ESTIMATING THE ECONOMIC BENEFIT OF LANDSCAPE PATTERN: AN

HEDONIC ANALYSIS OF SPATIAL LANDSCAPE INDICES

And

A COMPARISON OF BUILD- OUT SCENARIOS FOR THE PROTECTION OF ECOSYSTEM FUNCTIONS

Nanette Nelson Institute of Ecology

Elizabeth Kramer Institute of Ecology

Jeffrey Dorfman Department of Agricultural & Applied Economics

Bill Bumback Institute of Ecology

The University of Georgia

March 2004

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Report Overview This report is a summary of results for the study titled: Evaluating greenspace protection tools: What is the value to humans and the environment. We had four original research objectives:

1) Develop a set of landscape indices to estimate and describe specific ecosystem functions.

2) Estimate the degree to which real estate values vary with ecosystem function.

3) Compare the effectiveness of two conservation tools in protecting ecosystem functions and their impact on landscape indices: the Fulton County Tree Protection Ordinance and a conservation subdivision ordinance.

4) Conduct a comparative analysis of build-outs using traditional and conservation subdivision design for one of Georgia’s urban Counties.

Part 1 of this report addresses objectives 1,2, and part of 3. The analysis of the conservation subdivision ordinance was to be funded by the Georgia Forestry Commission’s Urban Forestry Grants, and that project was not funded. Part 2 of this report addresses objective 4. Part 1- The Hedonic Study INTRODUCTION Estimating the public’s value for forested areas informs managers of these resources of the trade-offs individuals would be willing to make in terms of tree canopy protection. The quantification of benefits provided by forests has been the subject of several studies. The aesthetic value of trees was measured by Anderson and Cordell (1985). They found homes sold on average from $347 to $420 per tree present in the landscaping. A more recent study in Georgia found that homes in neighborhoods that were built in the ‘spirit’ of the Fulton County, Georgia tree protection ordinance sold for $104,920 more than homes in neighborhoods that did not protect mature trees (Nelson, et al, 2002). In this study we seek to determine whether individuals in their decision to purchase a home are taking into account attributes of ecosystem function as approximated by indices of landscape pattern. Landscape ecology emphasizes ecological effects of spatial patterning of ecosystems. Landscapes are considered spatially heterogeneous areas with three characteristics: structure, function, and change. Structure refers to the spatial relationships between ecosystems, function refers to the interactions between these spatial elements,

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such as flows of energy or organisms, and change refers to the alteration of structure and function over time (Turner, 1989). Human alterations of landscapes often modify critical ecosystem functions and services, such as biodiversity protection, water quality protection, and air quality protection. Because landscape function is determined from landscape structure it is critical to measure landscape patterns to understand human impacts on ecosystem functions. Therefore, this study is interested in determining if there is a relationship between landscape pattern and individual valuation decisions. Using regression techniques, the hedonic pricing method can estimate the contribution of environmental amenities such as the pattern of the surrounding landscape to the selling price of a house. This value can then be used to infer an individual’s marginal willingness to pay for that environmental amenity and thus the overall value society places on that particular amenity1. Over the last several decades, hedonic models have been used to estimate changes in environmental quality in soil, air, and water; the effects of surrounding land use both agreeable (e.g., open space, parks, and forested areas) and disagreeable (landfills, hog operations, waste water effluent); and most recently, the effects of pattern in the surrounding landscape. These later studies have found that landscape pattern in addition to more traditional measures of neighborhood characteristics such as income levels and cultural composition influenced the sale price of homes (Bockstael, 1996; Geoghegan et al., 1997; Acharya and Bennett, 2001). METHODS STUDY AREA This study focuses on two Georgia counties, Gwinnett and Fulton (Figure 1). Development in these two counties has resulted in differing patterns of land use. The city of Atlanta is in Fulton County, its central location essentially splitting the county in half. Development in this county has been primarily in the north and east resulting in vastly different development rates and densities between the north and south portions of the county. The southern portion of Fulton County, for example, still has significant portions of land in agriculture and forestry production whereas to the north only small, isolated pockets of unimproved property exist. Gwinnett County, on the other hand, has experienced a steady march of development from west to east, essentially avoiding leapfrog development patterns (Figure 2).

