This article explores the techniques utilized to create effective data visualizations. It takes a deep dive into cognitive science principles, visual encoding and the gestalt principles. It also explains the difference between exploratory and explanatory data visualizations.
The oxford dictionary defines data visualization as the representation of information in the form of a chart, diagram, picture, etc.
Historically, our survival depended on quickly detecting and decoding visual cues, which is why our brains are wired for visual processing. It is estimated that 30-60% of the brain’s capacity is devoted to visual processing, compared to touch which is 8% and hearing which is only about 2%. Our brains are wired for fast visual processing.
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In the next section we will look at some commonly used visualizations such as text, table, heat map, scatter plots, and a few others!
A simple text or number visualization is sometimes all you really need for a high-level overview. Since the information density is low, text based visuals are easy to consume!
Tables convey a lot of information; however, the larger a table gets the less effective it becomes. A 2x2 table has a lot of power because it reduces the user's cognitive load!
A heat map takes information that you have in a table and then colors it based on a certain set of parameters. It primarily uses color to visually emphasize information!
Scatter plots are best for continuous variable information. It is great for discovering potential data relationships and making inferences.
Line graphs are generally used when data must be visualized across time. This graph can help spot trends with time variables, allowing for potential forecasting.
Bar charts usually present categorical variables, discrete variables or continuous variables grouped in class intervals. Bar graphs can be vertical, horizontal and stacked. The amount and type of information needed determines whether a bar graph should be horizontal or vertical.
A waterfall chart shows a running total as values are added or subtracted. It's useful for understanding how an initial value is affected by a series of positive and negative values.
Maps are crucial for spatial analysis and can be used with spatial components, geography, latitude and longitude.
In cognitive psychology, cognitive load refers to the amount of working memory resources used. There are 3 types of cognitive loads.
1) Intrinsic: Generated by the amount of memory that we need to understand something
2) Extraneous: Generated by the manner in which information is presented to learners
3) Germane: Generated when the brain looks for patterns to develop context
Intrinsic cognitive load is thought to be immutable. However, designers can manipulate extraneous and germane load. Reducing clutter will reduce the user’s cognitive load. Designers can reduce clutter by only keep essential information. Next, we will discuss how memory plays a role in our understanding of data visualizations.
In this section, we will discuss three types of memory and why targeting certain types of memory helps the user retain information more easily.
1) Sensory: Sensory visual memory is the visual sensory memory register pertaining to the visual domain and a fast-decaying store of visual information.
2) Short-term: Short term visual memory representations are longer lasting, more abstract, and more durable in comparison to sensory memory.
3) Long-term: Long-term visual memory is the ability to recall images or places that have been viewed in the distant past.
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Pre-attentive attributes are visual properties we notice without using conscious effort to do so! Some examples of pre-attentive attributes are color, form, spatial positioning, and movement. The goal is to build visualizations using pre-attentive attributes that hit the sensory and short term memories. Targeting long term memory is generally not required for data visualizations.
Visual encoding is the way in which data is mapped into visual structures. Whenever we visualize, we are encoding data using visual cues such as size, shape or color, and so on. A great way to accomplish visual encoding is by using gestalt principles. Gestalt Principles are principles/laws of human perception that describe how humans group similar elements, recognize patterns and simplify complex images when we perceive objects.
For the purpose of this project we will focus on 6 specific gestalt principles Proximity, Similarity, Enclosure, Closure, Continuity, Connection
The principle of proximity states that things that are close together appear to be more related than things that are spaced farther apart.
Similar elements are visually grouped, regardless of their proximity to each other. They can be grouped by color, shape, or size.
Enclosure refers to how we perceive objects enclosed within a common area as a group.
The Law of Closure is the states that if there is a break in the object, we perceive the object as continuing in a smooth pattern.
The principle of continuity states that elements that are arranged on a line or curve are perceived to be more related than elements not on the line or curve.
The law states that elements that are connected to each other by color, lines, frames, or other means are perceived as more related and grouped than elements with no connection.
Some simple questions to ask:
• What are their interests, needs, skills, knowledge and goals?
• What is the expected level of familiarity with the subject matter?
• What is the appropriate level of information density to fit your user’s needs?
When designing a visualization it is helpful to keep in mind what the user's end goal is. It's important to keep in mind there are two types of visualizations; exploratory and explanatory!
1) Exploratory Visualizations
Exploratory visualizations allow the user to explore the dat to identify trends and outliers. Exploratory visualizations should offer a fair degree of user control and interactivity. Such as, giving the user the ability to filter, pivot and zoom.
2) Explanatory Visualizations
Explanatory visualizations are created to communicate the results of analysis. Used to present the information in a clear and concise manner and inspire action. Interactivity may not be needed for explanatory visualizations
Data visualization is a great way for the human brain to understand large amounts of data. We can utilize what we know about the human brain, visual encoding, gestalt principles, and user needs to craft efficient visualizations.
Sources:
https://www.coursera.org/specializations/data-visualization?#instructors
https://www.toptal.com/designers/ui/gestalt-principles-of-design
https://www.history.com/news/prehistoric-cave-paintings-early-humans
https://computethought.blog/2020/05/18/cognitive-load-and-coding/
https://www.worldometers.info/world-population/
https://www.syncfusion.com/jquery-ui-widgets/heatmap
https://visage.co/data-visualization-101-scatter-plots/
https://hackernoon.com/the-top-8-social-media-data-charts-and-live-charts-you-need-to-know
https://www.howtogeek.com/747405/how-to-create-and-customize-a-waterfall-chart-in-microsoft-excel/
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