Optimal Map Classification Incorporating Uncertainty Information

A choropleth map frequently is used to portray the spatial pattern of attributes, and its mapping result heavily relies on map classification. Uncertainty in an attribute has an influence on map classification and, accordingly, can generate an unreliable spatial pattern. Only a few studies, however, have explored the implications of uncertainty in map classification. Recent studies present methods to incorporate uncertainty in map classification and generate a more reliable spatial pattern. Nevertheless, these methods often produce an undesirable result, with most observations assigned to one class, and struggle to find an optimal result. The purpose of this article is to expand the discussion about finding an optimal classification result considering data uncertainty in a map classification. Specifically, this article proposes optimal classification methods based on a shortest path problem in an acyclic network. These methods use dissimilarity measures and various cost and objective functions that simultaneously can consider attribute estimates and their uncertainty. Implementation of the proposed methods is in an ArcGIS environment with interactive graphic tools, illustrated with a mapping application of the American Community Survey data in Texas. The proposed methods successfully produce map classification results, achieving improved homogeneity within a class.