{"id":2124,"date":"2019-12-10T02:16:32","date_gmt":"2019-12-10T02:16:32","guid":{"rendered":"http:\/\/kusuaks7\/?p=1729"},"modified":"2024-02-12T13:30:38","modified_gmt":"2024-02-12T13:30:38","slug":"the-power-of-visualization-in-data-science","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/the-power-of-visualization-in-data-science\/","title":{"rendered":"The Power of Visualization"},"content":{"rendered":"\t\t
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A picture really does say a thousand words.<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\u201cVisualization is really about external cognition, that is, how resources outside the mind can be used to boost the cognitive capabilities of the mind.\u201d \u2014\u00a0Stuart Card<\/strong><\/p>\n<\/blockquote>\n<\/section>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This article focuses on the importance of visualization with data. The amount and complexity of information produced in science, engineering, business, and everyday human activity is increasing at staggering rates. Good visualizations not only present a visual interpretation of data, but do so by improving comprehension, communication, and decision making.<\/p>\n

The importance of visualization is a topic taught to almost every data scientist in an entry-level course at university but is mastered by very few individuals. It is often regarded as obvious or unimportant due to its inherently subjective nature. In this article, I hope to dispel some of those thoughts and show you that visualization is incredibly important, not just in the field of data science, but for communicating any form of information.<\/p>\n

I will aim to show the reader, through multiple examples, the impact a well-designed visualization can have at communicating an idea or piece of information. In addition, I will discuss the best practices for making effective visualizations, and how one can go about developing their own visualizations and the resources that are available to go about doing this.<\/p>\n

I hope you enjoy this visual journey and learn something in the process.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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What is Visualization?<\/strong><\/h1>\n<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The American Heritage Dictionary defines visualization as:<\/p>\n

(1)\u00a0<\/strong>The formation of mental visual images.<\/p>\n

In the context of visualization with data, it is necessary to add something to this definition, so that it becomes:<\/p>\n

The formation of mental visual images to convey information through graphical representations of data.<\/strong><\/mark><\/p>\n

If you are pursuing a career in data science, this is one of the most crucial skills that you can master, and it is transferable to virtually any discipline. Let us imagine that you are trying to convince your manager to invest in a company and you present them a spreadsheet full of numbers to explain to them why this is such a good investment opportunity. How would you respond if you were the manager?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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If presented in visual form, information is often much easier to digest, especially if it makes use of patterns and structures that humans can interpret intuitively. If you want a quick and easy visualization that requires little to no effort, you can go with something like a pie chart or a bar chart. In terms of developing visualizations, this is often as far as most people go, and often, it is as far as they made need to go for there field of expertise.<\/p>\n

Another factor that inhibits our use of visualizations is the amount of data we have available. How do I know if visualization is an appropriate method to communicate a message?<\/p>\n

This is a difficult question to answer. One design study recommends that we assess the viability of using visualizations based on the clarity of our task and the location of the information.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Design Study Methodology: Reflections from the Trenches and the Stacks, Michael Sedlmair, Miriah Meyer, and Tamara Munzner. IEEE Trans. Visualization and Computer Graphics, 2012.<\/p>\n

If we are in the top right corner of this diagram, it becomes feasible to develop and program interactive visualizations, which is the realm in which data scientists are now entering due to the persistently increasing scale of data resulting from the information explosion.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Information Explosion.<\/p>\n

We are now living in a data-driven world, and it is only likely to become more data-driven. This is clear from multiple areas, such as important advances in developing large-scale sensor networks as well as artificial intelligence agents that interact with the world (such as self-driving cars).<\/p>\n

In a world where data is sovereign, having the power to develop clear and impactful visualizations is becoming an increasingly necessary skill.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Good and Bad Visualizations<\/strong><\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Humans have been creating visualizations for thousands of years, and whilst the drawings of cavemen are slightly less spectacular than what we have nowadays, it is still good to appreciate just how powerful some of the early visualizations were, as well as how impactful they have been on the modern world.<\/p>\n

