Deane Merrill

USE OF DENSITY EQUALIZING MAP PROJECTIONS (DEMP) IN THE ANALYSIS OF CHILDHOOD CANCER IN FOUR CALIFORNIA COUNTIES

In studying geographic disease distributions, one normally compares rates among arbitrarily defined geographic subareas (for example, census tracts), thereby sacrificing 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). Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case location plotted on the transformed map should have a uniform distribution if the underlying disease rates are constant. The present report describes the application of the DEMP technique to 401 childhood cancer cases occurring between 1980 and 1988 in four California counties, with the use of map files and population data for the 262 tracts of the 1980 Census. A kth nearest neighbour analysis provides strong evidence for geographic non-uniformity in tract rates (p < 10(-4)). No such effect is observed for artificial cases generated under the assumption of constant rates. Work is in progress to repeat the analysis with improved population estimates derived from both 1980 and 1990 Census data. Final epidemiologic conclusions will be reported when that analysis is complete.

The University of California CD-ROM Information System

The University of California CD-ROM Information System replaces the equivalent of 260,000 books of published federal statistics with a CD-ROM-based online information system. The size of this database is currently 270 CD-ROMs (135GB). It contains 1990 U.S. census data (approximately 3,000 items of socio-economic and demographic information, including race-ethnicity, employment, income, educational level, and poverty) for every block and census tract in the U.S., as well as U.S. foreign trade data by commodity from every city in the U.S. to every country in the world. It also contains the digitized map outline boundary data for city blocks for the entire U.S. (census TIGER files).

Interpoint squared distance as a measure of spatial clustering

The expectation and variance for the mean interpoint squared distance are presented. In order to evaluate these expressions it is necessary to calculate the moments of a bivariate uniform distribution defined over an arbitrary polygon. Expressions for these moments are presented, allowing the mean interpoint squared distance to be used as a measure of spatial clustering. The distribution and power of this test statistic is explored on the unit square, and the spatial distribution of 11 cases of non-Hodgkins lymphoma is investigated to illustrate an application of the approach.