The Importance of Varying Spatial Levels in GIS Analysis of Environmental Epidemiological Data

摘要

Geographical Information Systems (GIS) are powerful tools for analyzing spatially related data. GIS has many potential applications in environmental epidemiology, because contaminant exposure is often spatially related. For example, persons living close to a source of toxic air emissions would likely be exposed to higher concentrations, resulting in greater likelihood of developing illnesses. In using GIS to analyze environmental epidemiological data, a spatial scale must be chosen. A large scale, such as that of a metropolitan region, requires less data, and is thus easier and quicker in terms of computational time. It may not provide, however, a fine enough resolution to detect patterns of disease due to an emission source. A smaller scale, such as a block group, can provide finer resolution. A smaller scale requires more data, time for analysis, and computing power; however, it is also more likely to uncover relationships between individual emission sources and populations with higher incidence of disease. This paper will discuss the issue of spatial scales in the context of a case study, involving proximity of childhood leukemia cases to airports. (The entire study was described in a 2012 AWMA conference paper; this paper will focus on the issue of spatial scales.) Using data provided by the Texas Department of State Health Services, observed/expected cancer ratios were calculated in GIS. Scatter plots of observed/expected ratios were then generated as a function of distance from airports. Similar analyses were performed at the levels of block group, census tract, and county, including counties with large and small populations. At the scale of large counties, no relationship was observed between leukemia cases and proximity to airports. However, at the block group level, a relationship became apparent. The case study thus illustrates potential advantages of analyzing smaller areas when teasing out relationships between diseases and pollutant emission sources.