TY - JOUR
T1 - Introductory overview
T2 - Recommendations for approaching scientific visualization with large environmental datasets
AU - Kelleher, Christa
AU - Braswell, Anna
N1 - Publisher Copyright:
© 2021 Elsevier Ltd
PY - 2021/9
Y1 - 2021/9
N2 - Scientific visualizations are the foundation for communicating results and findings to a variety of audiences. As the creation of novel and large environmental datasets has grown, this has necessitated new schemes and recommendations for creating effective visualizations. In this overview, we review the foundations of scientific visualization and considerations for visualization of large datasets within the context of the four Vs of big data (volume, variety, veracity, and velocity). Using big datasets requires making decisions as to whether to aggregate or preserve details, approaches for grouping to enable comparisons, and considering how best to show complex data in many-dimensional space. To enable more effective visualizations, we provide several considerations regarding common decisions faced during the visualization process. These recommendations are accompanied by examples applied to existing large datasets. While our recommendations are just that, they encourage intentionality and awareness of the choices faced when visualizing scientific datasets.
AB - Scientific visualizations are the foundation for communicating results and findings to a variety of audiences. As the creation of novel and large environmental datasets has grown, this has necessitated new schemes and recommendations for creating effective visualizations. In this overview, we review the foundations of scientific visualization and considerations for visualization of large datasets within the context of the four Vs of big data (volume, variety, veracity, and velocity). Using big datasets requires making decisions as to whether to aggregate or preserve details, approaches for grouping to enable comparisons, and considering how best to show complex data in many-dimensional space. To enable more effective visualizations, we provide several considerations regarding common decisions faced during the visualization process. These recommendations are accompanied by examples applied to existing large datasets. While our recommendations are just that, they encourage intentionality and awareness of the choices faced when visualizing scientific datasets.
KW - Graphics
KW - Multidimensional
KW - Plots
KW - Scientific visualization
KW - Visual analytics
KW - Visual communication
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U2 - 10.1016/j.envsoft.2021.105113
DO - 10.1016/j.envsoft.2021.105113
M3 - Article
AN - SCOPUS:85109203546
SN - 1364-8152
VL - 143
JO - Environmental Modelling and Software
JF - Environmental Modelling and Software
M1 - 105113
ER -