Take Leonardo da Vinci, for example, an Italian polymath who was not only the first one to come up with incredible inventions such as the airplane, helicopter, and tank, but was also incredibly skilled at drawing. His engineering and anatomical drawings, like the ones below, are incredibly realistic and yet simple to understand.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Being skilled at drawing was very necessary for the purpose of visualizations back hundreds of years ago when we did not have computers to draw things for us. Take a moment to admire Galileo\u2019s sketches of the moon during different phases of the lunar calendar.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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It is not often that we really stare at ancient drawings of the Moon, so is there really still a need for these types of visualizations in the modern world? And if there is, can we not just leave it to artists, graphic designers, and the like?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The answer is obviously yes. Even ten or fifteen years ago, learning something like chemistry was incredibly difficult, despite being able to picture molecules in your head, it is still tough to translate between complex scientific words and your mental picture of what is occurring. Nowadays, one can go on Youtube and type in a few words and watch a visualization or a visual walkthrough of essentially any aspect of chemistry. This same idea applies for essentially any abstract idea in science.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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So now we have convinced ourselves that visualizations are pretty useful for conveying information, and can also be used to explain complex ideas in a more interpretable manner.<\/p>\n

What are some examples of good visualizations?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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I currently live in Boston, so several of the following visualizations are related to the city of Boston. These are just some visualizations that I consider good, and, due to their subjective nature, you may disagree with me.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The below visualization captures not only the time taken to each stop from the city center in the form of concentric spheres but also follows the correct direction for each line. Looking at this diagram, it is pretty quick to work out which line to take, which direction it goes, and how long it will take to get there.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This second visualization shows the movement of individuals that are born in Massachusetts over the last century. We see that in 1940, 82% of the people born in Massachusetts were expected to live in Massachusetts. Now fast forward to the modern day, we see that this number has decreased to 64%, and we can get a reasonable idea of where these individuals have migrated.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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One of the most famous visualizations ever made was by Joseph Minard, and it depicts the journey of Napoleon as he marched towards Russia for his\u00a0Russian campaign of 1812<\/a>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The illustration depicts Napoleon\u2019s army departing the Polish-Russian border. A thick band illustrates the size of his army at specific geographic points during their advance and retreat. It displays six types of data in two dimensions: the number of Napoleon\u2019s troops; the distance traveled; temperature; latitude and longitude; the direction of travel; and location relative to specific dates without making mention of Napoleon. Minard\u2019s interest lay with the travails and sacrifices of the soldiers. This type of band graph for illustration of flows was later called a\u00a0Sankey diagram<\/a>.<\/p>\n

A trend that you might have noticed with all of the above visualizations is that they convey multiple types of data in a relatively simple fashion. Doing this is an intelligible way is not an easy task.<\/p>\n

Now let us consider the transformation of a poor visualization into a visualization better suited for its purpose. This is easiest to do with subway maps so I will consider the subway map of London, and we will see why it was changed and how their new design has improved upon the original design.<\/p>\n

This was the original map of the London Underground that dates back to 1927. As you may have already noticed, the major problem with this diagram is that there is a large cluster of closely spaced underground stations due to their close proximity. This stems from the fact that the map is plotted based on the geographic locations of the stations. However, when going far out of the city, there are vast amounts of space left unused on the map.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In 1933, Harry Beck came up with a new design for the map of the London Underground. Beck decided that passengers on the underground were not concerned with geographical accuracy and were most interested in how to get from one station to another and where to change trains. He took inspiration from electrical diagrams and decided to show each of the lines in an individual color, and to show their connections to other lines as one would on an electrical diagram. The plot maintains the directional information for where each line goes, but the distance information is lost as it was deemed unnecessary by Beck.<\/p>\n

As someone who used to live in London and used a modern version of this map to get around with relative ease, I can vouch for its brilliance.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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We can see a very similar debate that occurred with the New York subway map. Which of these do you think is better?<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Despite the fact we have looked at several subway maps, there is obviously not a clear-cut solution that can be applied to all situations. After all, it depends on what data is most relevant to the audience. Harry Beck decided that passengers did not care about distance or geographical information, only that they knew how to go from station A to station B and what connections to make. Maybe this idea would not work for New Yorkers because they are more concerned with knowing the distance and geographic location than Londoners.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Anscombe\u2019s Quartet<\/strong><\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Numbers can be incredibly misleading, as was demonstrated by Gertrude Anscombe in the form of the now famously known\u00a0Anscombe\u2019s Quartet.\u00a0<\/strong>The quartet is a set of four data samples that have exactly the same mean, variance, correlation, and linear regression line.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Anscombe\u2019s Quartet in tabular form (Anscombe, 1973).<\/p>\n

