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\begin{document}

\title{Viewscape privacy drives exurban development}



\author[1]{Jelena Vukomanovic}%
\author[2]{rkmeente}%
\affil[1]{North Carolina State University}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 17, 2024}


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~

\section*{Abstract}

{\label{140006}}

\section*{}

{\label{140006}}

\section*{}

{\label{140006}}

\section*{1. Introduction}

{\label{140006}}

\emph{\textbf{1. Exurban growth is uniquely characterized by aesthetic
preferences and individual choice. One of those important preferences in
privacy.}}

\par\null

Regions across the United States with scenic beauty and other natural
amenities are experiencing rapid population growth and residential
development. Exurban growth, or exurbanization, in particular in
characterized by low-density residential settlement in rural areas
valued for their aesthetic, recreational, and other consumption-oriented
values (McCarthy 2008; Taylor 2011). A complex and varied picture of the
drivers of exurbanization is emerging and the reasons that people move
to scenic rural areas are as numerous as the communities that they
form.~

-For many exurbanites, natural amenities, such as scenic beauty,
expansive vistas (Vukomanovic and Orr 2014), wilderness, recreational
opportunities, and climate play an important role in in the decision to
migrate. {[}ADD: Gosnell and Abrams 2011, McCarthy 2008; McGranahan
2008{]}

-Social and cultural connections to small-town rural life (Hines 2007)
and a desire for a sense of community (Vogt 2011) can also be a draw for
some exurbanites.~

Privacy and solitude, often described as being unaware of other people
when at home (Kondo et al. 2012), are important to many exurbanites who
seek a seclusion or a ``frontier living'' experience (Hines 2007; Hines
2011). As many as 46\% of amenity migrants in Washington State described
finding ``privacy'' or ``peace-and-quiet'' as a primary purchase goal.
Exurban development represents a unique land use often characterized by
aesthetics - whether strictly related to perceptions of beauty or more
broadly as principles or worldview expressed through outward appearance
and actions - and driven by individual choice.~

\par\null\par\null

\emph{\textbf{2.~ {[}AND{]} It's a rapidly-growing land use with
significant impacts to both communities and ecosystems. Impacts depend
on the spatial distribution of development.}}

\par\null

a) area (and percentage) of land at exurban densities {[}and at other
densities, for comparison{]}

b) how rapidly exurban areas are growing (e.g. rate of growth)

c) what are the land types that are converting to exurban

\par\null

The per capita land conversion in exurban areas is much greater than for
urban areas and that growth is seldom guided by growth management plans
(Kondo et al. 2012). The rapid growth and dispersed nature of exurban
development raises numerous ecological concerns, including changes to
water quality and quantity {[}REF{]}, altered fuel loads and fire
regimes {[}REF{]}, habitat fragmentation, and the spread of invasive
species {[}REF{]}.~~

-There are also impacts on communities (e.g. conflict between long-term
residents and newcomers) - examples {[}REFS{]}

\par\null

-Residential development drives the growth of other infrastructure and
many of the ecological impacts of residential development depend on the
spatial configuration of houses, associated infrastructure, in
particular road networks (Vukomanovic et al. 2013; OTHER REFS). This
spatial arrangement of houses depends on the drivers of exurbanization,
where different preferences can lead to very different spatial
arrangements~

\par\null

\emph{\textbf{3. {[}BUT{]} Studies to date have been largely descriptive
or done at scales where you lose that individual perspective.}}

\par\null\par\null

-While work has been done to identify the drivers of exurbanization,
very little is known about the spatial distribution of these preferences
or the relative importance of some drivers relative to others.~

-There have been limited attempts to integrate what has been learned
directly from exurbanites about their reasons for moving to rural
landscapes and the spatial pattern of development (Walker 2011).
Interviews, survey, participant observation, focus groups, and other
narratives provide valuable place-based information and context, however
time and resource limitation make these approaches difficult to
implement over landscape scales. Whether by norms of disciplinary
practice or protocols designed to protect participant identity, reported
results very seldom georeference narrative and survey data. Demographic
and aggregated spatial analyses of the impacts and drivers of
exurbanization have relied largely on country-level Census data
(McGranahan 2008; Rudzitis et al. 2011), are valuable for understanding
common drivers, such as climate (McGranahan 1999) or proximity to water
(Mueser and Graves 1995), but miss individual perspectives. This earlier
work is essential to identify what drivers to examine and questions to
ask. However, these approaches limit our understanding of how preference
are spatially distributed at finer scales, as well as the ability to ask
questions about trade-offs.

