200 page Doctoral dissertation. Treemaps are a graphically based method for the visualization of hierarchical or categorical data spaces. Treemap presentations of data shift mental workload from the cognitive to the perceptual systems, taking advantage of the human visual processing system to increase the bandwidth of the human-computer interface. Efficient use of display space allows for the simultaneous presentation of thousands of data records, as well as facilitating the presentation of semantic information. Treemaps let users see the forest and the trees by providing local detail in the context of a global overview, providing a visually engaging environment in which to analyze, search, explore and manipulate large hierarchical and categorical data spaces. The treemap method of hierarchical visualization, at its core, is based on the property of containment. This property of containment is a fundamental idea which powerfully encapsulates many of our reasons for constructing information hierarchies. All members of the treemap family of algorithms partition multi-dimensional display spaces based on weighted hierarchical data sets. In addition to generating treemaps and standard traditional hierarchical diagrams, the treemap algorithms extend non-hierarchical techniques such as bar and pie charts into the domain of hierarchical presentation. Treemap algorithms can be used to generate bar charts, outlines, traditional 2-D node and link diagrams, pie charts, cone trees, cam trees, drum trees, etc. Generating existing diagrams via treemap transformations is an excercise meant to show the power, ease, and generality with which alternative presentations can be generated from the basic treemap algorithms. Controlled experiments with novice treemap users and real data highlight the strengths of treemaps and provide direction for improvement. Experimental results show that treemaps are a powerful visualization tool for large data sets, significantly reducing user performance times for global comparison tasks. Effective visualizations of large data sets can help users gain insight into relevant features of the data, construct accurate mental models of the information, and locate regions of particular interest. Treemaps are based on simple, fundamental ideas, but they are the building blocks with which an entire world of unique and exciting visualizations can be built.