426781
and McFarlaneJournal of Planning Education and Research
JPEXXX10.1177/0739456X11426781Haines
Article
Factors Influencing Parcelization
in Amenity-Rich Rural Areas
Journal of Planning Education and Research
32(1) 81­–90
© The Author(s) 2012
Reprints and permission: http://www.
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DOI: 10.1177/0739456X11426781
http://jpe.sagepub.com
Anna L. Haines1 and Dan McFarlane1
Abstract
The process of rural parcelization provides an ongoing challenge for planners targeting both habitat and farmland preservation.
An understanding of which landscape features influence parcelization can help planners create more nuanced land division
and zoning ordinances. We used GIS to reconstruct a historical parcel database for three towns in Wisconsin and then
developed a logistic regression model to determine the extent to which parcel characteristics influence parcel subdivision.
Influential predictors included proximity to roads, water, and agricultural areas as well as parcel size.
Keywords
rural land, parcelization, natural resources, land protection
Introduction
Land division is a growing concern among planning and
natural resource professionals in rural communities (Gobster
and Schmidt 2000; Butler and Leatherberry 2004). Land
division or parcelization is the process in which large parcels
are divided into smaller ones. Subsequently, parcelization is
considered a crucial step in the transformation of rural landscapes from places that rely on natural resource use and
extraction to areas that package and sell the landscape itself
for real estate development. Past research has documented
the impacts of parcel and landscape fragmentation (Haines,
Kennedy, and McFarlane 2011; Gobster and Schmidt 2000;
Brown 2003), but little attention has been given to the actual
process of parcelization. In particular, amenity-rich rural
areas have experienced changes in demographics, local
economies, and building construction fueled by in-migration.
Residents and local government officials perceive these
changes on an incremental basis in their communities, but
are unsure of the cumulative implications. While the perception of an increased rate of parcelization is common, empirical evidence of such changes are largely tied to the growing
number of land owners or changes in mean parcel size. The
process of parcelization has rarely been studied, and what
research exists is limited and the analysis seldom extends
further back than twenty years (Drzyzga and Brown 1999).
A critical question is: Do landscape features influence rural
parcelization?
The purpose of this research is to quantify the landscape
features that influence rural parcelization. An understanding
of which landscape features influence parcelization can help
planners create more nuanced land division and zoning ordinances. To address this issue, we developed a multiple logistic regression model to determine if parcel characteristics
such as size, proximity to water, elevation, and road adjacency influence parcel subdivision. We focused on land
characteristics because we wanted to develop a model using
easily accessible data at the county level rather than an
agency-based model that would necessitate a costly landowner survey. If we can understand which features and the
importance of each feature, planners and other professionals
and decision makers can better target lands for protection
and use more appropriate forms of development. The remainder of this article reviews literature on rural community
change as it affects parcelization, describes our research
region, presents our analytical model, and discusses our
results and implications.
Rural Community Change
Community change is underway in rural America, with a
growing gap between those areas rich with amenity endowments and those without (McGranahan 1999). Against a
backdrop of long-term depopulation and economic decline
in most agriculture and forest-based rural communities,
places with significant shorelands, favorable climates, public lands with recreation opportunities, and other natural
amenities are demonstrating that not all rural areas share the
same fate. Nationwide, amenity-rich rural areas outpace most
Initial submission, June 2009; revised submissions, November 2010,
March, April, and August 2011; final acceptance, August 2011
1
University of Wisconsin, Stevens Point, WI, USA
Corresponding Author:
Anna L. Haines, University of Wisconsin, 800 Reserve St.,
TNR Room 205, Stevens Point, Stevens Point, WI 54481, USA
Email: ahaines@uwsp.edu
82
other rural places in terms of population growth, housing
values, and economic activity (Beale and Johnson 1998).
The growth in second and retirement housing in such communities serves as both an indicator and consequence of the
attractive power that amenities play in an increasingly footloose housing market.
Such growth and development are not without costs.
