Abstract

The study of the diversity of multivariate objects shares common characteristics across disciplines, including ecology, microbiology, and organizational management. Nevertheless, to some degree, experts in the three disciplines have adopted separate diversity concepts and analysis techniques, hindering the ability of potentially sharing and cross comparing these concerns. Moreover, while complex diversity data may benefit from exploratory data analysis, most of the existing techniques emphasize confirmatory analysis based on statistical metrics and models. To bridge these gaps, interactive visualization is especially appealing because of its potential to allow users to explore diversity data in a direct and holistic way.

This dissertation addresses the problem of designing multivariate visualizations that support exploration and communication of diversity patterns and processes in multivariate data. To this aim, the dissertation presents design considerations as well as implementation and evaluation of interactive visualizations targeting diversity analysis. The contributing visualization techniques and tools include (1) Diversity Map–a novel multivariate space-filling representation emphasizing diversity patterns in separate attributes; (2) Ecological Distributions and Trends Explorer (EcoDATE)–a web-based visual-analysis tool that is built upon Diversity Map and facilitates the exploratory analysis of long-term ecological data with an emphasis on diversity patterns and temporal trends; and (3) HIST–a visual representation for communicating team diversity faultlines across multiple attributes that is based on multiple linked, stacked histograms. Drawn upon these designs, this dissertation cross compares the literature of species diversity (ecology), microbial diversity (microbiology), and workgroup diversity (organizational management) and introduces a unified taxonomy of analytical tasks  to guide the creation and evaluation of future diversity visualizations. The design considerations, visualization techniques, tools, and task taxonomy are rigorously evaluated and refined in empirical user studies involving human participants and subject-matter experts.

Dissertation

Interactive Visualization of Diversity in Multivariate Data Sets Unified across Fields of Study.
ScholarsArchive@OSU: http://hdl.handle.net/1957/40688

Journal Articles

T. Pham, J. Jones, R. Metoyer, F. Colwell. Toward exploratory analysis of diversity unified across fields of study: an information visualization approach. Environmental Earth Sciences (Thematic Issue on Scientific Visualization). To Appear. [paper]

T. Pham, R. Metoyer, K. Bezrukova, C Spell. Visualization of Cluster Structure and Separation in Multivariate Mixed Data: A Case Study of Diversity Faultlines in Work Teams. Computers & Graphics (Special Section on Visual Analytics), Vol. 38, Feb 2014, pp. 117–130 [paper]

T. Pham, J. Jones, R. Metoyer, F. Swanson, and R. Pabst. Interactive Visual Analysis Promotes Exploration of Long-Term Ecological Data. Ecosphere, 4(9), 2013. [paper]

T. Pham, R. Hess, C. Ju, E. Zhang, R. Metoyer. Visualization of Diversity in Large Multivariate Data Sets. IEEE Transactions on Visualization and Computer Graphics, vol.16, no.6, pp.1053-1062, 2010. [paper]

Peer-reviewed Conference Papers and Abstracts

T. Pham, R. Metoyer, K. Bezrukova, C Spell. “Show Me the Cracks in Our Teams”: Visual Representations of Demographic Diversity Faultlines. Information Visualization 2012, Poster Abstract [abstract]

T. Pham, S. Highland, R. Metoyer, D. Henshaw, J. Miller, J. Jones. Interactive Visualization of Spatial and Temporal Patterns of Diversity and Abundance. In Proceedings of Environmental Information Management, pp. 104-110,  Publisher of University of California, 2011 [paper]

T.Pham. Exploring Diversity in Large Multivariate Data Sets. Thesis proposal at Visweek 2010 Doctoral Colloquium.

T. Pham, R. Hess, C. Ju, R. Metoyer, J. Gilbert. What Does Diversity Look Like?. Information Visualization 2009, Poster Abstract.  [abstract]

Prototypes and Tools

EcoDATE: Ecological Distributions and Trends Explorer

HIST: Faultlines Visual Representation based on Multiple Linked Stacked Histograms