Morse, P and Reading, A and Lueg, C, Animated analysis of geoscientific datasets: an interactive graphical application, Computers and Geosciences, 109 pp. 87-94. ISSN 0098-3004 (2017) [Refereed Article]
© 2017 The Authors. Published by Elsevier Ltd. Licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) http://creativecommons.org/licenses/by-nc-nd/4.0/
Geoscientists are required to analyze and draw conclusions from increasingly large volumes of data. There is a need to recognise and characterise features and changing patterns of Earth observables within such large datasets. It is also necessary to identify significant subsets of the data for more detailed analysis.
We present an innovative, interactive software tool and workflow to visualise, characterise, sample and tag large geoscientific datasets from both local and cloud-based repositories. It uses an animated interface and human-computer interaction to utilise the capacity of human expert observers to identify features via enhanced visual analytics. ‘Tagger’ enables users to analyze datasets that are too large in volume to be drawn legibly on a reasonable number of single static plots. Users interact with the moving graphical display, tagging data ranges of interest for subsequent attention. The tool provides a rapid pre-pass process using fast GPU-based OpenGL graphics and data-handling and is coded in the Quartz Composer visual programing language (VPL) on Mac OSX. It makes use of interoperable data formats, and cloud-based (or local) data storage and compute.
In a case study, Tagger was used to characterise a decade (2000–2009) of data recorded by the Cape Sorell Waverider Buoy, located approximately 10 km off the west coast of Tasmania, Australia. These data serve as a proxy for the understanding of Southern Ocean storminess, which has both local and global implications. This example shows use of the tool to identify and characterise 4 different types of storm and non-storm events during this time. Events characterised in this way are compared with conventional analysis, noting advantages and limitations of data analysis using animation and human interaction. Tagger provides a new ability to make use of humans as feature detectors in computer-based analysis of large-volume geosciences and other data.
|Item Type:||Refereed Article|
|Keywords:||visual analytics, interactive, animated, time series analysis|
|Research Division:||Earth Sciences|
|Research Group:||Other earth sciences|
|Research Field:||Other earth sciences not elsewhere classified|
|Objective Division:||Expanding Knowledge|
|Objective Group:||Expanding knowledge|
|Objective Field:||Expanding knowledge in the earth sciences|
|UTAS Author:||Morse, P (Dr Peter Morse)|
|UTAS Author:||Reading, A (Professor Anya Reading)|
|UTAS Author:||Lueg, C (Professor Christopher Lueg)|
|Web of Science® Times Cited:||1|
|Deposited By:||Earth Sciences|
|Downloads:||126 View Download Statistics|
Repository Staff Only: item control page