Many rural sociologists have highlighted the potential for
community discord when the views of amenity migrants,
tourists, and long-term residents clash (Green et al. 1996;
Spain 1993). From an economic perspective, the development of the landscape creates new challenges for traditional
resource-based industries such as farming and forestry
(Mather 2001). New residents may disapprove of land management practices, and the fragmentation of the landscape
can impede attempts to capitalize on economies of scale
(Rickenbach and Gobster 2003). Biologists and others
have pointed to a different set of concerns wherein the longterm consequences of development and change portend significant and irreversible changes to the ecosystems embedded
in amenities that attract humans (Odell, Theobald, and
Knight 2003). These environmental concerns are all the
more salient considering that the same natural amenities that
attract the modern tourist and migrant—coastal, riparian,
and alpine regions—are among the least resilient to human
development and interaction (Hersperger 1994).
Whether agricultural or forestry based, amenity-driven
exurbanization represents a substantial change in the social,
economic, and environmental sustainability of rural America
(Daniels 1999). This is a wrinkle for those who perceive the
more appreciative, postproductive economy of amenitybased development to be somehow easier on the environment than the extractive, boom and bust rural economy that
it stands to replace (Power 1996). Typical examples of communities experiencing amenity-driven change and development can be found throughout the nation, but rural Wisconsin
is a particularly relevant case to consider. The state’s bifurcated landscape of rich agricultural soils in the southeast and
sandy or rocky forest soils in the north provides a wide range
of economic and resource contexts for observing and understanding these changes.
Scattered second home, retirement, and exurban development is already fragmenting large tracts of forestland in the
state’s northern region (Klase and Guries 1999). Such development limits the potential for efficient timber management,
places households in fire-prone areas, and displaces wildlife
from their habitats (Davis, Nelson, and Dueker 1994). When
this development occurs near sensitive resources such as lakes
and wetlands, erosion and runoff can degrade water quality
and threaten aquatic ecosystems (Arnold and Gibbons 1996).
Low-density housing development in Wisconsin’s agricultural areas increases the need for and cost of public
services. Subsequent tax increases reduce profits from animal and row crop production, accelerating the already
Journal of Planning Education and Research 32(1)
startling decline in the number of agricultural producers in
what once was the nation’s leading milk-producing state
(Shi, Phipps, and Colyer 1997). The growth in nonagricultural households throughout the countryside increases the
potential for conflict between farmers and their exurban
neighbors over agricultural practices such as manure and
pesticide application (Daniels 1999).
Process of Parcelization
and Rural Land Development
While the consequences of amenity-driven development
are fairly well documented, the actual process of land-use
change in such rural places remains poorly understood. This
stands in contrast to the standard urban-based model of community growth, where the development of the landscape is
seen as a consequence of infrastructure, household preferences, and population changes driven by natural growth and
job-related migration (Capozza and Helsley 1989). Because
amenity-based growth is driven by seasonal and retirement
housing, it can occur well beyond the commutershed of a
metropolitan area. Unlike traditional rural development
models, the relative and absolute productive capacity of the
land resource plays little part in the decision making of
incoming residents (Dillman 1979). In fact, the values that
vacationers and exurban migrants place on land may be the
opposite of what would be assumed of a rational economic
actor. The farmer and forester sought well-drained, easily
accessible lands with quality soils free of rocks and boulders. The amenity migrant seeks water, terrain, and perhaps
gives little concern to the productive capacity of the soil. It
is the magnitude and accessibility of the amenities themselves
that motivates tourists and migrants to a given rural area.
How, then, is rural amenity growth understood?
Oftentimes, the tourist becomes the migrant as his or her
familiarity with an area grows with multiple recreationbased visits (Stewart and Stynes 1994). Another theory holds
that amenity-rich areas experience “life-cycles” of growth
and change driven more by the overall popular perception of
a region than by an individual’s preferences (Butler 1980). In
both cases, the growth in amenity-rich rural areas is externally driven and the well-being of such communities
becomes more closely associated with the well-being of distant urban economies. This is not altogether different from
the productive form of rural economic growth wherein external markets for raw and finished products determine commodity prices enjoyed by rural landowners and laborers. The
difference in the postproductive world is that the urban economies are now consuming the land itself rather than just the
fruits of that land.
