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)
The concept of the categorical gradient field is introduced to encompass spatially continuous fields of probabilities or membership values in a fixed number of categories. Three models for implementing categorical gradient fields are examined: raster grids, epsilon bands and gradient polygons. Of these, the gradient polygon model shows promise but has not(More)
This study investigated the effects of widespread forest clearance and fragmentation on forest compositional change between pre-settlement and the present (ca. 1800–1993) in western New York. Forest compositional turnover metrics were calculated to evaluate whether soil conditions accounted for the forest compositional change, to investigate how forest(More)
A spatial modeling technique is proposed to represent boundary uncertainty or gradation on area class maps using a simple polygon tessellation with designated zones of indeterminacy, or transition zones. The transition zone can be conceptualized as a dual of the epsilon band, but is more flexible and allows for a wide range of polygonal configurations,(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)