Use of measured environmental data and mathematic algorithms for estimating contaminant levels in exposure assessment is another area requiring specialized expertise in most cases. Since the advent of the computer age, packaged software has become more and more prevalent for such applications, but the old modeler adage “garbage in, garbage out” is perpetual truth. Even with the color maps produced using a GIS, “mapped garbage” is still “garbage.” In this article we propose several fundamental principles of environmental science and modeling that should be adhered to when using GIS in exposure assessment for epidemiology studies. Perhaps the most important of these principals can be captured by the term “validation.” In each of our example studies, environmental data were used to develop an exposure metric for use in epidemiology. The data used were collected for other purposes, commonly for administrative or regulatory use. These studies demonstrate the range of measurement data quality and degree of validation that may be possible from relatively low (Elliott et al. 2001) to high (Nyberg et al. 2000). They also demonstrate the likely consequences across this range in terms of risk estimates in an epidemiology study. In Elliott et al. (2001), a database on landfill sites was obtained from the environmental protection agencies, which collected the data from site operators in the licensing process. Thus, data that would have been useful for exposure assessment were not readily available (e.g., volumes and types of waste actually received at the landfill sites, measurement data for specific chemicals being released into the environment, or the extent of contamination). Instead, the likely limit of dispersion for landfill emissions (2 km) was estimated based on published information and used as an exposure boundary around each site, degree of hazard for exposure was derived from the type of license held by the operator, and the epidemiologic analysis assumed a common relative risk for all landfill sites. The researchers did not validate these exposure metrics. It is likely that sites licensed to carry special (hazardous) waste did not necessarily do so, and that sites licensed to carry nonspecial waste actually did carry some hazardous waste as well. The resulting exposure misclassification was most likely nondifferential, which could result in a bias risk estimate toward the null (Copeland et al. 1977). The findings of the study, small excess risks for some birth outcomes after exposure to landfills, seem to verify this conclusion.