The core process of this phenomenon is parcelization: the
division of land into smaller parcels and their subsequent
sale on the market. It is through parcelization that the relatively raw resource of land is refined and packaged for
83
Haines and McFarlane
wholesale and retail consumption as real estate. The characteristics of parcels can have significant impacts on the uses
available to a parcel owner. Parcel size is often a critical factor: a parcel too small is impractical to manage for farming
or forestry, while parcels too large may be impractical for
housing or other consumptive uses (Gobster and Rickenbach
2003). Parcel sizes help determine market values: land has
not only a per-acre value, but also a value that derives from
the necessary parceling for purchase, also known as plattage
(Chicoine 1981). The per acre land value for agriculture is
different when that land converts to development. The premium can be two or more times the agricultural value when
subdivided. Thus, parcels too small for one use may be subdivided to smaller parcels for another use.
Regulations Directed at Parcelization
Planners and policy makers have long sought to better manage the development of land through regulations at the federal, state, and local levels. At the federal level, laws such
as the Clean Water Act and the National Environmental
Protection Act set broad parameters for what can be done
with land resources at the local level. State agencies provide
further guidelines in accordance with state laws and regulations. The bulk of land-use control activities are carried out
through local administration of zoning, subdivision, and
other ordinances (Cullingworth 1993). These ordinances are
employed largely in reference to particular parcels, though
some may have greater influence on portions or features
found on a parcel.
Zoning has not been as popularly embraced in rural contexts as it has in the urban communities where it was established and legitimized in the early part of the twentieth
century. Though Wisconsin was one of the first states to
authorize and encourage rural county zoning, many rural
areas in the state have no zoning at all (Ohm and Schmidke
1998). Zoning is not a simple tool for local government to
use, in part because the classification of land into zones is
largely viewed as a temporary status ascribed to parcels that
landowners can have changed more or less at will, depending
on their own persuasive powers and the reactions of the
neighboring landowners and the community (Babcock 1966).
Land or subdivision regulations are applied less often
than zoning, but they remain a useful way for communities
to implement land-use and comprehensive plans (Freilich
and Shultz 1995). Through subdivision regulations, specific
attention is given to the size of new parcels, their location on
the landscape, and their potential impacts on public resources.
Wisconsin has adopted two potentially significant policies to influence parcelization. Statewide review of subdivisions was first initiated in 1956. This law required minimum
lot sizes and dimensional standards for new parcels, as well
as special requirements for new parcels created in riparian
areas and alongside state highways (Kuczenski 1999).
Reacting to the pace of shoreland development in rural areas,
the state passed legislation in the early 1970s to further regulate parcel creation in floodplains and riparian zones
(Yanggen and Kusler 1968). More recently, the state has
enacted planning legislation requiring that subdivision and
zoning regulations be consistent with local comprehensive
plans meeting statutory definitions (Ohm 2000).
Given the attention paid to land regulations and lot division controls, one might be surprised to find that there is
little empirical evidence demonstrating that land division
regulations have any impacts on the rate, extent, or nature of
parcelization. While perceptions of an increased rate of parcelization are common, empirical evidence of such changes
in Wisconsin or elsewhere are largely tied to the growing
number of land owners or changes in mean parcel size.
Rarely does the analysis extend further than twenty years
back (Drzyzga and Brown 1999; Klase and Guries 1999).
Recently, Haines, Kennedy, and McFarlane (2011) examined three towns in Bayfield County, Wisconsin, over a seventy-year period and found that parcelization led to forest
fragmentation and to developed land use over time. The fact
that few if any people have been actively tracking and analyzing the creation of parcels in a longitudinal sense makes it
difficult to say with any certainty whether or not parcelization rates and location are any different now than in the past.
However, the diffusion of geographic information systems
(GIS) and their ability to archive parcel patterns provides
greater potential for monitoring future changes in parcelization. Given this discussion, we wanted to find out what factors influence rural parcelization.
