research-article

Interactive Colormapping: Enabling Multiple Data Range and Detailed Views of Ocean Salinity

Authors:

Francesca Samsel,

Sebastian Klaassen,

Terece L. Turton,

David H. Rogers,

James AhrensAuthors Info & Claims

CHI EA '16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems

Pages 700 - 709

https://doi.org/10.1145/2851581.2851587

Published: 07 May 2016 Publication History

Abstract

Ocean salinity is a critical component to understanding climate change. Salinity concentrations and temperature drive large ocean currents which in turn drive global weather patterns. Melting ice caps lower salinity at the poles while river deltas bring fresh water into the ocean worldwide. These processes slow ocean currents, changing weather patterns and producing extreme climate events which disproportionally affect those living in poverty. Analysis of salinity presents a unique visualization challenge. Important data are found in narrow data ranges, varying with global location. Changing values of salinity are important in understanding ocean currents, but are difficult to map to colors using traditional tools. Commonly used colormaps may not provide sufficient detail for this data. Current editing tools do not easily enable a scientist to explore the subtleties of salinity. We present a workflow, enabled by an interactive colormap tool that allows a scientist to interactively apply sophisticated colormaps to scalar data. The intuitive and immediate interaction of the scientist with the data is a critical contribution of this work.

References

[1]

2011 (accessed 08 October 2015). Chroma.js. http: //old.driven-by-data.net/about/chromajs/#/1

[2]

accessed 08 October 2015. Color Tool. http: //colorusage.arc.nasa.gov/ColorTool.php

[3]

accessed 08 October 2015. Kitware Corporation. www. kitware.com

[4]

accessed October 2015. Amazon Mechanical Turk Website. www.mturk.com/mturk/welcome

[5]

accessed October 2015. Qualtrics Website. www. qualtrics.com

[6]

D. Bader, W. Collins, R. Jacob, P. Jones, P. Rasch, M. Taylor, P. Thornton, and D. Williams. 2014. Accelerated Climate Modeling for Energy. Technical Report. USA Department of Energy. http://climatemodeling.science.energy.gov/sites/default/files/publications/acme-project-strategy-plan.pdf.

[7]

L.D. Bergman, B. Rogowitz, and L.A. Treinish. 1995. A rule-based tool for assisting colormap selection. In Visualization, 1995. Visualization '95. Proceedings., IEEE Conference on. 118-125, 444. org/10.1109/VISUAL.1995.480803

Digital Library

[8]

D. Borland and R.M. Taylor. 2007. Rainbow Color Map (Still) Considered Harmful. Computer Graphics and Applications, IEEE 27, 2 (March 2007), 14-17.

Digital Library

[9]

M.J.C. Crump, J.V. McDonnell, and T.M. Gureckis. 2013. Evaluating Amazon's Mechanical Turk as a Tool for Experimental Behavioral Research. PLoS ONE 8, 3 (2013).

[10]

C.B. Field (Ed.). 2014. IPCC, 2014: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A. Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. Chapter 12.

[11]

M. Harrower and C.A. Brewer. 2011. ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps. John Wiley & Sons, Ltd, 261-268.

[12]

C.G. Healey. 1996. Choosing effective colours for data visualization. In Visualization '96. Proceedings. 263- 270.

[13]

M.E. Maltrud and J.L. McClean. 2005. An eddy resolving global 1/10? ocean simulation. Ocean Model. 8, 1-2 (2005), 31-54. ocemod.2003.12.001

[14]

K. Moreland. 2009. Diverging Color Maps for Scientific Visualization. In Proceedings of the 5th International Symposium on Advances in Visual Computing, Part II (ISVC '09). 92-103.

Digital Library

[15]

G. Paolacci, J. Chandler, and Ipeirotis. 2010. Running Experiments on Amazon Mechanical Turk. Judgment and Decision Making 5, 5 (2010), 411-419.

[16]

B.E. Rogowitz, A.D. Kalvin, A. Pelah, and A. Cohen. 1999. Which trajectories through which perceptually uniform color spaces produce appropriate color scales for interval data? 321-326.

[17]

B.E. Rogowitz, L.A. Treinish, and S. Bryson. 1996. How Not to Lie with Visualization. Comput. Phys. 10, 3 (June 1996), 268-273.

[18]

F. Samsel and U. Ayachit. 2015. http://www.kitware.com/blog/home/post/886. (May 2015). Kitware Blog.

[19]

F. Samsel, M. Petersen, T. Geld, G. Abram, J. Wendelberger, and J. Ahrens. 2015. Colormaps That Improve Perception of High-Resolution Ocean Data. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA '15). 703-710. http://doi.acm.org/10. 1145/2702613.2702975

Digital Library

[20]

S. Silva, J. Madeira, and B.S. Santos. 2007. There is More to Color Scales Than Meets the Eye: A Review on the Use of Color in Visualization. In Proceedings of the 11th International Conference Information Visualization (IV '07). IEEE Computer Society, Washington, DC, USA, 943-950.

Digital Library

[21]

R. Simmon. 2013. Subteties of Color: Tools and Techniques. (2013). http://blog.visual.ly/subtleties-of-color-tools-and-techniques

[22]

D. Thompson, J. Bennett, C. Seshadhri, and A. Pinar. A provably-robust sampling method for generating colormaps of large data. In 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV). 77-84.

[23]

C. Ware. 1988. Color sequences for univariate maps: theory, experiments and principles. Computer Graphics and Applications, IEEE 8, 5 (Sept 1988), 41-49.

