| In studying geographic disease distributions, one normally compares rates among arbitrarily defined geographic subareas (e.g. census tracts), thereby sacrificing some of the geographic detail of the original data. The sparser the data, the larger the subareas must be in order to calculate stable rates. This dilemma is avoided with the technique of Density Equalizing Map Projections (DEMP);The DEMP algorithm was applied to a data set previously analyzed with conventional techniques; namely, 401 childhood cancer cases in four counties of California. The distribution of cases on the transformed map was analyzed visually and statistically. To check the validity of the method, the identical analysis was performed on 401 artificial cases randomly generated under the assumption of uniformity risk. No statistically significant evidence for geographic non-uniformity of rates was found, in agreement with the original analysis perFormed by the California Department of Health Services (DHS).;Appendix A documents the electronic locations, of not only the data files used in this analysis, but of documents and data assembled during 30 years of related projects at Lawrence Berkeley National Laboratory (LBNL). These data are from SEEDIS (Socio-Economic Environmental Demographic Information System) and the PAREP (Populations at Risk to Environmental Pollution) project, and include comprehensive 1970 and 1980 U.S. Census data. Over 3200 tapes of historical government data, some of them unique and irreplaceable, have been archived and are documented online. |
