Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation

In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.

[1]  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 .

[2]  Jeff Sauro,et al.  Average task times in usability tests: what to report? , 2010, CHI 2010.

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

[4]  Jeffrey Heer,et al.  Crowdsourcing graphical perception: using mechanical turk to assess visualization design , 2010, CHI.

[5]  Benjamin Watson,et al.  Emerging Research in Mobile Visualization , 2015, MobileHCI Adjunct.

[6]  Anastasia Bezerianos,et al.  A Systematic Review of Experimental Studies on Data Glyphs , 2017, IEEE Transactions on Visualization and Computer Graphics.

[7]  Larissa K. Barber,et al.  Sleep consistency and sufficiency: are both necessary for less psychological strain? , 2009 .

[8]  Wanda Pratt,et al.  SleepTight: low-burden, self-monitoring technology for capturing and reflecting on sleep behaviors , 2015, UbiComp.

[9]  Pierre Dragicevic,et al.  Fair Statistical Communication in HCI , 2016 .

[10]  Monica M. C. Schraefel,et al.  TouchViz: a case study comparing two interfaces for data analytics on tablets , 2013, CHI.

[11]  Bongshin Lee,et al.  Data Visualization on Mobile Devices , 2018, CHI Extended Abstracts.

[12]  Martin Krzywinski,et al.  Points of Significance: Error bars , 2013, Nature Methods.

[13]  Sean A. Munson,et al.  When (ish) is My Bus?: User-centered Visualizations of Uncertainty in Everyday, Mobile Predictive Systems , 2016, CHI.

[14]  Pierre Dragicevic,et al.  La Différence Significative entre Valeurs p et Intervalles de Confiance , 2017 .

[15]  M. Sheelagh T. Carpendale,et al.  TouchWave: kinetic multi-touch manipulation for hierarchical stacked graphs , 2012, ITS.

[16]  Kerstin Blumenstein,et al.  Evaluating Information Visualization on Mobile Devices: Gaps and Challenges in the Empirical Evaluation Design Space , 2016, BELIV '16.

[17]  Luca Chittaro,et al.  Visualization of patient data at different temporal granularities on mobile devices , 2006, AVI '06.

[18]  Daniel J Buysse,et al.  Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for a Healthy Adult: Methodology and Discussion. , 2015, Sleep.

[19]  Bongshin Lee,et al.  WordlePlus: Expanding Wordle's Use through Natural Interaction and Animation , 2015, IEEE Computer Graphics and Applications.

[20]  Bongshin Lee,et al.  Crowdsourcing for Information Visualization: Promises and Pitfalls , 2017, Crowdsourcing and Human-Centered Experiments.

[21]  Lynne Baillie,et al.  Investigating Time Series Visualisations to Improve the User Experience , 2016, CHI.

[22]  Richard F. Riesenfeld,et al.  A Survey of Radial Methods for Information Visualization , 2009, IEEE Transactions on Visualization and Computer Graphics.

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

[24]  W. Cleveland,et al.  Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods , 1984 .

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

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

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

[28]  Tanzeem Choudhury,et al.  Towards circadian computing: "early to bed and early to rise" makes some of us unhealthy and sleep deprived , 2014, UbiComp.

[29]  David S. Ebert,et al.  Visual Analytics on Mobile Devices for Emergency Response , 2007, 2007 IEEE Symposium on Visual Analytics Science and Technology.

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

[31]  Jeffrey Heer,et al.  D³ Data-Driven Documents , 2011, IEEE Transactions on Visualization and Computer Graphics.

[32]  Michael Burch,et al.  Uncovering Strengths and Weaknesses of Radial Visualizations---an Empirical Approach , 2010, IEEE Transactions on Visualization and Computer Graphics.

[33]  Yang Chen,et al.  Visualizing Large Time-series Data on Very Small Screens , 2017, EuroVis.