rate The annual population growth in the county 2% decrease No growth
2% increase 4% increase 6% increase
2. Amount of projected grasslands
The percent change of county land designated as protected grasslands
No change 5% increase 10% increase
3. Amount of bison The percent change in total number of bison in the county
No change 3% increase 5% increase
4. Distance to recreation area
The distance to the nearest recreation area from the resident’s home
20 miles 7 miles 1 mile
5. Amount of agriculture The percentage of land in the county used for agricultural production
30% land 50% land 70 % land
6. Unemployment rate The percentage of people unemployed in the county
2% unemployed 4% unemployed 8% unemployed
In the model, respondents’ choices were regressed on the six attribute variables. The
hypothesized relationships among these attributes were guided by evidence from previous
research with focus group dialogues with community stakeholders (see Table 2.2). For the
pooled data sample, we predicted that increases in unemployment would negatively influence
choice and that the coefficients of all other attributes would be positive. Separate hypotheses
were developed for each study site because the coefficient signs were thought to differ slightly
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between Jasper and Will County respondents. Grasslands, in particular, were less salient in the
Will County focus groups. In the pooled sample, we predicted growth would positively influence
choices due to its potential to increase the county tax base. The second and third attributes,
grasslands and bison presence, were important for their ecological roles and attracting tourists,
especially for people outside of the two counties. Third, increased access to recreation was
positively regarded, because residents believed that these opportunities would make Jasper and
Will Counties more attractive to prospective residents. Fourth, stakeholders indicated the rural
qualities of each county, particularly related to a strong agricultural presence, were unique and
highly valued. Finally, we expected that low unemployment rates would be most desirable for
the future of these counties.
Table 2.2. Expected signs of variables in the model
Variable Pooled Sample Jasper County Will County
1. Growth + + + 2. Grasslands + + -
3. Bison + + + 4. Recreation + + + 5. Agriculture + + +
6. Unemployment - - -
The initial survey instrument and choice model were refined through two outlets prior to
data collection. First, the survey instrument was pre-tested with a convenience sample of
students, faculty, and staff at the host institution (n=8) following verbal protocol methods (Cahill
& Marion, 2007; Johnston et al., 2002). Next, the survey was pilot-tested at county fairs in Jasper
and Will Counties (n=120) using intercept sampling of adult fair-goers that were residents in the
two counties. Preliminary data provided insights on how best to revise the survey questionnaire
and were used to generate prior estimates necessary for producing an efficient design (Johnston
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et al., 2017; Louviere, Hensher, & Swait, 2000; Rose & Bliemer, 2013). Obtaining priors from
previous knowledge (i.e., literature and pilot testing) enabled us to create an optimal design that
minimized error (Arlinghaus, Beardmore, Riepe, Meyerhoff, & Pagel, 2014; Rose, Bliemer, &
Hensher, 2008).
After refining our survey, the final experimental design consisted of 18 choice sets, and
each respondent was asked to evaluate nine paired comparisons that were organized into two
survey blocks. The ordering of paired comparisons was reversed for each of the survey blocks to
minimize information order effects (Johnston et al., 2017). Figure 2.2 illustrates an example
question from the survey instrument that asked respondents to choose between two hypothetical
scenarios (A, B) or to opt-out (C) if neither scenario was acceptable. In line with previous
research (Dissanayake & Ando, 2014; Greiner et al., 2014; Peterson, Taylor, & Baudouin, 2015),
an opt-out or “no preference” choice was included in the model. The inclusion of a “no
preference” choice did not pressure respondents into choosing either scenario (Johnston et al.,
2017), and in doing so, maximized the fit of the model (Haaijer, Kamakura, & Wedel, 2001).
NGene 1.1.2 software was used to generate the experimental design.
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Figure 2.2. Sample choice question used in the survey instrument
Survey administration
Two county-wide, mail-back residential surveys were administered during spring 2018 to
a random sample of 1,500 residential addresses in each county. The survey was implemented
using an adaption of the ‘Tailored Design Method’ established by Dillman, Smyth, & Christian
(2014). There were five points of contact with residents over a three month period, including a 1)
hand-signed introductory letter endorsed by local partners, 2) questionnaire, 3) thank-you
reminder postcard, 4) second questionnaire to non-respondents, and 5) third questionnaire to the
remaining non-respondents. A cover letter and postage-paid return envelope were included in
each mailing. The survey process was administered by the Social and Economic Science
Research Center at Washington State University in cooperation with our institution. Monetary
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incentives were included in the first survey wave with the inclusion of $2 to increase the
likelihood of response (Edwards et al., 2007). A total of 967 surveys were collected from
residents in Jasper and Will Counties, with a response rate of 37.3% in Jasper County and 30.6%
in Will County.
Analysis Approach
Discrete choice experiments are analyzed using a variety of logit models whereby
individuals are presented with several alternatives and assumed to choose the alternative that
provides the greatest utility (Hensher & Greene, 2003; McFadden, 1978). The utility of a given
alternative includes both deterministic (i.e., observed) and stochastic (i.e., unobserved) parts
(Aas, Haider, & Hunt, 2000; Dissanayake & Ando, 2014). The multinomial (conditional) logit
model (MNL), known as the “workhorse” of choice models, shows the relationship among the
observed attributes of the choice scenarios, unobserved variables, and observed choice outcomes
(Hensher et al., 2005; Louviere et al., 2000). Though widely used, the MNL model has been
heavily critiqued on the basis of its restrictive assumptions (Johnston et al., 2017; Ryan et al.,
2008). Specifically, the model assumes that the attribute effects are consistent across a sample
population and uncertainty is identically and independently distributed (Bliemer & Rose, 2013).
In recent decades, there has been a shift towards implementing more flexible models to
predict stated preferences (Bliemer & Rose, 2013; Boxall & Adamowicz, 2002; Hensher, Rose,
& Greene, 2005; Johnston et al., 2017; Train, 1998). A mixed logit model is a generalized form
of all possible choice models (McFadden & Train, 2000). Within this group, the random
parameters logit (RPL) model allows uncertainty to be accommodated in the estimation of each
parameter as a random variable (Greiner et al., 2014; Hensher et al., 2005; Hunt, 2005). The
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distributions of these random parameters are commonly assumed to be normal but can account
for uniform, exponential, triangular, or other distributions as specified by the researcher (Bliemer
& Rose, 2013; Hensher et al., 2005). The RPL model offers significant advantages over a
traditional logit model, including the ability to account for (unobserved) preference heterogeneity
and more complex error structures (Greiner et al., 2014; Hensher et al., 2005). Though the RPL
model captures heterogeneity in preferences, it does not explain the variation among individuals
(Hensher et al., 2005; Hunt, 2005). Previous research has used unobserved segmentation (i.e.,
latent class analysis; Reichhart & Arnberger, 2010) and individual-specific variables (i.e.,
interaction of sociodemographic variables; Rambonilaza & Dachary-Bernard, 2007) to
understand potential sources of preference heterogeneity.
