Abstract¶
Outlines the foundational principles for creating compelling and accurate data visualizations. These principles are grounded in cartographic theory, information design, and empirical research in visual perception.
Purpose & Audience¶
Define your message before you design. Every effective map or chart begins with a clear understanding of:
What is the story? - What insight or finding do you want to communicate?
Who is the audience? - What is their background and knowledge level?
What action or decision will this visualization support?
According to cartographic design theory, a map or chart that cannot stand alone or fails to communicate a specific message to its intended audience has failed in its primary purpose—regardless of its aesthetic beauty or technical sophistication Bertin, 1967Tufte & Graves-Morris, 1983.
Data Integrity¶
Represent data truthfully and avoid distortion. It is easy to lie with maps, although all maps distort the truth somewhat, you just need to decide which truth you’re displaying. Distorted visualizations mislead audiences and undermine decision-making. This is not just an ethical issue—it destroys trust and credibility.
The most important rule in visualization is that your visualization must not lie. This means:
Accurate scaling - Visual dimensions must be proportional to data values
Proper context - Show data in appropriate context; don’t cherry-pick ranges
No misleading effects - Avoid visual tricks that exaggerate or minimize differences
Document your methods - Clearly explain data sources, processing, and any transformations
Common distortions to avoid include
Truncated axes that exaggerate small differences
3D effects that distort comparisons
Area/volume charts that don’t scale properly with data
Cherry-picked time ranges or data subsets without context
According to Tufte & Graves-Morris (1983) the Lie Factor is defined as:
Size of effect shown in graphic / Size of effect in dataA value of 1.0 indicates accurate data representation.
Visual Encoding¶
The way you encode data visually determines how effectively viewers can extract information. Different visual variables work better for different types of data. Research in graphical perception shows that certain visual properties are processed pre-attentively (instantly, without conscious effort). Bertin’s foundational work established that visual encoding must match the logical structure of the data being represented. This principle remains central to modern cartography and information design Bertin, 1967. Using the wrong encoding forces viewers to work harder to understand your data. These visual properties encode different types of information at different effectiveness levels Table 1.
Table 1:Visual variables from Bertin, 1967.
| Visual Variable | Best For | Effectiveness |
|---|---|---|
| Position | Precise comparisons, continuous data | Highest |
| Size | Quantities, hierarchies | High |
| Color (Hue) | Categorical differences, qualitative data | High |
| Value (lightness) | Ordered sequences, quantitative ranges | High |
| Texture/Pattern | Categorical distinctions | Medium |
| Orientation | Directional data, flow | Medium |
| Shape | Category identification | Lower (hard to compare) |
A key principle is to match the esncoding to your data type. These include:
Categorical data → Use hue (distinct colors), shape, or texture
Ordinal data (ranked) → Use value/lightness, size, or position
Quantitative data (continuous) → Use position, size, or value (lightness)
Temporal data → Use position (typically left to right) or animation
Simplicity¶
Minimizing clutter and too many items in a map maximizes clarity and focuses the audience on your key messsages. Viewers have limited cognitive capacity. Every element competing for attention reduces the clarity of your message. Research in visual perception consistently shows that simplified, focused visualizations communicate more effectively than complex, decorated ones.Your visualization should communicate its message as efficiently as possible. Every element should earn its place.
Edward Tufte’s foundational concept of the data-ink ratio advocates for maximizing the amount of ink devoted to representing data in any data visualization Tufte & Graves-Morris, 1983. Tufte argues that “non-data-ink” or decoration should be eliminated or minimized.
Some examples of items to eliminate:
Chartjunk - Decorative elements that don’t convey data (excessive text, north arrows, scale bars, 3D effects, background images, unnecessary grid lines)
Redundant encoding - Showing the same data in multiple ways
Competing visual elements - Elements that distract from the main message
Excessive labeling - Text that clutters rather than clarifies
For Maps:
Remove unnecessary boundary lines
Use basemap details that provide context (not distraction)
Simplify your color scheme
Zoom to your area of interest
Clear, minimal labeling
For Charts:
Remove decorative gridlines (or make them subtle)
Use only the necessary axes
Choose bar charts or dot plots over pie charts (when comparing values)
Consistent, limited color palettes
One insight per chart
A word about north arrows & scale bars 🧭
North arrows are nice, but add clutter to maps and often are not needed when it’s understood that up is north on the page. North arrows are only really necessary if you’ve rotated the map and north is a different direction. Scale bars also add to the clutter and are not necessary, particularly if your audience is familiar with the map’s region or you have an inset map showing the location of the area. There are always exceptions to layout cleanup, but it’s usually better to leave these out. Your map will be easier to read and cleaner as a result. Don’t add these items for dogmatic reasons, ask if they really add value and only add if needed.
Visual Hierarchy¶
Guide viewer attention and integrate all elements intentionally. Don’t add items such as north arrows and scale bars just because everyone else does. A well-designed visualization controls the order and emphasis of information, guiding viewers from the most important insights to supporting details. Modern visualization experts including Few (2013) emphasize the importance of visual hierarchy in directing attention and improving comprehension.
Use these techniques to emphasize importance:
Size - Important elements larger than supporting elements
Color - Highlight key data with contrasting colors; use muted tones for context
Position - Place primary insights prominently (top-left for readers of left-to-right languages)
Contrast - Use white space and contrast to separate important information
Saturation - More saturated colors draw attention; desaturated colors recede
All elements of a map should work together:
Title - Clear, specific (not “US Population” but “Population Growth in the US, 2010-2020”)
Legend - Essential but not intrusive; only show what’s necessary. Sometimes the legend can be part of the explanatory text.
Data Sources - Always cite your data sources
Annotations - Use strategically to highlight key insights without overwhelming
Color scheme - Consider colorblind-friendly palettes
A reminder of some basic design principles
Consistency - Use the same colors and symbols throughout a series of maps/charts
Balance - Distribute visual weight; avoid one-sided emphasis
Rhythm - Repetitive patterns (when intentional) can emphasize patterns in data
Gestalt Principles - Humans group similar elements together; use this intentionally
Viewers naturally scan visualizations with a specific eye pattern. By understanding this, you can guide them to your key insights first, then let them explore details. This makes your visualization more persuasive and memorable.
- Bertin, J. (1967). Sémiologie graphique: Les diagrammes les réseaux, les cartes. (No Title).
- Tufte, E. R., & Graves-Morris, P. R. (1983). The visual display of quantitative information (Vol. 2). Graphics press Cheshire, CT.
- Few, S. (2013). Information dashboard design (2nd ed.). Analytics Press.