1 Hedonic estimation is limited for computing welfare changes in discrete changes in environmental quality. As long as the discrete change is limited to a small area, then the welfare effect may be easily quantified. However, if the discrete change is over a large area, the hedonic price function shifts, and the estimated change in property values serves as an upper bound for benefits (Leggett and Bockstael, 2000).

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Figure 1. The location of Fulton and Gwinnett counties in Georgia

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Figure 2. Land cover maps for Fulton and Gwinnett counties. Green shades represent areas that are still mostly undeveloped while red and pink areas represent urbanized areas. LANDSCAPE METRICS Landscapes are often described as being composed of a mosaic of patches. These patches represent continuous areas of an ecosystem type. Therefore, internal patch heterogeneity is ignored. In this study our patch types include such categories as deciduous forest, evergreen forest, mixed forest, pasture, etc. (see Table 1 for the list of categories). Landscape pattern metrics focus on the spatial character and distribution of patches. These are defined at three levels, patch-level, class-level, and landscape-level. Patch-level metrics are defined for individual patches and characterize the spatial character and context of patches. Class-level metrics are integrated over all of the patches of a given type (class). Finally, landscape-metrics are integrated over all patch types or classes over the entire landscape.

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Figure 3. Aerial photos depicting the difference between a mixed forest (on the left) and a mixed forest residential (on the right) TABLE 1 – LAND COVER CLASSES

♦Deciduous forest ♦Forested wetland ♦Evergreen forest ♦Deciduous forest residential♦Mixed forest ♦Evergreen forest residential♦Pasture ♦Mixed forest residential

For this study we looked at two class-metrics, patch density and edge density and one landscape-metric, contagion. Patch density is a measure of configuration that provides information on the degree of fragmentation within a particular class. In contrast, edge density is a measure of the contrast amongst the patches of various classes. Finally, contagion measures the tendency of patch types to be spatially aggregated. To calculate the landscape metrics we measured the area of the land cover classes listed in Table 1. These land cover classes are shown for the study area in Figure 2. The cell size used in the land cover analysis was 30 meters by 30 meters. The residential designation for three of the forest cover types is to signify a patch that is less than 30 meters by 30 meters. The difference in the two designations is depicted in Figure 3. The landscape metrics, patch density, edge density and contagion, were calculated using Fragstats (McGarigal and Marks, 1995).

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Figure 4. Sales transaction with surrounding land use and buffered distances To address issues of scale, the landscape metrics were calculated for three separate distances. Buffers of 0.1, 0.25, and 1.0 mile where drawn around each housing transaction (see Figure 4). The smallest buffer is meant to capture what one would see from one’s house and will be referred to as the property throughout the remainder of this report. The middle distance captures what is seen immediately surrounding the property and will be referred to as the block. Finally, the 1.0-mile distance is what can be seen within an easy walk and will be referred to as the neighborhood. THE HEDONIC MODEL We can express the sale price of a home by equation 1, where P is an (n x 1) vector of house price; S is an (n x 1) vector of structural characteristics; N is an (n x 1) vector of neighborhood characteristics; and Q is an (n x 1) vector of landscape metrics.

ετγβα ++++= QNSP (1)

The structural characteristics included in this study are: living area, materials used for exterior, foundation, age of house, and lot size. The neighborhood characteristics include the ethnic composition (specifically, percent white) and the median income of the census block group from 2000 Census data. A county dummy variable is included in the regressions to capture the differing tax rates and public services, as well as, quality of school systems between the two

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counties. Four conventional spatial variables measure the distance to several amenties/disamenties including distance to a state highway or interstate, airport, active landfill, and commercial or industrial center2. Definitions of variables used in this study are given in Table 2. Descriptive statistics for the final data sets are presented in Table 3. The housing data used in this study were provided by the local tax assessor’s office in Gwinnett and Fulton counties. Sales were limited to 1998, the same year as the land cover analysis used in generating the landscape metrics. Sales data within the city of Atlanta were excluded to avoid the potential misspecification of the model due to unforeseen market interactions. The 1998 sales data were randomly sampled to produce 1,500 observations per county3. The locations of the housing transactions used in the estimation are shown in Figure 5.

