In 1854, Dr. John Snow solved a medical mystery and became perhaps the first person to solve an environmental health problem with maps. By mapping and spatially examining the distribution of deaths from cholera in relation to water pumps, Snow was able to find the source of, and end, an epidemic of cholera that had killed more than 500 Londoners in 10 days. While Snow believed that contaminated water could be a source of cholera, it was his graphical display “that provided direct and powerful testimony about a possible cause-effect relationship” (Tufte 1997).
Snow’s work provides an excellent example of the power of maps as both an analysis and communication tool. Maps and other forms of spatial analysis can help researchers to visualize problems and come up with creative solutions. But while the use of maps and visualization to solve health and environmental problems has a long history, recent advances in computing and statistical methods, along with extensive geographically referenced health, environmental and population data sets, have revolutionized spatial disease analysis.
For example, while many of the most widely used classical statistical techniques such as least squares and regression were developed in the 18th and 19th centuries, it was only in the 1960s that Krige and Matheron developed the spatial interpolation technique known as kriging, now one of the most widely used spatial statistical techniques (Cressie 1993, Matheron 1963). The 1960s also saw the advent of the first geographic information systems (GIS) software, now the standard for geographic analysis. The combination of new statistical techniques with increased computer power and GIS has made retrospective disease analysis significantly more feasible and enabled major advances in proactive disease surveillance (Waller and Jacquez 1995).