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Visual analytics of high-frequency lake monitoring data

A case study of multiple stressors on a large inland lake system

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Abstract

In recognizing the cumulative effects of multiple stressors on altering aquatic ecosystem function, scientists have become increasingly interested in capturing high-frequency response variables using a variety of sensors. This practice has led to a demand for novel ways to visualize and analyze the wealth of data in order to meet policy and management goals. Time series data collected as part of these monitoring activities are not easily analyzed with traditional methods. In this paper, a visual analytics system is described that leverages humans’ innate capability for pattern recognition and feature detection. High-frequency monitoring of weather and water conditions in Lake Nipissing, a large, shallow, inland lake in northeastern Ontario, Canada, is used as a case study. These visualizations are presented as Web-based tools to facilitate community-based participatory research among scientists, government agencies, and community stakeholders. These analytics techniques contribute to collaborative research endeavors and to the understanding of the response of lake conditions to environmental change.

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Notes

  1. http://gleon.org

  2. http://iwrc.nipissingu.ca/

  3. See http://survey.timeviz.net for examples.

  4. https://github.com/gka/chroma.js

  5. https://github.com/dntj/jsfft

References

  1. Accorsi, P., Lalande, N., Fabrègue, M., Braud, A., Poncelet, P., Sallaberry, A., Bringay, S., Teisseire, M., Cernesson, F., Le Ber, F.: Hydroqual: visual analysis of river water quality. In: Proceedings of the IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 123–132. (2014)

  2. Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, Berlin (2011)

    Book  Google Scholar 

  3. Andrienko, G., Andrienko, N., Keim, D., MacEachren, A.M., Wrobel, S.: Challenging problems of geospatial visual analytics. J. Vis. Lang. Comput. 22(4), 251–256 (2011)

    Article  Google Scholar 

  4. Blaas, J., Botha, C., Post, F.: Extensions of parallel coordinates for interactive exploration of large multi-timepoint data sets. IEEE Trans. Vis. Comput. Graph. 14(6), 1436–1451 (2008)

    Article  Google Scholar 

  5. Diehl, A., Pelorosso, L., Delrieux, C., Saulo, C., Ruiz, J., Gröller, M., Bruckner, S.: Visual analysis of spatio-temporal data: applications in weather forecasting. Comput. Graph. Forum 34(3), 381–390 (2015)

  6. Edsall, R.M.: The parallel coordinate plot in action: design and use for geographic visualization. Comput. Stat. Data Anal. 43(4), 605–619 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Fraterrigo, J.M., Rusak, J.A.: Disturbance-driven changes in the variability of ecological patterns and processes. Ecol. Lett. 11(7), 756–770 (2008)

    Article  Google Scholar 

  8. Gruendl, H., Riehmann, P., Pausch, Y., Froehlich, B.: Time-series plots integrated in parallel-coordinates displays. Comput. Graph. Forum 35(3), 321–330 (2016)

  9. Hamilton, D.P., Carey, C.C., Arvola, L., Arzberger, P., Brewer, C., Cole, J.J., Gaiser, E., Hanson, P.C., Ibelings, B.W., Jennings, E., et al.: A global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models. Inland Waters 5(1), 49–56 (2015)

    Article  Google Scholar 

  10. Hanson, P.C., Weathers, K.C., Kratz, T.K.: Networked lake science: how the global lake ecological observatory network (GLEON) works to understand, predict and communicate lake ecosystem response to global change. Inland Waters 6(4), 543–554 (2016)

    Article  Google Scholar 

  11. Heathwaite, A.: Multiple stressors on water availability at global to catchment scales: understanding human impact on nutrient cycles to protect water quality and water availability in the long term. Freshw. Biol. 55(s1), 241–257 (2010)

    Article  Google Scholar 

  12. Heinrich, J., Weiskopf, D.: State of the art of parallel coordinates. In: STAR Proceedings of Eurographics, pp. 95–116 (2013)

  13. Heinrich, J., Weiskopf, D.: Parallel coordinates for multidimensional data visualization: basic concepts. Comput. Sci. Eng. 17(3), 70–76 (2015). doi:10.1109/MCSE.2015.55

