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Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution
Citation:
Messier, K., L. Jackson, J. White, AND E Hilborn. Landscape Risk Factors for Lyme Disease in the Eastern Broadleaf Forest Province of the Hudson River Valley and the Effect of Explanatory Data Classification Resolution. Spatial and Spatio-temporal Epidemiology. Elsevier B.V., Amsterdam, Netherlands, 12:9-17, (2015).
Impact/Purpose:
Title modified for publication from: "Landscape risk Factors for Lyme Disease in the Hudson River Valley and the effect of Explanatory Data Classification Resolution" As We evaluate if raw national land cover data (NLCD) categories are as useful for describing Lyme disease occurrence distribution as combined NLCD categories. We report little difference, and suggest that land use planners and modelers may use the raw categories to model spatial Lyme disease risk.
Description:
This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter based R(2) (Ra(2)) and AIC. The maximum Ra(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. Theaggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The indwidual model variables included deciduous forest, deciduous forest-squared ,developed low intensity, pasture, y-coordinate, y-coordinate-squared , income, and income-squared. Resuls indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution._
