Communicating through infographics: visualizing scientific and engineering information Christa Kelleher Department of Earth and Ocean Sciences Nicholas School of the Environment Duke University hp://xkcd.com/688/ 1 Model diagnostics, catchment modeling, and visualization of large environmental datasets [Kelleher et al., 2012; Kelleher et al., 2013; Sawicz et al., 2013; Kelleher et al., in prep] 2 The importance of How much data is created every minute? visualization As of 2012 …. • 100,000 tweets • 2,000,000 google search queries • 47,000 app downloads • 571 new websites • 217 new mobile web users 3 Source: [Visual News, hp://www.visualnews.com/2012/06/19/how-much-data-created-every-minute/?view=infographic] The importance of How much data is created every minute? visualization 4 Source: [Mashable, hp://mashable.com/2014/04/23/data-online-every-minute/?] Resources Data analysis: Matlab R, Python Spaal analysis: ArcGIS R Mapping packages, qGIS, GRASS GIS, SAGA Fine tuning: Illustrator Inkscape, CorelDRAW 5 HOW DO WE CREATE EFFECTIVE VISUALIZATIONS? 6 Rule 1 Choose an effective plot type The type of plot you use should compliment the type of data you have (and the message you are conveying) [Source: Matlab, http://www.mathworks.com/help/matlab/creating_plots/figures-plots-and-graphs.html] 7 Rule 1 Choose an effective plot type Plots use attributes to ‘encode’ information… …BUT our ability to quantitatively perceive these attributes differs! 8 [Kelleher and Wagner (2011); Few (2009)] Posion Common Non-aligned Length Details Direcon Angle Our accuracy to rank quantitative perceptual tasks varies across Area different encoding objects and graphs More accurate Volume Patterns Shading Color saturaon 9 [Mackinlay (1986), ACM Transacons on Graphics] Rule 1 Choose an effective plot type 4 DETAILS 2 Line graph 0 -2 J F M A M J J A S O N D Annual VS PATTERNS Heat map (Pattern plot) 10 [Kelleher and Wagner (2011); Few (2009)] Rule 1 Choose an effective plot type Line plots highlight details in the timeseries – high values, low values, and value comparisons Comparing Trends in Air Temperature for 8 Sites 4 2 0 -2 J F M A M J J A S O N D Annual 11 [Kelleher and Wagner (2011); Few (2009)] Rule 1 Choose an effective plot type Heat maps (pattern plots) highlight patterns in the data, and allow easy comparison (2) (4) (6) (8) 14 Jan °C 10yrs-1 Feb 12 Mar > -1.5 Apr 10 -0.5 May -0.25 Jun 8 Jul 0.25 Aug 6 0.5 Sep 1.5 Oct 4 2.5 Nov < 3.5 Dec 2 Annual σ 0 0 2 4 6 8 (1) (3) (5) (7) 12 [Kelleher and Wagner (2011); Few (2009)] Rule 1 Choose an effective plot type Aggregating data, troops versus cost, 1980 - 2012 Overall trends …. Data source: Time Magazine Creator: Jorge Camoes Chart: http://www.excelcharts.com/blog/redraw-troops-vs-cost-time-magazine/ 13 … vs details Data source: Time Magazine Creator: Jorge Camoes Chart: http://www.excelcharts.com/blog/redraw-troops-vs-cost-time-magazine/ 14 Rule 1 Choose an effective plot type Select attributes which can be easily perceived depending on your graph’s message 15 [Robbins (2006), accessed at: h"p://www.perceptualedge.com/arQcles/b-eye/dot_plots.pdf] Rule 1 Choose an effective plot type Pie charts are usually considered ineffective 25% 25% ?? This one is up to you! [Few (2007), accessed at: h"p://www.perceptualedge.com/arQcles/visual_business_intelligence/ save_the_pies_for_dessert.pdf] 16 Rule 1 Choose an effective plot type Tables are a good alternative Companies Percentage Company B 40% Company C 25% Company D 17% Company A 10% Company E 7% Company F 1% Total 100% [Few (2007), accessed at: h"p://www.perceptualedge.com/arQcles/visual_business_intelligence/ save_the_pies_for_dessert.pdf] 17 Rule 2 Remove chart junk (Tufte, 1983) [S. Khosla, Global Post, hp://www.globalpost.com/dispatch/news/regions/americas/brazil/140702/graphic-brazil-red-yellow-cards18 ] Rule 2 Remove chart junk (Tufte, 1983) Chartjunk represents any ink on a given graph which displays redundant or non-data information Reduction in chartjunk 30 30 30 20 20 20 10 10 10 [Tue (1983); Bray, Data-Ink Rao Example 1, accessed at: h"p://www.tbray.org/ongoing/data-ink/di1] 19 Rule 2 Remove chart junk (Tufte, 1983) This includes 3D graphics [Kelleher and Wagner (2011), Environmental Modelling and Soware] 20 Display the same number of dimensions as the Rule 3 dataset With a single encoding attribute, we can easily identify the object that doesn’t follow the pattern Enclosure OrientationOrientation Enclosure ColourColor [Spence (2007), Informa+on Visualiza+on] 21 Display the same number of dimensions as the Rule 3 dataset FIND THE RED SQUARE! When we mix too many attributes, we make it harder to detect patterns [Spence (2007), Informa+on Visualiza+on] 22 Display the same number of dimensions as the Rule 3 dataset [DarkHorse Analytics, [The Economist, http://www.economist.com/blogs/ graphicdetail/2014/07/daily-chart-3] http://darkhorseanalytics.com/blog/data-looks-better-naked/] 23 Consider the use of color to fit your chart and the Rule 4 type of data you’re displaying Sequential Diverging Categorical Example: air Example: anomalies Example: colors, political temperature, traffic parties, car types/ counts manufacturors [ColorBrewer2, online at h"p://colorbrewer2.org/] 24 Consider the use of color to fit your chart and the Rule 4 type of data you’re displaying Colorbrewer2 (colorbrewer2.org/), developed by Cynthia Brewer (Penn State) [ColorBrewer2, online at h"p://colorbrewer2.org/] 25 Rule 5 Maintain axes when comparing subplots 10 10 9 9.5 8 7 9 6 (IMDB) Rating 8.5 5 4 8 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 Episode # Episode # Data source: IMDB Location: http://graphtv.kevinformatics.com/ 26 Rule 5 Maintain axes when comparing subplots 10 10 8 8 6 6 4 4 IMDB Rating IMDB 2 2 0 0 10 20 30 40 50 60 70 80 90 10 20 30 40 50 60 Episode # Episode # Data source: IMDB Location: http://graphtv.kevinformatics.com/ 27 Rule 5 Maintain axes when comparing subplots Period 1 Precipitation Period 2 50 Period 1 50 Period 2 50 50 7 1948 - 1957 7 Visualization programs usually 7 7 6 45 45 6 45 6 45require that you manually set 6 6 5 the axes and ranges between 5 40 5 40 5 40 5 40 4 plots 4 4 4 35 4 35 35 35 33 3 3 3 30 30 30 2 30 2 2 2 2 25 25 1 25-130 -120 -110 -100 -90 -80 -70 25-130 -120 -110 -100 -90 -80 -70 1 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 Period 1 Period 2 Period 1 Period 2 50 50 50 50 7 7 7 7 Period 31958 - 1967 7 Period 4 50 Period 3 50 Period 4 45 5045 6 50 45 6 45 6 6 7 6 7 7 7 45 5 45 40 5 40 5 5 6 40 5 4540 6 45 6 6 4 4 40 454 40 5 35 4 35 5 35 4035 5 40 3 3 3 4 3 34 35 4 35 4 30 30 30 3530 2 35 2 232 3 2 3 3 30 1 30 25 3025 121 30 2 25-130 -120 -110 -100 -90 -80 -70 25-130 -120 -110 -100 -90 -80 -70 2 2 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 [C. Kelleher, K. Sawicz] 28 25 1 25 25-130 -120 -110 -100 -90 -80 -70 1 25-130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 Period 3 Period 4 50 Period 3 50 Period 4 50 50 7 7 7 7 45 45 45 6 45 6 6 6 40 5 40 5 40 5 40 5 4 4 35 4 35 4 35 35 3 3 3 3 30 30 30 2 30 2 2 2 25 1 25 25-130 -120 -110 -100 -90 -80 -70 1 25-130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 -130 -120 -110 -100 -90 -80 -70 Rules for specific plot types Magnitude Change Correlation Distribution 29 Rules for effective use Reference to zero Use rotated bar charts if there are more than 8-10 categories Include a legend Be aware of scaling issues (important if you’re showing more than one data series) [Few, 2009; Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 30 Reference the y-axis to zero on bar charts 2000 2000 1900 1500 1800 1000 1700 1600 500 1500 0 A B C A B C This accentuates the perceived differences between graphed entities [Kelleher and Wagener (2011), Environmental Modelling and Soware; Wainer (1984) The Am sta+s+cian] 31 Reference the y-axis to zero on bar charts [Yau (2012), FlowingData.com, accessed at: h"p://flowingdata.com/2012/08/06/fox-news-conQnues-charQng-excellence/] 32 Rotate your chart if displaying more than 8-10 categories [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 33 Rotate your chart if displaying more than 8-10 categories Relative Poverty Rates in Wealthy Nations, 2000 Finland Elderly Norway Children Sweden Switzerland* Belgium Austria France* Germany Luxembourg Netherlands* Canada U.K.* Australia* Spain Italy Ireland U.S. 0 10 20 30 40 [Gary Klass, Illinois State, h"p:// % living in families below 50% of median family income lilt.ilstu.edu/gmklass/pos138/ * most recent year datadisplay/secons/goodcharts.htm] source: Luxembourg Income Study 34 Stacked bar charts make sense when comparing the series closest to the x-axis [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 35 Bar charts and scaling issues … especially important when displaying two data series with very different magnitudes [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 36 Bar charts and scaling issues Highlights the fact that labor participation is large across all years Misses that the unemployment rate increases by a factor of 30% [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 37 Bar charts and scaling issues Displaying the y-axis on a log scale can help to highlight the rate of change Labor force participation and Percentage change in labor force unemployment, 1970 - 2000 participation and unemployment between decades 50 40 30 20 10 0 −10 1970 - 1980 1980 - 1990 1990 - 2000 [Gary Klass, Illinois State, h"p://lilt.ilstu.edu/gmklass/pos138/datadisplay/secons/goodcharts.htm] 38 Example – the paintings of Bob Ross An analysis of 381 Bob Ross episodes ‘The top-line results are to be expected — wouldn’t you know, he did paint a bunch of mountains, trees and lakes! — but then I put some numbers to Ross’s classic figures of speech.