In the present study, the choice data were analyzed using a RPL model. Main effects and
main effects with interaction effects were calculated in two separate models using NLogit 6
statistical software. For the first model, all six parameters were specified as random with normal
distributions for the first two ‘unlabeled’ alternatives–Option A and Option B. A constant
represented the third no-preference alternative–Option C. Marginal willingness-to-accept higher
unemployment rates was also calculated to understand the tradeoffs residents were willing to
make between unemployment and land use goods and services. Though willingness-to-pay
(WTP) is a more common approach in choice experiments (see Sergio Colombo, Hanley, &
Louviere, 2009; Dissanayake & Ando, 2014; Train, 1998), the inclusion of county-wide
unemployment was more relevant than a price attribute tied to each future growth scenario. In
the second model, we incorporated a county-of-residence variable because we expected
differences in growth preferences between study sites. We specifically interacted a dummy-
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coded variable (Jasper County = 0 and Will County = 1) with each of the six parameters to
understand preference heterogeneity on the basis of county-of-residence.
2.5 Results
Descriptive Results
Of the 967 respondents that returned the survey, 888 completed some or all of the choice
questions. A majority of respondents chose to mail back the completed surveys (n=761; 85.60%)
while 128 (14.40%) completed the online version of the survey. Table 2.3 describes the sample
population’s socio-demographic characteristics. A slight majority of respondents self-identified
as female (54.50%). The survey captured a wide age range (min=18; max=104) with the average
age being 57.70 years (SD=15.50; SE=0.53). In terms of education and income, just over half of
respondents earned at least a college degree (51.10%), and respondents primarily reported annual
household income to be within the middle-class income brackets. The majority of respondents
racially identified as being White (89.80%), followed by black or African American (2.80%) and
Asian (2.70%). Household size averaged about three people, and respondents reported living in
their current home 17.7 years (SD=14.70; SE=0.50).
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Table 2.3. Respondent socio-demographic characteristics
Variable Mean (SD; SE) N (%)
Gender Female 467 (54.5) Male 388 (45.3) Age (years) 57.7 (15.5; 0.53) Education Some high school 26 (3.0) High school graduate 227 (26.0) Some college 174 (19.9) Two-year college degree 99 (11.3) Bachelor’s degree 164 (18.8) Some graduate school 52 (6.0) Graduate or professional degree 131 (15.0) Annual Household Income Less than $24,999 77 (9.3) $25,000 - $99,999 469 (56.8) $100,000 - $249,999 252 (30.6) $250,000 or more 27 (3.3) Race White 798 (89.8) Black or African American 25 (2.8) Asian 24 (2.7) Other 46 (5.2) Household Size Number of adults 1.9 (0.7; 0.02) Number of children 1.2 (1.4; 0.06) Years Lived In current home 17.7 (14.7; 0.50) In the county 29.2 (23.2; 0.80)
Choice Modeling Results
Responses to the multiple choice sets presented in the survey yielded 7,384 choice set
observations. Respondents who marked ‘Option C’ for all nine choice questions were identified
as ‘protest voters’ (n=50; 5.2%) and were removed from the analysis (Greiner et al., 2014;
Jürgen Meyerhoff, Bartczak, & Liebe, 2012). Two different models were estimated using the
RPL model to address our study objectives (see Table 2.4). In Model 1, choice (among Options
A, B, and C) was regressed on the six landscape-scale attributes. The impact of the attributes on
respondents’ choices is reflected in the coefficients, which showed the mean utility estimate
across the sample of respondents. All attribute coefficients, except for the bison attribute, were
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significant predictors of choice at the 99% confidence level. The probability of choosing an
alternative increased with higher rates of growth (β = 0.024), more grasslands (β = 0.045), closer
recreation areas (β = -0.060), and more land in agriculture (β = 0.031). The probability of
choosing an alternative significantly decreased with higher unemployment rates (β = -0.394).
The standard deviation of the parameter distributions showed that preference for residential
growth, access to recreation, agriculture, and unemployment exhibited significant heterogeneity
(p < 0.01).
Tradeoffs between unemployment and the land use attributes were understood through
residents’ willingness-to-accept higher unemployment rates for increases in a particular good or
service. Marginal willingness-to-accept unemployment showed that residents were willing-to-
accept higher unemployment rates for increased residential growth rates (0.06%), more land in
grasslands (0.11%) and agriculture (0.08%), and closer recreation areas (0.15%). Across the
range of levels we measured, respondents were most willing to accept higher unemployment
rates for increases in agricultural land such that respondents would tradeoff higher
unemployment rates by 3.20% to have land in agricultural production increase from 30% to 70%.
Similarly, respondents would tradeoff increased unemployment rates by 2.85% to have the
closest recreation areas change from 20 miles to one mile away from their place of residence.
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Table 2.4. Estimated random parameters logit (RPL) models
Variables MODEL 1: Attributes only MODEL 2: Including interactions
Coeff. (SE) SD (SE) Coeff. (SE) SD (SE)
Residential growth 0.024*** (0.009) 0.174*** (0.011) 0.167*** (0.027) 0.165*** (0.011) Protected Grasslands 0.045*** (0.010) 0.009 (0.026) 0.095*** (0.031) 0.023 (0.025) Distance to Recreation -0.060*** (0.003) 0.047*** (0.005) -0.024** (0.010) 0.049*** (0.005) Agriculture 0.031*** (0.002) 0.028*** (0.002) 0.061*** (0.005) 0.029*** (0.002) Bison 0.006 (0.005) 0.008 (0.013) -0.011 (0.015) 0.019 (0.012) Unemployment -0.394*** (0.016) 0.243*** (0.015) -0.467*** (0.043) 0.249*** (0.016) Constant -1.929*** (0.139) 2.858*** (0.136) 1.737*** (0.401) 2.453*** (0.117) Residential growth * Will Co.+ -0.096*** (0.017) N/A Protected Grasslands * Will Co. -0.032* (0.020) N/A Distance to Recreation * Will Co. -0.023*** (0.006) N/A Agriculture * Will Co. -0.020*** (0.003) N/A Bison * Will Co. 0.012 (0.009) N/A Unemployment * Will Co. 0.050* (0.027) N/A Constant * Will Co. 2.511*** (0.270) N/A
LL = -5,852; AIC = 11,732; N = 7,384; Pseudo R2 = 0.279
LL = -5,810; AIC = 11,663; N = 7,384; Pseudo R2 = 0.284
+ Dummy-coded site-specific variable where 0 = respondent from Jasper County and 1 = respondent from Will County Significance at 1% = ***, at 5% = **, and at 10% = * ++ LL=Log likelihood; AIC=Akaike information criterion
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Table 2.5. Marginal willingness-to-accept higher unemployment rates
Variable Marginal Willingness-to-Accept Unemployment
Residential growth 0.0609 Protected grasslands 0.1140 Distance to recreation -0.1523 Agriculture 0.0787 Bison --
In alignment with our second objective, Model 2 illustrated the differences in preferences
between Jasper and Will County residents by interacting a site-specific variable with the six
attributes (see Table 2.4). When interacting the dummy-coded Will County variable with each
attribute, differences based on county-of-residence emerged. As illustrated by the interaction
effects in Model 2, choices made by Will County residents were less influenced by growth (β = -
0.096), grasslands (β = -0.032), agriculture (β = -0.020), and unemployment (β = 0.050).
