A Indrayan1, R Kumar. 1. Division of Biostatistics and Medical Informatics, Delhi University College of Medical Sciences, Dilshad Garden, India.
Abstract
BACKGROUND: The potential of maps in the study of regional variation and similarity in health and in understanding the underlying processes is being increasingly realized. It has thus become important that more care is exercised in drawing health maps and the subjective elements are minimized. Conventional choropleth maps based on qualitative data are mostly arbitrary with regard to the number of categories and the cutoff points. This can lead to substantially different pictures based on the same data set. METHODS: We suggest use of cluster methods to discover 'natural' groups of data points which to a large extent are suggested by the data themselves. These methods can determine not only the cutoff points but also the number of categories required to depict the variability in the data. The methods have natural extension to the multivariate set-up and thus can provide the strategy to construct integrated maps based on the simultaneous consideration of several variables. Since different cluster methods can yield different grouping we propose a simple method to identify cutoffs common to a majority of the methods. RESULTS: The details of the methods are explained on two real data sets. One is the indicators of mortality before one year of age in India and the other is years of life lost due to premature mortality in different countries. The maps obtained are compared with the conventional maps. CONCLUSION: The cutoff points obtained by a majority of cluster methods deserve attention for obtaining natural groups for choroplethic depiction. Maps based on such cutoffs seem to have promise for increasing the accuracy in perception and cognition of regional variation.
BACKGROUND: The potential of maps in the study of regional variation and similarity in health and in understanding the underlying processes is being increasingly realized. It has thus become important that more care is exercised in drawing health maps and the subjective elements are minimized. Conventional choropleth maps based on qualitative data are mostly arbitrary with regard to the number of categories and the cutoff points. This can lead to substantially different pictures based on the same data set. METHODS: We suggest use of cluster methods to discover 'natural' groups of data points which to a large extent are suggested by the data themselves. These methods can determine not only the cutoff points but also the number of categories required to depict the variability in the data. The methods have natural extension to the multivariate set-up and thus can provide the strategy to construct integrated maps based on the simultaneous consideration of several variables. Since different cluster methods can yield different grouping we propose a simple method to identify cutoffs common to a majority of the methods. RESULTS: The details of the methods are explained on two real data sets. One is the indicators of mortality before one year of age in India and the other is years of life lost due to premature mortality in different countries. The maps obtained are compared with the conventional maps. CONCLUSION: The cutoff points obtained by a majority of cluster methods deserve attention for obtaining natural groups for choroplethic depiction. Maps based on such cutoffs seem to have promise for increasing the accuracy in perception and cognition of regional variation.