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The Structure of Human-Environment Interactions in an Urbanizing Watershed

The Structure of Human-Environment Interactions in an Urbanizing Watershed. N.L. Law and L.E. Band University of North Carolina at Chapel Hill Department of Geography AGU Meeting Spring 1999 Boston, MA. Problem Statement.

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The Structure of Human-Environment Interactions in an Urbanizing Watershed

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  1. The Structure of Human-Environment Interactions in an Urbanizing Watershed N.L. Law and L.E. Band University of North Carolina at Chapel Hill Department of Geography AGU Meeting Spring 1999 Boston, MA

  2. Problem Statement • Establish link between socio-economic and biophysical characteristics of urban watersheds • Study relationship with vegetation • Exploratory statistical analysis to uncover associations to help understand system

  3. Gwynns Falls Watershed Baltimore

  4. Population density (persons/ha) in the Gwynns Falls Watershed

  5. Periods of residential development from 1940 to 1990.

  6. Observed vegetation cover in the Gwynns Falls watershed averaged per census block.

  7. Methods Data • Summer TM image to calculate NDVI • Averaged NDVI per census block • 1990 census block data • 9 independent variables, n =303 • 3 socio-economic indices • 4 periods of development • population density (pers/ha) • distance to nearest stream

  8. Demographic variables • Socio-economic index (socio) • occupation • household income • education • Household index (house) • marital status • one-family household • owner occupied dwelling • Ethnicity • ‘black’ or ‘other races’ • foreign born

  9. Methods Model Development • regression model using 9 independent variables • multi-variate model • tree regression

  10. Observed NDVI and population density.

  11. Predicted vs observed NDVINLS regression model based on population density

  12. Regression Tree-based Models • recursive partitioning • decision rules to split data into homogeneous subsets

  13. < 1.2 km > 1.2 km

  14. Predicted NDVI based on tree regression model averaged per census block.

  15. Observed vs predicted NDVI using regression tree model

  16. Tree Regression Model: Part 1 Predicted NDVI

  17. Tree Regression Model: Part II

  18. Conclusions • Multiple, integrative factors affect spatial distribution of vegetation cover • Tree regression model suggests: • lower NDVI • city • mixed aged of development • urban parks • higher NDVI • county • newer development

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