When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs. Ample context and commentary are often needed to fully appreciate an insight.
When narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important. It’s important to understand how these different elements combine and work together in data storytelling. As to size, humans can tell a symbol is larger or smaller than the other, but cannot precisely determine how much they differ, especially when the shape of symbols are irregular (Steele & Iliinsky, 2011 “The science of data visualization”, 2018).Data storytelling is a structured approach for communicating data insights, and it involves a combination of three key elements: data, visuals, and narrative. Watch the video “ The science of data visualization” (2018) (from 15:50 to 21:07) for more suggestions and caveats of using colors. So the bottom line is not to use too many colors. Though there is no concrete maximum number of colors that a designer should use, humans can only distinguish about eight colors. The image below suggests where you should put information that you want to draw readers’ most attention. In general, the power of these elements in visualization is ordered as position > color > size > shape (“The science of data visualization”, 2018). More description can be found here.īasic elements in visualization include position, color, size, and shape. In addition, ratio data have an absolute zero (e.g., height and weight). Ratio data have all information that interval data have. For example, the difference between 90 ☏ and 92 ☏ is the same as 64 ☏ and 66 ☏. In contrast, interval data are not only ordered but also indicate the exact difference between intervals and the difference between each interval is equal. Ordinal data have ordered relationship among values, but the difference between each value is unknown (e.g., satisfied, neutral, and unsatisfied). Nominal are known as “labels” or “names”, such as states and hair colors. The first two are qualitative data and the latter two are quantitative data. There are four data measurement scales: nominal, ordinal, interval and ratio. Knowing your data type is essential for choosing an appropriate way of representing your data. It is your job to know your data and to determine what information you want your readers to learn from your visualization.
So it is important to put yourself in the shoes of your readers. If it is explanatory, be aware that you create visualization for others, but not yourself. The first step is to determine whether your visualization is exploratory or explanatory. The Designer-Reader-Data Trinity below helps you think through your design at the beginning.
#BASIC DATA VISUALIZATION HOW TO#
See this article about how to increase your research impact using infographic. It has gained popularity on the web as an effective way of communication. It usually combines statistics and graphics to narrate a story and/or to persuade audiences a specific message or argument. Infographic is a specific type of visualization. Graphics of findings included in your presentations and publications are typical explanation visualization. John Snow demonstrates the power of spatial analysis in the study of epidemiology.Įxplanation visualization applies when you already know stories in your data and your try to tell others. In spatial data analysis (which can be considered as a subdomain of data analysis), the well-known Snow’s map of the Cholera outbreak in 1854 created by Dr. The famous Anscombe's Quartet illustrates the important role of visualization in data analysis. Exploration visualization is typically a part of data analysis by which you find stories in your data. If you have no idea about data visualization, check this link about data visualization examples in daily life.Īccording to Steele & Iliinsky (2011), data visualization has two categories in general: exploration visualization and explanation visualization. Data visualization is simply the graphical representation of data.
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