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Rural forested areas are becoming increasingly fragmented throughout the United States. (Hammer, Stewart, Hawbaker, & Radeloff 2009). Larger parcels of land are split into smaller parcels, often to be sold. This process is 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, economical and logistical factors all contribute to parcelization probability. Specific characteristics that have been shown by Kennedy (2014) to influence parcelization probability include: 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 pre- dicting future parcelization probability in the Town of Delta. Through these processes, we seek to answer the following research questions: 1. What effect do multidisciplinary factors have on the likelihood of parcelization in the Town of Delta? 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? Distance to Amenities Land cover Land value Zoning Lake-shore frontage Parcel Acreage An Examination of Factors Influencing Parcelization Probability in the Town of Delta, Bayfield County, Wisconsin Results Introduction Acknowledgments Sources Ben Bruening, Timothy Kennedy Methods Discussion Study Site Our study site is the Town of Delta in Bayfield County, northern Wiscon- sin. 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. 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. Duluth Our results indicate that 12 of the variables we tested have a significant relationship to parcelization probability. Some interesting findings regarding these variables: 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 parcelization. This seems to indicate that parcels which are more homogeneous with regards to land-use have a higher probability of parcelization. Parcels zoned as agriculture or residential have a higher probability of parcelization Residentially zoned land is likely to be split as larger parcels of land are divided and sold as smaller parcels. 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 surprising, since enrollment in MFL prohibits development for human residence on the en rolled parcel. (Wisconsin DNR 2013). Thus, MFL enrollment should intuitively be negatively related to parcelization probabilty. 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). Variable B Significance Exp(B) Total Land Value** 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** .999 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 1.030 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 Agricultural zoning* 3.293 0.0222* 26.919 Commercial zoning -16.055 0.9993 .000 Distance to public land* -0.002 0.0073** .998 Distance to agricultural land** 0.001 0.0000** 1.001 Constant -41.297 0.9776 .000 Table 1. Results of binary logistic regression model including significance of the variables plugged in to our model. Signifi- cant variables are highlighted in blue. * Indicates significance (.001< p ≤ .05). **Indicates high significance (p ≤ .001). Land cover Land value Zoning Parcel Acreage Lake-shore frontage Surface area of nearest lake over 4 acres Distance to water, public land, roads, agriculture, and Duluth (the nearest urban area to Delta). MFL (managed forest law) Enrollment 2007 2014 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. Using ArcMap, we overlayed 2007 tax parcel data (n=1028) with 2014 data (n=1036) and deter- mined 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: These attributes were examined for adverse correlation using Pearson’s correlation analysis. We en- tered these values into a binary logistic regression model using IBM’s SPSS statistics software to obtain each characteristic’s relationship to parcelization. Using this model we predicted the proba- bility of parcelization for each parcel in Delta. Lakes Public Land 54% - 70% 41% - 50% 33% - 40% 25% - 32% 15% - 24% 5% - 14% 0% - 4% Probability of parcelization We would like to thank Bayfield County, the Wisconsin DNR, and the Center for Land-Use Ed- ucation for providing us with the data for this study. We would also like to thank the UW- Stevens Point College of Letters and Science Undergraduate Education Initiative for provid- ing funding for research and travel costs. Conclusion Our model found 12 variables significantly related to parcel split in the town of Delta (table 1). This included the surface of nearby lakes, which suggests 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 environ- mental and economical effects associated with 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. Haines, A. L., & MacFarlane, D. (2012). Factors influencing parcelization in amenity-rich rural areas. Journal of Planning Education and Research, 32(81), 81-90. 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. 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. 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. Schnaiberg, J., Riera, J., Turner, M. G., & Voss, P. R. (2002). Explaining human settlement patterns in a recre- ational lake district: Vilas County, Wisconsin, USA. Environmental Management, 30(1), 24-34. 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. 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. Wisconsin DNR. (2013). Wisconsin’s managed forest law: a program summary. (Publication No. PUB-FR-295). Table 2. Classification Table Observed Split No Split % Correct Split 10 57 14.92 No Split 14 869 98.96 Overall % Correct 93.5 Predicted Nagelkerke R 2 0.440 Table 3. Nagelkerke R² value. This is a pseudo R² value used for logistic regression models. Table 4. Descriptive statistics of continuous variables Minimum Maximum Mean Std. Deviation Total Land Value ($) 0.000 399000.000 50063.127 53240.369 Parcel Acreage 0.010 70.481 18.702 17.317 Lake-shore Frontage (ft) 0.000 3565.560 168.143 408.041 Distance to Water (ft) 0.000 16769.810 2663.234 4246.682 Distance to Road (ft) 0.000 6287.480 545.841 933.887 Travel time to Duluth (min) 58.569 82.220 70.560 6.182 Percent Developed 0.000 100.000 20.371 33.566 Percent Agriculture 0.000 100.000 3.959 17.117 Percent Forest 0.000 100.000 65.606 37.334 Percent Hydric Soil 0.000 100.000 4.828 14.485 Surface Area of Nearest Lake (ac.) 4.053 167.239 59.284 55.808 Distance to Public Land 8.482 8435.122 53.782 1461.463 Distance to Agricultural Land 0.000 18230.082 6760.371 4312.026 Figure 3 . Map of probability of future parcel spit in Delta as predicted by our statistical model.

