An interactive data visualization framework for exploring geospatial environmental datasets and model predictions
Water
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Abstract
With the rise of large-scale environmental models comes new challenges for how we best utilize this information in research, management and decision making. Interactive data visualizations can make large and complex datasets easier to access and explore, which can lead to knowledge discovery, hypothesis formation and improved understanding. Here, we present a web-based interactive data visualization framework, the Interactive Catchment Explorer (ICE), for exploring environmental datasets and model outputs. Using a client-based architecture, the ICE framework provides a highly interactive user experience for discovering spatial patterns, evaluating relationships between variables and identifying specific locations using multivariate criteria. Through a series of case studies, we demonstrate the application of the ICE framework to datasets and models associated with three separate research projects covering different regions in North America. From these case studies, we provide specific examples of the broader impacts that tools like these can have, including fostering discussion and collaboration among stakeholders and playing a central role in the iterative process of data collection, analysis and decision making. Overall, the ICE framework demonstrates the potential benefits and impacts of using web-based interactive data visualization tools to place environmental datasets and model outputs directly into the hands of stakeholders, managers, decision makers and other researchers.
Suggested Citation
Walker, J.D., Letcher, B., Rodgers, K., Muhlfeld, C.C., D’Angelo, V.S., 2020, An interactive data visualization framework for exploring geospatial environmental datasets and model predictions: Water, v. 12, no. 10, 2928, 20 p., https://doi.org/10.3390/w12102928.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | An interactive data visualization framework for exploring geospatial environmental datasets and model predictions |
| Series title | Water |
| DOI | 10.3390/w12102928 |
| Volume | 12 |
| Issue | 10 |
| Publication Date | October 20, 2020 |
| Year Published | 2020 |
| Language | English |
| Publisher | MDPI |
| Contributing office(s) | Leetown Science Center, Northern Rocky Mountain Science Center, Lower Mississippi-Gulf Water Science Center |
| Description | 2928, 20 p. |
| Country | United States, Canada |