Digital Library

[24]

C. Ware. 2008. Visual Thinking: For Design. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.

Digital Library

Cited By

Stegers BSauvé KHouben S(2022)Ecorbis: A Data Sculpture of Environmental Behavior in the Home ContextProceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533508(1669-1683)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3532106.3533508
Ferreira MCoelho MNisi VJardim Nunes NSas CMaiden N(2021)Climate Change Communication in HCI: a Visual Analysis of the Past DecadeProceedings of the 13th Conference on Creativity and Cognition10.1145/3450741.3466774(1-16)Online publication date: 22-Jun-2021
https://dl.acm.org/doi/10.1145/3450741.3466774
Nardini PChen MSamsel FBujack RBottinger MScheuermann G(2021)The Making of Continuous ColormapsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.296167427:6(3048-3063)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TVCG.2019.2961674
Show More Cited By

Index Terms

Interactive Colormapping: Enabling Multiple Data Range and Detailed Views of Ocean Salinity
1. Computing methodologies
  1. Computer graphics
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
      1. User interface management systems

Recommendations

Colormaps that Improve Perception of High-Resolution Ocean Data
CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

Scientists from the Climate, Ocean and Sea Ice Modeling Team (COSIM) at the Los Alamos National Laboratory (LANL) are interested in gaining a deeper understanding of three primary ocean currents: the Gulf Stream, the Kuroshio Current, and the Agulhas ...
Comprehensive Influence of Salinity and Organic Load on Anaerobic Sequencing Batch Biofilm Reactor (ASBBR) Treating Mustard Tuber Wastewater
ISDEA '13: Proceedings of the 2013 Third International Conference on Intelligent System Design and Engineering Applications

According to the characteristics of high concentration of organic matters and high biodegradability, a new and efficient anaerobic sequencing batch biofilm reactor (ASBBR) was chosen as an anaerobic pretreatment unit to treat mustard tuber wastewater. ...
Comprehensive simulation of salinity transport in irrigated watersheds using an updated version of SWAT-MODFLOW
Abstract
Salinity can have major detrimental impacts on soil health, crop yield, and environmental ecosystem services. In this paper we introduce SWAT-MODFLOW-Salt, a version of SWAT-MODFLOW that simulates the fate and transport of major salt ...
Highlights
- We provide a new version of SWAT-MODFLOW that simulates salt ion transport in watershed systems

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI EA '16: Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems

May 2016

3954 pages

ISBN:9781450340823

DOI:10.1145/2851581

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
Program Chairs:
Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

Copyright © 2016 ACM.

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 May 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Department of Energy Office of Science

Conference

CHI'16

Sponsor:

SIGCHI

CHI'16: CHI Conference on Human Factors in Computing Systems

May 7 - 12, 2016

California, San Jose, USA

Acceptance Rates

CHI EA '16 Paper Acceptance Rate 1,000 of 5,000 submissions, 20%;

Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
225
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Stegers BSauvé KHouben S(2022)Ecorbis: A Data Sculpture of Environmental Behavior in the Home ContextProceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533508(1669-1683)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3532106.3533508
Ferreira MCoelho MNisi VJardim Nunes NSas CMaiden N(2021)Climate Change Communication in HCI: a Visual Analysis of the Past DecadeProceedings of the 13th Conference on Creativity and Cognition10.1145/3450741.3466774(1-16)Online publication date: 22-Jun-2021
https://dl.acm.org/doi/10.1145/3450741.3466774
Nardini PChen MSamsel FBujack RBottinger MScheuermann G(2021)The Making of Continuous ColormapsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2019.296167427:6(3048-3063)Online publication date: 1-Jun-2021
https://doi.org/10.1109/TVCG.2019.2961674
Zeng QWang YZhang JZhang WTu CViola IWang Y(2019)Data-Driven Colormap Optimization for 2D Scalar Field Visualization2019 IEEE Visualization Conference (VIS)10.1109/VISUAL.2019.8933764(266-270)Online publication date: Oct-2019
https://doi.org/10.1109/VISUAL.2019.8933764
Samsel FWolfram PBares ATurton TBujack R(2019)Colormapping resources and strategies for organized intuitive environmental visualizationEnvironmental Earth Sciences10.1007/s12665-019-8237-978:9Online publication date: 20-Apr-2019
https://doi.org/10.1007/s12665-019-8237-9
Kirilenko ADesell TKim HStepchenkova S(2017)Crowdsourcing Analysis of Twitter Data on Climate Change: Paid Workers vs. VolunteersSustainability10.3390/su91120199:11(2019)Online publication date: 3-Nov-2017
https://doi.org/10.3390/su9112019
Turton TBerres ARogers DAhrens JKozlíková BSchreck TWischgoll T(2017)ETKProceedings of the Eurographics/IEEE VGTC Conference on Visualization: Short Papers10.2312/eurovisshort.20171131(43-47)Online publication date: 12-Jun-2017
https://dl.acm.org/doi/10.2312/eurovisshort.20171131
Samsel FTurton TWolfram PBujack RRink KMiddel AZeckzer DBujack R(2017)Intuitive colormaps for environmental visualizationProceedings of the Workshop on Visualisation in Environmental Sciences10.2312/envirvis.20171105(55-59)Online publication date: 12-Jun-2017
https://dl.acm.org/doi/10.2312/envirvis.20171105
Nagoor OBorgo RJones MWan TVidal F(2017)Data painterProceedings of the Conference on Computer Graphics & Visual Computing10.2312/cgvc.20171280(69-76)Online publication date: 14-Sep-2017
https://dl.acm.org/doi/10.2312/cgvc.20171280
Samsel FPatchett JRogers DTsai KMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact SimulationsProceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems10.1145/3027063.3053337(1126-1134)Online publication date: 6-May-2017
https://dl.acm.org/doi/10.1145/3027063.3053337
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents