Disease intensity in space and time

This video shows a spatiotemporal estimation of disease intensity from P. ramorum infection of California bay laurel leaves across a 275-km2 study area in Sonoma County, California. Dr. Ross Meentemeyer (NC State University) established 200 plots across this area from 2003 to 2004, and collected data on P. ramorum and SOD related symptoms each spring through 2012: symptomatic leaf count of bay laurel and canker infection on oak and tanoak. Using the symptomatic leaf count data, we rendered this map by clustering plots within 1500-m of each other in space and leaf counts within two years of each other in time. This method is called space-time kernel density estimation, and in this application it provides visualization of which regions of the study area experienced the highest disease intensity (“hot spots”) based on the data. The x-y axes depict space and the z-axis shows time starting with the data from 2004 at the base to the data from 2012 at the top. Further analysis is required to determine if the disease intensity in one plot is related to the disease intensity in nearby plots. Whalen Dillon (NC State University) and Eric Delmelle (UNC at Charlotte) developed this visualization using Voxler 3D Visualization Software (Golden Software, Golden, CO).