Data Visualization

An effective chart includes not only the plot area ( the actual visualization) but also chart elements like font types, element alignment, density of elements and so on.  These peripheral elements of the chart give context and clarity to the visualization and are just as important to effective visual communication.

This section covers some important aspects of using elements to create effective visualizations:

• Typography : The style and alignment of written elements of the chart
• Marker Styles : The physical characteristics of chart elements that help distinguish different variables
• Gridlines : Helping he viewer match the variable to specific values and their context relative to other variables
• Axes :  Similar to gridlines, though they help define the scale of the data and clarify difference in magnitude between data points.

Typography  refers to all aspects of letter styles including typeface, size, color, and spacing.   Picking a good font for your visualization and

• Font Selection

 Choose fonts that are clear and legible. Serif fonts like Times New Roman are often used for formal or traditional visualizations, while sans-serif fonts like Arial or Helvetica are more modern and linearly simple.  Serifs are ornamental strokes added to the ends or vertices of letters to increase legibility, while san-serif mean without serif and refs to letters that are more linearly simple :

                                  Serif font                                                                                 Sans-serif font 

Sans-serifs have been preferred in digital environments because the limited resolution of early computer graphics made it difficult to render the fine details of serif fonts.  However, advanced in computer graphics have made it possible to display serif fonts with a high degree of detail.  Never-the-less serif fonts can still appear cluttered, while san-serif letters can  be difficult to distinguish.  For  example in most sans-serif fonts capital "I" and lower case "l" will appear very similar:

 

Avoid stylistic fonts simulating historical typefaces or handwriting, except for novelty visualizations as needed:

• Font Style

 Use font styles (bold, italic, underline, caps and lower) sparingly for emphasis. 

In the image above left, the labels and title are unnecessarily ornamented with bold and italics, whereas the image above right, with the title alone in bold is adequate for most purposes.

• Font Size

Use font sizes that are easily readable. Titles and headings should be larger and more prominent than labels or annotations. Avoid making text too small, especially if the visualization will be viewed on smaller screens or in print.

a	a	a	a	a	a	a	a	a	a	a	a	a	a	a
6	8	10	11	12	14	16	18	20	22	24	26	28	36	48

A	A	A	A	A	A	A	A	A	A	A	A	A	A	A
6	8	10	11	12	14	16	18	20	22	24	26	28	36	48

• Contrast

Consider text color in relation to the background and other elements in the visualization. Ensure there is enough contrast for readability. For example, use dark text on a light background or vice versa.

• Alignment

Align text appropriately within the visualization. Titles and headings are typically centered, while labels and annotations may be left-aligned or right-aligned depending on the context.

In the chart above we can see that the y-axis label is at the bottom, when it would be better placed at the middle of the axis.

• Hierarchy

Establish a typographical hierarchy to guide the reader's eye through the visualization. Titles should stand out the most, followed by headings, labels, and annotations. Use font size and style to create this hierarchy.

• Spacing

Pay attention to line spacing (leading) and letter spacing (kerning). Adequate spacing between lines of text and characters can improve readability.

In the graph above left, the lines in the title are not well spaced and overlap, whereas in the graph on the right, they are well spaced and more legible.

• Consistency

Maintain consistency in typography across all elements of the visualization. Use the same font, size, and style for similar elements throughout the visualization.

In the graph above left three different typefaces are used and in different colors.  This is corrected in the graph, above right, where the same font style is used for the labels and a different style is used for the title.   

• Clutter

Avoid overcrowding the visualization with text. Use concise and clear labels, and consider using tooltips or interactive elements for additional information to avoid clutter.  

In the example above, excessive annotation both in the title and labels and in the graph itself, reduces comprehensibility with redundant  and unnecessary information.

• Annotations

Provide explanatory annotations or captions as needed to help the audience understand the context and significance of the data. Annotations can include explanations of trends, outliers, or important observations.

• Language and Terminology

Use language and terminology that are clear and meaningful to your target audience. Avoid jargon or technical terms that may not be understood by all readers.

In the example  above left, the chart uses overwrought, highly technical language to communicate the topic of the data and the relationship between the variables.  In the example, above right, the language has been simplified to make the graph more comprehensible.

Choose appropriate marker types

• Circles are neutral and commonly used, making them a good default choice for a wide range of data types.
• Squares might represent structured or "solid" concepts, such as financial data points or tangible items.
• Triangles can indicate change or direction, useful for time series data where trends are important.
• Crosses or Xs might be used to highlight outliers or points of conflict within the dataset.
• Stars could represent peak values, special highlights, or significant achievements.

Marker Size

Adjust the size of markers to represent data values or significance. Larger markers can indicate higher values or greater importance.  Be cautious with extreme size variations, as very small or very large markers can lead to visual clutter or misinterpretation.  In the below example, the markers (circles) should have been made proportional to the min and max values instead of representing raw values.

Marker Color

Use marker colors effectively to encode additional information, such as categories or groups within your data.
Be mindful of color choices for markers. Ensure that colors are distinguishable and follow a logical color scheme (e.g., a color legend) when representing different data groups.  In the example below, the markers have different colors for markers that are part o the same series, making it difficult to see which series of data ware related to each other.

Transparency (Alpha)

Consider using transparency (alpha) to show overlapping data points when markers overlap. This can prevent over-plotting issues and help reveal data density. Adjust transparency levels carefully to maintain readability and avoid making data points too faint.  In the example below, an alpha value has been specified to better distinguish overlapping points.

Legend or Key

A legend or key is used to explain the meaning of different marker styles and colors. This helps users understand the significance of each marker or color representation type and its related data.  Large datasets may impact intelligibility or legends, and require aggregation, simplification, down-sampling or sub-sampling.

The population pyramid below has a very simple legend in the upper right, illustrating the values of the color representations.

A gridline can make interpretation of the graph easier especially for scatter pots and line charts, and bar charts and related visualizations like histograms.  Gridlines come in several varieties: horizontal, vertical and crossed, and each works well with different visualizations.

 

Horizontal Gridlines

Use horizontal gridlines when you want to aid comparison of values along the y-axis.  They are particularly useful when you have a large number of data points and want to easily track the values across different categories or time periods. Horizontal gridlines can help viewers align data points horizontally and assess trends or patterns more accurately.

Vertical Gridlines

Use vertical gridlines when you want to aid comparison of values along the x-axis.  They are helpful for aligning data points vertically and assessing trends or patterns across different categories or time periods.  Vertical gridlines can also be useful for tracking specific values at different points in time or categories.

 

Cross Gridlines

 Cross gridlines are formed by the intersection of horizontal and vertical gridlines.  They provide a reference grid for assessing both x-axis and y-axis values simultaneously.  Cross gridlines are useful when you want to analyze relationships between variables or assess the position of data points in a multi-dimensional space.

Most Chart types have two axes with tick marks.   The number of tick marks depends on the range of the values.  In general a range of  100 should have every tenth tick labelled.

 

                                                                   

                           Axis with units specified                                                 Axis without Units     

 

Similarly X-axes should be labelled every nth place. e.g. 5h, 10th, 25th, etc.  The interval will be determined by the scale of the range  so larger scales will have larger intervals.

 

In the below example every tick is labelled and this can compromise legibility:

 A better approach would be to label every 5th tick, for example, as seen below: