title: A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping.
abstract: 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 evaluate the likelihood that estimates in two classes are statistical different. Unfortunately, choropleth maps created according to the separability criterion usually have highly unbalanced classes. To produce reasonably separable but more balanced classes, we propose a heuristic classification approach to consider not just the class separability criterion but also other classification criteria such as evenness and intra-class variability. A geovisual-analytic package was developed to support the heuristic mapping process to evaluate the trade-off between relevant criteria and to select the most preferable classification. Class break values can be adjusted to improve the performance of a classification.
authors.affiliation: Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA.
authors.forename: Min
authors.initials: M
authors.lastname: Sun
authors.affiliation: Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA.
authors.forename: David
authors.initials: D
authors.lastname: Wong
authors.affiliation: Department of Geology/Geography, Eastern Illinois University, Charleston, IL, USA.
authors.forename: Barry
authors.initials: B
authors.lastname: Kronenfeld
keywords: Class separability; choropleth maps; data reliability; multi-criteria classification