A heuristic multi-criteria classification approach incorporating data quality information for choropleth mapping. Issue 3 (4th May 2017)

• Record Type: Journal Article
• Authors: Sun, Min; Wong, David; Kronenfeld, Barry
• Is Part Of: Cartography and geographic information science. Volume 44:Issue 3(2017)
• Page Start: 246
• Page End: 258
• Publication Date: 2017-05-04
• Subjects: Class separability -- multi-criteria classification -- data reliability -- choropleth maps; Cartography -- Periodicals; Geographic information systems -- Periodicals; 526
• DOI: 10.1080/15230406.2016.1145072
• Languages: English
• ISSNs: 1523-0406

Abstract

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.