Data Visualization
Turning data into visual form that reads clearly, choosing what fits the purpose and blocking misreadings.
Data visualization loads numbers onto visual channels like position, length, color, and shape so people can read them. You decide what to show, pick a visual encoding that suits the data type, strip out the misreadings that axes and colors create, and check that the same information reaches readers who can't see the picture. The weight sits on designing and delivering a chart that will be read, not on reading and interpreting one.
Level Overview
| Level | Stage | Hashtags |
|---|---|---|
| Lv.1 | Chart Decoding | #ChartAnatomy#AxisReading#ChartTypeIdentification#VisualChannelSpotting |
| Lv.2 | Accurate Chart Making | #ChartTypeSelection#AxisLabeling#ScaleIntegrity#ChartDeclutter |
| Lv.3 | Purpose-Driven Design | #EncodingChoice#MisleadingAxis#TextAlternative#ChartAnnotation |
| Lv.4 | Audience-Adapted Design | #SmallMultiples#ColorVisionSafety#InteractionDesign#AudienceAdaptation |
| Lv.5 | Standards and Review | #ChartStandards#VisualizationReview#DesignCritique#DataVizMentoring |
| Lv.6 | Organizational Visualization System | #VisualizationGovernance#EncodingGuideline#PerceptualEvidence#VizCurriculum |
| Lv.7 | Encoding Language Creation | #EncodingLexicon#NewChartForm#VisualizationCanon#DataVizPedagogy |
Levels
Level 1
#ChartAnatomy#AxisReading#ChartTypeIdentification#VisualChannelSpottingTaking a chart apart to see which value rides on which visual element.
You've just stepped into data visualization. On the encoding decision axis you're still checking mappings someone else chose, so you focus on telling bar, line, and scatter charts apart and on reading what axes and legends tell you. You break a single chart down into which values ride on position and which ride on color or size. Designing a chart yourself belongs to the next level.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.2 Accurate Chart Making, where you pick the chart type the data calls for yourself. Lee, Kim & Kwon, who built the VLAT visualization literacy test, split reading into separate items: find a value, read a trend, compare extremes. Throw those same tasks at your own chart one at a time and the reading checklist flips straight into a making checklist.
Level 2
#ChartTypeSelection#AxisLabeling#ScaleIntegrity#ChartDeclutterChoosing a chart that fits the data type and making it readable.
You start making charts yourself. You separate categorical from continuous data, pick the chart type each one calls for, and add a title, axis labels, units, and a source so the chart still makes sense on its own. On the encoding decision axis you still follow common convention. On the reception check axis you go no further than your own eyes.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.3 Purpose-Driven Design, where you fix the reader's judgment first and choose the encoding around it. Cleveland & McGill(1984) confirmed by experiment that reading accuracy differs across elementary perceptual tasks. Draw the same data three ways, have someone read each one, and write down how closely they recover the values. Choosing stops being taste and becomes judgment.
Level 3
#EncodingChoice#MisleadingAxis#TextAlternative#ChartAnnotationSetting a purpose, picking the encoding, and finishing a chart alone.
This is where dependence turns into autonomy. You write down what the reader has to judge, then load the comparison that judgment needs onto position or length. You strip out anything that distorts values: truncated axes, dual axes, areas inflated by volume. The reception check axis shifts here. You attach a short identifying description and a long one covering values, trends, and relationships, so information reaches readers who can't see the picture.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.4 Audience-Adapted Design, where you rebuild the same data for each audience. The W3C accessibility guidance requires complex images to carry a short identifying description and a long one together. That habit of preparing one piece of information in two layers runs straight into designing two versions per audience. Write the long description first and hand the chart only as much as that text can drop.
Level 4
#SmallMultiples#ColorVisionSafety#InteractionDesign#AudienceAdaptationReshaping layout by audience and medium to design complex visuals.
Rule application gives way to situational adaptation. On the task layer axis you define why the chart is needed at all, then split the same data into one version for decision makers and one for practitioners. You lay several categories out against a shared axis, or add interaction so readers change the conditions themselves. You encode shape and position on top of color so nothing rests on color alone, and you tune density to the medium.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.5 Standards and Review, where you judge other people's charts. Brehmer & Munzner's multi-level task typology splits a visualization into why it was made, how, and what goes in and stays out. Ask in that order, but treat encoding as the accuracy question Cleveland & McGill(1984) settled: put two options in front of one reader and record the gap. Taste complaints drop and fixable spots surface layer by layer.
Level 5
#ChartStandards#VisualizationReview#DesignCritique#DataVizMentoringSetting team chart standards and reviewing the visuals others make.
Your scope widens from your own output to the standard the team works to. The misreading responsibility axis splits here: you carry the accuracy of every chart the team ships, not just your own. You write color, axis, and label rules into a document, harden them into templates, and review other people's visuals against encoding accuracy, misreading risk, and accessibility. You also rule on the cases where no chart is the better answer.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.6 Organizational Visualization System, where you set guidelines that cross departments. The PNAS study designed the definition of visualization literacy, its exercises, and its assessments as one package. A standard you can teach comes with tasks you can grade. Rewrite each clause of the team standard as a single task that passes or fails, and the document grows from a rulebook into a course.
Level 6
#VisualizationGovernance#EncodingGuideline#PerceptualEvidence#VizCurriculumDesigning the organization's visualization system and joining field debate.
Your reach extends past the team into the organization and the field. You pull the forms each department used differently into one guideline, and attach visual perception research to every encoding rule so taste arguments turn into questions you can settle. You design a curriculum, measure ability before and after it, and put your methods into industry discussion. You also publish the exceptions where your own rules break down.
What Comes Next
If you've checked off most of this list, you're ready to enter Lv.7 Encoding Language Creation, where you build forms no typology lists yet. Cleveland & McGill(1984) showed that you can settle which form wins by reading accuracy rather than by argument. When you put out a new form, draw the same data in the existing form beside it, measure accuracy, and publish the procedure as well. That leaves evidence other people can reproduce.
Level 7
#EncodingLexicon#NewChartForm#VisualizationCanon#DataVizPedagogyFounding a new encoding language that changes how later readers see data.
You leave a mark on the language of data visualization itself. For data structures existing typologies can't hold, you found a new form of expression or a new encoding rule, and it becomes the default for other organizations and for the generation after you. No grade or credential names the next step, so the only evidence of arrival is how long the language you left keeps getting referenced.
What Comes Next
If you've checked off most of this list, you stand where the default changes: what people reach for when they look at data. The reading ability VLAT measures and the delivery requirements W3C sets both assume forms that already exist. Adding to those forms is confirmed by no grade, only by what the generation after you takes for granted.
References
Defines data visualization literacy and structures it into conceptual frameworks, exercises, and assessments. It is the primary basis for the level boundary where reading ability turns into design ability.
A psychometrically validated instrument that measures 8 visualization tasks across 12 chart types with 53 items. The list of tasks those items cover gives the lower level checklists their concrete basis.
A standard typology that sorts visualization tasks into why, how, and what layers. It supplies the vocabulary and the criteria for the design decisions the upper levels demand.
An international standard requiring charts and diagrams to carry both a short identifying alt text and a long description covering values, trends, and relationships. It grounds the L3-L5 items on delivery and misreading, which check that information lands without a visual channel.
The classic experiment establishing that reading accuracy differs across elementary perceptual tasks. It backs the point that choosing which value rides on which visual channel is a measurable question, not a matter of taste. Upper levels apply the same standard to rule evidence and to testing new forms.