Distance and Risk Measures for the Analysis of Spatial Data: A Study of Childhood Cancers


Complete text of this article in Social Science and Medicine, 34(7), 769-777, 1992.


This work compares distance and risk measures as ways to detect spatial clusters of disease associated with a point source exposure. Also included is an application of these two approaches to childhood cancer data for the city of San Francisco (1973-88). The distributions of incident cases of leukemia (51 cases), brain cancer (35 cases) and lymphatic cancer (35 cases) among individuals less than 21 years of age are described using three statistical measures: distance on a geopolitical map, distance on a density equalized transformed map, and relative risk. The point source of exposure investigated is a large microwave tower located southwest of the center of the city (Sutro Tower). These three statistical methods are contrasted to explore the advantages and disadvantages of using distance and risk measures in the analysis of spatial data. All three measures of spatial clustering are shown to perform similarly when a specific area of exposure can be defined. Tests based on the normal distribution and simulation methods are used to analyze the spatial distribution of the cancer incidence data. Both analytic approaches indicate that the pattern of the major childhood cancers is essentially random with respect to the point source.


Disease Modeling | Statistical Methodology | Statistical Theory

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