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Rural forested areas are becoming increasingly fragmented throughout the United States. (Hammer, Stewart, Hawbaker, & Radelo� 2009). Larger parcels of land are split into smaller parcels, often to be sold. This process is known as parcelization. These smaller parcels of land are often converted to di�erent land uses, leading to increased landscape fragmentation.

This parcelization has been shown to lead to negative environmental e�ects. 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, Ta�, Kilgore, and Snyder 2010; Germain,

Anderson, & Bevilacqua 2007).

The drivers of parcelization are complex and multi-dimensional. Social, economical and logistical factors all contribute to parcelization probability. Speci�c characteristics that have been shown by Kennedy (2014) to in�uence parcelization probability include:

In this study, we use GIS analysis to determine the e�ects these factors and others have on parcelization in the Town of Delta in Bay�eld County, Wisconsin. We use these e�ects to create a statistical model pre-dicting future parcelization probability in the Town of Delta. Through these processes, we seek to answer the following research questions:

1. What e�ect do multidisciplinary factors have on the likelihood of parcelization in the Townof Delta?

2. What e�ect 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?

• Distance to Amenities• Land cover• Land value

• Zoning• Lake-shore frontage• Parcel Acreage

An Examination of Factors Influencing Parcelization Probability in the Town of Delta, Bayfield County, Wisconsin

Results

Introduction

Acknowledgments

Sources

Ben Bruening, Timothy Kennedy

Methods Discussion

Study Site

Our study site is the Town of Delta in Bay�eld County, northern Wiscon-sin. 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.

Figure 1. The top image is an aerial photograph of Delta overlayed with a landcover layer to make the land cover di�erences 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.

Duluth

Our results indicate that 12 of the variables we tested have a signi�cant relationship to parcelization probability. Some interesting �ndings regarding these variables:

• Distance to amenities such as water, roads, and public land are all negatively related withprobability of parcelization. This supports conclusions of previous research which foundthat decreased distance to amenities leads to increased probability of parcelization (Haines &Macfarlane 2011; Hammer, Stewart, Hawbaker, & Radelo� 2009).

• The percentage of developed land, the percentage of agriculture, and the percentage of forestare all positively related to probability of parcelization. This seems to indicate that parcelswhich are more homogeneous with regards to land-use have a higher probability ofparcelization.

• Parcels zoned as agriculture or residential have a higher probability of parcelizationResidentially zoned land is likely to be split as larger parcels of land are divided and sold assmaller parcels. 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. Landnear larger lakes is likely to be more valuable because of the increased capacity for boatingand other activities. This leads to increased land division for the purpose of selling anddeveloping (Schnaiberg, Riera, Turner, & Voss 2002).

• Our model shows no signi�cant relationship between MFL enrollment and parcel split. This issurprising, since enrollment in MFL prohibits development for human residence on the enrolled parcel. (Wisconsin DNR 2013). Thus, MFL enrollment should intuitively benegatively related to parcelization probabilty.

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).

Variable B Significance Exp(B)

Total Land Value** 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** .999

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 1.030

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

Agricultural zoning* 3.293 0.0222* 26.919

Commercial zoning -16.055 0.9993 .000

Distance to public land* -0.002 0.0073** .998

Distance to agricultural land** 0.001 0.0000** 1.001

Constant -41.297 0.9776 .000

Table 1. Results of binary logistic regression model including signi�cance of the variables plugged in to our model. Signi�-cant variables are highlighted in blue. * Indicates signi�cance (.001< p ≤ .05). **Indicates high signi�cance (p ≤ .001).

