The document discusses techniques for creating effective data visualizations. It provides examples and suggestions for how to increase the data-ink ratio and effectively display data through proper use of granularity, size and scale, position, angle, color, annotation, and by reducing non-data pixels and enhancing data pixels. The goal is to design visualizations that selectively draw attention to the data and allow viewers to understand the data through pre-attentive processing.
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Hinweis der Redaktion
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A central part of analyzing data is thinking about how we need to look at the numbers to understandthem. Data visualization is powerful because it can condense a lot of complicated information into asmall space and so answer important questions. But this can only happen when the design allows thoseanswers to show through.The variety of charts out there may seem endless, but they really boil down to six core visual elements:grouping, size, position, angle, color, and annotation. When we visualize data, these elements are ourbuilding blocks. In this post, I’ll step through each of these visual elements and show how we can usethem to design thoughtful visualizations of our data.
The visual differences between the shapes of the various numbers that appear (for instance, the difference in shape between a "3" and a "5") are too complex to process preattentively.
This time perception was easy and immediate, because the 5's were encoded with a different preattentive visual attribute from the other numbers—in this case, a different color. Why is this important to note? Because if you want to visually encode information in a manner that can be perceived instantly and easily by your readers, you now know that you should visually encode the data using preattentive attributes, and if you want some of the data to stand out from the rest, you should encode it using different preattentive attributes.
Data-Ink Ratio
Data-Ink Ratio
Data-Ink Ratio
Data-Ink Ratio
How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
It all depends about the answer you’re looking for. The chart on the left is great if you want to see quarterly trends, but what if you want to find out how the Facebook ad campaign you launched last Tuesday did?
How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
How we aggregate underlying dataShould we look at data in a yearly, quarterly, monthly, weekly granularity?