Figure 5. Locations of housing transactions

2 The location of the commercial/industrial center was taken from the analysis of land cover in Georgia using 1998 Landsat imagery (NARSAL, 2002). 3 Transactions that were found to be incomplete or suspect were dropped from the final data set resulting in a final sample size slightly smaller than 1,500 per county.

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TABLE 2 DEFINITIONS OF VARIABLES Housing Variables Variable Attributes Type LOT Area of parcel measured in square meters C YR_BUILT Year house was built C LIV_AREA Living area of house measured in square meters C STUCCO Stucco exterior D BRICK Brick exterior D SLAB Foundation construction D Neighborhood Variables Variable Attributes Type Z_WHITE Area of parcel measured in square meters C MED_INC Year house was built C COUNTY House located in Gwinnett County D D_SH_INT Distance to nearest state highway or interstate (meters) C D_AIRPRT Distance to nearest airport (meters) C D_ACTFILL Distance to nearest active landfill (meters) C D_COM_IN Distance to nearest commercial or industrial area (meters) C Landscape Variables for ‘property’ (0.10-mile buffer) Variable Attributes Type CO_10 Contagion C PD_10_DF Patch density of deciduous forest C PD_10_EF Patch density of evergreen forest C PD_10_MF Patch density of mixed forest C PD_10_P Patch density of pasture C PD_10_FW Patch density of freshwater wetland C PD_10_DFR Patch density of deciduous forest residential C PD_10_EFR Patch density of evergreen forest residential C PD_10_MFR Patch density of mixed forest residential C ED_10_DF Edge density of deciduous forest C ED_10_EF Edge density of evergreen forest C ED_10_MF Edge density of mixed forest C ED_10_P Edge density of pasture C ED_10_FW Edge density of freshwater wetland C ED_10_DFR Edge density of deciduous forest residential C ED_10_EFR Edge density of evergreen forest residential C ED_10_MFR Edge density of mixed forest residential C Landscape variables for ‘block’ and ‘neighborhood’ (0.25- and 1.0-mile buffers) are represented by replacing the “10” in the above list of landscape variables with a “25” and “100”, respectively.

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TABLE 3 - DESCRIPTIVE STATISTICS Housing Variables Variable Minimum Maximum Mean Median Std DevSALE_AMT $40,000 $2,000,000 $198,694 $160,500 $135,992LOT 56 77,177 2,079 1,491 3,439YR_BUILT 1880 2002 1988 1993 13.2LIV_AREA 69.7 1,144 237 217 104Z_WHITE 0.0 1.0 0.77 0.82 0.20MED_INC $20,787 $200,000 $79,613 $74,598 $28,154D_SH_INT 1.0 5,619 1,258 1,053 903D_AIRPRT 359 39,708 17,286 16,314 10,058D_ACTFILL 789 24,187 13,305 13,967 4,765D_COM_IN 4.0 1,114 137 106 126 Housing Dummy Variables Variable Count PercentageSTUCCO 190 0.06BRICK 623 0.21SLAB 1054 0.35COUNTY 1495 0.50 Landscape variables for ‘property’ (0.10-mile buffer) Variable Minimum Maximum Mean Median Std DevCO_10 4.73 75.53 25.69 24.97 7.09PD_10_DF 0.00 93.37 28.67 27.55 17.39PD_10_EF 0.00 89.61 22.70 19.67 15.46PD_10_MF 0.00 65.36 10.70 9.11 11.50PD_10_P 0.00 109.29 15.06 9.11 18.45PD_10_FW 0.00 58.48 1.57 0.00 5.32PD_10_DFR 0.00 134.99 56.64 56.98 24.41PD_10_EFR 0.00 119.38 33.29 31.30 22.98PD_10_MFR 0.00 121.21 13.80 0.00 24.25ED_10_DF 0.00 245.86 72.70 66.14 50.49ED_10_EF 0.00 216.29 56.47 49.28 44.50ED_10_MF 0.00 168.72 21.11 11.90 26.52ED_10_P 0.00 230.77 28.37 11.02 41.58ED_10_FW 0.00 111.11 2.47 0.00 9.84ED_10_DFR 0.00 293.58 122.91 120.45 61.19ED_10_EFR 0.00 279.41 68.51 54.55 58.99ED_10_MFR 0.00 221.31 21.68 0.00 39.60

