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


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.


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



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.


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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

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