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Valuation of Spatial Configurations and Forest Types in the Southern Appalachian Highlands

Environmental Management volume 43, pages 628–644 (2009)Cite this article

Abstract

Site-specific estimates of the values of spatial configuration and forest composition are presented. Amenity values of forest patches are found to vary the most by urban and sprawling development patterns of specific areas and forest types. For example, smaller patches of deciduous forest are more highly valued in the urban and sprawling areas of Greensboro, North Carolina, whereas larger patches of deciduous forest are more highly valued in the urban and sprawling areas of Greenville, South Carolina. Within the Greenville and Greensboro areas, visible landscape complexity is highly valued for deciduous and evergreen forest patches, whereas lower visible landscape complexity, i.e., smoothly trimmed forest patch boundaries, is highly valued for mixed forest patches.

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References

  • Acharya G, Bennett LL (2001) Valuing open space and land-use patterns in urban watersheds. Journal of Real Estate Finance and Economics 22(2/3):221–237

    Article  Google Scholar 

  • Anderson AT, West SE (2006) Open space, residential property values, and spatial context. Regional Science and Urban Economics 36:773–789

    Article  Google Scholar 

  • Anselin L (1988) Spatial Econometrics. Methods and models. Kluwer Academic, Boston

    Google Scholar 

  • Anselin L (1995) Local indicators of Spatial Association (LISA). Geographical Analysis 27:93–115

    Google Scholar 

  • Baldassare M (1992) Suburban communities. Annual Review of Sociology 18:475–494

    Article  Google Scholar 

  • Bin O, Polasky S (2004) Effects of flood hazards on property values: evidence before and after hurricane flood. Land Economics 80:490–500

    Article  Google Scholar 

  • Blakely EJ (1994) Planning local economic development: theory and practice, 2nd edn. SAGE, London

    Google Scholar 

  • Brunsdon C, Fotheringham AS, Charlton ME (1996) Geographically weighed regression: a method for exploring spatial nonstationarity. Geographical Analysis 28(4):281–298

    Google Scholar 

  • Brunsdon C, Fotheringham AS, Charlton ME (1999) Some notes on parametric significance tests for geographically weighted regression. Journal of Regional Science 39:497–524

    Article  Google Scholar 

  • Cameron A, Trivedi PK (2005) Microeconometrics. Cambridge University Press, Cambridge

    Google Scholar 

  • Cao TV, Cory DC (1981) Mixed land uses. Land-use externalities and residential property values: a re-evaluation. Annals of Regional Science 16:1–24

    Article  Google Scholar 

  • Cho S, Newman DH, Wear DN (2005) Community choices and housing demands: A spatial analysis of the southern Appalachian Highlands. Housing Studies 20:549–569

    Article  Google Scholar 

  • Cho S, Bowker JM, Park WM (2006) Measuring the contribution of water and green space to housing values: an application and comparison of spatially-weighted Hedonic models. Journal of Agricultural and Resource Economics 31:485–507

    Google Scholar 

  • Cho S, Roberts RK (2007) Cure for urban sprawl: measuring the ratio of marginal implicit prices of density-to-lot-size. Review of Agricultural Economics 29:572–579

    Article  Google Scholar 

  • Cho S, Poudyal NC, Roberts RK (2008) Spatial analysis of the amenity value of green open space. Ecological Economics. In Press: Available online

  • Cleveland WS, Devlin SJ (1988) Locally weighted regression: an approach to regression analysis by local fitting. Journal of the American Statistical Association 83:596–610

    Article  Google Scholar 

  • Daniels T (2001) Smart growth: a new American approach to regional planning. Planning Practice & Research 16:271–279

    Article  Google Scholar 

  • Deller SC, Lledo V (2007) Amenities and rural appalachian growth. Agricultural and Resource Economics Review 36(1):107–132

    Google Scholar 

  • EPA (2000) Air Data—County Emission Map. http://www.epa.gov/air/data/emisdist.html?us~usa~United%20States. Accessed online 10 October 2007

  • ESRI (2004) GIS and Mapping Software. http://www.esri.com. Accessed online 15 September 2007

  • Ewing R, Pendall R, Chen D (2002) Measuring Sprawl and its Impact. Smart Growth America, Washington

    Google Scholar 

  • Farber S, Páez A (2007) A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations. Journal of Geographical Systems 9(4):371–396