Conversely, recreation (β = -0.023) was a strong driver of choice indicating Will County
residents preferred closer recreation areas more than Jasper County residents. Similar patterns
emerged between Models 1 and 2 when considering the main effects of the attributes, in that five
of the six attributes were significant predictors of choice with the expected signs. Moreover, the
heterogeneity displayed in the protected grasslands parameter distribution was no longer present.
That is, when an individual’s county-of-residence was accounted for, the SD for grasslands
became non-significant.
2.6 Discussion
This study advanced knowledge of how residents in two rural Midwestern counties
envisioned the future and made tradeoffs between competing landscape conditions. Results from
a stated choice experiment presented growth preferences in relation to different landscape and
economic conditions. Residents in Jasper County, IA and Will County, IL responded favorably
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to hypothetical scenarios that included higher residential growth rates, more grassland areas
under protection, less distance between home and recreation areas, more land in agriculture, and
lower unemployment rates. Moreover, expanding existing bison herds did not significantly
influence respondent choices for future growth scenarios. Although the two study sites exhibited
similar characteristics (e.g., strong agricultural ties, emphasis on conservation, historic reliance
on industry), the attributes of our choice model were evaluated differently by residents in our two
case study sites. Generally, residents from Jasper County responded more positively to growth
than did residents in Will County.
Situating our results in the context of urbanization, this study offers insight on the
preferences reported by residents living in changing landscapes on the rural-urban fringe (Soini
et al., 2012). As both Jasper and Will Counties face pressure from adjacent, expanding
metropolitan centers, decision makers are increasingly challenged to respond to the needs of their
stakeholders. Our results showed that preferences for landscape change did not always align with
changes that often accompany urbanization. For example, urbanization is linked to less
dependence on agriculture (Krannich et al., 2006; Lichter & Brown, 2011), but respondents
preferred scenarios with more land in agriculture, regarding 70% of county land in agricultural
production as more preferable than 30% or 50%. Urbanization also has consequences for natural
environments such as grasslands, and similar to the work of Slemp et al. (2012), respondents
preferred more protection of these natural landscapes when envisioning the future of their
counties. In other words, respondents preferred both more agricultural land and natural
grasslands while at the same time preferred higher rates of residential growth.
Five out of six hypotheses for the pooled sample of respondents were supported. As
expected, respondents were more likely to choose scenarios with increased agriculture,
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grasslands, and access to recreation (see Table 2.2). Previous studies have similarly
demonstrated the desire for open space protection in communities experiencing growth and
development (Lokocz et al., 2011; Slemp et al., 2012). The effect of residential growth, also a
positive predictor of preferred growth scenarios, was in accordance with qualitative findings in
which leaders indicated a need to attract prospective residents to their county (Strauser et al.,
2018). Additionally, greater unemployment rates were not preferred in future scenarios. The one
hypothesis not supported by our findings was that residents would have positive value for growth
of bison herds. However, the bison attribute in our model was not statistically significant. It
could be that the perceived benefits derived from the presence of bison may have been captured
in the protected grasslands attribute. Alternately, county residents may have been unaware that
bison existed in their county and, as a result, disregarded this attribute when making choices. An
implication of this finding is for planners and managers at the two case study sites to raise
visibility of existing bison herds given potentially limited public awareness.
Our comparison between study sites indicated that Jasper County residents had stronger
preferences for growth than those from Will County. Because Will County has experienced rapid
growth in recent years, residents might be more hesitant toward development and land-use
changes than those living in Jasper. However, Will County residents responded strongly toward
changes in access to recreation, preferring greater access to recreation areas in the future. They
were more driven by this attribute than were Jasper County respondents. The amenity migration
literature suggests that recreation and green space are natural amenities that attract people to
places on the fringe of urban centers (i.e., Will County) where residents can benefit from both
urban and rural amenities (Gosnell & Abrams, 2011; Tu et al., 2016). With many residents living
on the rural-urban fringe, Will County may have a greater demand for and capacity to support
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recreation opportunities compared to its more rural counterpart, Jasper County. Finally, we
expected increases in protected grasslands to have a negative influence on Will County
respondents’ choices. While this effect was non-significant, grassland protection was
significantly less important for residents in Will County than in Jasper, suggesting that Will
County respondents were indifferent to grassland conservation when they envisioned their
futures.
The methodological approach we adopted to carry out the choice experiment produced
meaningful results. We inductively identified attributes for the experimental design (Dissanayake
& Ando, 2014; Johnston et al., 2017), followed by pilot testing with a representative sample to
strengthen the design (Louviere et al., 2000; Rose & Bliemer, 2013). Future studies
implementing choice experiments should strive to ground elements of the design (i.e., attributes
and levels) in site-specific contexts to maintain relevancy and credibility (Greiner et al., 2014).
In our quantitative assessment of the choice data, we expected differences in preferences across
our sample. Thus, a traditional model with fixed parameters estimates (i.e., multinomial logit)
was likely unsuitable. The random parameters logit (RPL) model accounted for respondent
heterogeneity and allowed parameter estimates to vary across individuals (Bliemer & Rose,
2013; Hunt, 2005). Though the RPL model was useful to assess preference heterogeneity, it did
not provide insights into the reasons why respondents’ preferences vary (Boxall & Adamowicz,
2002). We explained some preference heterogeneity using respondents’ county-of-residence;
however, other variables such as income (Rambonilaza & Dachary-Bernard, 2007), gender
(Hensher et al., 2005), age (Hunt, 2005), and distance to features (Dissanayake & Ando, 2014)
have been cited as sources of heterogeneity in landscape preferences.
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Opportunities for Future Research
Our research was limited in several ways and thus created opportunities for future
research. First, including an opt-out option was consistent with previous literature (Greiner et al.,
2014; Peterson et al., 2015); however, this research approach did not allow us to understand why
respondents opted-out instead of choosing a growth scenario. Further, the verbiage used for the
opt-out option - No Preference - was ambiguous, making it difficult to interpret in the results. In
the future, more specific wording (see Dissanayake & Ando, 2014) or inclusion of reference
attributes and levels (see Lizin, Brouwer, Liekens, & Broeckx, 2016) is recommended. A second
limitation was related to potential biases in our sample of respondents. For example, individuals
that did not respond to the survey might have had systematic differences in preferences than did
the respondents. Future research should evaluate non-response bias and continue the quest for
maintaining high response rates. Finally, we did not consider attributes that may have been
ignored by respondents when choosing between growth scenarios. This ‘attribute non-
attendance’ should be empirically assessed in future work by asking respondents to state which
attributes they did not consider when making choices (Greiner et al., 2014).
2.7 Conclusion
Choice experiments are a useful tool for understanding stakeholder preferences and
democratizing the planning process. We engaged residents in two Midwestern counties
experiencing land-use changes by implementing a choice experiment that represented local
concern identified during an earlier, qualitative phase of this research. The study attributes
represent local priorities that warrant attention from resource planning and management
agencies. We not only advance the stated choice modeling literature but also provide practical
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evidence for critically assessing the growth trajectory of rural communities in the coming years,
particularly around recreation, conservation, agriculture, and al opportunities alongside increases
in population. Unemployment rates are a particularly salient issue that should be carefully
considered in future communication about resource management. Our comparison between study
sites also illuminated preferences for growth in two different contexts, which broadened our
ability to generalize the findings of this research to other locales. As resources and lifestyles on
the rural-urban fringe continue to change, results from this research can be applied to enhance
regional scale plans for addressing growth challenges and inform strategies for stakeholder
involvement in decision-making.
