Many people think of data visualization as a logical “next step” in the data manipulation process. They’ve gone through the process of collecting data, cleaning data, mining data, and analyzing data, all of which can be a pretty demanding ordeal. So when they arrive at the visualization step, they start imagining bars, graphs and pie charts, similar to the ones you’d make in school. Easy as pie, right?
While it’s not necessarily unusual to find bar graphs, line graphs, and pie charts in visualized data, those are often far from sufficient. Some kinds of data require the visualization of things that simply cannot be accounted for using “traditional” graphing or visualization methods. Some data sets are so vast that they simply cannot be contained within the confines of the simplistic graphing methods that we learn in high school.
Diving Further Than A Graph
There are plenty of instances in which a simple graph would not be enough to accurately convey the meaning of a set of data. Although this seems strange at first, the primary role of data visualization is to make the information easier for humans to cognitively process and thoroughly understand. Colors, shapes, labels, and lines all make sense to the human brain, especially when compared to the alternative: raw data consisting of numbers in tables or spreadsheets.
So how do you solve that problem? How do you make a graphic that is complex enough to account for all of the various aspects and variables of the data you’re trying to visualize? On top of that, how do you ensure that it’s relatively easy to understand and that it won’t lose any of its significance? That’s where algorithms come into play.
The Differentiations and Characteristics of Findings
Analytic findings derived from “big data” can be so precise that plotting them cannot be left up to the eye. These points have very special places in very particular spots of a very specific graphic. Each has a very particular hue assigned, to differentiate it from some values and associate it with others. All of these characteristics and more must be spot on; thus, they’re determined using algorithms.
Enrico Bertini, an assistant professor at New York University, outlined the various kinds of algorithms used in the visualization of data. He published an article on his website titled “The Role of Algorithms in Data Visualization,” in which he classifies algorithms into distinct varieties.
The first variety of visualization algorithm deals with the spatial layout of the data. As previously mentioned, the spatial design of simple data can be handled quite easily. “Traditional” visualization methods can be employed here: a bar graph to show amount per variable; a line graph to show growth or lack thereof; a pie chart to visualize percentages of a whole. While that’s all well and good, these simple approaches cannot always accommodate complex datasets.
The Role of Treemaps
Treemaps are an example of a type of algorithm-based visualization. They’re often chosen because of how efficiently they use the space provided. However, their space-saving qualities allow them to visualize data that is incredibly precise and often jam-packed with information. A treemap would be difficult to plot by hand in the first place, and the level of difficulty increases proportionately to how much data is being displayed. When observing a treemap, the amount of data is typically substantial; mathematical precision is required to display the data clearly and accurately.
Treemaps are far from the only visualization method that uses algorithms for spatial placement, but they’re arguably the easiest example for a layperson to understand. Force-directed layouts, which use algorithms to place nodes and edges in relation to the data, are another notable example. Despite their strengths for displaying large amounts of data, there is still room for improvement. When it comes to presenting information in a visually comprehensible manner, even treemaps have their limit.
In these instances, where space is of the utmost importance, the next best option for visualization is through abstraction. To put it simply, abstraction may be described as separating the data points from one another and putting it all into intervals so that it is easier to understand. This allows us to keep within the confines of what can possibly be displayed at one time, allowing enormous amounts of data to be viewed in a relatively small space.
Data visualization based on abstraction can include some kind of user interactivity aspect, which tends to make the concepts easier for individuals to understand. Think about a graphic of the United States of America. You’re trying to impart an impression of how many people live in each state to the individuals looking at the graphic. You choose a simple method: you include dots, with each one of the dots representing a certain number of people. Problem solved, right? Sort of.
While those people now know how many people are living in each state, the graphic doesn’t convey other information like population density. For example, the population density around the major cities will be drastically larger when compared to population density in a rural area. However, the initial example fails to communicate that. While the individual looking at the graphic knows how many people live in New York state, he might not realize how much of the population is concentrated within New York City.
This problem can be solved through the use of algorithms. You can alter the graphic so that instead of identical dots inside of the state representing people, you now have different colored dots representing a certain number of individuals within an exact area. These are sometimes referred to as heat maps.
Plotting and Algorithms
Now, when observing our graphic, individuals will see a sharper color in metropolitan centers, while in rural areas, the color starts to fade to something more neutral. This gives them a clearer idea of population density as well as of the population in general. Plotting something like this without use of an algorithm is impossible. The algorithm is what enables you to take the data you’ve gathered about state and city populations and insert it into a unique visualization that helps you get your point across.
These kinds of visualizations are incredibly common. Next time you see any sort of heat map-style visualization of data, take a moment to appreciate the complexity behind it. After all, it wasn’t something someone drew up and said, “Yes, that’s close enough.” It has complex mathematics behind it to ensure that it’s as accurate as possible. Clearly, visualization is much more complicated than it seems at face value. Once you start to delve into the complexities of the subject, and examine some of the limitations of various visualization techniques, it becomes clear that algorithms are essential for visualizing large or complex data sets.
In fact, algorithms are occasionally used in applications where they aren’t entirely necessary but where they can aid in ensuring accuracy and effectiveness. Smart algorithms can plot graphics that could be graphed by hand to remove the possibility of human error. The use of algorithms to aid in data visualization is only growing in popularity. Even if there were an alternative, algorithms are here to stay.
You Don’t Always Have to Do It Alone
Even though the concept of using algorithms to plot data is complex, and can be difficult to understand, there’s no need to feel discouraged. It’s the last step of the data manipulation process, which means you’re almost ready to present your findings. If you have no idea where to begin with the visualization of your data, you can always consult a professional in the field. Data analysts are highly familiar with the best ways to visually represent individual sets of data, and can take care of the specifics of visualization once they’ve gone through the rest of the data manipulation process. If hiring a professional is not particularly appealing to you, there are a number of tools available to you. Most of them do come at a price, but the software is well worth the purchase. The right user-friendly software program can turn the idea of visualizing your data from an incomprehensible nightmare into a relatively straightforward process.
There’s absolutely no doubt that for complex data visualization mathematical algorithms are a necessity. Use this article as a reference to determine what sort of algorithm would suit your dataset best, and if you’re still not certain, start researching your options further. The science and math behind algorithm-based visualization is certainly fascinating, so you’ll at least be intellectually challenged and entertained as you explore the many possibilities for visualizing incredibly complex data.
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