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TABLE 3 - DESCRIPTIVE STATISTICS (CONTINUED) Landscape variables for ‘block’ (0.25-mile buffer) Variable Minimum Maximum Mean Median Std DevCO_25 10.68 63.41 26.95 26.37 5.91PD_25_DF 0.00 57.83 23.61 23.26 8.57PD_25_EF 0.00 43.54 19.14 19.22 7.66PD_25_MF 0.00 36.68 10.16 8.86 6.62PD_25_P 0.00 62.79 13.06 10.50 10.94PD_25_FW 0.00 27.65 1.88 0.00 3.55PD_25_DFR 0.00 87.77 45.52 46.00 15.42PD_25_EFR 0.00 68.91 28.37 29.14 14.67PD_25_MFR 0.00 81.91 12.02 0.00 19.96ED_25_DF 0.00 193.65 84.91 84.38 36.97ED_25_EF 0.00 165.70 65.88 64.80 32.00ED_25_MF 0.00 126.57 25.10 20.44 19.58ED_25_P 0.00 177.74 30.57 19.00 32.67ED_25_FW 0.00 95.24 3.74 0.00 9.13ED_25_DFR 0.00 251.22 112.50 112.27 47.01ED_25_EFR 0.00 244.48 68.50 62.20 47.94ED_25_MFR 0.00 156.80 20.69 0.00 35.25 Landscape variables for ‘neighborhood’ (1.0-mile buffer) Variable Minimum Maximum Mean Median Std DevCO_100 19.49 54.58 27.18 26.05 4.64PD_100_DF 7.10 34.10 19.70 19.49 3.83PD_100_EF 0.24 24.76 16.59 16.72 3.46PD_100_MF 1.69 25.67 10.30 10.22 4.04PD_100_P 1.81 30.54 11.69 10.62 5.78PD_100_FW 0.00 15.30 1.99 1.08 2.38PD_100_DFR 1.27 62.86 36.09 36.17 11.03PD_100_EFR 0.47 48.70 23.14 24.39 9.90PD_100_MFR 0.00 53.01 9.74 0.00 15.43ED_100_DF 26.14 158.53 92.76 92.85 24.07ED_100_EF 0.65 132.83 73.58 75.31 18.81ED_100_MF 2.24 116.15 29.31 27.48 14.71ED_100_P 2.29 117.09 32.31 26.70 22.96ED_100_FW 0.00 38.42 4.47 1.89 6.14ED_100_DFR 1.69 183.98 91.12 88.48 34.12ED_100_EFR 0.57 173.74 62.52 62.45 34.82ED_100_MFR 0.00 100.84 17.42 0.00 28.09

