Multivariate and Spatiotemporal Visualization for Ecological Informatics: A Quantitative Content Analysis of Visualization Practices using the Neotoma Paleoecology Database
Abstract
Now more than ever, our understanding of the Earth and its ecosystems is driven by data. Increasingly, researchers in ecology and related Earth sciences are contributing their primary research data to open community repositories. Thanks to these collaborative efforts, the bottleneck for ecological informatics is arguably no longer with data acquisition and compilation, but instead with data analytics and visualization needed to harness the potential of these repositories for research, education, and policy.
We contribute to this gap in ecological informatics through a case study using the Neotoma Paleoecology Database (https://www.neotomadb.org/ ), an open access, community-curated data resource providing site level paleoecological records spanning the Quaternary and Pliocene. Neotoma and paleoecology offer a useful case study potentially transferable to other Earth science contexts given the importance of multivariate, geospatial, and temporal analysis and visualization. We evaluated visualization practices and trends in scholarly publications contributing to Neotoma using quantitative content analysis (QCA), an established social science method for systematically deconstructing and interpreting a coherent corpus of artifacts by their constituent visual elements. Our QCA included 702 unique charts, maps, etc., published in 108 unique Neotoma contributed papers. The QCA was informed by design tenets from cartography, data science, and information visualization, and included four broad themes: data type (space, time, attributes), representation (chart type, visual variables), uncertainty (accuracy, precision, trustworthiness), and interactivity (for web and mobile only). The QCA of Neotoma visualization practices identified the most common design solutions, gaps in current solutions based on design recommendations, and unique examples potentially useful for a wider range of contexts. The QCA also identified innovative multivariate and spatiotemporal techniques, methods for scaling visualizations to growing databases, support for visualization on mobile devices, and opportunities for harnessing big data visualization for multiple research, education, and policy use cases. This research was funded by NSF Awards #1550707, #1550855, and #1550913.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMIN21B..20R
- Keywords:
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- 0850 Geoscience education research;
- EDUCATION;
- 9820 Techniques applicable in three or more fields;
- GENERAL OR MISCELLANEOUS;
- 1992 Virtual globes;
- INFORMATICS;
- 1994 Visualization and portrayal;
- INFORMATICS