However, it is clear from a visual representation of the results that the distributions of the four sets of data are completely different.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Anscombe\u2019s Quartet in visual form (Anscombe, 1973).<\/p>\n

To reinforce this point I have developed six of my own data plots, all of which have the same mean, variance, correlation, and linear regression line. As you can see below, these are, again, completely different.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This idea that visualizations can be used in place of quantitative metrics to make the structure of the data clearer and more meaningful leads us to naturally into the realm of misleading visualizations. Just as numbers can be used to mislead us about the structure of our data, as we have seen from Anscombe\u2019s Quartet, it also works the other way around, visualizations can be cleverly made to distort the underlying structure in the data. As we shall see, this is a very common occurrence, especially in areas that are prone to discourse such as politics and scientific debates.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Misleading Visualizations<\/strong><\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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There are numerous examples of people using statistics to mislead individuals. Indeed, this is an extremely common tactic used in politics. Possibly one of the most famous examples of this was related to tax cuts proposed by President George Bush, in which a 5% increase was made to look much larger by distorting the axis of a bar chart.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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(Left) The visualization shown to viewers, and (right) a less deceptive visualization.<\/p>\n

These deceptive tactics often involve distortion of the measurement axes, as in the above example. Here is another example of axis distortion related to job losses (ironically, also related to U.S. politics).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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In reality, the plot should look like this.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Another way that people are deceived through the use of visualizations is through the omission of data.<\/p>\n

I, myself, am an environmental scientist, so I am only too aware of the deception people can espouse with carefully crafted visualizations. Here is a prime example of one showing that global warming is a myth (which, just to clarify, it is not, and this idea has not been contested by environmental scientists since the 1990s).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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A less deceptive diagram is shown below.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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As of now we just discussed visualizations that are actively designed to deceive us. How about when it is done accidentally?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Beware of Rainbows<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Rainbow color maps are probably the most annoying visualization I come across on a daily basis. As an environmental scientist, I see these pretty much everywhere. Not only are these bad to use because colorblind people (such as myself) can have issues differentiating many of the colors, but assigning a color to a quantitative value is nonsensical.<\/p>\n

The rainbow colormap is perceptually non-linear. Who decided that blue represents a quantitatively lower value than yellow or red? When do the transitions occur and how sudden are they?<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Rainbow color maps.<\/p>\n

The best way to tackle this is to stick to just two colors and use a linear change in color to represent quantitative values. In this sense, the plot can be colored but the quantitative nature of the plot is described by the brightness of the color, with darker regions typically indicating higher values. This idea is illustrated below.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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This essentially makes them the same as a heat plot or a choropleth map like the one below.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Smoky Mountains<\/a><\/p>\n

A nice example of this is shown for U.S. votes following the 2016 presidential election.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Color Blindness<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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It is always good to keep in mind the fact that a reasonable amount of people are colorblind and to avoid using color combinations that can be problematic for these individuals. Take the following visualization as an example.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Catalogtree<\/a><\/p>\n

The colors used in this diagram are a terrible combination for someone who is suffering from red-green color blindness (can be deuteranopia or protanopia). It is best to be mindful of at least using combinations of red and green since this type of color blindness is the most prevalent.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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If you are interested in learning more about color blindness, there is a nifty website for simulating color blindness.\u00a0Coblis – Color Blindness Simulator<\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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If you are not suffering from a color vision deficiency it is very hard to imagine how it looks like to be colorblind\u2026www.color-blindness.com<\/a>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Edge Bundling<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The idea behind edge bundling is essentially wrapping an elastic band around all of the paths that follow the same route from one node to another node. This is used on network diagrams and has the advantage of making the visualization look less a cluttered hairball, and is more inherently pleasing as we can see below.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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The downsides to this method are that you cannot follow the exact links or paths after bundling, which mean that our visualization does not reflect the underlying data (we forfeit some graphical integrity).<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Overview of the Visualization Process<\/h1><\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Now that we have looked at a bunch of visualizations and understand the difference between a good and bad visualization, it is a good time to discuss what actually makes a good visualization.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Visualization Goals<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Essentially, there are three goals to visualization:<\/p>\n\n