\par\null

\textbf{\emph{4. {[}THEREFORE{]} We turn to a viewscapes approach that
is informed by social science, but relies on a spatial representation to
understand the interactions and trade-offs between drivers.}}

\par\null

Analyses of demographic trends and~

\par\null

5. We a) mapped the historic distribution of suburban, exurban, and
rural development prior to 2010 (great recession), b) analyzed size and
privacy of exurban viewscapes relative to those in suburban and rural
settings, c) we developed a predictive model of the probability of
exurban settlements based on quantitative metrics of viewscape visual
qualities. Application of the model is made possible by a new algorithm
for computing spatially continuous, all-possible viewscape coverages for
a region {[}highlight in abstract{]} ~

c) pro

\par\null

To include in paragraph 5:~The region is well-suited to studying drivers
of exurban viewscape decisions as the rugged topography and low-height
desert vegetation provide numerous scenic vistas AND local land-use
policy does not restrict development of low-density housing and private
roads.

Like much of the American Southwest, population growth is rapid\ldots{}

\par\null

\section*{}

{\label{140006}}

\section*{}

{\label{140006}}

\section*{2. Methods}

{\label{140006}}

\section*{2.1 Study Region}

{\label{140006}}

The Sonoita Plain covers 700 km\textsuperscript{2} of southeastern
Arizona~with rolling hills ranging from 1100-1600 m in elevation in the
Plain and steep terrain reaching 3000 m in the the Santa Rita and
Huachuca Mountains. The natural landscape includes plant communities of
desert grassland, plains grassland and desert scrub vegetation (Bock and
Bock 2000; Figure 1). Residential development is concentrated in the
central Plain where groundwater extraction from a single large aquifer
is more available(Vukomanovic et al. 2013). Population of surrounding
Santa Cruz County increased 46.5\%, 45.1\%, 29.3\%, and 23.6\% each
decade from 1980 to 2010 (U.S. Decennial Census 2010), as rangelands and
ranches made way for residential and limited commercial developments.
With low cost of living, great scenic beauty, and plenty of available
land, the Sonoita Plain became a booming housing market and destination
for amenity migrants seeking new low-density residences and vacation
homes. Local real estate companies highlight the region's rural
lifestyle, emerging winery/vineyard industry, and variety of
recreational opportunities on public lands (Wildhorse Realty 2016;
Sonoita Realty 2016).

\par\null

The region is well-suited to studying drivers of exurban viewscape
decisions as the rugged topography and low-height desert vegetation
provide numerous scenic vistas AND local land-use policy does not
restrict development of low-density housing and private roads. Land
ownership is roughly 50\% public (US Forest Service, Bureau of Land
Management, State Lands) and 50\% private (ASLD 2011), with almost all
private land zoned for residential development (SCCZDC 2015). Sonoita
Plain residents are generally older and wealthier compared to the state
of Arizona, giving them greater mobility and freedom in their housing
decisions.~As a result of these factors, residential development in the
area is driven more by individual choice than town or regional planning
(reference).~

\par\null

\section*{2.2 Housing Data}

{\label{140006}}

We heads-up digitized the historic distribution of suburban, exurban,
and rural houses in the Sonoita Plain study prior to 2010 using
high-resolution (1-m) aerial imagery obtained from the USDA Farm Service
Agency, National Agricultural Imagery Program (NAIP). We
cross-referenced the digitizing with 2010 U.S. Bureau of the Census data
to match the number of houses mapped to the number reported in each
Census block. Using the same digitizing methods, we mapped the
distribution of newly built homes between 2010 and 2016.~Following
Theobald (2005) and Leinwand, Theobald, Mitchell, and Knight (2010), we
categorized homes as one of three housing-density classes: rural
(0-0.0618 units/ha), exurban (0.0618-1.47 units/ha), and suburban
(1.47-10 units/ha) (Figure X).