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Since the hedonic price function consists of both supply and demand considerations, the appropriate functional form cannot be specified on theoretical grounds (Rosen, 1974). Cropper, et al. (1988) found that a simple linear form consistently outperformed other forms when misspecification is present. The simple linear form is also preferable to other forms for the ease of communicating the results to our target audience: forest managers, local elected officials, planners, and the public. RESULTS Three separate models containing variables from one of the three buffer distances (0.1, 0.25, and 1.0-mile) plus the structural and neighborhood variables were estimated using Gauss4. Estimation results are summarized in Tables 4, 5, and 6. All three models performed well accounting for approximately 75 percent of the variation in real estate prices. Because we used a simple linear functional form, the estimated coefficients represent the value in dollars of marginally increasing the associated independent variable. The estimated coefficients for the structural and neighborhood characteristics are significant and of the expected sign with a couple of exceptions. The variable representing the age of the house (YRBUILT) in the property model was not significant. The variable identifying which county the property is in (COUNTY) is not significant in the neighborhood estimation. The variable for distance to state highway or interstate (D_SH_INT) was not significant in all three models. In all three model specifications, several of the landscape variables are statistically significant at the 10 percent level and may be interpreted as follows. Variables associated with patch density that are positive in sign indicate a preference for smaller, more numerous patches of the corresponding patch type. To a landscape ecologist, a higher patch density translates into a more fragmented landscape. Conversely, a negative sign indicates a preference for larger and fewer patches of the corresponding patch type and thus a more intact landscape. Edge density variables that are positive indicate a preference for rougher, more natural patches while a negative sign indicates a smoother, more managed edge is preferred. The contagion index, which measures the overall clumpiness of the landscape, was not significant in all three model specifications. Within the property, marginal increases in patch density of residential deciduous and residential mixed forests improve house prices by $143 and $517, respectively. House prices within the block increase by $849 and $2,143 with a marginal increase in the patch density of residential mixed forest and mixed forest, respectively. Increasing patch density of deciduous forest and mixed 4 The landscape metrics for the three buffer distances were run in separate models due to high multicolinearity among the variables.

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Table 4 – Estimation results for the property (0.10-mile buffer)

Variable Coefficient Estimate

T-value

P-value

Intercept -258,925.25 -0.9394 0.3476LOT 3.65 9.0623 0.0000YR_BUILT 74.62 0.5330 0.5941LIV_AREA 898.14 51.1374 0.0000STUCCO 42,132.70 7.1442 0.0000BRICK 25,244.76 6.9964 0.0000SLAB -7,901.60 -2.5933 0.0096Z_WHITE 38,896.07 4.0936 0.0000MED_INC 0.47 6.6343 0.0000COUNTY -9,239.35 -2.0957 0.0362D_SH_INT -2.06 -1.3758 0.1690D_AIRPRT 0.57 2.7629 0.0058D_ACTFILL 1.25 4.1094 0.0000D_COM_IN -21.06 -1.9363 0.0529CO_10 313.27 1.3553 0.1754PD_10_DF -135.70 -1.3539 0.1759PD_10_EF -167.85 -1.3881 0.1652PD_10_MF 226.84 0.9355 0.3496PD_10_P -24.05 -0.1710 0.8642PD_10_FW 664.90 1.3553 0.1754PD_10_DFR 142.82 1.8789 0.0604PD_10_EFR 11.19 0.1052 0.9163PD_10_MFR 516.67 2.7544 0.0059ED_10_DF 74.64 2.0028 0.0453ED_10_EF -72.28 -1.5396 0.1238ED_10_MF 79.70 0.7321 0.4642ED_10_P -72.81 -1.1727 0.2410ED_10_FW -177.28 -0.6773 0.4983ED_10_DFR -90.93 -2.6237 0.0087ED_10_EFR -42.28 -0.9253 0.3549ED_10_MFR -224.30 -2.0056 0.0450df = 2952 R-sq = 0.7444 DW = 1.8251

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Table 5 – Estimation results for the block (0.25-mile buffer)

Variable Coefficient Estimate

T-value

P-value

Intercept -699,477.53 -2.4796 0.0132LOT 3.96 9.8539 0.0000YR_BUILT 305.79 2.1318 0.0331LIV_AREA 890.73 50.6667 0.0000STUCCO 41,556.17 7.0595 0.0000BRICK 24,560.89 6.8135 0.0000SLAB -7,951.60 -2.6162 0.0089Z_WHITE 44,744.02 4.5399 0.0000MED_INC 0.49 6.9046 0.0000COUNTY -10,900.94 -2.3395 0.0194D_SH_INT -1.57 -1.0339 0.3013D_AIRPRT 0.77 3.5355 0.0004D_ACTFILL 1.27 4.0527 0.0001D_COM_IN -24.41 -2.2025 0.0277CO_25 67.28 0.1948 0.8456PD_25_DF 273.07 1.3593 0.1741PD_25_EF -499.11 -2.0168 0.0438PD_25_MF 2,143.27 4.1356 0.0000PD_25_P -446.56 -1.6955 0.0901PD_25_FW 531.86 0.7356 0.4620PD_25_DFR 222.91 1.3369 0.1814PD_25_EFR -166.88 -0.7674 0.4429PD_25_MFR 848.70 1.9408 0.0524ED_25_DF -49.52 -0.8884 0.3744ED_25_EF -210.65 -3.0956 0.0020ED_25_MF -344.08 -1.8898 0.0589ED_25_P -107.08 -1.2962 0.1950ED_25_FW -130.57 -0.5011 0.6163ED_25_DFR -169.88 -3.0263 0.0025ED_25_EFR -32.10 -0.4899 0.6242ED_25_MFR -282.68 -1.1619 0.2454df = 2952 R-sq = 0.7461 DW = 1.8492

