Journal of Geographical Systems

, Volume 14, Issue 3, pp 243–264

Validation tests of an improved kernel density estimation method for identifying disease clusters

Original Article

DOI: 10.1007/s10109-010-0146-0

Cite this article as:
Cai, Q., Rushton, G. & Bhaduri, B. J Geogr Syst (2012) 14: 243. doi:10.1007/s10109-010-0146-0

Abstract

The spatial filter method, which belongs to the class of kernel density estimation methods, has been used to make morbidity and mortality maps in several recent studies. We propose improvements in the method to include spatially adaptive filters to achieve constant standard error of the relative risk estimates; a staircase weight method for weighting observations to reduce estimation bias; and a parameter selection tool to enhance disease cluster detection performance, measured by sensitivity, specificity, and false discovery rate. We test the performance of the method using Monte Carlo simulations of hypothetical disease clusters over a test area of four counties in Iowa. The simulations include different types of spatial disease patterns and high-resolution population distribution data. Results confirm that the new features of the spatial filter method do substantially improve its performance in realistic situations comparable to those where the method is likely to be used.

Keywords

GISKernel density estimationSpatial filterDisease rateDisease clusters

JEL Classification

C13C14C15

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.National Minority Quality ForumWashingtonUSA
  2. 2.Department of GeographyThe University of IowaIowa CityUSA
  3. 3.Geographic Information Science and Technology GroupOak Ridge National LaboratoryOak RidgeUSA