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CHAPTER 3: ASSESSING SPATIAL PREFERENCE HETEROGENEITY IN A MIXED-
USE LANDSCAPE2
3.1 Abstract
Discrete choice experiments are a well-known method for analyzing landscape
preferences. Although people’s preferences are known to vary across space, this body of work
imposes strong assumptions regarding the spatial distribution of preferences, often operating
under spatial homogeneity. Thus, localized approaches are needed to better understand spatial
heterogeneity in preferences. I analyzed landscape preferences in an American Midwestern
county–Will County, Illinois–using residential surveys. Drawing from the results of a discrete
choice model, I obtained and geo-located individual-specific parameter estimates for select land
use and economic attributes of Will County–residential growth, protected grasslands, recreation,
agriculture, bison reintroduction, and unemployment rates. Subsequently, I used both global and
local spatial autocorrelation patterns to analyze the spatial relationships of the landscape
preferences. Results showed that preferences for all model attributes were heterogeneous within
the sample. Local spatial autocorrelation revealed local clustering of high and low preferences,
especially apparent in the agriculture and residential growth attributes. This study gives insight
on how location of residence relates to stakeholder preferences for landscape attributes and
provides management implications for county leaders and resource managers tasked with
allocating resources for diverse and changing landscapes.
Keywords: discrete choice experiment, Moran’s I, growth preferences, hotspot analysis,
preference heterogeneity, spatial autocorrelation
2 Formatted in line with the requirements of the target journal, Land Use Policy.
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3.2 Introduction
People’s preferences for landscapes are complex and vary based on an array of factors
that range from internal (i.e., biological needs) to external processes (i.e., previous experiences)
(Abildtrup et al., 2013; Arnberger & Eder, 2011; Boxall & Adamowicz, 2002). In particular,
spatial patterns in the landscape have been found to influence preferences for landscape
characteristics and benefits (Plieninger, Dijks, Oteros-Rozas, & Bieling, 2013; Schläpfer &
Hanley, 2003). In line with the idea of transactionalism (Zube, Sell, & Taylor, 1982), people
influence their environments, and in turn, environments influence people over time. Given that
individual preferences are influenced by the local landscape (Bockstael, 1996; Schläpfer &
Hanley, 2003), approaches that capture spatial preference heterogeneity are essential for an
assessment of landscape preferences. Yet, few studies address this gap (Bateman, Jones, Lovett,
Lake, & Day, 2002). Understanding landscape preferences and how they vary is important for
engaging policy makers and program administrators as well as advancing analytical techniques
used to capture public views on planning and management.
One tool that has been previously used to address the research gap noted above is discrete
choice modeling because it is a well-researched method for capturing individual preferences
(Louviere et al., 2000). Choice experiments were originally developed by economists to allow
researchers to understand people’s preferences for competing options (McFadden, 1986). Recent
advances in choice modeling have accommodated differences in individual preferences, known
as ‘preference heterogeneity,’ which identifies variation within a sample (Hensher & Greene,
2003; Sagebiel, Glenk, & Meyerhoff, 2017; Train, 1998). Although choice experiments have
effectively modeled heterogeneity in landscape preferences (Arnberger & Eder, 2011; Sayadi et
al., 2009), less attention has been dedicated to understanding spatial heterogeneity (Bateman et
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al., 2002; Campbell et al., 2008). This is problematic because operating under preference
homogeneity overlooks how the local landscape influences people’s preferences (Bockstael,
1996; Schläpfer & Hanley, 2003). Discrete choice models that incorporate spatial relationships
can reveal localized patterns that are otherwise invisible (Johnston & Ramachandran, 2014;
Meyerhoff, 2013). These patterns have potential to uncover local clusters of high (low)
preferences within a spatial unit, such as a municipality or county, and to further a broader
understanding of how local landscapes influence preferences.
Spatial heterogeneity has been accounted for in discrete choice experiments through a
range of techniques, but most often, interaction terms have been applied to choice models to
reveal how spatial relationships influence decisions (Broch, Strange, Jacobsen, & Wilson, 2013;
Brouwer et al., 2010; Liao et al., 2015; Schaafsma, Brouwer, & Rose, 2012). For example, Broch
et al. (2013) studied how farmers’ preferences for improving ecosystem services on their land
through afforestation were influenced by spatial variables (e.g., forest cover). These authors
found that two spatial interaction effects were significant, in that population density negatively
influenced farmers’ willingness to provide recreation services while presence of hunting
increased the level of compensation farmers were willing to accept for agreeing to an
afforestation contract (Broch et al., 2013).
The integration of spatial variables in discrete choice models has also been applied to
understanding preferences as a function of distance to an asset such as recreation sites
(Schaafsma et al., 2012), restored grasslands (Dissanayake & Ando, 2014), and wetlands
(Bateman, Day, Georgiou, & Lake, 2006). Previous research has demonstrated the importance of
‘distance decay’ which describes how preferences decrease with increases in distance (Brouwer
et al., 2010; Dissanayake & Ando, 2014; J Meyerhoff, 2013). In other words, respondents tend to
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place more value on conditions within close proximity to their place of residence. Often, distance
is interacted with respondents’ willingness-to-pay (WTP), which evaluates the amount that an
individual would pay for a public good or service (Hanemann, 1991). Bateman et al. (2006), for
example, conducted two case studies and found significant distance-decay in respondents’
willingness-to-pay for preserving wetlands and improving river conditions. While spatial
interaction terms in choice experiments can provide valuable information on how preferences
vary across space, generating these aggregate effects for the entire study area is not sufficient for
evaluating preferences that exhibit patchiness or clustering patterns (Campbell et al., 2008;
Schaafsma et al., 2012).
Knowledge of preferences at the individual-level, rather than in aggregate, has paved the
way to an expanding literature in spatial econometrics (Abildtrup et al., 2013; Campbell et al.,
2009, 2008; Czajkowski et al., 2017; Johnston et al., 2015; Johnston & Ramachandran, 2014;
Meyerhoff, 2013; Vollmer, Ryffel, Djaja, & Gret-Ragamey, 2016; Yao et al., 2014). Researchers
have utilized individual-specific outputs from logit models in various posterior analyses to
capture preference heterogeneity (Train, 1998). Specifically, individual-specific estimates have
been used in second-stage regression analyses to understand spatial factors contributing to
preferences (Abildtrup et al., 2013; Czajkowski et al., 2017; Yao et al., 2014) and in exploratory
spatial analyses testing the spatial dependence of preferences within a distinct study area
(Campbell et al., 2009, 2008; Czajkowski et al., 2017; Johnston et al., 2015; Johnston &
Ramachandran, 2014; Meyerhoff, 2013). The latter collection of studies has provided insight on
how individual preferences, based on location of residence, vary across space. Campbell et al.