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Table 6 – Estimation results for the neighborhood (1.0-mile buffer)

Variable Coefficient Estimate

T-value

P-value

Intercept -966,983.20 -3.3311 0.0009LOT 4.23 10.6002 0.0000YR_BUILT 445.60 3.0202 0.0025LIV_AREA 872.56 49.5754 0.0000STUCCO 39,921.02 6.8645 0.0000BRICK 21,623.64 6.0759 0.0000SLAB -9,456.24 -3.1696 0.0015Z_WHITE 51,284.47 4.6942 0.0000MED_INC 0.43 5.8841 0.0000COUNTY 2,220.40 0.3701 0.7113D_SH_INT 1.24 0.7477 0.4547D_AIRPRT 1.24 4.6307 0.0000D_ACTFILL 1.35 3.7909 0.0002D_COM_IN -27.23 -2.5685 0.0103CO_100 -538.30 -1.0162 0.3096PD_100_DF 1,127.87 1.9913 0.0465PD_100_EF -2,053.30 -2.8030 0.0051PD_100_MF 8,192.73 4.9900 0.0000PD_100_P -1,148.67 -1.9904 0.0466PD_100_FW 571.79 0.4260 0.6701PD_100_DFR 544.19 1.1410 0.2539PD_100_EFR 469.01 0.7546 0.4506PD_100_MFR -1,006.06 -0.6682 0.5041ED_100_DF -57.87 -0.4903 0.6240ED_100_EF -840.03 -6.4991 0.0000ED_100_MF -1,056.11 -2.5596 0.0105ED_100_P 93.28 0.7319 0.4643ED_100_FW -373.76 -0.8724 0.3830ED_100_DFR -385.39 -2.7666 0.0057ED_100_EFR -7.7621 -0.0503 0.9599ED_100_MFR 852.31 1.0547 0.2917df = 2952 R-sq = 0.7518 DW = 1.8890

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forest within the neighborhood results in an upward shift of sales price by $1,128 and $8,193, respectively. Larger and fewer patches of evergreen forest and pasture improve house prices from $447 to $2,053 within both the block and the neighborhood. Smoother, more managed edges improved house prices in all three model specifications for patches of residential deciduous forest, residential mixed forest, mixed forest, and evergreen forest. Values range from $91 to $1,056 for a marginal decrease in edge density. Only within the property did a rougher, more natural edge for deciduous forest positively affect house prices. In this case, a marginal increase in edge density positively influences sale price by $75. DISCUSSION In this study we have shown that the pattern of surrounding land use as well as its composition influences house prices and thus individual preferences. Homeowners prefer to have hardwoods to pines in their yard with the trees dispersed rather than aggregated in one area. Within their block, homeowners like a mixture of hardwoods and pines in small patches as opposed to a few, large patches. Around the neighborhood both hardwood and mixed forests are preferred in a dispersed pattern rather than a few, large assemblages. The presence of a pine forest or pasture of significant size within the block or neighborhood is also considered a benefit and positively influences house price. At all three scales, property, block, and neighborhood, a managed look is preferred to a natural edge. This result is more likely a consequence of this study being conducted in a primarily urbanized as compared to rural area where properties and streets are linear and engineered. These results show that people care very much about the look of the surrounding landscape. To this end, urban foresters may encounter less resistance from developers when they design tree protection ordinances that produce a landscape that homebuyers are willing to pay premium prices for. The trend, as shown by this study, seems to be a preference for numerous small patches of hardwoods with some pine scattered across the landscape and some pasture or pine forest of significant acreage in close proximity.