Methods
Study Area
This research focuses on exurban communities facing significant growth from urban fringe development and rural
residential development. Wisconsin has two archetypical
regions facing such development: the northern forested lakes
region and the southern agricultural region. We focus on the
latter for this study. From the agricultural region, we
selected a county to analyze based on a number of criteria:
whether or not they had completed a modern-day digital
parcel map layer, and whether they proactively engaged in
planning. Selection of a case study community focused on
the county and town unit for several reasons. First, the
county’s Land Information Office maintained much of
the data required for this project. The selection of multiple
towns within a single county reduced the data collection and
coordination efforts significantly. In addition, towns within
the same county were presumed to share similar economic
conditions, regional development pressures and other contextual forces. By examining a cross-section of towns within
a county, we focused on local factors that drive or inhibit
parcelization while controlling for these external forces and
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Journal of Planning Education and Research 32(1)
Figure 1. Location of study area within Wisconsin and Columbia County
pooled the data of the three towns for the analysis. Because
of the amount of data involved in our effort, we focused on
three towns within Columbia County, each representing a
different degree of contemporary land division (high [Lodi],
average [West Point], and low [Springvale]) (Figure 1) by
calculating parcel density (number of parcels per square
mile) at the town level.
Columbia County, located immediately north of Dane
County, Wisconsin, which is home to the state capital
(Madison), was selected as our typical rural, agricultural
community. Dane County is consistently among the state’s
fastest growing places in terms of housing and population.
While a number of planning efforts have taken place within
Dane County, the surrounding counties have a more varied
planning history, and this part of the state remains the
only region where there is no regional planning entity.
Consequently, spillover growth from Dane County has
become an increasingly visible issue as lower land prices and
property taxes lure homebuyers further and further from
downtown Madison.
Though changing, Columbia County remains a predominantly rural place with an overall population density under
70 persons per square mile, nearly 350,000 acres of farmland, and more than $100 million in annual agricultural sales
(National Agricultural Statistics Service 2002). The county’s
western edge is defined by the Wisconsin River and Lake
Wisconsin, a 9,000-acre impoundment formed by a dam at
the county’s southwest corner. The lake and the nearby
Wisconsin Dells have long been tourist attractions: tourists
and travelers spend an estimated $160 million in the county
(Wisconsin Department of Tourism 2007).
Database Development
We used historic tax assessment rolls provided by Columbia
County and a current geographic information systems (GIS)
digital parcel layer to dissolve parcel lines according to the
time period in which they were created. Working backwards, we created digital parcel layers for 1953, 1961, 1972,
1983, 1991, 2000, and 2005. Table 1 summarizes the number of parent parcels (parcels that subdivide) and the amount
of lots created during each time period.
We also reconstructed digital land cover maps for the years
1955, 1968, 1978, 1992, 2002, and 2005 using georeferenced
historic aerial photographs. Aerial photographs were widely
available throughout the county, but were inconsistent in
terms of scale and time coverage. For spatial utility, we orthorecitified each photo (a process by which software removes
85
Haines and McFarlane
Table 1. Number of Parcelization Events from 1953-2005 in the
Study Area
Time Period
1953-1961
1962-1972
1973-1983
1984-1991
1992-2000
2001-2005
Total
No. of Parent Parcels
No. of Lots Created
81
91
117
93
128
152
662
536
553
494
254
480
439
2,756
errors caused by camera angle distortions). Erdas Image software, elevation control points, and existing ortho-photography
were used to rectify historical aerial photography.
A significant amount of research has examined the link
between natural amenities and rural development (Beale and
Johnson 1998; Dearien, Rudzitis, and Hintz 2005; Deller and
Lledo 2005; Gobster and Schmidt 2000). Because many people are believed to be attracted to the countryside for recreational and aesthetic reasons, properties that have amenity
characteristics should be more desirable to own. Similarly,
existing landowners or developers may look at these characteristics of parcels in deciding where to purchase or subdivide land for future profits. It is widely recognized that
proximity to or distance from open water, forests, and public
land are the greatest influences of rural parcelization. As
these amenities become further away from a particular parcel, the lesser the likelihood for that parcel to split or subdivide. We also included nonamenity variables necessary for
land division and development, including the availability of
roads and public utilities, which we hypothesize positively
influence rural parcelization.