\par\null

\section*{2.3 Line-of-Sight Viewscape
Modeling}

{\label{140006}}

We calculated line-of-sight viewscapes for all 2XXX houses using~ a 10-m
DEM~ {[}Source of DEM{]} and a computationally efficient line-sweeping
algorithm implemented in r.viewshed module in GRASS GIS (Haverkort,
Toma, \& Zhuang, 2009). This DEM represents the highest-resolution
elevation data available since publicly available LiDAR data do not
exist for the study area. We set the observer height at 3 m above the
surface to simulate a typical household viewpoint and we restricted the
maximum visibility distance to 10 km in all directions. Though
top-of-canopy (ToC) surface models derived from LiDAR data have been
shown to more accurately model viewscapes in the presence of forest
(Vukomanovic et al. 2018), this region is dominated by low stature
desert plant communities. From the line-of-site visibility analysis ,
we~ calculated four metrics of visual quality: viewscape privacy, size,
greenness, and ruggedness.

\par\null

It's a line-of-sight, so the number of people that you can see = the
number of people that can see you.~

\par\null

\par\null\par\null

\section*{2.4 Statistical Analysis and Model
Development}

Using pairwise Wilcoxon rank sum tests, we compared differences in
viewscape size and privacy between exurban,~ suburban, rural, and
randomly-located homes (built through 2010). Next, using autologistic
regression, we tested which metrics of visual quality (viewscape
privacy, size, greenness, and ruggedness) best predicted the probability
of exurban development between 2010 and 2016. The autologistic form of
ordinary logistic regression allowed our model to simultaneously account
for expected spatial dependence in the housing data and avoid
autocorrelated residuals (Besag, J., 1974, Gumpertz et al. 1997). We
also considered the significance of a home's proximity to a primary road
to account for the potential importance of accessibility in remote
locations. Our analysis included the examination of all-possible models
testing every subset of predictors and identifying the best model based
on Akaike Information Citerion (AIC)
(\emph{sensu~}\href{http://onlinelibrary.wiley.com/doi/10.1111/j.1365-2745.2006.01206.x/full\#b52}{Quinn
\& Keough 2002}).

\par\null

\section*{2.5 Model Application with spatially continuous, All-Possible
Viewscapes~}

{\label{508956}}

Within our study area we created a regular grid of viewpoints with 30m
spacing and computed viewsheds for each of these viewpoints using the
10-m DEM.The resulting 30-m resolution total viewshed was then derived
by storing the number of visible cells from each view point rather than
using raster algebra to add each individual viewshed one at a time
(Llobera et al, 2010). Based on the computed viewsheds, we derived
several variables for each viewpoint - viewshed size, the number of
visible houses, greenness based on NDVI, and terrain ruggedness. By
assigning each of the individual computed values on the grid, we created
several 30-m resolution raster layers representing spatially continuous
visual attributes of the landscape - visual prominence (Llobera 2003),
privacy index, greenness, and terrain complexity, respectively.

\par\null

Since deriving the total viewshed and other visual attributes for our
study area required to compute 40000 individual viewsheds, we
parallelized the computation in high-performance computing environment
to reduce the required time from several months to several days. (I can
add more precise numbers if needed). The individual viewshed
computations were performed using GRASS GIS module r.viewshed with 3-m
observer's height and while considering the curvature of the earth.
Since the maximum visibility distance was set to 10 km in all
directions, we sufficiently increased the extent of the DEM to avoid any
edge effects.

\par\null

For each 30-m pixel, computed the viewscape geometry and a value for
each of the considered variables (privacy, greenness, terrain
ruggedness, viewscape size, and primary roads). {[}Figure DD can show
the number of neighbors visible from each pixel based on it's size and
configuration{]}

\par\null

\section*{}

{\label{140006}}\par\null

\section*{3. Results}

{\label{140006}}\par\null

Our heads-up digitizing identified a total of 1,867 homes in 2010 (5X\%
exurban).~Exurban homes built prior to 2010 have significantly larger
viewscapes than randomly-distributed (Z times on average), suburban (X
times on average), and rural (Y times on average) homes (Figure
BBa).~These same exurban homes were visible to significantly fewer
neighbors compared to suburban homes, but more visible than rural homes.
Exurban homes were also visible to XX fewer neighbors than what may be
expected by chance (Figure BBb).

