Geospatial relationships lie at the core of many public health issues, and the integration of remote sensing data, epidemiology, and geographic analysis of disease presents a rich opportunity for collaborative activity at the interface of earth science and public health. Hypotheses generated by the analysis of such geospatial relationships can be tested and refined by analytical and experimental research as the basis for identifying causal relationships. The tools and methods that facilitate analysis include conventional epidemiology, with its subspecialties of genetic, occupational, and environmental epidemiology, as well as remote sensing, Geographic Information Science (GIScience¹), and the broad field of geospatial analysis that includes spatial statistics and spatial modeling.

Geographic Information Systems (GISs) used in an epidemiological context are “a simple extension of statistical analyses that join epidemiological, sociological, clinical, and economic data with references to space. A GIS system does not create data but merely relates data using a system of references that describe spatial relationships” (Ricketts, 2003, p. 3).