We extracted natural amenity variables associated with
topography and landform (i.e., slope, soils, forest, agriculture, water) from a direct overlay of the parcel data layers that
most closely corresponded to the historic landcover layer
using ArcGIS 9.3 (ESRI, Inc., Redlands, CA). Public land
boundaries were delineated using archived plat books and
historic tax assessment rolls. Public lands include public
parks, county forestland, and Wisconsin Department of
Natural Resources–managed lands (Table 2). Slope values
were derived from a 30-m digital elevation model and averaged for each parcel using the Spatial Analyst extension.
Soil variables were obtained from the Soil Survey Geographic
(SSURGO) Database. We extracted hydric soils from the
SSURGO database to represent areas that form under flooding or conditions of saturation and are considered a development limitation. The area of hydric soils was summed for
each parcel and calculated as a percentage. The location of
prime farmland soils were also extracted from the SSURGO
database. Prime farmland soils are best suited to produce
food, fiber, and feed. Percentage prime farmland for each
parcel was calculated using GIS overlay methods. Adjacency
and distance variables (public land, forest, water, and agriculture) were calculated by creating a grid of distance values
(5-m resolution) and calculating a minimum for each parcel.
Parcels with a minimum of zero were coded 1 when they
were adjacent independent variables. Percentage forest cover
was calculated by extracting forested areas from each land
cover layer. Parcel layers that most closely corresponded to
the mapped land cover year were overlaid and the area of
forest per parcel was calculated as a percentage. We reconstructed the location and timing of infrastructure expansion
(roads, sewer/water boundaries) based on historical aerial
photos and interviews with local officials.
Statistical Analysis
Parcelization events were identified from the spatial interpretation of parcel layers. We assigned parcels into one of
two classes: parcels that split and parcels that did not split.
Because our dependent data were binary (i.e., split or no
split), we used logistic regression as a predictive approach
for estimating the probability that a given parcel would subdivide. Prior to model development, we evaluated Spearman
rank correlations among all potential independent variables
and excluded an initial set (Table 2) based on correlations
greater than 0.6 with other candidate variables. To further
ensure minimal multicollinearity, we calculated variance
inflation factors (VIF) for each of the predictors. All independent variables had VIF values less than 3. For each parcelization subset, we randomly selected two-thirds of the
parcel splits and an equal number of nonparcel splits (those
parcels that did not split over the entire period), using the
remainder as a model validation set. We developed our
model using a forward selection logistic procedure in SPSS,
with a probability to enter or stay in the model of P ≤0.05.
We evaluated model performance by examining the area
under the receiver operator characteristic (ROC) curve. The
ROC curve represents the relationship between the sensitivity (proportion of splits correctly predicted as such) relative
to the specificity fraction (proportion of nonsplits incorrectly
predicted as splits). When these fractions are plotted, the
area under the curve corresponds to the ability of the model
to distinguish between split and nonsplit events (Venier et al.
2004). ROC value ranges from 0.5 to 1, where 1 indicates a
perfect fit and 0.5 represents a random fit.