    Article  Google Scholar 

  14. Hipsey, M.R., Hamilton, D.P., Hanson, P.C., Carey, C.C., Coletti, J.Z., Read, J.S., Ibelings, B.W., Valesini, F.J., Brookes, J.D.: Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories. Water Resour. Res. 51(9), 7023–7043 (2015)

    Article  Google Scholar 

  15. Javed, W., McDonnel, B., Elmqvist, N.: Graphical perception of multiple time series. IEEE Trans. Vis. Comput. Graph. 16(6), 927–934 (2010)

    Article  Google Scholar 

  16. Jennings, E., Jones, S., Arvola, L., Staehr, P.A., Gaiser, E., Jones, I.D., Weathers, K.C., Weyhenmeyer, G.A., Chiu, C.Y., De Eyto, E.: Effects of weather-related episodic events in lakes: an analysis based on high-frequency data. Freshw. Biol. 57(3), 589–601 (2012)

    Article  Google Scholar 

  17. Johansson, J., Forsell, C.: Evaluation of parallel coordinates: overview, categorization and guidelines for future research. IEEE Trans. Vis. Comput. Graph. 22(1), 579–588 (2016). doi:10.1109/TVCG.2015.2466992

    Article  Google Scholar 

  18. Johansson, J., Forsell, C., Cooper, M.: On the usability of three-dimensional display in parallel coordinates: evaluating the efficiency of identifying two-dimensional relationships. Inf. Vis. 13(1), 29–41 (2014)

    Article  Google Scholar 

  19. Johansson, J., Ljung, P., Jern, M., Cooper, M.: Revealing structure within clustered parallel coordinates displays. In: Proceedings of the IEEE Symposium on Information Visualization (INFOVIS), pp. 125–132. (2005)

  20. Jones, R.C., Graziano, A.P.: Diel and seasonal patterns in water quality continuously monitored at a fixed site on the tidal freshwater Potomac River. Inland Waters 3, 421–436 (2013)

    Article  Google Scholar 

  21. Kelly-Hooper, F.: The water quality of Lake Nipissing and the contributing watershed. The Wilderness Preservation Committee of Ontario, Toronto (2001)

    Google Scholar 

  22. Köthur, P., Witt, C., Sips, M., Marwan, N., Schinkel, S., Dransch, D.: Visual analytics for correlation-based comparison of time series ensembles. Comput. Graph. Forum 34(3), 411–420 (2015)

  23. Mansmann, F., Fischer, F., Keim, D.A.: Dynamic visual analytics—facing the real-time challenge. In: Dill, J., Earnshaw, R., Kasik, D., Vince, J., Chung Wong, P. (eds.) Expanding the Frontiers of Visual Analytics and Visualization, pp. 69–80. Springer, London (2012)

  24. Meyer, M., Sedlmair, M., Quinan, P.S., Munzner, T.: The nested blocks and guidelines model. Inf. Vis. 14(3), 234–249 (2015)

    Article  Google Scholar 

  25. Moreland, K.: Diverging color maps for scientific visualization expanded. Adv. Vis. Comput. 5876, 92–103 (2009)

    Article  Google Scholar 

  26. Morgan, G.E., Bay, N.: Lake Nipissing Data Review 1967 to 2011, pp. 1–46. Ontario Ministry of Natural Resources, North Bay (2013)

    Google Scholar 

  27. Munzner, T.: A nested model for visualization design and validation. IEEE Trans. Vis. Comput. Graph. 15(6), 921–928 (2009)

  28. Munzner, T.: Visualization Analysis and Design. CRC Press, Boca Raton (2014)

    Google Scholar 

  29. Nürnberg, G.K., Molot, L.A., O’Connor, E., Jarjanazi, H., Winter, J., Young, J.: Evidence for internal phosphorus loading, hypoxia and effects on phytoplankton in partially polymictic lake simcoe, ontario. J. Gt Lakes. Res. 39(2), 259–270 (2013)

    Article  Google Scholar 

  30. Paerl, H.W., Huisman, J., et al.: Blooms like it hot. Science 320(5872), 57 (2008)

    Article  Google Scholar 

  31. Prescott, M.: Characterizing mixing and stratification in Lake Nipissing embayments through employment of the lake analyzer package and an analysis of meteorological controls. Master’s thesis, Nipissing University (2015)

