Environmental data visualisation for non-scientific contexts: Literature review and design framework

Environmental science is an applied discipline, which therefore requires interacting with actors outside of the scientific community. Visualisations are increasingly seen as powerful tools to engage users with unfamiliar and complex subject matter. Despite recent research advances, scientists are yet to fully harness the potential of visualisation when interacting with non-scientists. To address this issue, we review the main principles of visualisation, discuss specific graphical challenges for environmental science and highlight some best practice from non-professional contexts. We provide a design framework to enhance the communication and application of scientific information within professional contexts. These guidelines can help scientists incorporate effective visualisations within improved dissemination and knowledge exchange platforms. We conclude that the uptake of science within environmental decision-making requires a highly iterative and collaborative design approach towards the development of tailored visualisations. This enables users to not only generate actionable understanding but also explore information on their own terms. Effective visualisations can engage non-scientists with unfamiliar and complex subject matter.We review the main principles of visualisation and specific graphical challenges for environmental science.We provide a design framework to help scientists develop effective visualisations within nonscientific, professional contexts.The uptake of science within environmental decision-making requires a highly iterative and collaborative design approach.

[1]  Anthony C. Robinson,et al.  Developing Map Symbol Standards through an Iterative Collaboration Process , 2012 .

[2]  David W. Cash,et al.  Knowledge systems for sustainable development , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Sara L. Su,et al.  Understanding Visualization by Understanding Individual Users , 2012, IEEE Computer Graphics and Applications.

[4]  Samuel S. Silva,et al.  Using color in visualization: A survey , 2011, Comput. Graph..

[5]  Gary E. Bolton,et al.  A Laboratory Study of the Benefits of Including Uncertainty Information in Weather Forecasts , 2006 .

[6]  Jang-Sun Hwang,et al.  Measures of Perceived Interactivity: An Exploration of the Role of Direction of Communication, User Control, and Time in Shaping Perceptions of Interactivity , 2002 .

[7]  Ulf Jeppsson,et al.  Environmental decision support systems , 2017 .

[8]  Björn-Ola Linnér,et al.  Communicating Climate Change through ICT-Based Visualization: Towards an Analytical Framework , 2013 .

[9]  Tomasz Opach,et al.  Decision support for adaptive action – assessing the potential of geographic visualization , 2015 .

[10]  Jessica S. Ancker,et al.  The Practice of Informatics: Design Features of Graphs in Health Risk Communication: A Systematic Review , 2006, J. Am. Medical Informatics Assoc..

[11]  Kamran Sedig,et al.  Human-information interaction: An emerging focus for educational cognitive tools , 2011 .

[12]  Kathleen Sherman-Morris,et al.  Measuring the Effectiveness of the Graphical Communication of Hurricane Storm Surge Threat , 2015 .

[13]  James D. Brown,et al.  Prospects for the open treatment of uncertainty in environmental research , 2010 .

[14]  Isabel Meirelles,et al.  Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations , 2013 .

[15]  Dirk Willenbockel,et al.  Managing uncertainty: a review of food system scenario analysis and modelling , 2010, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  S. Shyam Sundar,et al.  Theorizing Interactivity's Effects , 2004, Inf. Soc..

[17]  John T. Stasko,et al.  Casual Information Visualization: Depictions of Data in Everyday Life , 2007, IEEE Transactions on Visualization and Computer Graphics.

[18]  Karen Holtzblatt,et al.  Contextual design , 1997, INTR.

[19]  Thorsten Wagener,et al.  Ten guidelines for effective data visualization in scientific publications , 2011, Environ. Model. Softw..

[20]  Susanne Lorenz,et al.  Tailoring the visual communication of climate projections for local adaptation practitioners in Germany and the UK , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[21]  Robin Williams.  The non-designer's design book, third edition , 2008 .

[22]  S. Dessai,et al.  Actionable Knowledge for Environmental Decision Making: Broadening the Usability of Climate Science , 2013 .