    Article  Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton ME (1998) Geographically weighted regression: a natural evolution of the expansion method for spatial data analysis. Environment and Planning A 30:1905–1927

    Article  Google Scholar 

  • Fotheringham AS, Brunsdon C (1999) Local forms of spatial analysis. Geographical Analysis 31:340–358

    Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton ME (2000) Quantitative geography. Perspectives on spatial data analysis. Sage Publications, Ltd, London

    Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton ME (2002) Geographically weighted regression: the analysis of spatially varying relationships. John Wiley and Sons Ltd, West Sussex, England

    Google Scholar 

  • Goodman AC (1977) A comparison of block group and census tract data in a Hedonic housing price model. Land Economics 53:483–487

    Article  Google Scholar 

  • Gordon P, Richardson HW (1998) Prove it. Brookings Review 16:23–26

    Google Scholar 

  • Geoghegan J, Weinger L, Bockstael N (1997) Spatial landscape indices in a Hedonic framework: an ecological economics analysis using GIS. Ecological Economics 23:251–264

    Article  Google Scholar 

  • Green GP (2001) Amenities and community economic development: strategies for sustainability. Journal of Regional Analysis & Policy 31(2):61–75

    Google Scholar 

  • Handy S (2005) Smart growth and the transportation—land use connection: what does the research tell us? International Regional Science Review 28(2):146–167

    Article  Google Scholar 

  • Huang Y, Leung Y (2002) Analyzing regional industrialization in Jiangsu province using geographically weighted regression. Journal of Geographical Systems 4:233–249

    Article  Google Scholar 

  • Iwata S, Murao H, Wang Q (2000) Nonparametric assessment of the effects of neighborhood land uses on the residential house values. In: Fomby TB, Hill RC (eds) Advances in econometrics: applying Kernel, nonparametric estimation to economic topics, vol 14. JAI Press, Greenwich

    Google Scholar 

  • Judge GG, Lutkepohl H, Hill RC, Lee T, Griffiths WE (1982) Introduction to the theory and practice of econometrics. John Wiley and Sons, New York

    Google Scholar 

  • Kaplan R, Kaplan S (1989) The experience of nature: a psychological perspective. Cambridge University Press, New York

    Google Scholar 

  • Kelejian HH, Robinson D (2004) The influence of spatially correlated heteroskedasticity on tests for spatial correlation. Advances in Spatial Econometrics, Springer

    Google Scholar 

  • Kestens Y, Theriault M, Rosiers FD (2004) The impact of surrounding land use and vegetation on single-family house prices. Environment and Planning B. Planning and Design 31:539–567

    Article  Google Scholar 

  • Kim K, Marcouiller DW, Deller SC (2005) Natural amenities and rural development: understanding spatial and distributional attributes. Growth and Change 36(2):275–298

    Article  Google Scholar 

  • Lambert DM, McNamara KT, Garrett MI (2006) An application of Spatial Poisson Models to manufacturing investment location analysis. Journal of Agricultural and Applied Economic 38(1):105–121

    Google Scholar 

  • Leung Y, Mei CL, Zhang WX (2000a) Testing for spatial autocorrelation among the residuals of the geographically weighted regression. Environment and Planning A 32(5):871–890

    Article  Google Scholar 

  • Leung Y, Mei CL, Zhang WX (2000b) Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environment and Planning A 32(1):9–32

    Article  Google Scholar 

  • Liu JY, Liu ML, Tian HQ, Zhuang DF, Zhang ZX, Zhang W, Tang XM, Deng XZ (2005) Spatial and temporal patterns of China’s cropland during 1990–2000: an analysis based on Landsat TM data. Remote Sensing of Environment 98:442–456

    Article  Google Scholar 

  • Mahan BL, Polasky S, Adams RM (2000) Valuing urban wetlands: a property price approach. Land Economics 76(1):100–113

    Article  Google Scholar 

  • McMillen DP (1996) One hundred and fifty years of land values in Chicago: a nonparametric approach. Journal of Urban Economics 40(1):100–124

    Article  Google Scholar 

  • Monchuk D, Miranowski J (2007) Amenities and non-farm employment growth in the U.S. Midwest: an analysis of recreational amenities in surrounding counties. The Review of Regional Studies 37(2):120–45

    Google Scholar 

  • MRLC, Multi-Resolution Land Characteristics Consortium (2001) USGS Earth Resource Observation and Science (EROS) Center. http://www.mrlc.gov. Accessed online 13 October 2007