Results
Table 3 summarizes the overall model statistics of the logistic regression conducted in this study. A total of 1,280 cases
were analyzed (N = 640 splitters and 640 nonsplitters) and
the full model was considered to be significantly reliable, with
an overall chi-square of 312.643. The high model chi-square
value indicates an improvement over the intercept-only
model. The intercept-only model serves as a good baseline
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Journal of Planning Education and Research 32(1)
Table 2. Factors Influencing Rural Land Parcelization
Natural resource factors
Public_distance
Forest_distance
Water_distance
Agriculture_distance
Soil_limitations
Prime_soil
Slope
Cover
Infrastructure factors
Road_distance
Sewer_distance
Other factors
Size
Description
Units
Impact on Parcelization
Distance to public land
Distance to forest
Distance to water
Distance to agriculture
Percentage of parcel unsuitable for development
Percentage of parcel with prime farmland soil
Mean parcel slope
Percentage of parcel in forest cover
feet
feet
feet
feet
%
%
%
%
Distance to road
Distance to municipal sewer boundary
feet
feet
Parcel size at beginning of time period
Index 1-2(1 if <20 and 2 if ≥20)
+
+
+
Table 3. Logistic Regression Model Predicting the Probability of Parcelization
Predictor Variable
Constant
Public_distance
Water_distance
Agriculture_distance
Road_distance
Sewer_distance
Size
Test
Overall model evaluation
Model
–2 log likelihood
–Cox & Snell R2
–Nagelkerke R2
–Goodness-of-fit test
Hosmer and Lemeshow
–ROC
–Percentage correctly predicted
Β
SE β
Wald’s χ²
df
p
Odds Ratio
2.058
−0.117
−0.332
−0.428
−1.783
0.132
1.395
0.634
0.044
0.053
0.082
0.561
0.046
0.621
10.531
8.95
27.007
21.358
10.425
8.117
1.185
1
1
1
1
1
1
1
0.001
0.003
0.000
0.000
0.001
0.004
0.000
7.827
0.877
0.761
0.686
0.163
1.141
4.035
χ²
df
p
312.643
6
0.000
38.764
8
0.064
1461.813
0.217
0.289
0.771
34%
Note: ROC = receiver operating characteristic.
because it contains no predictors. The Hosmer and Lemeshow
chi-square test had a p value of 0.064, indicating that the
data fit the model well (Hosmer and Lemeshow 2000).
The Cox & Snell and Nagelkerke R2 values were 0.217
and 0.289, respectively. When adjusted R2 values exceed
0.2, it generally indicates a good model fit (Clark and
Hosking 1986).
As an alternative method, we examined the area under
the ROC curve of the final logistic regression model. For
all parcel splits, the area under the ROC curve was 0.771,
indicating a correlation between the independent variables
(as a group) and the dependent variable. The specificity
value was 0.74, indicating that the model correctly predicted
parcels that did not split with a greater than 70 percent success rate (Table 4). Similarly, the sensitivity value was 0.71,
indicating that the logistic regression model correctly predicted where parcels split about 70 percent of the time. These
observations are supported by both false-positive and falsenegative results of 27.2 and 28.4 percent, respectively. The
false positive measures the proportion of misclassified
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Haines and McFarlane
Table 4. Observed and Predicted Frequencies for Parcelization
by Logistic Regression with a Cutoff of 0.5
Predicted
Observed
Split
No Split
% Correct
Split
No split
Overall % correct
454
170
186
470
70.9
73.4
72.2
Note: Sensitivity = 70.9%; specificity = 73.4%; false positive = 27.2%; false
negative = 28.4%.
parcelization events over all nonparcelization events. The
false-negative measure, on the other hand, indicates the proportion of misclassified parcelization over all those classified
as nonparcelization events. The overall correction prediction
was 72.2 percent, an improvement over a random chance level.
The logistic regression analysis highlighted the importance of several factors that influence the occurrence of parcel splits in the study area based on model coefficients.
The relative importance of the independent variables can be
assessed using the corresponding coefficients in the logistic
regression model. In this study, all coefficients, except the
ones belonging to sewer district boundaries and parcel size,
are negative (Table 3), indicating that they are negatively
related to the probability of land subdivision through the log
transformation. Specifically, the coefficient belonging to
distance to roads strongly departs from zero and led us to the
assumption that the road network has a stronger effect on
parcelization than any other parameter. Proximity to sewer
district boundaries had the opposite effect than our hypothesized relationship. Thus, in the study area sewer districts are
not influencing parcelization; instead land division is likely
to occur farther away. The parcel size variable indicated a
positive relationship, meaning that larger lots have a higher
probability of parcelization than do smaller ones.
Among the natural amenity parameters, proximity to agriculture played the most important role for parcel subdivision. Proximity to water and public land were also important
factors in the model. However, the regression model differs
from our expected hypothesis and revealed that distance
from forestland, and percentage slope, soil limitations, and
forest cover had little influence on parcelization events.
