Choropleth maps are commonly used to show statistical variation among map enumerations such as countries or other administrative units. Mapmakers take into account numerous considerations and make many decisions to produce a product that will effectively communicate spatially complex information to the map user. One design consideration is the choice between classed or unclassed choropleth maps, which has been a topic of much discussion in the cartographic community during the last forty years. Unclassed maps assign a unique color, shade, or pattern based on each unit's value. These maps are rich in information but might not be optimal for visual discrimination of regions or identifying values from a legend. Classed maps classify enumeration units based on unit values and in some cases consider geographic area per class or contiguity. These classed maps better delineate regions and interclass variation but are designed to eliminate visibility of intraclass variations. We present a method designed to use colors for choropleth classes and soft shadows to show intraclass variations associated with adjacent or nearby polygons. We conceptualize the choropleth data as a three-dimensional prism model under simulated illumination, with the height of each enumeration unit a function of its mapped value. Our user studies have demonstrated that participants were able to use soft shadows to better identify which of two adjacent units was of greater population density, regardless of whether units were in the same or different classes. Additionally, the resulting soft shadows rarely interfere with the map reader's ability to match color classes to a legend or to compare estimated differences in mean and variance of population density between two regions. Such visual discriminations are not useful for widely spaced units, but are useful for those adjacent to or within the shadow of nearby units.
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