Matches, Mismatches, and Methods: Multiple-View Workflows for Energy Portfolio Analysis

The energy performance of large building portfolios is challenging to analyze and monitor, as current analysis tools are not scalable or they present derived and aggregated data at too coarse of a level. We conducted a visualization design study, beginning with a thorough work domain analysis and a characterization of data and task abstractions. We describe generalizable visual encoding design choices for time-oriented data framed in terms of matches and mismatches, as well as considerations for workflow design. Our designs address several research questions pertaining to scalability, view coordination, and the inappropriateness of line charts for derived and aggregated data due to a combination of data semantics and domain convention. We also present guidelines relating to familiarity and trust, as well as methodological considerations for visualization design studies. Our designs were adopted by our collaborators and incorporated into the design of an energy analysis software application that will be deployed to tens of thousands of energy workers in their client base.

[1]  Petra Isenberg,et al.  Evaluation of alternative glyph designs for time series data in a small multiple setting , 2013, CHI.

[2]  Tamara Munzner,et al.  The nested blocks and guidelines model , 2015, Inf. Vis..

[3]  K. J. Vicente,et al.  Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work , 1999 .

[4]  Martin Wattenberg.  Baby names, visualization, and social data analysis , 2005, IEEE Symposium on Information Visualization, 2005. INFOVIS 2005..

[5]  Jason Dykes,et al.  Human-Centered Approaches in Geovisualization Design: Investigating Multiple Methods Through a Long-Term Case Study , 2011, IEEE Transactions on Visualization and Computer Graphics.

[6]  Lisa Stryjewski,et al.  40 years of boxplots , 2010 .

[7]  Tamara Munzner,et al.  A Multi-Level Typology of Abstract Visualization Tasks , 2013, IEEE Transactions on Visualization and Computer Graphics.

[8]  Hanspeter Pfister,et al.  LineUp: Visual Analysis of Multi-Attribute Rankings , 2013, IEEE Transactions on Visualization and Computer Graphics.

[9]  Edward R. Tufte,et al.  Envisioning Information , 1990 .

[10]  Susan M. Stevens-Adams,et al.  Where do I Start? Practical Methods for Design Studies in InfoVis and Visual Analytics. , 2014 .

[11]  Mary Czerwinski,et al.  Understanding the verbal language and structure of end-user descriptions of data visualizations , 2012, CHI.

[12]  O'Connell Se,et al.  Where do I start , 1991 .

[13]  HeerJeffrey,et al.  D3 Data-Driven Documents , 2011 .

[14]  Sung-Hee Kim,et al.  A Comparative Study of Three Sorting Techniques in Performing Cognitive Tasks on a Tabular Representation , 2013, Int. J. Hum. Comput. Interact..

[15]  Martin Wattenberg,et al.  Stacked Graphs – Geometry & Aesthetics , 2008, IEEE Transactions on Visualization and Computer Graphics.

[16]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[17]  James Agutter,et al.  Transactions on Visualization and Computer Graphics Design Activity Framework for Visualization Design , 2014 .

[18]  Leland Wilkinson,et al.  The History of the Cluster Heat Map , 2009 .

[19]  Steven Franconeri,et al.  Comparing averages in time series data , 2012, CHI.

[20]  H. Levkowitz,et al.  Coordinated views to assist exploration of spatio-temporal data: a case study , 2004, Proceedings. Second International Conference on Coordinated and Multiple Views in Exploratory Visualization, 2004..

[21]  Silvia Miksch,et al.  Hierarchical Temporal Patterns and Interactive Aggregated Views for Pixel-Based Visualizations , 2009, 2009 13th International Conference Information Visualisation.

[22]  Tamara Munzner,et al.  LiveRAC: interactive visual exploration of system management time-series data , 2008, CHI.

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

[24]  S E O'Connell,et al.  Where do I start? , 1991, HRMagazine : on human resource management.

[25]  Heidrun Schumann,et al.  Visualization of Time-Oriented Data , 2011, Human-Computer Interaction Series.

[26]  Andreas Butz,et al.  Information visualization evaluation in large companies: Challenges, experiences and recommendations , 2011, Inf. Vis..

[27]  K. Ellegård,et al.  Visualizing energy consumption activities as a tool for making everyday life more sustainable , 2011 .

[28]  Marc Streit,et al.  Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations , 2014, IEEE Transactions on Visualization and Computer Graphics.

[29]  Sara Jones,et al.  Creative User-Centered Visualization Design for Energy Analysts and Modelers , 2013, IEEE Transactions on Visualization and Computer Graphics.

[30]  Edward Rolf Tufte,et al.  The visual display of quantitative information , 1985 .

[31]  Jeffrey Heer,et al.  Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations , 2009, CHI.

[32]  Jeffrey Heer,et al.  Interpretation and trust: designing model-driven visualizations for text analysis , 2012, CHI.

[33]  Niklas Elmqvist,et al.  Graphical Perception of Multiple Time Series , 2010, IEEE Transactions on Visualization and Computer Graphics.

[34]  M. Sheelagh T. Carpendale,et al.  Pre-design empiricism for information visualization: scenarios, methods, and challenges , 2014, BELIV '14.

[35]  Tamara Munzner,et al.  A Nested Model for Visualization Design and Validation , 2009, IEEE Transactions on Visualization and Computer Graphics.

[36]  Milind R. Naphade,et al.  The dubuque electricity portal: evaluation of a city-scale residential electricity consumption feedback system , 2013, CHI.

[37]  Daniel A. Keim,et al.  Multi-Resolution Techniques for Visual Exploration of Large Time-Series Data , 2007, EuroVis.

[38]  Michael Gleicher,et al.  Task-driven evaluation of aggregation in time series visualization , 2014, CHI.

[39]  Jarke J. van Wijk,et al.  Cluster and Calendar Based Visualization of Time Series Data , 1999, INFOVIS.

[40]  Tamara Munzner,et al.  Visualization Analysis and Design , 2014, A.K. Peters visualization series.

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

[42]  Jeffrey Heer,et al.  SpanningAspectRatioBank Easing FunctionS ArrayIn ColorIn Date Interpolator MatrixInterpola NumObjecPointI Rectang ISchedu Parallel Pause Scheduler Sequen Transition Transitioner Transiti Tween Co DelimGraphMLCon IData JSONCon DataField DataSc Dat DataSource Data DataUtil DirtySprite LineS RectSprite , 2011 .

[43]  Tamara Munzner,et al.  Overview Use in Multiple Visual Information Resolution Interfaces , 2007, IEEE Transactions on Visualization and Computer Graphics.