The validation process of the model was performed for the
span of 2000-2005. We were able to conduct this analysis
because we had all the parcels in our database and had sampled
from that population to conduct the logistic regression. The
candidate parcels and their land division status of 2000-2005
were first determined. For each parcel, the probability of parcelization was computed using the logistic model (1).
log it(P) = 2.058 – 0.117Public_distance – 0.332Water_
distance – 0.428Agriculture_distance – 1.783Road_
distance + 0.132Sewer_distance + 1.395Size
(1)
Subsequently, we selected the parcels with the highest
calculated probabilities and compared them to the actual
observed splits for the same 5-year time span. Figure 2 is a
visual representation of the calculated probability of parcelization. Although the overall 72.2 percent correct prediction
is relatively high, we find that the accuracy of correct prediction for the occurrence of parcelization is lower, at 34 percent for this time period. However, this model predicts the
particular parcel that will subdivide, and what we are actually interested in are areas that are likely to subdivide. By
selecting the adjacent parcels to the predicted splitters,
56 percent of the parcels are correctly identified. Notably,
nearly 4,000 acres of the study area is identified as having a
very high probability of parcelization. This is especially the
case in the towns of Lodi and West Point, which have considerable natural resource amenities like water and agricultural lands.
Discussion and Conclusion
A combination of natural resource and infrastructural factors
influenced land parcelization in rural Columbia County. The
relative importance of these factors may extend beyond our
study region to other areas similar in typology and landscape
characteristics. Because parcelization is a precursor to landuse change, understanding which factors influence land
subdivision is critical because it can reveal the geographic
features favored for potential development. This information
can provide critical information that can help planners and
resource professionals better manage rural landscapes under
development pressure.
The process of parcelization was analyzed by examining
various spatial characteristics of parcels to derive a static
predicting model of rural land division. Built using historic
land records and GIS, the logistic model turned out to be
reasonably successful in revealing influencing factors. In our
study, we found that proximity to roads was the most influential factor, followed by distance to water, and agriculture.
The strong influence of roads is not surprising because building new roads opens previously less accessible areas to
development.
Distance to water is also an important explanatory variable. This is not surprising given our initial observations and
the high value placed on waterfront properties (GonzalezAbraham et al. 2007). However, in this analysis, proximity
to water was not the most important variable. Housing and
parcel densities are high in shoreland areas and were parcelized in most instances prior to shoreland zoning regulations
in the early 1970s. Though still significant and influential,
distance to water has a reduced predictive power because
contemporary parcel splits are now occurring at farther
distances from water. Most of the parcels along the waterfront have been subdivided to their maximum extent; thus,
moving away from water is the only alternative.
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Journal of Planning Education and Research 32(1)
Springvale
L egend
Very low probability
Low probability
Medium probability
High probability
Very High probability
0
W est Point
Lodi
1
2 Miles
–
Figure 2. Probability of parcel subdivsion map based on logistic regression model parameters
Another variable with explanatory power was the proximity to agriculture. We found that the closer to agriculture, the
more likely a parcel will split. This is not too surprising,
since many agricultural regions near metropolitan areas and
cities have been under conversion pressure for quite some
time. The influence of agriculture on parcelization may be
inflated because of a common trend where farmers split their
parcels in order to take advantage of use value taxation. In
this case, farmers have their homesteads parceled off from
their surrounding farmland. Similarly, farmers are also more
likely to subdivide their properties on retirement in order to
generate extra income or divide the land among several children. This finding should cause further concern about the
potential loss of agricultural land.
In general, a higher probability of parcelization tended to
occur farther away from sewer district boundaries (e.g., city/
village borders). The positive relationship supports current
trends and people’s desire for exurban living far from existing cities and urban fringe areas (Heimlich and Anderson
2001). However, we found that when a parcel splits within a
sewer district boundary, it usually subdivides into the maximum allowable number of lots all at once. In contrast, a parcel outside sewer district areas tends to split numerous times
over many years until it becomes too small for further subdivision. Therefore, sewer utilities may be more influential on
parcel size in a given time period.
