The Gestalt principles of grouping include four types: similarity, proximity, continuity, and closure. For example, when you see one dot, you perceive it as such, but when you see five dots together, you group them together by saying a "row of dots." Without this tendency to group our perceptions, that same row would be seen as "dot, dot, dot, dot, dot," taking both longer to process and reducing our perceptive ability. This allows us to interpret the information completely without unneeded repetition. In order to interpret what we receive through our senses, they theorized that we attempt to organize this information into certain groups. The German word "Gestalt" roughly translates to "whole" or "form," and the Gestalt psychologist's sincerely believed that the whole is greater than the sum of its parts. In this section, we will discuss the various theories on how our sensation are organized and interpreted, and therefore, how we make sense of what we see, hear, taste, touch, and smell. The way we perceive our environment is what makes us different from other animals and different from each other. This spatial problem is difficult to solve, especially with complex datasets, and must be planned for in deploying any data visualization.As mentioned in the introduction, perception refers to interpretation of what we take in through our senses. Instead, similarity is graphically denoted with a container or a visible line connecting one element to another. One major challenge of deploying more complex data visualization methods, such as force-directed networks, sankey diagrams, or circle-packing, is that often times with such charts proximity does not mean similarity. In the case of ordering by value, bars are nearest to the bars that they have similar values with, while categorical ordering groups bars based on attribute similarity not conveyed in the length of the bar. Clean chart design that groups bars into categories or sorts them by descending or ascending values works because it aligns the chart to accord with what the reader visually expects (that things near each other are more similar to each other). We don't typically think that bars in a bar chart are similar simply because they are next to each other, nor do we assume slices in a pie chart are similar to each other because they are neighbors, but that's actually what's being conveyed. The circles on the right have been split into two groups by simply making the 10 circles on the left closer to each other than the 30 circles on the right. Some of these unintentional graphical signals are already present in this simple figure: the implied columns and rows seeming to indicate 8 or 5 other groups The color red, because of its hue, implies activation, while the subdued gray implies deactivation The memory of all circles being initially gray with only half transitioning to red reinforces this activation signal.Ī graphical element being close to another graphical element is a strong indication of similarity. Once we formalize how we are using graphical features to indicate category, quantity, or topology-even the most fundamental like color similarity-we also notice features that unintentionally convey meaning. But while gestalt principles themselves are important to crafting effective data visualization, I think the gestalt gaze is equally important. This basic example seems uncontroversial to the point that it might seem too facile. Hue and saturation are very bad at denoting quantitative values, but very good at denoting categories. This could have also utilized shared symbols (for instance leveraging d3.svg.symbol or the like) to show shared category or shared stroke color or width icons and so on. Here we see the use of color similarity to indicate two classes of elements: the red ones and the gray ones. The most intuitive gestalt principle is that graphical elements with shared visual properties will be considered in the same group.
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