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REFERENCES Acharya, G. and L. L. Bennett, 2001. Valuing open space and land-use patterns in urban

watersheds. Journal of Real Estate Finance and Economics, 22(2/3), 221-237. Anderson, L. M. and H. K. Cordell, 1985. Residential property values improved by

landscaping with trees. Southern Journal of Applied Forestry, 9(3), 162-166. Bockstael, N. E., 1996. Economics and ecological modeling: the importance of spatial

perspective. American Journal of Agricultural Economics 78(5), 1168-1180. Geoghegan, J., L. A. Wainger, N. E. Bockstael, 1997. Spatial landscape indices in a

hedonic framework: an ecological economics analysis using GIS. Ecological Economics 23(3), 251-264.

Leggett, C. G. and N. E. Bockstael, 2000. Evidence of the effects of water quality on

residential land prices. Journal of Environmental Economics and Management 39, 121-144.

McGarigal, K. and B. J. Marks. 1995. FRAGSTATS: spatial pattern analysis program for

quantifying landscape structure. USDA For. Serv. Gen. Tech. Rep. PNW-351. (http://www.umass.edu/landeco/research/fragstats/fragstats.html)

NARSAL (Natural Resource Spatial Analysis Lab), 2002. A GAP analysis of Georgia. The

University of Georgia, Athens, Georgia. Nelson, N., J. Dorfman, L. Fowler, 2002. The potential for community forests to be self-

financing: an hedonic analysis of the enhancement value of Georgia’s trees. The University of Georgia. Working paper.

Turner, M.G. 1989. Landscape ecology: The effect of pattern on process. Annual Review

of Ecology and Systematics. 20:171-97. Turner, M. G., R. H. Gardner, R. V. O’Neill, 2001. Landscape ecology in theory and

practice. Springer-Verlag, New York.

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PART 2 - COMPARATIVE ANALYSIS OF BUILD-OUTS INTRODUCTION Current computer technologies allow us to create alternative scenarios based upon different types of policies and regulations and to analyze the impacts of these scenarios. Because both 3-D visualizations and modeled results are extremely computer intensive we were limited in the area that we modeled for this study. We selected a small watershed in Clarke County Georgia for our demonstration. The build-outs represent current land use regulations for the county and regulations modified for conservation subdivisions. We looked at differences in forest cover and potential stormwater impacts for these two scenarios. Because a companion study on the economic effects of conservation subdivisions was not funded, we are not able to develop comparisons for economic differences between these two scenarios. METHODS A comparison of forest cover and stormwater impacts was conducted using ArcView version 3.3 and the Community Viz version 1.3 extension to examine build out under two different land use policy scenarios. The study area for this investigation was defined as the Upper Shoal Creek Watershed, an area of approximately 2,230 acres located in Eastern Athens-Clarke County (Figure 6). This area was chosen because it represents a sparsely developed region of Athens-Clarke County that is under increasing residential development pressure. Existing spatial data layers and county zoning information were compiled and used to configure Community Viz. Locations of existing structures were digitized from 1998 color infrared aerial photography (Figure 7). County land use policy information including zoning and protected areas were collected and used in determining the amount and spatial distribution of future development across the study area. The zoning criteria for the first build-out scenario representing a generalized current policy scenario were a development density of one unit per acre and a minimum building separation of 20 feet. The development constraints restricting land from potential development included a 75-foot buffer around streams as determined from the USGS 7.5 minute quadrangle maps and all wetlands as determined from the 1998 Georgia Landcover Map. The zoning criteria for the second build-out scenario representing an alternative open space preservation scenario (conservation subdivisions) were a development density of two units per acre and a minimum building separation of 20 feet. The development constraints restricting land from potential development included a 150-foot buffer around streams as determined from the USGS 7.5 minute quadrangle maps and all wetlands plus a 100 foot buffer as determined from the 1998 Georgia Landcover Map. These assumptions and constraints are summarized in Table 7. In addition, development under the alternative scenario was restricted to areas along existing roads or adjacent to existing development to ensure that existing infrastructure would be put to the most efficient use before further extending infrastructure.

