An Examination of Factors Influencing Parcelization Probability in the Town of Delta, Bayfield County, Wisconsin
Ben Bruening, Timothy Kennedy
Introduction
Rural forested areas are becoming increasingly fragmented throughout the United States. (Hammer,
Stewart, Hawbaker, & Radeloff 2009). Larger parcels of land are split and sold as smaller parcels, a process
known as parcelization. These smaller parcels of land are often converted to different land uses, leading to
increased landscape fragmentation.
This parcelization has been shown to lead to negative environmental effects. They include:
•
•
•
Reduced wildlife habitat (Theobold, Miller, & Hobbs 1997)
Reduced water quality (Wear, Turner, & Naiman 1998)
Reduced capacity to use land for wood products (Mundell, Taff, Kilgore, and Snyder 2010; Germain,
Anderson, & Bevilacqua 2007).
The drivers of parcelization are complex and multi-dimensional. Social, environmental and logistical factors all contribute to parcelization probability. Specific characteristics that have been shown by Kennedy
(2014) to influence parcelization probability include:
•
•
•
Distance to Amenities
Land cover
Land value
•
•
•
2007
Methods
• Land cover
• Lake-shore frontage
• Land value
• Surface area of nearest lake over 4 acres
• Zoning
• Distance to water, public land, roads, agriculture, and
Duluth (the nearest urban area to Delta).
• Parcel Acreage
• MFL (managed forest law) Enrollment
These attributes were examined for adverse correlation using Pearson’s correlation analysis. We entered these values into a binary logistic regression model using IBM’s SPSS statistics software to
obtain each characteristic’s relationship to parcel splitting. Using this model we predicted the
probability of parcel split for each parcel in Delta.
Figure 2. Example of a parcel split. The upper left
image is from the 2007 parcel layer, the upper right is
the same location from the 2014 layer. This indicates
the parcel has split since 2014.
1. What effect do multidisciplinary factors have on the likelihood of parcelization in the Town
of Delta?
•
Distance to amenities such as water, roads, and public land are all negatively related with
probability of parcelization. This supports conclusions of previous research which found
that decreased distance to amenities leads to increased probability of parcelization(Haines &
Macfarlane 2011; Hammer, Stewart, Hawbaker, & Radeloff 2009).
•
The percentage of developed land, the percentage of agriculture, and the percentage of forest
are all positively related to probability of parceliazation. This seems to indicate that Parcels
which are more homogenous with regards to land-use have a higher probability of parceliza
tion.
•
Parcels zoned as agriculture or residential have a higher probability of parcelization Residen
tially zoned land is likely to be split as larger parcels of land are divided and sold as smaller par
cels. Agriculturally zoned land is likely to be split for the same purpose.
•
The surface area of the nearest lake is positively related to parcel split probability. Land
near larger lakes is likely to be more valuable because of the increased capacity for boating
and other activities. This leads to increased land division for the purpose of selling and
developing (Schnaiberg, Riera, Turner, & Voss 2002).
• Our model shows no significant relationship between MFL enrollment and parcel split. This is
surprisingThus, MFL enrollment should theoretically be negatively related to par celization
probability.
Source of error:
-Low number of splits- Because we wanted to study parcelization at a local scale in a recent time
period the number of parcel splits our model is based on is rather small (67 total).
2. What effect does the size of nearby lakes have on the parcelization likelihood of nearby land ?
3. What is the likelihood of future parcel split on a per-parcel basis in the Town of Delta?
Study Site
Discussion
Our results indicate that 12 of the variables we tested have a significant relationship to parcelization
probability. Some interesting findings regarding these variables:
Using ArcMap, we overlayed 2007 tax parcel data (n=1028) with 2014 data (n=1036) and determined which parcels had split over this time period. Each parcel was classified as “split” or “not
split”. Public land was removed from consideration, since public parcels are not expected to split.
We then calculated and added attribute data on a per-parcel basis. The attributes included:
Zoning
Lake-shore frontage
Parcel Acreage
In this study, we use GIS analysis to determine the effects these factors and others have on parcelization
in the Town of Delta in Bayfield County, Wisconsin. We use these effects to create a statistical model predicting future parcel split probability in the Town of Delta. Through these processes, weseek to answer
the following research questions:
2014
Results
Table 1. Results of binary logistic regression model including
significance of the variables plugged in to our model. Significant variables are highlighted in blue. * Indicates significance
(.001< p ≤ .05). **Indicates high significance (p ≤ .001).
