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Title: Density equalizing map projections (cartograms) in public health applications

Abstract

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){copyright}. Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease risk is constant. On the transformed map, the statistical analysis of the observed distribution is greatly simplified. Even for sparse distributions, the statistical significance of a supposed disease cluster can be calculated with validity. 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 uniform risk. No statistically significant evidence for geographic non-uniformity of rates was found, in agreement with the original analysismore » performed by the California Department of Health Services.« less

Authors:
Publication Date:
Research Org.:
Lawrence Berkeley National Lab., Information and Computing Sciences Div., Berkeley, CA (United States)
Sponsoring Org.:
USDOE Assistant Secretary for Environment, Safety, and Health, Washington, DC (United States)
OSTI Identifier:
290959
Report Number(s):
LBNL-41624
ON: DE98056105; TRN: AHC29901%%164
DOE Contract Number:  
AC03-76SF00098
Resource Type:
Technical Report
Resource Relation:
Other Information: TH: Thesis (Ph.D.); PBD: May 1998
Country of Publication:
United States
Language:
English
Subject:
55 BIOLOGY AND MEDICINE, BASIC STUDIES; PUBLIC HEALTH; EPIDEMIOLOGY; NEOPLASMS; CHILDREN; NUMERICAL DATA; CALIFORNIA; MAPS; GEOGRAPHICAL VARIATIONS; POPULATION DENSITY; ALGORITHMS

Citation Formats

Merrill, D W. Density equalizing map projections (cartograms) in public health applications. United States: N. p., 1998. Web. doi:10.2172/290959.
Merrill, D W. Density equalizing map projections (cartograms) in public health applications. United States. https://doi.org/10.2172/290959
Merrill, D W. 1998. "Density equalizing map projections (cartograms) in public health applications". United States. https://doi.org/10.2172/290959. https://www.osti.gov/servlets/purl/290959.
@article{osti_290959,
title = {Density equalizing map projections (cartograms) in public health applications},
author = {Merrill, D W},
abstractNote = {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){copyright}. Boundaries of geographic subregions are adjusted to equalize population density over the entire study area. Case locations plotted on the transformed map should have a uniform distribution if the underlying disease risk is constant. On the transformed map, the statistical analysis of the observed distribution is greatly simplified. Even for sparse distributions, the statistical significance of a supposed disease cluster can be calculated with validity. 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 uniform 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.},
doi = {10.2172/290959},
url = {https://www.osti.gov/biblio/290959}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri May 01 00:00:00 EDT 1998},
month = {Fri May 01 00:00:00 EDT 1998}
}