[23]  Anna Mikulak.  Mismatches between ‘Scientific’ and ‘Non-Scientific’ Ways of Knowing and Their Contributions to Public Understanding of Science , 2011, Integrative psychological & behavioral science.

[24]  Mike Pearson,et al.  Visualizing Uncertainty About the Future , 2022 .

[25]  Andrew N Freedman,et al.  Communication of Uncertainty Regarding Individualized Cancer Risk Estimates , 2011, Medical decision making : an international journal of the Society for Medical Decision Making.

[26]  Detlof von Winterfeldt,et al.  Bridging the gap between science and decision making , 2013 .

[27]  Ben Shneiderman,et al.  Interactive Dynamics for Visual Analysis , 2012 .

[28]  Kwan-Liu Ma,et al.  Living Liquid: Design and Evaluation of an Exploratory Visualization Tool for Museum Visitors , 2012, IEEE Transactions on Visualization and Computer Graphics.

[29]  Phillip Gough,et al.  From the Analytical to the Artistic: A Review of Literature on Information Visualization , 2017, Leonardo.

[30]  W. Muller,et al.  Visualization methods for time-dependent data - an overview , 2003, Proceedings of the 2003 Winter Simulation Conference, 2003..

[31]  Ken Brodlie,et al.  A Review of Uncertainty in Data Visualization , 2012, Expanding the Frontiers of Visual Analytics and Visualization.

[32]  Yuqiong Liu,et al.  Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management , 2008, Environ. Model. Softw..

[33]  Jesper Simonsen,et al.  Challenges and Opportunities in Contemporary Participatory Design , 2012, Design Issues.

[34]  David Borland,et al.  Rainbow Color Map (Still) Considered Harmful , 2007 .

[35]  P. Fayers,et al.  The Visual Display of Quantitative Information , 1990 .

[36]  L. Manovich,et al.  What is visualisation? , 2011 .

[37]  Ron Janssen,et al.  Effectiveness of collaborative map-based decision support tools: Results of an experiment , 2013, Environ. Model. Softw..

[38]  M. Sheelagh T. Carpendale,et al.  More Than Telling a Story: Transforming Data into Visually Shared Stories , 2015, IEEE Computer Graphics and Applications.

[39]  Jeffrey Heer,et al.  Selecting Semantically‐Resonant Colors for Data Visualization , 2013, Comput. Graph. Forum.

[40]  Niels Henze,et al.  Investigating Representation Alternatives for Communicating Uncertainty to Non-experts , 2015, INTERACT.

[41]  K. Lowell,et al.  Visualisation of spatial uncertainty in hydrological modelling , 2011 .

[42]  Roger W. Jones,et al.  Foundations for decision making , 2014 .

[43]  S. Sheppard.  Visualizing Climate Change: A Guide to Visual Communication of Climate Change and Developing Local Solutions , 2012 .

[44]  Robert Kosara.  InfoVis Is So Much More: A Comment on Gelman and Unwin and an Invitation to Consider the Opportunities , 2013 .

[45]  Johnny Rodgers,et al.  Ambient and Artistic Visualization of Residential Resource Use , 2010 .

[46]  Martin Krzywinski,et al.  Points of view: Elements of visual style , 2013, Nature Methods.

[47]  H. D. Cooper,et al.  Scenarios for Global Biodiversity in the 21st Century , 2010, Science.

[48]  Daniel A. Keim,et al.  Visual Analytics: Definition, Process, and Challenges , 2008, Information Visualization.

[49]  Alex T. Pang,et al.  Approaches to uncertainty visualization , 1996, The Visual Computer.

[50]  Johanna Alkan Olsson,et al.  Possibilities and problems with the use of models as a communication tool in water resource management , 2006 .

[51]  A. Treisman.  Preattentive processing in vision , 1985, Comput. Vis. Graph. Image Process..