There is a documented link between population growth
and public lands (Frentz et al. 2004; Mundell et al. 2009).
Our model results also indicated a significant negative relationship with parcelization and distance to public lands,
meaning that land splits are more likely to occur in proximity
to protected open spaces. This is an important finding for
public agencies and conservation organizations. Public lands
are often conserved because of unique or environmentally
89
Haines and McFarlane
sensitive areas. Expanding these lands may become impossible because of the pattern of land ownership and
development.
Several variables had little influence on parcelization,
namely, distance from forestland, percentage slope, soil limitations, and forest cover. In part, these variables may not
affect parcelization like agriculture and water because of the
lack of those particular amenities or constraints. In towns or
counties where mountains and forests are the primary amenities, it’s possible we may find a different result. Haines,
Kennedy, and McFarlane (2011) examined a highly forested
county in northern Wisconsin and found that parcelization
directly lead to forest fragmentation and a decrease in overall
average parcel size. In that study, forest cover is perhaps
akin to agriculture in this study. If the primary land cover is
forest or agriculture, perhaps it’s inevitable that that particular amenity will have a significant influence on parcelization. If that is the case, the results of this study provide
further evidence of the importance of road and water amenities as drivers of parcelization. Additional research is necessary to assess the influence of these nonsignificant variables.
Our study relied heavily on historic data sets that oftentimes
consisted of poor spatial resolution and interpretation. Going
forward, new research tools and higher accuracy layers, like
land cover, soils, and typography, as well as digital cadastres, will permit for more robust sampling and modeling
applications.
It is important to note that predicting the exact parcel for
subdividing is not necessarily the point; instead identifying
regions with high or low probabilities will help to better target land conservation efforts whether for working lands or
wildlife habitat. As discussed earlier, creating a parcelization
probability map by applying the logistic regression function
based on the predictors to the current parcel layer, planners
can visualize the probability of land subdivision and start to
target areas for working lands, for example. The Columbia
County comprehensive plan identifies many areas as agriculture in its future land-use map. In Wisconsin, for example,
this type of analysis could be used by planners to identify
exclusive agricultural lands for farmland preservation plans
and for identifying regions of agricultural land for inclusion
into an agricultural enterprise zone, into a Purchase of
Agricultural Conservation Easements (PACE) program, or
as a sending area for a Transfer of Development Rights
program.
The preparation of a parcelization probability map is
major step forward in land-use planning and resource protection. Our model was limited to only a subset of natural
resource and infrastructural variables because of the historic
nature of the data and the amount of time generating parameters. The study used logistic regression to identify factors
that influence rural land parcelization and to generate a susceptibility map. We used paper records to identify and recreate historic parcel layers in a GIS. We are now working to
encourage more communities to regularly archive digital
copies of their parcel layers and related GIS records to allow
for future research analyzing parcelization, land-use change,
and fragmentation. Of particular interest is the potential for
parcel analysis to help assess and illustrate the degree to
which a town or county has achieved or is at least moving
toward the goals expressed in their comprehensive plans,
such as preserving farmland or wildlife habitat.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
project was supported by the National Research Initiative of the
Cooperative State Research, Education and Extension Service,
USDA, Grant 2005-35401-15924: Factors Influencing Land
Parcelization in Amenity Rich Rural Areas and the Potential
Consequences of Planning Variables.
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Bios
Anna L. Haines is a professor in the College of Natural Resources
at the University of Wisconsin, Stevens Point, and is the director of
the Center for Land Use Education. Her research interests include
parcelization and fragmentation of the rural landscape, planning
and zoning evaluation, and sustainable communities.
Dan McFarlane is a GIS/research specialist in the College of
Natural Resources and an adjunct instructor of GIS in the College
of Science and Letters at the University of Wisconsin, Stevens
Point. He also serves as a GIS/engineering technician for Waupaca
County, Wisconsin. His professional interests include the use of
GIS technology for visualizing and measuring spatial landscape
patterns.