  • Nelson N, Kramer E, Dorfman J, Bumback B (2004) Estimating the economic benefit of landscape pattern: an Hedonic analysis of spatial landscape indices. Institute of Ecology, The University of Georgia

  • Páez A, Uchida T, Miyamoto K (2002a) A general framework for estimation and inference of geographically weighted regression models: 1. Location-specific Kernel bandwidths and a test for locational heterogeneity. Environment and Planning A 34(4):733–754

    Article  Google Scholar 

  • Páez A, Uchida T, Miyamoto K (2002b) A general framework for estimation and inference of geographically weighted regression models: 2. Spatial Association and model specification tests. Environment and Planning A 34(5):883–904

    Article  Google Scholar 

  • Páez A, Long F, Farber S (2008) Moving Window approaches for Hedonic price estimation: an empirical comparison of modeling techniques. Urban Studies. In Press

  • Palmer JF (2004) Using spatial metrics to predict scenic perception in a changing landscape: Dennis, Massachusetts. Landscape and Urban Planning 69:201–218

    Article  Google Scholar 

  • Rasker R, Hansen AJ (2000) Natural amenities and population growth in the Greater Yellowstone region. Human Ecology Review 7:30–40

    Google Scholar 

  • Rempel RS, Carr AP (2003) Patch Analyst extension for ArcView version 3

  • RTI (2005) Rural Technology Initiative—LMS Analyst. http://www.ruraltech.org. Accessed online 15 October 2007

  • Skaburskis A (2000) Housing prices and housing density: do higher prices make cities more compact? Canadian Journal of Regional Science 23:455–487

    Google Scholar 

  • Tian G, Yang Z, Zhang Y (2007) The spatio-temporal dynamic pattern of rural residential land in China in the 1990s using Landsat TM images and GIS. Environmental Management 40:803–813

    Article  Google Scholar 

  • U.S. Census (2000) U.S. Census Bureau—USA QuickFacts. http://quickfacts.census.gov/qfd/index.html. Accessed online 15 September 2007

  • Weitz J (1999) From quiet revolution to smart growth: state growth management programs, 1960 to 1999. Journal of Planning Literature 14(2):268–338

    Article  Google Scholar 

  • Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. Journal of Geographical Systems 7:161–187

    Article  Google Scholar 

  • Wu J (2006) Environmental amenities, urban Sprawl, and community characteristics. Journal of Environmental Economics and Management 52:527–547

    Article  Google Scholar 

  • Yu D, Wu C (2004) Understanding population segregation from Landsat ETM + imagery: a geographically weighted regression approach. GIScience and Remote Sensing 41(3):187–206

    Article  CAS  Google Scholar 

Download references

Acknowledgments

We would like to thank David. B. Eastwood for his valuable comments and seminar participants at the 2008 annual meetings of Southern Regional Science Association. The views expressed here do not necessarily represent those of the University of Tennessee.

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  1. Department of Agricultural Economics, The University of Tennessee, Knoxville, TN, 37996-4518, USA

    Seong-Hoon Cho, Suhyun Jung & Seung Gyu Kim

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  1. Seong-Hoon Cho
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  2. Suhyun Jung
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  3. Seung Gyu Kim
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Corresponding author

Correspondence to Seong-Hoon Cho.

Appendix

Appendix

figure 9

Cluster Maps for Coefficients of Mean Patch Size of Deciduous Forest (top) and Mean Patch Size of Mixed Forest (bottom). Maps generated by CV and LM procedures are on the left and right, respectively

figure 10

Cluster Maps for Coefficients of Patch Density of Evergreen Forest (top) and Patch Density of Mixed Forest (bottom). Maps generated by CV and LM procedures are on the left and right, respectively

figure 11

Cluster Maps for Coefficients of Edge Density of Deciduous Forest (top), Evergreen Forest (middle), and Mixed Forest (bottom). Maps generated by CV and LM procedures are on the left and right, respectively

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Cho, SH., Jung, S. & Kim, S.G. Valuation of Spatial Configurations and Forest Types in the Southern Appalachian Highlands. Environmental Management 43, 628–644 (2009). https://doi.org/10.1007/s00267-008-9209-0

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  • DOI: https://doi.org/10.1007/s00267-008-9209-0

Keywords

  • Landscape values
  • Amenity resources
  • Forest fragmentation
  • Open space
  • GWR
  • Hedonic model