Methods for Multiclass Geospatial Data Visualization

Geographical visualizations are critical for multi-criteria analysis, optimization, and decision making, where the translation of spatial data into a visual form allows analysts to quickly see patterns, explore summaries and relate domain knowledge about underlying geographical phenomena. However, several critical challenges arise when visualizing large spatiotemporal datasets. While, the underlying geographical component of the data lends itself well to univariate visualization in the form of traditional cartographic representations (e.g., choropleth, isopleth, dasymetric maps), as the data becomes multivariate, cartographic representations become more complex, requiring new approaches for multiclass map visualization and exploration. In this thesis, novel visual analytics methods and frameworks are proposed to support multiclass map analysis. An interactive conservation portfolio development system that combines visualization, multicriteria analysis, optimization, and decision making is developed that showcases a novel visualization and interaction design to compare different purchasing profiles under various optimization constraints. Such multiclass map analysis is then extended using concepts from scalar field topology for hotspot analysis including the introduction of a novel visualization construct combining Merge Trees and Streamgraphs.