Accuracy can be defined as how well the GIS data represent reality in terms of positional, attribute, and temporal accuracy. Positional accuracy relates to the agreement between data representation in the GIS and actual location of the data, or “ground truth.” Attribute accuracy is a measure of how well information linked to the data representation format is correct (e.g., is the line segment tagged with the correct street information?). Temporal accuracy concerns the appropriateness of using a particular snapshot or snapshots of time for a particular GIS-based analysis or modeling effort. For example, temporal accuracy would reflect how well using a single-year crop map would reflect proximity to pesticide use for exposure assessment of a particular disease outcome. Errors in GIS can be categorized as source errors or processing errors. Source errors relate to the accuracy of the data per se, that is, the differences between the data in the GIS and reality. For example, geocoding is often used to estimate the location of residences and pollutant sources; however, the positional error generated at this first step in the exposure assessment process is rarely evaluated. A study by Krieger et al. (2001) compared geocoding firms and found widely varying geocoding success rates as well as large differences in the accuracy of census tract assignment. The positional accuracy of geocoded addresses in epidemiology studies was evaluated in a breast cancer case–control study in western New York (Bonner et al. 2003) and in a non-Hodgkin lymphoma case–control study in Iowa (Ward et al., in press). The positional errors were comparable in the two studies; the majority of homes were geocoded to within 100 meters of their location determined by GPS. However, positional errors were greater for homes outside the large metropolitan areas (Bonner et al. 2003), and rural addresses in Iowa had a median positional error of around 200 meters (Ward et al. submitted).