Data visualization is the art and science of transforming data into meaningful and engaging visuals that communicate insights and stories. But how do you choose the best way to encode your data for maximum impact? In this article, you will learn about some of the most common and effective data encoding methods, and how to apply them to your data visualization projects.
Data encoding is the process of mapping data values to visual attributes, such as color, shape, size, position, or texture. Data encoding helps you to highlight patterns, trends, outliers, or relationships in your data, and to make your visuals more intuitive and memorable for your audience. Data encoding can be classified into two main types: categorical and quantitative.
There are some obvious examples: red-yellow-green are so widely used that we connect them to the bad-caution-good paradigm. But what else? Radial or spiral charts, where position towards the center indicates intensity, coupled with color saturation (fainter pastels towards the margins), conveys meaning as well. But mostly: Context matters.
Categorical encoding is a useful technique for data that has discrete or nominal values, such as names, categories, labels, or groups. It can help to illustrate differences, similarities, or associations among data items. Three of the most common methods for categorical encoding are color hue, shape, and texture. For instance, color hue can show the distribution of different types of fruits in a pie chart; shape can demonstrate the correlation between different variables in a scatter plot; and texture can show the composition of different regions in a stacked bar chart.
Quantitative encoding is a data representation technique used for continuous or ordinal values, such as numbers, percentages, or rankings. This method can help to illustrate magnitude, proportion, or change. Common quantitative encoding methods include color saturation, size, and position. For example, color saturation can show population density in a choropleth map; size can show the relative importance of words in a word cloud; and position can show the ranking of countries in a line chart.
When choosing the best encoding method for your data, several factors should be taken into consideration, such as the type and structure of your data, the purpose and message of your visualization, and the audience and context of your visualization. As a general rule, you should use the most effective and accurate encoding method for your data type and visualization goal. Additionally, use the most perceptually salient and distinctive encoding method for your data importance and message. Furthermore, use the most consistent and intuitive encoding method for your data meaning and audience expectation. For instance, darker colors or larger sizes can be used for higher values while lighter colors or smaller sizes can be used for lower values.
In the 1984 paper titled "Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods", statisticians William Cleveland and Robert McGill studied the cognitive processes people use to understand a chart and ranked them, from the easiest to the most challenging: 1. Position along a common scale (bar chart, dot plot) 2. Position along nonaligned, identical scales (differential charts) 3. Length, direction, angle (pie chart) 4. Area (treemap) 5. Volume, curvature (3D bar chart, surface plot) 6. Shading, color saturation (heatmap, choropleth map) Use this ranking to choose the best encoding methods.
Data encoding can be beneficial for data visualization, however, it can also bring some challenges or pitfalls such as over-encoding, under-encoding, and misleading-encoding. To avoid these issues, you should simplify your encoding, validate your encoding, and test your encoding. This includes using the minimum number and type of encoding methods that convey your data clearly and effectively. Additionally, you should use the appropriate scale and range for your encoding methods to ensure accuracy and honesty. Finally, use best practices and guidelines for your encoding methods to ensure data readability and accessibility.
Hm. This paragraph bears the distinctive marks of an AI-generated response to a textual inquiry. What's missing here are actual, concrete examples of what an "encoding pitfall" might be; a common failing of this type of content. So here goes: avoid graphs where there are too many elements (lines, bars, boxes, etc.) that are too similar on color, or where the colors blend into the background and are thus indiscernible to the human eye. We've all seen the squiggly line charts where the lines are faint pastels that are nearly impossible (or that take significant cognitive processing) to translate into some (any?) kind of insight.
Data encoding is a key skill for data visualization, but it is also a complex and creative process that requires practice and experimentation. To hone your encoding skills, experts suggest learning from the best data visualization practitioners and researchers, such as Edward Tufte, Alberto Cairo, or Cole Nussbaumer Knaflic. Additionally, you should explore and try different data encoding methods with the various data visualization tools and platforms available, such as Tableau, Power BI, or D3.js. Finally, it is important to get feedback from peers, clients, or users and seek constructive criticism and suggestions for improvement. By understanding and applying the different data encoding methods, principles, tips, and best practices, you can create impactful and engaging data visualizations that will enhance your skills and projects.
Once again: context matters. Your experience as a human being ... it matters (ironic to type that into a soulless AI-generated article, but here we are). If you want to build your skills, start by using your critical thinking skills on galleries of data visualizations. Can you quickly & intuitively grasp what the charts & graphs mean? Or do you struggle to find meaning amidst the chaos of a "spaghetti chart"? Then find a forum where there are other data viz nerds (I recommend the DVS Slack instance). Go there. Talk to others. Bounce your ideas & perceptions off of them, and then integrate that into your field knowledge. It takes time, as do all things of value.