(2008), in particular, assessed how preferences for landscape improvements were distributed in
Ireland. However, few discrete choice experiments have incorporated the spatial dynamics of
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preferences and their influences despite the benefits that would emerge from recognizing the
inherent spatial arrangement of landscape patterns.
This study analyzed preferences for land use and economic conditions in an American
Midwestern county that has been historically dominated by agriculture but increasingly
accommodates other industries (e.g., manufacturing) and land uses. Given the county’s diverse
landscape, preferences were expected to vary within the county. I developed a discrete choice
experiment that allowed for preference heterogeneity using a random parameters logit model.
Similar to previous work (Campbell et al., 2009; Czajkowski et al., 2017), I explored spatial
heterogeneity in a posterior test that analyzed the spatial dependence of individuals’ preferences.
Diverging from most spatially-explicit choice experiments, I assessed preferences at a local
spatial scale (see Johnston, Jarvis, Wallmo, & Lew, 2015; Johnston & Ramachandran, 2014;
Meyerhoff, 2013 for exceptions). Therefore, I was able to identify spatial regional trends, local
outliers of preferences, and the locations where preferences were particularly high (or low). I
hypothesized that individuals living in close proximity would be more similar than those living
further away (Tobler’s first law of geography; Tobler, 1970), and that preferences would exhibit
local clustering.
Study Objectives
This study used discrete choice modeling and spatial analysis methods to examine
heterogeneity of landscape preferences in Will County, Illinois. Specifically, preferences were
analyzed using a random parameters logit model as well as both global and local spatial
autocorrelation tests. Three objectives directed my research: 1) estimate residents’ preferences
for county-wide landscape characteristics (referred to herein as “attributes”); 2) assess the spatial
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dependence of individual preferences; and 3) analyze and map the local spatial patterns of
preferences for the model attributes. This is one of the few studies (e.g., Johnston, Jarvis,
Wallmo, & Lew, 2015; Johnston & Ramachandran, 2014; Meyerhoff, 2013) to test local spatial
autocorrelation using data from a discrete choice experiment. Moreover, my research advances
the discrete choice literature by illustrating heterogeneity in preferences for future landscape
scenarios and providing meaningful outcomes for decision-makers (i.e., targeting policy efforts)
working in mixed-use contexts.
3.3 Methods
Study Site
Will County is Illinois’s fourth most populous county and is situated within the Chicago
Metropolitan Statistical Area (see Fig. 3.1; “Will County, IL,” 2018). This site is a mixed-use
landscape; it is a productive agricultural region, transportation hub, economic engine, and place
for many valuable recreation and conservation activities (Strauser et al., 2018). A majority of the
county’s population and opportunities for employment are located in the northern region while
the south is largely characterized by agrarian land use practices (Chicoine, 1981). In 2012, farms
occupied approximately 43% of Will County and were mostly dedicated to growing field corn,
followed by soybeans and foraging crops such as hay (“2012 State and County Profiles,” 2018).
Due to its close proximity to Chicago and central location in the Midwest, Will County is an
important transportation center for the region. The county boasts multiple interstate systems,
well-developed rail lines, a national intermodal transportation facility, and an active river route
and continues to be at the forefront of the transportation industry, as evidenced by prolonged
plans for a new interstate system and major airport. Though these two projects could stimulate
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employment opportunities, they have experienced opposition, primarily from rural residents who
have expressed skepticism of the benefits for local residents (Dolan, 2018; Steele, 2016).
Amidst the residential areas and industry presence, a patchwork of protected areas exist
in the county. The largest green space is Midewin National Tallgrass Prairie, which encompasses
over 72 square kilometers in the southwest region of the county. Managed by the US Forest
Service since 1996, the protected area embodies the idea of ‘multiple-use and sustained yield’
(Multiple Use Sustained-Yield Act of 1960) as it contains recreational trails, a restored tallgrass
prairie system, existing infrastructure from the land’s previous use as a federal arsenal, and a
bison herd on 1,200 acres of the prairie. Bison were reintroduced in 2015 and have increased
visitation threefold since their arrival (Lafferty, 2016). In addition to Midewin, the Will County
Forest Preserve District manages over 21,000 acres with one-third of the land in active
restoration (“Land Management,” 2017). Will County also has an extensive trail system that
includes the historic Illinois and Michigan (I&M) Canal Trail and the 23-mile long Wauponsee
Glacial Trail. These sites are increasingly important for providing recreational opportunities and
ensuring conservation of natural resources as the county continues to grow and develop.
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Figure 3.1. Land cover in Will County, Illinois
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Choice Modeling Experiment
The design of the choice experiment was guided by mixed methods through semi-
structured interviews and groups (see Strauser et al. (2018) for overview). After the scope of the
choice model was developed, pilot testing using a verbal protocol assessment (Cahill & Marion,
2007) and full administration of the survey to refine the survey questions and choice model
design (Campbell et al., 2009; Dissanayake & Ando, 2014; Garrod, Ruto, Willis, & Powe, 2012;
Greiner et al., 2014). The choice design was developed using NGene 1.1.2 software which used
priors from pilot testing to create an optimal design (Greiner et al., 2014; Wang & Swallow,
2016). My final choice design included 18 choice questions, blocked into two different survey
versions. Thus, each survey respondents evaluated nine choice questions. The six attributes were
Residential Growth, Protected Grasslands, Bison Presence, Distance to Recreation, Agriculture,
and Unemployment Rate, each of which had three or five levels that represented the range of
conditions that survey respondents may encounter in the study area (see Table 3.1). Responses
were collected through a mail-back survey that tasked respondents with choosing their preferred
scenario among two experimentally designed options and an ‘opt-out’ option (see Fig. 3.2).
Survey respondents were identified using a random address-based sampling approach and
surveyed using an adaption of the ‘Tailored Design Method’ (Dillman et al., 2014). Survey were
sent to 1,500 addresses in Will County. Individuals also had the option to respond to an online
version of the survey administered via Qualtrics.
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Table 3.1. Choice model attributes and levels for the survey instrument
Attribute Description Levels
1. Residential growth rate The annual population growth in the county 2% decrease No growth
2% increase 4% increase 6% increase
2. Amount of projected grasslands
The percent change of county land designated as protected grasslands
No change 5% increase 10% increase
3. Amount of bison The percent change in total number of bison in the county
No change 3% increase 5% increase
4. Distance to recreation area
The distance to the nearest recreation area from the resident’s home
20 miles 7 miles 1 mile
5. Amount of agriculture The percentage of land in the county used for agricultural production
30% land 50% land 70 % land
6. Unemployment rate The percentage of people unemployed in the county
2% unemployed 4% unemployed 8% unemployed
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Figure 3.2. Example choice question from the survey
Discrete choice experiments are guided by random utility theory which assumes that
individuals under identical conditions make different choices to maximize personal benefits
(Thurstone, 1927). Multinomial logit (MNL) models have been used most often to capture stated
choices (Brouwer et al., 2010; Louviere et al., 2000), though an increasing number of studies
have adopted more flexible models such as the random parameters logit (RPL) model (Brouwer
et al., 2010; Sergio Colombo et al., 2009; Hensher & Greene, 2003). The RPL model is preferred
to traditional approaches because it accounts for heterogeneity across individual preferences and
operates under less restrictive assumptions (Bliemer & Rose, 2013; Campbell et al., 2009;
Hensher & Greene, 2003; Train, 1998). Because individuals have different tastes and
preferences, not accounting for heterogeneity in a choice model can lead to biased results (Boxall
& Adamowicz, 2002). In allowing parameters to vary across individuals, each random parameter
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has a distribution with mean and standard deviation values (Bliemer & Rose, 2013). A random
parameters logit model was employed in the present study to analyze the choice dataset using
NLogit 6 software. All study attributes were specified as random and followed normal
distributions (Johnston & Ramachandran, 2014; Sagebiel, Glenk, & Meyerhoff, 2017).