• Land cover• Land value• Zoning• Parcel Acreage

• Lake-shore frontage• Surface area of nearest lake over 4 acres• Distance to water, public land, roads, agriculture, and

Duluth (the nearest urban area to Delta).• MFL (managed forest law) Enrollment

2007 2014

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.

Using ArcMap, we overlayed 2007 tax parcel data (n=1028) with 2014 data (n=1036) and deter-mined which parcels had split over this time period. Each parcel was classi�ed 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:

These attributes were examined for adverse correlation using Pearson’s correlation analysis. We en-tered these values into a binary logistic regression model using IBM’s SPSS statistics software to obtain each characteristic’s relationship to parcelization. Using this model we predicted the proba-bility of parcelization for each parcel in Delta.

Lakes

Public Land

54% - 70%

41% - 50%

33% - 40%

25% - 32%

15% - 24%

5% - 14%

0% - 4%

Probability of parcelization

We would like to thank Bay�eld County, the Wisconsin DNR, and the Center for Land-Use Ed-ucation for providing us with the data for this study. We would also like to thank the UW- Stevens Point College of Letters and Science Undergraduate Education Initiative for provid-ing funding for research and travel costs.

ConclusionOur model found 12 variables signi�cantly related to parcel split in the town of Delta (table 1). This included the surface of nearby lakes, which suggests 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 (�gure 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 environ-mental and economical e�ects associated with parcelization.

Germain, R. H., Anderson, N., & Bevilacqua, E. (2007). The e�ect of forestland parcelization and ownership transfers on nonindustrial private forestland forest stocking in New York. Journal Of Forestry, 105(8), 403-408.

Haines, A. L., & MacFarlane, D. (2012). Factors in�uencing parcelization in amenity-rich rural areas. Journal of Planning Education and Research, 32(81), 81-90.

Hammer, R. B., Stewart, S. I., Hawbaker, T. J., & Radelo�, V. C. (2009). Housing growth, forests, and public lands in Northern Wisconsin from 1940 to 2000. Journal of Environmental Management, 90, 2690-2698.

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.

Mundell, J., Ta�, 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.

Schnaiberg, J., Riera, J., Turner, M. G., & Voss, P. R. (2002). Explaining human settlement patterns in a recre-ational lake district: Vilas County, Wisconsin, USA. Environmental Management, 30(1), 24-34.

Theobald, D. M., Miller, J.R., & Hobbs, N.T. (1997). Estimating the cumulative e�ects of development on wildlife habitat. Landscape and Urban Planning, 39, 25-36.

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.

Wisconsin DNR. (2013). Wisconsin’s managed forest law: a program summary. (Publication No. PUB-FR-295).

Table 2. Classi�cation Table

Observed Split No Split % CorrectSplit 10 57 14.92No Split 14 869 98.96Overall % Correct 93.5

Predicted

Nagelkerke R2

0.440

Table 3. Nagelkerke R² value. This is a pseudo R² value used for logistic regression models.

Table 4. Descriptive statistics of continuous variables

Minimum Maximum Mean Std. Deviation

Total Land Value ($) 0.000 399000.000 50063.127 53240.369Parcel Acreage 0.010 70.481 18.702 17.317Lake-shore Frontage (ft) 0.000 3565.560 168.143 408.041Distance to Water (ft) 0.000 16769.810 2663.234 4246.682Distance to Road (ft) 0.000 6287.480 545.841 933.887Travel time to Duluth (min) 58.569 82.220 70.560 6.182Percent Developed 0.000 100.000 20.371 33.566Percent Agriculture 0.000 100.000 3.959 17.117Percent Forest 0.000 100.000 65.606 37.334Percent Hydric Soil 0.000 100.000 4.828 14.485Surface Area of Nearest Lake (ac.) 4.053 167.239 59.284 55.808Distance to Public Land 8.482 8435.122 53.782 1461.463Distance to Agricultural Land 0.000 18230.082 6760.371 4312.026

Figure 3 . Map of probability of future parcel spit in Delta as predicted by our statistical model.