Advertisement

Visual analytics of high-frequency lake monitoring data

A case study of multiple stressors on a large inland lake system
  • Mark P. WachowiakEmail author
  • April L. James
  • Renata Wachowiak-Smolíková
  • Dan F. Walters
  • Krystopher J. Chutko
  • James A. Rusak
Regular Paper

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.

Keywords

Environmental monitoring Visual analytics Data analytics Web systems Community-based participatory research High-resolution data 

Notes

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.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

References

  1. 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)Google Scholar
  2. 2.
    Aigner, W., Miksch, S., Schumann, H., Tominski, C.: Visualization of Time-Oriented Data. Springer, Berlin (2011)CrossRefGoogle Scholar
  3. 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)CrossRefGoogle Scholar
  4. 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)CrossRefGoogle Scholar
  5. 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)Google Scholar
  6. 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)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 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)CrossRefGoogle Scholar
  8. 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)Google Scholar
  9. 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)CrossRefGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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)CrossRefGoogle Scholar
  12. 12.
    Heinrich, J., Weiskopf, D.: State of the art of parallel coordinates. In: STAR Proceedings of Eurographics, pp. 95–116 (2013)Google Scholar
  13. 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 CrossRefGoogle Scholar
  14. 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)CrossRefGoogle Scholar
  15. 15.
    Javed, W., McDonnel, B., Elmqvist, N.: Graphical perception of multiple time series. IEEE Trans. Vis. Comput. Graph. 16(6), 927–934 (2010)CrossRefGoogle Scholar
  16. 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)CrossRefGoogle Scholar
  17. 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 CrossRefGoogle Scholar
  18. 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)CrossRefGoogle Scholar
  19. 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)Google Scholar
  20. 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)CrossRefGoogle Scholar
  21. 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. 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)Google Scholar
  23. 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)Google Scholar
  24. 24.
    Meyer, M., Sedlmair, M., Quinan, P.S., Munzner, T.: The nested blocks and guidelines model. Inf. Vis. 14(3), 234–249 (2015)CrossRefGoogle Scholar
  25. 25.
    Moreland, K.: Diverging color maps for scientific visualization expanded. Adv. Vis. Comput. 5876, 92–103 (2009)CrossRefGoogle Scholar
  26. 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. 27.
    Munzner, T.: A nested model for visualization design and validation. IEEE Trans. Vis. Comput. Graph. 15(6), 921–928 (2009)Google Scholar
  28. 28.
    Munzner, T.: Visualization Analysis and Design. CRC Press, Boca Raton (2014)Google Scholar
  29. 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)CrossRefGoogle Scholar
  30. 30.
    Paerl, H.W., Huisman, J., et al.: Blooms like it hot. Science 320(5872), 57 (2008)CrossRefGoogle Scholar
  31. 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)Google Scholar
  32. 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)CrossRefGoogle Scholar
  33. 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)CrossRefGoogle Scholar
  34. 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)CrossRefGoogle Scholar
  35. 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)CrossRefGoogle Scholar
  36. 36.
    Thomas, J.J., Cook, K.A.: A visual analytics agenda. IEEE Comput. Graph. Appl. 26(1), 10–13 (2006)CrossRefGoogle Scholar
  37. 37.
    Tufte, E.: Envisioning Information. Graphics Press, Cheshire (1990)Google Scholar
  38. 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)CrossRefGoogle Scholar
  39. 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)CrossRefGoogle Scholar
  40. 40.
    Vanderkam, D.: Dygraphs Javascript charting library. http://dygraphs.com (2006). Accessed 9 Sept 2017
  41. 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)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer Science and MathematicsNipissing UniversityNorth BayCanada
  2. 2.Department of GeographyNipissing UniversityNorth BayCanada
  3. 3.Department of Geography and PlanningUniversity of SaskatchewanSaskatoonCanada
  4. 4.Dorset Environmental Science CentreOntario Ministry of the Environment and Climate ChangeDorsetCanada

Personalised recommendations