  32. Rigosi, A., Hanson, P., Hamilton, D.P., Hipsey, M., Rusak, J.A., Bois, J., Sparber, K., Chorus, I., Watkinson, A.J., Qin, B., et al.: Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems. Ecol. Appl. 25(1), 186–199 (2015)

    Article  Google Scholar 

  33. Sips, M., Köthur, P., Unger, A., Hege, H.C., Dransch, D.: A visual analytics approach to multiscale exploration of environmental time series. IEEE Trans. Vis. Comput. Graph. 18(12), 2899–2907 (2012)

    Article  Google Scholar 

  34. Smith, J.P., Hunter, T.S., Clites, A.H., Stow, C.A., Slawecki, T., Muhr, G.C., Gronewold, A.D.: An expandable web-based platform for visually analyzing basin-scale hydro-climate time series data. Environ. Model. Softw. 78, 97–105 (2016)

    Article  Google Scholar 

  35. Theus, M.: Statistical data exploration and geographical information visualization. In: Dykes, J., MacEachren, A.M., Kraak, M.-J. (eds.) Exploring Geovisualization, pp. 127–142. Elsevier, Amsterdam (2005)

    Chapter  Google Scholar 

  36. Thomas, J.J., Cook, K.A.: A visual analytics agenda. IEEE Comput. Graph. Appl. 26(1), 10–13 (2006)

    Article  Google Scholar 

  37. Tufte, E.: Envisioning Information. Graphics Press, Cheshire (1990)

    Google Scholar 

  38. Tuna, G., Arkoc, O., Gulez, K.: Continuous monitoring of water quality using portable and low-cost approaches. Int. J. Distrib. Sens. Netw. 9, 249598 (2013)

    Article  Google Scholar 

  39. Tuna, G., Nefzi, B., Arkoc, O., Potirakis, S.M.: Wireless sensor network-based water quality monitoring system. Key Eng. Mater. 605, 47–50 (2014)

    Article  Google Scholar 

  40. Vanderkam, D.: Dygraphs Javascript charting library. http://dygraphs.com (2006). Accessed 9 Sept 2017

  41. Weathers, K., Hanson, P.C., Arzberger, P., Brentrup, J., Brookes, J.D., Carey, C.C., Gaiser, E., Hamilton, D.P., Hong, G.S., Ibelings, B., et al.: The global lake ecological observatory network (GLEON): the evolution of grassroots network science. Limnol. Oceanogr. Bull. 22(3),71–73 (2013)

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Acknowledgements

The authors are grateful to the anonymous reviewers for their constructive criticisms and helpful suggestions. MPW is supported by NSERC Discovery Grant 386586-2011. ALJ is supported by the Canada Research Chairs program, Nipissing University, the Canada Foundation for Innovation, and NSERC. The authors thank M. Prescott for QA/QC of the 2014 data, C. McConnell for buoy design, and B. Dobbs, T. Singhe, M. Timson, D. DuVal, and J. Moggridge for programming assistance. The authors also thank GLEON for providing a mechanism and platform for stimulating interdisciplinary collaborations using high-frequency data.

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Authors and Affiliations

  1. Department of Computer Science and Mathematics, Nipissing University, North Bay, ON, Canada

    Mark P. Wachowiak & Renata Wachowiak-Smolíková

  2. Department of Geography, Nipissing University, North Bay, ON, Canada

    April L. James & Dan F. Walters

  3. Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK, Canada

    Krystopher J. Chutko

  4. Dorset Environmental Science Centre, Ontario Ministry of the Environment and Climate Change, Dorset, ON, Canada

    James A. Rusak

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  1. Mark P. Wachowiak
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Correspondence to Mark P. Wachowiak.

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Wachowiak, M.P., James, A.L., Wachowiak-Smolíková, R. et al. Visual analytics of high-frequency lake monitoring data. Int J Data Sci Anal 5, 99–110 (2018). https://doi.org/10.1007/s41060-017-0072-z

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