Variable
B
Significance
Exp(B)
0.000
0.0000**
1.000
MFL Enrollment
-15.285
0.9967
.000
Parcel Acreage
-0.028
0.2165
.973
Lake-shore Frontage
0.000
0.6355
1.000
Distance to Water**
-0.001
0.0145**
Distance to Road**
-0.002
0.0012**
.998
Travel time to Duluth**
0.214
0.0000**
1.239
Percent developed**
0.042
0.0075**
1.043
Percent agriculture**
0.144
0.0011**
1.155
Percent forest*
0.035
0.0249*
1.035
Percent hydric soils
0.030
0.0864
Surface area of nearest lake*
0.006
0.0190*
1.006
Shoreland Zoning
18.551
0.9899
113883596.217
Residential zoning*
1.285
0.0183*
3.616
26.919
Total Land Value**
.999
1.030
Agricultural zoning*
3.293
0.0222*
Commercial zoning
-16.055
0.9993
Distance to public land*
-0.002
0.0073**
.998
Distance to agricultural land**
0.001
0.0000**
1.001
0.9776
.000
Constant
-41.297
.000
Table 2. Classification Table
Predicted
Observed
Split
No Split
Overall % Correct
Split
10
14
No Split
57
869
% Correct
14.92
98.96
93.5
Table 3. Nagelkerke R² value.
This is a pseudo R² value used
for logistic regression models.
Nagelkerke R2
0.440
Table 4. Descriptive statistics of continuous variables
Minimum
Total Land Value ($)
Parcel Acreage
Lake-shore Frontage (ft)
Distance to Water (ft)
Distance to Road (ft)
Travel time to Duluth (min)
Percent Developed
Percent Agriculture
Percent Forest
Percent Hydric Soil
Surface Area of Nearest Lake (ac.)
Distance to Public Land
Distance to Agricultural Land
0.000
0.010
0.000
0.000
0.000
58.569
0.000
0.000
0.000
0.000
4.053
8.482
0.000
Maximum
Mean
399000.000 50063.127
70.481
18.702
3565.560
168.143
16769.810 2663.234
6287.480
545.841
82.220
70.560
100.000
20.371
100.000
3.959
100.000
65.606
100.000
4.828
167.239
59.284
8435.122
53.782
18230.082 6760.371
Conclusion
Our model found 12 variables significantly related to parcel split in the town of Delta (table
1). This included the size of nearby lakes, which indicated that larger lakes near a parcel increase that parcel’s parcelization probability. Using a model based on these variables, we
were able to approximate the likelihood of parcelization of parcels within Delta in the future
(figure 3). This will allow for more targeted management of parcelization by government
agencies such as the Wisconsin DNR, which may help prevent some of the negative environmental and cultural effects associated with parcelization.
Std. Deviation
53240.369
17.317
408.041
4246.682
933.887
6.182
33.566
17.117
37.334
14.485
55.808
1461.463
4312.026
Acknowledgments
We would like to thank Bayfield County, the Wisconsin DNR, and the Center for Land-Use
Education for providing us with the parcel data and GIS layers used in this study. We would
also like to thank the UW- Stevens Point College of Letters and Science Undergraduate Education Initiative for providing funding for the research and travel costs.
0.40
0.20
0.00
22
24
Sources
Probability of
parcelization
Germain, R. H., Anderson, N., & Bevilacqua, E. (2007). The effect of forestland parcelization and ownership transfers on nonindustrial private forestland forest stocking in New York. Journal Of Forestry,
105(8), 403-408.
0% - 4%
5% - 14%
Haines, A. L., & McFarlane, D. (2012). Factors influencing parcelization in amenity-rich rural areas.
Journal of Planning Education and Research, 32(81), 81-90.
15% - 24%
Duluth
Figure 1. The top image is an aerial photograph of Delta overlayed with a
landcover layer to make the land cover differences more noticeable. The black
lines represent roads. The bottom image is the 2014 parcel map of the same
area. Public parcels are gray, private are blue.
Hammer, R. B., Stewart, S. I., Hawbaker, T. J., & Radeloff, V. C. (2009). Housing growth, forests, and
public lands in Northern Wisconsin from 1940 to 2000. Journal of Environmental Management, 90,
2690-2698.
25% - 32%
33% - 40%
Kennedy, T.T. (2014). Modeling the multi-dimensional factors of parcelization and the spatial connection to land-use change in rural Wisconsin (Doctoral dissertation). Retrieved from Proquest.
41% - 50%
54% - 70%
Mundell, J., Taff, S. J., Kilgore, M. A., & Snyder, S. A. (2010). Using real estate records to assess forest
land parcelization and development: A Minnesota case study. Landscape and Urban Planning, 94,
71-76.
Public Land
Our study site is the Town of Delta in Bayfield County, northern Wisconsin. Landcover consists primarily of forested land interspersed with
wetland and agriculture. Development is concentrated at lakeshore
areas. Delta is amenity rich, containing a large number of lakes and a
high percentage of public land. The nearest urban area is Duluth, MN.
Schnaiberg, J., Riera, J., Turner, M. G., & Voss, P. R. (2002). Explaining human settlement patterns in a
recreational lake district: Vilas County, Wisconsin, USA. Environmental Management, 30(1), 24-34.
Lakes
Theobald, D. M., Miller, J.R., & Hobbs, N.T. (1997). Estimating the cumulative effects of development
on wildlife habitat. Landscape and Urban Planning, 39, 25-36.
Figure 3 . Map of probability of future parcel spit in Delta as predicted by our statistical model.
Wear, D.N., Turner, M.G., and R.J. Naiman. 1998. Land cover along an urban-rural gradient:
Implications for water quality. Ecological Applications 8(3): 619-630.