Another benefit of the random parameters logit model is its ability to estimate parameters
specific to each person in a sample. Individual-specific parameters are based upon the mean
parameter of a subgroup of individuals who chose the same option when faced with the same
choice set (Hensher et al., 2005; Train, 2009; Vollmer et al., 2016). These estimates are
conditional on individuals’ known choices and become increasingly accurate with a larger
number of choice questions (Johnston, Jarvis, Wallmo, & Lew, 2015). Empirically, individual-
specific parameters from RPL models are used to understand the shape of a parameter
distribution or to conduct posterior tests (Scarpa, Willis, & Acutt, 2005; Vollmer et al., 2016).
Using the latter approach, we derived individual-specific parameters for all six attributes to test
for spatial dependence of landscape preferences. Parameters were mapped in ArcMap 10.6
software based on geocoded respondent addresses (Johnston, Holland, & Yao, 2016; Meyerhoff,
2013). Respondents’ preferences were connected to their place of residence.
Global and Local Spatial Autocorrelation
Spatial autocorrelation methods were used to assess variation in landscape preferences
across Will County. This method measures the relationship between a variable and itself across
space. Similar to Campbell, Scarpa, & Hutchinson (2008), Johnston & Ramachandran (2014),
and others, I did not explain spatial variation in preferences but, rather, evaluated preferences in
a univariate exploratory analysis. In this study, I used both global and local methods to identify
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spatial clustering and dispersion of individual-specific parameters. Global methods apply across
the study area while local methods depict trends around each observation in space (Fotheringham
& Brunsdon, 2010). I used univariate Moran’s I and Gi* analysis to analyze global and local
spatial autocorrelation, respectively. Global Moran’s I is the correlation of a value at location n
and its neighboring values. A Moran’s I value above zero indicates positive spatial
autocorrelation in which similar values are clustered together while a value below zero specifies
negative autocorrelation where nearby locations have dissimilar values (Campbell et al., 2008;
Haining, 2003).
In addition to analyzing global spatial autocorrelation of the individual-specific
parameters, local trends were analyzed. Gi*, a common indicator of local spatial autocorrelation,
was used to identify clusters of significantly low and high preference (Getis & Ord, 1992). In this
analysis, the average value for clusters of observations, that is, observation n and its neighbors,
was compared to the global average. Significant clusters existed if the local average was
significantly different from the overall sample (Johnston & Ramachandran, 2014). Both spatial
autocorrelation tests were completed using GeoDa spatial software (Anselin, Syabri, & Kho,
2006). Local ‘neighbors’ were defined using a spatial weights matrix, known as the ‘spatial lag.’
A queen contiguity first-order spatial weights matrix was used for the spatial analyses in this
study, and Monte Carlo methods were used for significance testing. The inference process
randomly re-assigned the values (i.e., individual-specific parameters) among the points (i.e.,
respondent households). This process was repeated 99,999 times to create a reference
distribution, and the actual Moran’s I and Gi* values were compared with the reference
distributions to understand how much different the spatial dependence of preferences was from
random. These values were generated for each of the six landscape attributes.
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3.4 Results
Random address-based sampling yielded 440 survey responses (30.6% response rate)
from residents in Will County, Illinois. Of those surveys, 386 were used in our analysis after
removing incomplete data (n=37) and ‘protest votes’ (n=17; Greiner, Bliemer, & Ballweg,
2014). Over 80% of respondents returned a mail-back version of the survey compared to 19%
who chose the online option. Upon geocoding the respondents’ address locations, we found the
majority of respondents were from the northern part of the county. Also, respondent lived in
different environments. Over half of respondents resided in developed regions characterized by
low intensity development, followed by respondents living on medium-intensity developed land
(see Table 3.2).
Table 3.2. Respondent residential locations and land cover type (N=386) Land cover type1 N %
Developed, Open Space 13 3.4 Developed, Low Intensity 197 51.0 Developed, Medium Intensity 154 39.9 Developed, High Intensity 10 2.6 Deciduous Forest 1 0.3 Mixed Forest 1 0.3 Herbaceous 3 0.8 Cultivated Crops 4 1.0 Woody Wetlands 3 0.8 1National Land Cover Database (2011)
Background information was collected from Will County residents. The mean age of
respondents was 56.2 years (SD=14.7), ranging from 18 to 93 years old. The average household
size was two adults (SD=0.8) and one child (SD=1.3). A slight majority of respondents identified
as female (52.5%) and the majority identified as White (78%). Seventy-three percent of
respondents reported having completed at least some college, and the largest group of
respondents reported their yearly household income as $50,000-$99,999. On average,
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respondents had lived in their current home for 16.7 years (SD=13.9) and in Will County for 19.3
years (SD=18.4). The majority of respondents stated they were currently employed (65.8%) with
education being the greatest employment sector for respondents (17%). In the first page of the
survey, respondents were asked about their knowledge on the attributes used in the choice model
with a five-point Likert Scale. Overall, knowledge of the attributes in Will County was low, but
respondents reported highest levels of knowledge on residential growth (M=2.3; SD=1.3),
followed by recreation and tourism (M=2.2; SD=1.2) and protected grasslands (M=2.0; SD=1.1).
Preferences for Landscape Scenarios
In line with Objective 1, I employed a random parameters model, revealing a range of
landscape preferences in Will County, IL (see Table 3.3). Results from the choice experiment
were generated from 3,421 choice set observations, and the McFadden’s pseudo R2 value of
0.306 indicated a well-fitting model (Hensher & Johnson, 1981). All six model attributes were
significantly different from zero (p<0.10) and had varying effects on the dependent variable of
“choice” (see Table 3.3). Respondent choices were negatively driven by higher Residential
Growth and Unemployment Rates while increases in Protected Grasslands, shorter Distances to
Recreational Areas, more land in Agriculture, and greater Bison Presence increased the
likelihood of a respondent choosing a given scenario. The model attributes also exhibited
significant standard deviations of the random parameter distributions, indicating that preference
heterogeneity existed among all parameters.
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Table 3.3. The mean and spread of the six random parameters from the random parameters logit
model (N=386) Attributes Coefficients (st. err.) Std. Deviation (st. err.)