\par\null

An additional 2XX homes were built between 2010 and 2016 with XX\%
located in low density exurban and rural settings. T-test show that
exurban homes built after 2010 had significantly larger viewscapes (P
\textless{} 0.00X) but possessed less privacy (P\textless{} 0.00X) as
the region grew. The auto-logistic model of new growth showed that
viewscape privacy (P = 0.003) and NDVI (P \textless{} 0.001) were the
most significant predictors of the probability of exurban development
between 2010 and 2016 (write equation here). Proximity to primary roads
and the size and ruggedness of exurban viewscapes were not significant
predictors after accounting for privacy and NDVI. Application of the
model in the GIS produced a predictive map of the exurban development
probability across the study region (Figure DD).~

\par\null

\section*{}

{\label{140006}}

\section*{4. Discussion}

{\label{140006}}

1. Exurban viewscapes are larger, but they see fewer neighbors~

Would expect random homes that are ``pushed'' to the edges of the study
area to have larger viewscapes. Supports findings in Vukomanovic and Orr
(2014) about the importance of visual scale as a visual quality metric.~

\par\null

2. The fact that they see fewer neighbors suggests an optimization

\par\null

3. The all-possible(``total'') viewscapes~

-Results

-What they mean

-How they can be applied

\par\null

4. People have done a lot of work to understand individual (or a small
number of drivers). As that understanding has grown, understanding
trade-offs in preference is the next step. This is a unique way that
geospatial approaches can contribute\ldots{}in survey and other social
science methods, difficult to ascertain trade-offs (plus memory fades
with time). Contribute to understanding of amenity migration with this
approach.

\par\null

Location matters and the spatial configuration of exurban houses, roads,
and associated infrastructure will depend on the drivers of migration.
Different patterns of exurban growth will have different impacts on both
ecosystems and rural communities. In area places where land availability
allow choice with regard to house location, information about landscape
drivers and exurban preference could prove helpful for infrastructure
planning and growth management efforts.~

\par\null

scrap: Homes built through 2010 represent a period of first gradual
(1970-XXXX) and then rapid (XXXX) growth, which virtually came to a stop
with the~great recession. This pre-2010 period of housing growth
represents a wide-range of drivers, while XX\% of the new homes after
2010 has been exurban.

\section*{}

{\label{140006}}

\section*{Figures:}

{\label{140006}}

Figure AA: Study Area Map

Figure BB: Photos of exurban development

Figure CC: a) Viewscape Size by Density Class; b) Privacy by Density
Class ) {[}do 3 pairwise Wilcoxon tests and include bar graph (include
the statistics in the caption){]}

Figure DD: Spatially continuous, all-possible viewscapes visualization:
a) viewscape size, b) privacy, c) greenness, d) terrain ruggedness

Figure EE: Predictive map of the probability of exurban home development
(INCLUDE EQUATION AGAIN SO FIGURE STANDS ON ITS OWN).~\emph{Locations
with the highest likelihood of exurban development occur in places that
exhibit opportunities to A) view the scenic beauty of vegetated
``green'' landscapes\ldots{}.., B) build in highly private, low density
settings, and c) the combination of green and private viewscapes.}

\par\null\par\null

Jelena To-Do:

-Figure BB and the wilcoxon tests

\par\null

SCRAPS:

\par\null

The region provides a unique opportunity for studying drivers of exurban
viewscapes as the surrounding mountains provide scenic vistas with
vertical visual boundaries that constrain residents' views to the
interior of the Plain and the inward-facing mountain slopes

\par\null

\subsection*{REFERENCES}

{\label{260849}}

Gumpertz, M.L., Graham, J.M., Ristaino, J.B., 1997. Autologistic model
of spatial pattern of phytophthora epidemic in bell pepper: effects of
soil variables on disease presence. J. Agric. Biol. Environ. Stat. 2,
131--156.

\par\null

Besag, J., 1974. Spatial interaction and the statistical analysis of
lattice systems. J. R. Stat. Soc. B 36, 192--236.

\par\null

Llobera, Marcos, David Wheatley, James Steele, Simon Cox, and Oz
Parchment. ``Calculating the inherent visual structure of a landscape
(inherent viewshed) using high-throughput computing.'' Archaeolingua,
2010.

Llobera, Marcos. ``Extending GIS-based visual analysis: the concept of
visualscapes.'' International journal of geographical information
science 17, no. 1 (2003): 25-48.

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