[52]  Ben Miller,et al.  Visualizing uncertainty in spatio-temporal data , 2014 .

[53]  Paul K J Han,et al.  Communicating the Uncertainty of Harms and Benefits of Medical Interventions , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[54]  A. MacEachren,et al.  Visualization in Modern Cartography: Setting the Agenda , 1994 .

[55]  Mark Gahegan,et al.  Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know , 2005 .

[56]  Alexander Toet,et al.  The Perception of Visual UncertaintyRepresentation by Non-Experts , 2014, IEEE Transactions on Visualization and Computer Graphics.

[57]  E. Herricks,et al.  Interaction Between Scientists and Nonscientists in Community-Based Watershed Management: Emergence of the Concept of Stream Naturalization , 1999, Environmental management.

[58]  Jeffrey Heer,et al.  Narrative Visualization: Telling Stories with Data , 2010, IEEE Transactions on Visualization and Computer Graphics.

[59]  David J. Spiegelhalter,et al.  A Collaboratively-Derived Science-Policy Research Agenda , 2012, PloS one.

[60]  Miriah D. Meyer,et al.  Information visualisation for science and policy: engaging users and avoiding bias. , 2014, Trends in ecology & evolution.

[61]  Victor Sposito,et al.  Developing a multi-scale visualisation framework for use in climate change response , 2012, Landscape Ecology.

[62]  Christian Hergarten,et al.  Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development , 2014, Front. Earth Sci..

[63]  Keith Beven,et al.  Environmental Modelling , 2007 .

[64]  D. Demeritt,et al.  Models of best practice in flood risk communication and management , 2014 .

[65]  M S Reed,et al.  Participatory scenario development for environmental management: a methodological framework illustrated with experience from the UK uplands. , 2013, Journal of environmental management.

[66]  Johnny Rodgers,et al.  Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback , 2011, IEEE Transactions on Visualization and Computer Graphics.

[67]  Sarah H. Creem-Regehr,et al.  The influence of different graphical displays on nonexpert decision making under uncertainty. , 2015, Journal of experimental psychology. Applied.

[68]  Tomasz Bednarz,et al.  Art and Chartjunk: a Guide for NEUVis , 2014, VINCI '14.

[69]  Tamara Munzner,et al.  Design Study Methodology: Reflections from the Trenches and the Stacks , 2012, IEEE Transactions on Visualization and Computer Graphics.

[70]  Robin Williams.  The Non-Designer's Design Book , 1994 .

[71]  Susan L Joslyn,et al.  Uncertainty forecasts improve weather-related decisions and attenuate the effects of forecast error. , 2012, Journal of experimental psychology. Applied.

[72]  Deborah P. Dixon,et al.  One Sinister Hurricane: Simondon and Collaborative Visualization , 2015 .

[73]  Suraje Dessai,et al.  Communicating uncertainty in seasonal and interannual climate forecasts in Europe , 2015, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[74]  H. Pfister,et al.  The multiplicity of emotions: A framework of emotional functions in decision making , 2008, Judgment and Decision Making.

[75]  Andrew A. Lovett,et al.  Developing scenarios and visualisations to illustrate potential policy and climatic influences on future agricultural landscapes , 2006 .

[76]  Patricia Gober,et al.  Credibility, salience, and legitimacy of boundary objects: water managers' assessment of a simulation model in an immersive decision theater , 2010 .

[77]  Yarden Katz.  Against storytelling of scientific results , 2013, Nature Methods.

[78]  Sophie A. Nicholson-Cole.  Representing climate change futures: a critique on the use of images for visual communication , 2005, Comput. Environ. Urban Syst..

[79]  Alexander L. Davis,et al.  Communicating scientific uncertainty , 2014, Proceedings of the National Academy of Sciences.

[80]  Ian D. Bishop,et al.  Evaluation of Data Visualisation Options for Land-Use Policy and Decision Making in Response to Climate Change , 2013 .