Residential Growth -0.031** (0.013) 0.181*** (0.017) Protected Grasslands 0.035** (0.015) 0.059** (0.027) Distance to Recreation -0.072*** (0.005) 0.060*** (0.007) Agriculture 0.023*** (0.003) 0.029*** (0.003) Bison Presence 0.013* (0.007) 0.028* (0.015) Unemployment Rate -0.374*** (0.023) 0.250*** (0.025) Constant -3.762*** (0.245) 2.883*** (0.200)
Log-likelihood = -2,609; Akaike information criterion (AIC) = 5,246; N = 3,421; Pseudo R2 = 0.306
*** = p<0.0001, ** = p<0.05, * = p<0.10 Spatial Dependence of Preferences
To understand the preference heterogeneity that existed among individuals in Will
County, I spatially located individual-specific parameter values based on respondents’ location
of residence, as indicated by Objective 2 of the study. A global spatial autocorrelation
assessment of the geocoded parameters revealed overall spatial dependence for each attribute
(see Table 3.4). Based on the Moran’s I statistics of individual preferences, there was a low
degrees of spatial dependence among the parameters. Further, patterns of spatial dependence
neither trended towards positive or negative spatial autocorrelation. The Distance to Recreation
attribute had a statistically significant trend (p<0.009), exhibiting negative spatial
autocorrelation. That is, preferences for Recreation were not similar among nearby households.
Individuals with high and low preferences for Recreation tended to be located near one another.
Conversely, the Agriculture attribute was positively correlated (p<0.068), indicating that nearby
households had similar preferences for Agriculture.
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Table 3.4. Global Moran’s I statistics, z-values, and p-values for preferences of the six growth
attributes (N=386)
Attributes Moran’s I z-value p-value
Residential Growth 0.004 0.2164 0.408 Protected Grasslands -0.032 -0.9912 0.160 Distance to Recreation -0.072 -2.283 0.009 Agriculture 0.043 1.515 0.068 Bison Presence -0.004 -0.056 0.485 Unemployment Rate 0.009 0.386 0.342
Significance testing with 99,999 permutations; spatial weights defined using queens contiguity (first order)
Local Patterns of Preferences
Aligning with Objective 3, I analyzed landscape preferences using local spatial
autocorrelation tests to identify clusters of significantly high (or low) preferences. The number of
local cluster types are reported in Table 3.5 for each of the six landscape attributes. Significant
high-high clusters (or hotspots) and low-low clusters (or coldspots) existed for all of the
attributes. High-high clusters indicated an individual with high preferences surrounded by
neighboring high preferences, and low-low clusters were locations where an individual with low
preferences was surrounded by neighboring low preferences. Thus, individual preferences were
similar to neighboring individuals in specific areas of the county. Preference for Agriculture
exhibited the most significant local clusters (n=56), followed by Residential Growth clusters
(n=52).
Figure 3.3 illustrates the spatial clustering of landscape preferences across Will County.
The most prominent spatial clustering of landscape preferences existed in the Agriculture
attribute. Hotspots of preferences for Agriculture coincided with the southern region of the
county where the landscape was dominated by cropland (see Fig. 3.3d). Agricultural coldspots–
clusters of low preference–were found only in the northern half of the county. Spatial clustering
of the Residential Growth attribute also followed clear patterns within the county (see Fig. 3.3a).
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Distinct bands of hotspots and coldspots emerged with a cluster of contiguous hotspots to the
northwest of Joliet and another hotspot band located in the center of the county. Coldspots for
Residential Growth were located primarily in a band extending northeast from Joliet and another
in the southeast region of the county. Spatial clustering of preferences for the other attributes
existed, but regional trends were less apparent. For example, preferences for Recreation were
mixed in the far northern part of the county, but a cluster of hotspots emerged in the center of
Will County (see Fig. 3.3c). Spatial patterns for the conservation-related attributes, Grasslands
(see Fig. 3.3b) and Bison (see Fig. 3.3e), were also mixed with less evident clustering of hotspots
and coldspots. Finally, clusters of preferences for lower Unemployment rates surrounded the
greater Joliet region while clusters of ‘willingness to accept’ higher Unemployment rates existed
in the central region of the county and in the northwest corner west of Bolingbrook (see Fig.
3.3f). These results suggest at least part of the preference heterogeneity present in respondent
preferences can be understood by local spatial differences.
Table 3.5. Local clusters1 of landscape preferences
Attributes High-High Clusters Low-Low Clusters Non-Significant
N (%) N (%) N (%)
Residential Growth 27 (7.0%) 25 (6.5%) 334 (86.5%) Grasslands 23 (6.0%) 21 (5.4%) 342 (88.6%) Recreation2 13 (3.4%) 14 (3.6%) 359 (93.0%) Agriculture 31 (8.0%) 25 (6.5%) 330 (85.5%) Bison 15 (3.9%) 16 (4.1%) 355 (92.0%) Unemployment 20 (5.2%) 19 (4.9%) 347 (89.9%) 1 Clusters are significant at p<0.05; spatial weights defined using queens contiguity (first order) 2 Recreation was reverse-coded so that high-high clusters represent preference for closer recreation areas
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Figure 3.3. Gi* hotspot analysis for hypothetical growth preferences (99,999 permutations;
queens contiguity with first order effects); recreation was reverse-coded so that high-high
clusters represent preference for closer recreation areas
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3.5 Discussion
The purpose of this study was to better understand the spatial patterns of landscape
preferences reported by residents in Will County, Illinois. Given the heterogeneous nature of the
county’s landscape, preferences were expected to vary across space. A discrete choice
experiment revealed preference heterogeneity for all model attributes–Residential Growth,
Protected Grasslands, Bison Presence, Recreation, Agriculture, and Unemployment–while
spatial autocorrelation methods illustrated spatial heterogeneity in individual preferences for
these attributes. Global spatial autocorrelation methods showed little evidence of overall spatial
dependence in individual preferences, but local methods illustrated significant patterns. The
Residential Growth and Agriculture attributes exhibited the most apparent spatial clustering
patterns with high and low preferences for these attributes clustered in distinct areas of the
county. Our results offer insight on preferences in a mixed-use landscape where land use types
and ecosystem services are diverse.
The random parameters logit model revealed that all model attributes were significant
predictors of choice, thus, validating the research approach in its ability to understand the drivers
of individual decisions. Will County residents were more likely to choose scenarios with more
land in Agriculture, more Grasslands, a greater Bison Presence, and closer Recreation
opportunities but less likely to choose scenarios with greater Residential Growth and higher
Unemployment Rates. Using this model, significant preference heterogeneity was captured for all
six attributes. Thus, the impact of these attributes on choice was not the same across individuals.
For instance, the mean coefficient of Residential Growth was positive, but preferences for
Residential Growth were significantly different, suggesting that a subset of the population is
negatively driven by population growth within Will County.
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Global spatial autocorrelation indicated that overall spatial dependence of the six
attributes was weak. This finding extends other studies that have analyzed global spatial
autocorrelation of individual-specific parameters (Johnston et al., 2015; Johnston &
Ramachandran, 2014; Meyerhoff, 2013). Although the Moran’s I values for Distance to
Recreation and Agriculture preferences were significant at the 90% confidence level, both values
were within 0.1 from zero, indicating relatively weak autocorrelation as values above 0.3 or
below -0.3 are known to represent strong autocorrelation patterns (O’Sullivan & Unwin, 2014).
