Preserving Change Information in Multi-temporal Choropleth Maps Through an Extended Data Classification Method

Abstract

Diverse user requirements has led to an increasing availability of multi-temporal data, the analysis of which often requires visualization, e.g. in multi-temporal choropleth maps. However, if using standard data classification methods for the creation of these maps, problems arise: significant changes can be lost by data classification (change loss) or non-significant changes can be emphasized (change exaggeration). In this paper, an extended method for data classification is presented, which can reduce these effects as far as possible. In the first step, class differences are set for important or necessary changes. The actual data classification considers these class differences in the context of a sweep line algorithm, whose optimal solution is determined with the help of a measure called Preservation of Change Classes (POCC). By assigning weights during computation of this measure, different tasks or change analyses (e.g. emphasize only highly significant changes) can be processed.