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The Value of Data Visualization
– Two basic types – find a story the data is telling you – tell a story to an audience – Represents large quantities of data coherently – Help the user to in the data – Does not distort what the data has to say – Takes into account your
Audience Considerations• What information does
the reader need to be successful?• How much detail does the reader need? and what values need action?• What learned or cultural ? – Identity, Motivation, Language and learned Social Context – What do mean? – Which are familiar?• Don’t forget (quite common) – Color palettes friendly to those with color blindness at http://colorlab.wickline.org/colorblind/colorlab
Gestalt Principles of Perception :
Objects that are close together or connected are perceived as a group : Objects that share similar attributes, color or shape, are perceived as a group : Objects that appear to have a boundary or a continuation around them are perceived as a group : Open structures can be easily perceived as closed, complete
Common Data Visualization Issues• Inappropriate
display choices that distort reality i.e. , 3-D charts• Variety for the sake of variety display choices that use noisy fill patterns, line styles, or saturated/bright colors quantitative data and placement• Inconsistent or reversed scales• Proportional axis scaling• Using counts vs. percentages when comparing periods with different totals
Chart with Integrity VS.http://danspira.com/2009/07/08/same-data-different-graphsAn example
of the “charting tricks” politicians use:1. Vertical scaling: Both graphs use a y-axis that is proportionately bigger than the x-axis, exaggerating slope of recent job losses.2. Absolute values: One graph counts actual number of jobs lost, instead of the percentage of jobs lost. The workforce has grown considerably over the years, this exaggerates the downward slope of recent job losses.3. Narrower context: One graph uses fewer past recessions in the comparison, and leaves out the more-severe 1981 recession, and two shorter recessions. This skews extrapolation of what might happen next.
Design Considerations• Part to whole
• Exception highlighting• Bar Charts • Avoid meaningless variety• Line Charts • Empty points• Disparate Values • Do not use 3-D charts• Gridlines • Appropriate use of color• Sparklines • Multiple chart areas• Bullet graphs • Use text sparingly• Small multiples • Avoid Too Much Information• Pareto charts • Physical position is easiest to• Status indicators perceive and most powerful visual property
Bar Chart for comparing across
categories, discrete data or continuous data• Orientation: Horizontal for• Proximity – Set white space width separating contiguous bars equal to 50%-150% width of bars (unless bullet graph like)• Fills – Use distinct, but no intense colors for highlighting• Borders – Avoid, – Always start at the axis unless it’s a range bar chart• Tick marks – Don’t overdo the number of tick marks or tick mark labels – Use consistent, sensible tick mark values
Line Chart Best Practices• Lines
should mostly be used for connecting on interval scale except Pareto chart• Intervals should be equal in size• Combination charts should used synchronized axis• Lines should only values in adjacent intervals – If data is missing, indicate it is missing
Dashboard Design• Designed by consider•
Strategic, Analytical and Operational• Combination of individual data visualizations• Must fit• Monitored at a glance so or• Important to and keep context items near each other
Common Dashboard Design PitfallsStephen Few,
Perceptual Edge1. Exceeding the boundaries of a single screen2. Supplying for the data3. Displaying or precision4. Expressing measures indirectly5. Choosing of display6. Introducing7. Using poorly designed display media8. Encoding quantitative data inaccurately9. Arranging the data poorly what’s important11. Cluttering the screen with12. Misusing or overusing color13. Designing an unappealing visual display http://www.perceptualedge.com/articles/ Whitepapers/Common_Pitfalls.pdf
Additional Resources• Jen Underwood Blog