Weak global clustering might have been linked to the spatial resolution of individual households,
the units of observation. At this scale, global patterns did not demonstrate a strong relationship
between preferences of individuals and their neighbors’ preferences, as defined by the spatial
weights matrix. It could be that patterns existed at a broader spatial scale. Previous research has
indicated that aggregating preferences across spatial units (e.g., census blocks, electoral districts)
has shown stronger influence on global spatial dependence (Campbell et al., 2009; Czajkowski et
al., 2017). However, in line with Johnston et al. (2015), Johnston & Ramachandran (2014), and
Meyerhoff (2013), I chose individual households as the unit of analysis to not mask local
variability in preferences. Future research should carefully consider the unit of analysis for
understanding autocorrelation patterns of preferences.
My analysis approach allowed local, place-based patterns of preferences to surface which
has important implications for management and policy. The results of our study showed
clustering, regional trends, and outliers of preferences for specific landscape attributes. Such
information is helpful for targeting policy efforts and assessing the feasibility of policy proposals
or local projects (Campbell et al., 2008; Johnston et al., 2016; Sagebiel et al., 2017). Specifically,
the study’s results can assist in identifying where to focus opportunities for building future
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recreational facilities, expanding existing open spaces and trail systems, and conserving natural
landscapes such as wetlands and prairies. Also, the outputs of the study have potential to
illuminate where resistance to these and other opportunities might be greatest. Spatial differences
in residents’ ‘willingness-to-accept’ greater Unemployment rates could illustrate locations that
are most vulnerable to economic downturns while patterns in Agricultural preferences identify
areas in which agrarian lifestyles are essential to local residents. In response to these results,
leaders in Will County and other counties in the Midwest, will be better able to apply their local
knowledge about phenomenon in the landscape that influence preferences in the locations
highlighted by my spatial analysis.
This study presents several opportunities for future research that applies spatial
autocorrelation methods. First, my assessment of both global and local spatial dependence of
preferences identified distributional patterns but did not explain why these patterns occurred.
Other methods such as spatial regression (Abildtrup et al., 2013; Czajkowski et al., 2017) and
latent class analysis (Scarpa et al., 2005) can be used in tandem with spatial autocorrelation for
future research to better understand the factors that influence the spatial distribution of
preferences. Second, the spatial weights matrix plays an important role in shaping the
interpretation of results and should be carefully considered in spatial autocorrelation tests. In line
with previous research (Johnston & Ramachandran, 2014; Raudsepp-Hearne, Peterson, &
Bennett, 2010), I defined ‘neighboring’ individuals using a queens contiguity spatial matrix.
Although this analysis approach yielded useful results, other spatial weights matrices are
available. Third, I analyzed uneven spatial units, which had implications for interpreting the map
outputs (see Fig. 3.3). Some spatial patterns were more easily observed in areas that were less
dense and had fewer observations. This challenge is particularly relevant for contexts such as
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Will County that span a rural-urban gradient. Future research on the rural-urban fringe should
consider using a spatially stratified sampling approach to generate a more equal spatial
representation of preferences.
3.6 Conclusion
The use of spatial autocorrelation methods in a discrete choice experiment demonstrates
one approach for understanding spatial heterogeneity of preferences. I mapped preferences based
on place of residence within a mixed-use landscape in Illinois and found that individual
preferences varied across Will County. Moreover, these results illustrated clusters of high (and
low) preferences for six attributes measured at the landscape level. I therefore underline the
importance of allowing preferences to vary across space so that local variability in preferences
that are not easily explained by global statistics can be captured. Research in choice modeling
should continue to incorporate spatial methods for more representative and policy-oriented
efforts.
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CHAPTER 4: CONCLUSION
The purpose of this thesis was to understand how preferences for growth on the rural-
urban fringe are influenced by landscape-scale attributes, and how these preferences vary across
space. Specifically, a random parameters logit model was implemented in the American Midwest
to analyze the effects of attributes on respondents’ choices for hypothetical growth scenarios. My
results showed that residents in Jasper County, IA and Will County, IL were more likely to
choose scenarios with higher residential growth rates, more grassland areas under protection, less
distance between home and recreation areas, more land in agriculture, and lower unemployment
rates. Though the two study sites had similar characteristics, the model revealed significant
differences between the two counties, and indicated that residents in Will County preferred lower
residential growth rates, less land in agriculture, and closer recreation areas when compared to
Jasper County residents. The second analytical component of my thesis explained how
preferences for growth varied across Will County’s diverse landscape. Tests of spatial
dependence showed little evidence of overarching, global patterns between individuals’
preferences and preferences of neighboring households but illustrated clear spatial clustering in
specific locations. For example, clusters of high agricultural preference were primarily located in
the southern region of the county while clusters of low preference for agriculture dominated the
northern half of the county. Thus, in specific locations of county, individual preferences were
strongly linked with the preferences of their neighbors. In summary, my study findings
illuminate that preferences are heterogeneous and that some of these differences can be
understood through spatial patterns.
This research provides opportunities for future research in the field of choice modeling.
First, my research found that implementing mixed-methods was a useful approach for informing
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an experimental design. Future studies should continue using preliminary qualitative data and
pilot testing to develop relevant and meaningful discrete choice experiments (Cahill & Marion,
2007; Greiner et al., 2014; Ryan et al., 2008). Second, this research validated the use of the
random parameters logit model to account for individual heterogeneity. If differences in
preferences across a sample are anticipated, a RPL model is recommended because of its ability
to account for unobserved heterogeneity (Hensher & Greene, 2003; Ryan et al., 2008). Third,
few studies have analyzed the spatial dependence of individual preferences and have produced
mixed results. While several scholars found strong spatial dependence among individual-specific
parameters (Campbell et al., 2009; Czajkowski et al., 2017), my research aligns with others who
have discovered relatively weak spatial dependence among preferences across a given study area
(Johnston & Ramachandran, 2014; Meyerhoff, 2013). Thus, additional research on the spatial
dependence of preferences is needed to better understand how preferences within close proximity
are related. Finally, this study calls for similar research on the rural-urban fringe because these
areas are dynamic and, thus, preferences are likely to shift and change.
Extending beyond the pursuit of knowledge, this project has important implications for
management and policy. Broadly, my thesis prioritized stakeholder involvement at the regional
level (Nelson & Duncan, 1995). Studying preferences at this broader geographic scale is
especially important for planners and visionaries interested in combatting ‘leapfrog
development’ and supporting decision-making beyond the municipal-level (Bengston et al.,
2004). Further, managers and policy-makers can use these results to better understand the
differences that exist among stakeholders regarding their preferences for future growth and
development. Given that I showed stakeholder preferences were not homogenous, people may
respond differently to changes in policy or to proposed projects. My results can be used to
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spatially target proposed policies based on spatial clusters of high and low preferences that were
identified. Specifically, the study findings strengthen understanding of where support for specific
projects might be greatest. Both researchers and managers can build upon this study to better
understand how preferences differ across dynamic landscapes such as the rural-urban fringe and
specifically how individuals prefer to grow and develop in the face of change.
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APPENDIX A: WILL COUNTY SURVEY QUESTIONNAIRE