[81]  I. Lipkus.  Numeric, Verbal, and Visual Formats of Conveying Health Risks: Suggested Best Practices and Future Recommendations , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[82]  M. Mulligan,et al.  Modelling multiple threats to water security in the Peruvian Amazon using the WaterWorld policy support system , 2014 .

[83]  Colin Ware,et al.  Information Visualization: Perception for Design , 2000 .

[84]  Victoria Wibeck,et al.  Images of climate change – a pilot study of young people’s perceptions of ICT-based climate visualization , 2015, Climatic Change.

[85]  Alan M. MacEachren,et al.  How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies , 2014 .

[86]  Rachelle S. Heller,et al.  Using a theoretical multimedia taxonomy framework , 2001, JERC.

[87]  M. Oppenheimer,et al.  The ethics of scientific communication under uncertainty , 2014 .

[88]  Patrick J. Bartlein,et al.  The end of the rainbow? Color schemes for improved data graphics , 2004 .

[89]  Mei-Shiang Chang,et al.  A scenario planning approach for the flood emergency logistics preparation problem under uncertainty , 2007 .

[90]  Nigel Holmes.  Designer's Guide to Creating Charts and Diagrams , 1984 .

[91]  Keith Beven,et al.  Communicating uncertainty in flood inundation mapping: a case study , 2015 .

[92]  Deborah Hemming,et al.  Mapping the climate: guidance on appropriate techniques to map climate variables and their uncertainty , 2011 .

[93]  Holtzblatt Karen,et al.  Contextual Inquiry: A Participatory Technique for System Design , 2017 .

[94]  Martin Wattenberg,et al.  Artistic Data Visualization: Beyond Visual Analytics , 2007, HCI.

[95]  Roy Brouwer,et al.  Integrated modelling of risk and uncertainty underlying the cost and effectiveness of water quality measures , 2008, Environ. Model. Softw..

[96]  Bang Wong.  Points of View: Salience , 2010, Nature Methods.

[97]  Heidrun Schumann,et al.  Visualization for modeling and simulation: visualization methods for time-dependent data - an overview , 2003, WSC '03.

[98]  Amber Wutich,et al.  Water management decision makers' evaluations of uncertainty in a decision support system: the case of WaterSim in the Decision Theater , 2015 .

[99]  J. Bruner.  Actual minds, possible worlds , 1985 .

[100]  M. Hilferink,et al.  Affective appraisal of 3D land use visualization , 2010, Comput. Environ. Urban Syst..

[101]  Ana Figueiras,et al.  How to Tell Stories Using Visualization , 2014, 2014 18th International Conference on Information Visualisation.

[102]  Jock D. Mackinlay,et al.  Storytelling: The Next Step for Visualization , 2013, Computer.

[103]  Heidrun Schumann,et al.  Visualizing time-oriented data - A systematic view , 2007, Comput. Graph..

[104]  E. McNie.  Reconciling the supply of scientific information with user demands: an analysis of the problem and review of the literature , 2007 .

[105]  B. Fischhoff,et al.  The role of social and decision sciences in communicating uncertain climate risks , 2011 .

[106]  Anthony J. Jakeman,et al.  Environmental decision support systems (EDSS) development - Challenges and best practices , 2011, Environ. Model. Softw..

[107]  Wouter Buytaert,et al.  Environmental Virtual Observatories (EVOs): prospects for knowledge co-creation and resilience in the Information Age , 2016 .

[108]  Baruch Fischhoff,et al.  Applying the science of communication to the communication of science , 2011 .

[109]  Ketil Stølen,et al.  Geographic Visualization of Risk as Decision Support in Emergency Situations , 2012, 2012 5th International Conference on Human System Interactions.

[110]  Robert Kosara,et al.  Embedding Information Visualization within Visual Representation , 2009, Advances in Information and Intelligent Systems.

