Barry J. Kronenfeld

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Despite conceptual and technology advancements in cartography over the decades, choropleth map design and classification fail to address a fundamental issue: estimates that are statistically indifferent may be assigned to different classes on maps or vice versa. Recently, the class separability concept was introduced as a map classification criterion to(More)
BACKGROUND Health officials and epidemiological researchers often use maps of disease rates to identify potential disease clusters. Because these maps exaggerate the prominence of low-density districts and hide potential clusters in urban (high-density) areas, many researchers have used density-equalizing maps (cartograms) as a basis for epidemiological(More)
The increasing use of fuzzy classification methods to generalize environmental data has led to a persistent question of how to determine class membership values, as well as how to interpret these values once they have been determined. This paper integrates the above two problems as complementary aspects of the same data reduction process. Within this(More)
Data quality should be considered in compiling maps in order to reveal reliable information about the spatial variation of a phenomenon. However, creating classes in a choropleth map by maximizing data reliability (i.e. the statistical differences of observed values between classes) often lead to useless maps with very uneven number of observations in(More)