earth sciences and epidemiology/environmental health into a cause-and-effect relationship with one another. The spatial distribution of Lyme disease can be modeled accurately using GIS and remote sensing at a range of scales to model tick dispersion with reference to a series of environmental variables (e.g., Cortinas et al., 2002; Guerra et al., 2002). The same is true of modeling the effects of climate change on disease distribution, although there is some debate about the accuracy of such models (e.g., Hay et al., 2002; Patz et al., 2002; Tanser et al., 2003; Pascual et al., 2006). Geospatial analysis, or more simply “spatial analysis,” uses mathematics and statistics to analyze data patterns that underlie GIS. Many spatial measures and spatial models are available to help summarize and understand complex spatial distributions, including central tendency, dispersion, and clustering (Cromley and McLafferty, 2002; Rushton, 2003).
Remote sensing encompasses the full array of technologies for data collection using aircraft or satellites and includes visible wavelength data as well as a broad range of other types of sensors. It is particularly useful for data describing land use, soil, and hydrological features. Satellite imagery is available over an increasing number of wavelengths and at increasing levels of resolution. Remote sensing, coupled with GIS, has been used widely to describe the environmental conditions associated with disease and to model the occurrence of disease, particularly infectious diseases that are sensitive to environmental conditions such as vectorborne and waterborne diseases.
Data layers are a basic element of GIS. A layer of population data may be superimposed on geological data for determining, for example, whether there is a relationship between bedrock type and population characteristics. Or earthquake vulnerability coefficients may be overlaid on layers showing the distribution of elderly or handicapped people for scenario planning for disaster response. Similarly, maps of land use may be overlaid on digital terrain maps in coastal areas, for example, to aid in efforts to mitigate the salinization problems that have been experienced in Sri Lanka and Malaysia following the flooding of rice paddies by the December 2004 tsunami. Njemanze et al. (1999) used a series of “probability layers” to assess the risk of diarrheal disease from water in rural Nigeria (see Box 7.1). The aggregate risk is a product of geological, hydrological, population, and pollutant characteristics, all of which vary spatially.