[111]  Cynthia A. Brewer,et al.  ColorBrewer.org: An Online Tool for Selecting Colour Schemes for Maps , 2003 .

[112]  Cristina Conati,et al.  Evaluating the Impact of User Characteristics and Different Layouts on an Interactive Visualization for Decision Making , 2014, Comput. Graph. Forum.

[113]  M. Granger Morgan,et al.  Graphical Communication of Uncertain Quantities to Nontechnical People , 1987 .

[114]  Y. Ariel,et al.  Information, Interactivity, and Social Media , 2015 .

[115]  Melanie C. Green,et al.  Narratives and Cancer Communication , 2006 .

[116]  M. Fishbein,et al.  Understanding tailoring in communicating about health. , 2008, Health education research.

[117]  Tamara Munzner,et al.  Visualization analysis & design , 2015 .

[118]  Michelle A. Borkin,et al.  What Makes a Visualization Memorable? , 2013, IEEE Transactions on Visualization and Computer Graphics.

[119]  Claus-Christian Carbon,et al.  Give Me Gestalt! Preference for Cubist Artworks Revealing High Detectability of Objects , 2013, Leonardo.

[120]  Keith Beven,et al.  On virtual observatories and modelled realities (or why discharge must be treated as a virtual variable) , 2012 .

[121]  Francis W. Zwiers,et al.  Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties , 2010 .

[122]  Mark Gahegan,et al.  Geovisualization for knowledge construction and decision support , 2004, IEEE Computer Graphics and Applications.

[123]  S. Carpenter,et al.  Decision-making under great uncertainty: environmental management in an era of global change. , 2011, Trends in ecology & evolution.

[124]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[125]  Florian Pappenberger,et al.  Visualizing probabilistic flood forecast information: expert preferences and perceptions of best practice in uncertainty communication , 2011 .

[126]  G. Nicol,et al.  Grand Challenges in Terrestrial Microbiology , 2011, Front. Microbio..

[127]  Michael F. Dahlstrom.  Using narratives and storytelling to communicate science with nonexpert audiences , 2014, Proceedings of the National Academy of Sciences.

[128]  J. G. Hollands,et al.  The visual communication of risk. , 1999, Journal of the National Cancer Institute. Monographs.

[129]  Martin Krzywinski,et al.  Reply to: "Against storytelling of scientific results" , 2013, Nature Methods.

[130]  S. Franklin.  Somewhere Over the Rainbow, Cells Do Fly , 2018 .

[131]  Daniel Lapidus.  Gapminder: Unveiling the beauty of statistics for a fact based world view Flash animations bringing enormous amounts of data to life , 2008 .

[132]  Baruch Fischhoff,et al.  The sciences of science communication , 2013, Proceedings of the National Academy of Sciences.

[133]  Julia Kastner,et al.  Show Me The Numbers Designing Tables And Graphs To Enlighten , 2016 .

[134]  Olaf Kolditz,et al.  Visualisation in environmental sciences , 2014, Environmental Earth Sciences.

[135]  Rachel Jones.  Visual attention: Now you see it... , 2002, Nature Reviews Neuroscience.

[136]  Tomasz Bednarz,et al.  Affective and Effective Visualisation: Communicating Science to Non-expert Users , 2014, 2014 IEEE Pacific Visualization Symposium.

[137]  Alexander Toet,et al.  The perception of visual uncertainty representation by non-experts , 2013 .

[138]  J. Mayr,et al.  Somewhere Over the Rainbow: How to Make Effective Use of Colors in Meteorological Visualizations , 2015 .

[139]  Erfan Abdi Dezfouli,et al.  A sense of change: media designers and artists communicating about complexity in social-ecological systems , 2014 .

[140]  Steve Kelling,et al.  Participatory design of DataONE - Enabling cyberinfrastructure for the biological and environmental sciences , 2012, Ecol. Informatics.