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Figure 6. Upper Shoal Creek Watershed, Athens-Clarke County, Georgia Table 7. Zoning Criteria and Development Constraints Scenario Density Set backs Stream buffer Wetland buffer Current policy 1 unit per acre 10 ft. 75 ft. 0 ft. Conservation Subdivision policy

2 units per acre 10 ft. 150 ft. 100 ft.

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Figure 7. Existing Structures in the Upper Shoal Creek Watershed, Athens-Clarke County, Georgia

RESULTS The analysis of the build-out results was based on several assumptions. All units predicted by the build-out simulation were assumed to have identical characteristics for each scenario. The assumed average impervious surface per lot of 6 percent for a house on a one-acre lot under the current policy scenario and 10 percent for a house on a half-acre lot under the alternative policy. The key assumption in the stormwater analysis was that all precipitation falling on an impervious surface contributes to total runoff volume while none of the precipitation falling on nonimpervious (pervious) surfaces contributes to runoff. This runoff assumption was held constant over both scenarios to allow for a relative comparison across scenarios. For the potential forest cover analysis, it was assumed that any area not built-out under each scenario would be potentially forested. The results of the comparison show fewer potential stormwater impacts and more potential for large blocks of contiguous forest cover while allowing more units to be constructed under the alternative policy simulation (1,144) than under the current

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policy simulation (1,086). The new structures are much more widely dispersed across the basin under the current policy build-out scenario while the alternative policy build-out scenario shows a clustering approach to development (Figure 8). Some of the environmental effects of clustering development at a higher density can be seen in looking at stormwater and forest cover impacts. The average volume of stormwater runoff resulting from a given precipitation event was lower under the alternative policy scenario at 896 gallons per acre compared to 1,075 gallons per acre under the current policy scenario (Figure 9). There was also a greater amount of land available for potential forest cover under the alternative policy scenario at 1,385 acres compared to 410 acres under the current policy scenario (Figure 10). In addition to the analysis of data, and the rendering of 2-D images, Community Viz software has the capabilities to create 3-D visualizations and fly-throughs of each of the scenarios. Figures 11, 12, and 13 are screenshots of each of these scenarios. This feature provides a useful educational tool for community planners. DISCUSSION The development of future build-out scenarios provides an opportunity to stakeholders, planners, and policy makers to visualize the impacts of regulations and policies on a community. In addition to the visualizations we can begin to create a series of indicators and measures to compare the environmental, economic, and social impacts that these decision might have. For example, conservation subdivision build-out allows for a greater number of housing units and provides larger blocks of forest and lower volumes of stormwater runoff. Although we were not able to develop an economic analysis for these build-outs, we can make some assumptions from our first study that these houses might sell for higher values. In addition, because these houses are built in clusters, infrastructure costs could be lower for the developer.

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Figure 8. Current Policy versus Alternative Policy (Conservation subdivision) Build-Out Scenarios for the Upper Shoal Creek Watershed, Athens-Clarke County, Georgia

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Figure 9. Runoff per Acre Under Current Policy and Alternative Policy (Conservation subdivision) Build-Out Scenarios for the Upper Shoal Creek Watershed, Athens-Clarke County, Georgia

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Figure 10. Potential Forest Area Under Current Policy and Alternative Policy (Conservation subdivision) Build-Out Scenarios for the Upper Shoal Creek Watershed, Athens-Clarke County, Georgia

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Figure 11. This image provides a 3-D view of the existing development pattern found in the Upper Shoal Creek Watershed.

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F Figure 12. This image provides a 3-D view of the Upper Shoal Creek watershed in a future build-out scenario using current development policies.

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Figure 13. This image provides a 3-D view of the alternative policy build-out (conservation subdivision) for the Upper Shoal Creek watershed.