For a GIS to accurately represent occurrences on the earth’s surface, the location of data must be reliable, accurate, and pertinent (Falbo et al. 1991). Geospatial science is the systematic study of geographic variables relating to, occupying, or having the character of space. Fundamental elements of geospatial sciences relevant to GIS applications in exposure assessment include data representation, scale, and accuracy. Data representation is the format of the unit of analysis used in the GIS. The most commonly used representations of space in a GIS are the raster and vector data models. In the raster model, grid cells serve as the basic units of analysis. An example would be pixels of remotely sensed imagery from satellite imagery. The vector model uses points, lines, or polygons based on continuous geometry of space to represent data. Other, more specialized data models are available in most GIS software. For example, the triangulated irregular network (TIN) model provides an efficient means of representing elevation data often used for terrain analysis. GIS software contain algorithms for translating between formats, for example, raster → vector, vector → raster, point → TIN, although some error may be introduced by these data transformation processes. More complete information on data models can be found in textbooks such as those by Chrisman (2002) and DeMers (2000).