📊Graphical Representation: Sense-making in Data Visualizations (Part 2: Guidelines)

Graphical Representation Series

 

Consider the following best practices in data visualizations (work in progress):

• avoid poor labeling and annotation practices
• label data points
• considering labeling at least the important number of points
• e.g. starts, ends, local/global minima/minima
• when labels clutter the chart or there's minimal variation
• avoid abbreviations
• unless they are defined clearly upfront, consistent and/or universally understood
• can hinder understanding
• abbreviations should help compress content without losing meaning
• use font types, font sizes, and text orientation that are easy to read
• avoid stylish design that makes content hard to read
• avoid redundant information
• text should never overshadow or distort the actual message or data
• use neutral, precise wording
• avoid the use of pre-attentive attributes 
• aka visual features that our brains process almost instantly
• color
• has identity value: used to distinguish one thing from another
• carries its own connotations
• gives a visual scale of measure
• the use of color doesn’t always help
• hue 
• refers to the dominant color family of a specific color, being processed by the brain based on the different wavelengths of light
• allows to differentiate categories
• use distinct hues to represent different categories
• intensity (aka brightness)
• refers to how strong or weak a color appears
• saturation (aka chroma, intensity) 
• refers to the purity or vividness of a color
• as saturation decreases, the color becomes more muted or washed out
• highly saturated colors have little or no gray in it
• highly desaturated colors are almost gray, with none of the original colors
• use high saturation for important elements like outliers, trends, or alerts
• use low saturation for background elements
• avoid pure colors that are bright and saturated
• drive attention to the respective elements 
• avoid colors that are too similar in tone or saturation
• avoid colors hard to distinguish for color-blind users
• e.g. red-green color blindness
• brown-green, orange-red, blue-purple combinations
• avoid red-green pairings for status indicators 
• e.g. success/error
• e.g. blue-yellow color blindness
• blue-green, yellow-ping, purple-blue
• e.g. total color blindness (aka monochromacy)
• all colors appear as shades of gray
• ⇒ users must rely entirely on contrast, shape, and texture
• use icons, labels, or patterns alongside color
• use tools to test for color issues
• e.g. Color Oracle, WebAIM Contrast Checker
• use colorblind-safe palettes 
• e.g. ColorBrewer or Viridis4
• for sequential or diverging data, use one hue and vary saturation or brightness to show magnitude
• start with all-gray data elements
• use color only when it corresponds to differences in data
• ⇐ helps draw attention to whatever isn’t gray
• dull and neutral colors give a sense of uniformity
• can modify/contradict readers' intuitive response
• choose colors to draw attention, to label, to show relationships 
• form
• shape
• allows to distinguish types of data points and encode information
• well-shaped data has functional and aesthetic character
• complex shapes can become more difficult to be perceived
• size
• attribute used to encode the magnitude or extent of elements 
• should be aligned to its probable use, importance, and amount of detail involved
• larger elements draw more attention
• its encoding should be meaningful
• e.g. magnitudes of deviations from the baseline
• overemphasis can lead to distortions
• choose a size range that is appropriate for the data
• avoid using size to represent nominal or categorical data where there's no inherent order to the sizes
• orientation
• angled or rotated items stand out.
• length/width
• useful in bar charts to show quantity
• avoid stacked bar graphs
• curvature
• curved lines can contrast with straight ones.
• collinearity
• alignment can suggest grouping or flow
• highlighting
• spatial positioning
• 2D position
• placement on axes or grids conveys value 
• 3D position in 2D space
• 
  
• grouping
• proximity implies relationships.
• keep columns, respectively bars close together
• enclosure
• borders or shaded areas signal clusters.
• depth (stereoscopic or shading)
• adds dimensionality
• avoid graphical features that are purely decorative
• aka elements that don't affect understanding, structure or usability
• stylistic embellishments
• borders/frames
• ornamental lines or patterns around content
• background images
• images used for ambiance, not content
• drop shadows and gradients
• enhance depth or style but don’t add meaning.
• icons without function
• decorative icons that don’t represent actions or concepts
• non-informative imagery
• stock photos
• generic visuals that aren’t referenced in the text.
• illustrations
• added for visual interest, not explanation.
• mascots or logos
• when repeated or not tied to specific content.
• layout elements
• spacers
• transparent or blank images used to control layout
• leave the right amount of 'white' space between chart elements
• custom bullets or list markers
• designed for flair, not clarity
• visual separators
• lines or shapes that divide sections without conveying hierarchy or meaning
• avoid bias
• sampling bias
• showing data that doesn’t represent the full population
• avoid cherry-picking data
• aka selecting only the data that support a particular viewpoint while ignoring others that might contradict it
• enable users to look at both sets of data and contrast them
• enable users to navigate the data
• avoid survivor bias
• aka focusing only on the data that 'survived' a process and ignoring the data that didn’t
• use representative data
• aka the dataset includes all relevant groups
• check for collection bias
• avoid data that only comes from one source 
• avoid data that excludes key demographics
• cognitive bias
• mental shortcut that sometimes affect interpretation
• incl. confirmation bias, framing bias, pattern bias
• balance visual hierarchies
• don’t make one group look more important by overemphasizing it
• show uncertainty
• by including confidence intervals or error bars to reflect variability
• separate comparisons
• when comparing groups, use adjacent charts rather than combining them into one that implies a hierarchy
• e.g. ethnicities, region
• visual bias
• design choices that unintentionally (or intentionally) distort meaning
• respectively how viewers interpret the data
• avoid manipulating axes 
• by truncating y-axis
• exaggerates differences
• by changing scale types
• linear vs. logarithmic
• a log scale compresses large values and expands small ones, which can flatten exponential growth or make small changes seem more significant
• uneven intervals
• using inconsistent spacing between tick marks can distort trends
• by zooming in/out
• adjusting the axis to focus on a specific range can highlight or hide variability and eventually obscure the bigger picture
• by using dual axes
• if the scales differ too much, it can falsely imply correlation or exaggerate relationships 
• by distorting the aspect ration
• stretching or compressing the chart area can visually amplify or flatten trends
• e.g. a steep slope might look flat if the x-axis is stretched
• avoid inconsistent scales
• label axes clearly
• explain scale choices
• avoid overemphasis 
• avoid unnecessary repetition 
• e.g. of the same graph, of content
• avoid focusing on outliers, (short-term) trends
• avoid truncating axes, exaggerating scales
• avoid manipulating the visual hierarchy 
• avoid color bias
• bright colors draw attention unfairly
• avoid overplotting 
• too much data obscures patterns
• avoid clutter
• creates cognitive friction
• users struggle to focus on what matters because their attention is pulled in too many directions
• is about design excess
• avoid unnecessary or distracting elements 
• they don’t contribute to understanding the data
• avoid overloading 
• attempting to show too much data at once
• is about data excess
• overwhelms readers' processing capacity, making it hard to extract insights or spot patterns
• algorithmic bias 
• the use of ML or other data processing techniques can reinforce certain aspects (e.g. social inequalities, stereotypes)
• visualize uncertainty
• include error bars, confidence intervals, and notes on limitations
• audit data and algorithms
• look for bias in inputs, model assumptions and outputs
• intergroup bias
• charts tend to reflect or reinforce societal biases
• e.g. racial or gender disparities
• use thoughtful ordering, inclusive labeling
• avoid deficit-based comparisons
• avoid overcomplicating the visualizations 
• e.g. by including too much data, details, other elements
• avoid comparisons across varying dimensions 
• e.g. (two) circles of different radius, bar charts of different height, column charts of different length, 
• don't make users compare angles, areas, volumes

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