Exploring the Sensitivity of Choropleths under Attribute Uncertainty

摘要

The choropleth map is an essential tool for spatial data analysis. However, the underlying attribute values of a spatial unit greatly influence the statistical analyses and map classification procedures when generating a choropleth map. If the attribute values incorporate a range of uncertainty, a critical task is determining how much the uncertainty impacts both the map visualization and the statistical analysis. In this paper, we present a visual analytics system that enhances our understanding of the impact of attribute uncertainty on data visualization and statistical analyses of these data. Our system consists of a parallel coordinates-based uncertainty specification view, an impact river and impact matrix visualization for region-based and simulation-based analysis, and a dual-choropleth map and t-SNE plot for visualizing the changes in classification and spatial autocorrelation over the range of uncertainty in the attribute values. We demonstrate our system through three use cases illustrating the impact of attribute uncertainty in geographic analysis.