Data Visualization Summary iHub

Editor's Notes

• #3: Lastly, before we get started – a few notes on the course. We’re going to use a variety of formats: lectures, discussion, demos, homework, coding, presentations. (The first class will be more lecture than usual.) Also, please jump in with questions and discussion anytime. Please continue to monitor the course website. It will have links to everything we cover in class as well as extra material. It will also have links to code samples and libraries, and we’ll put all the projects up at the end of the class. The website will be available after the class ends for your reference.
• #4: We’ll learn about what it means for DV to be effective - to be purposefully designed, to use appropriate techniques, and to achieve a specific purpose.
• #5: Who I am, where I’m from, what I do, what I’m about.
• #7: Alright, let’s get started – what is data visualization?
• #8: It’s difficult to define precisely: as a field, DV has many related and overlapping goals and descriptions. It is often used interchangeably with different terms, and it falls under many different disciplines.
• #9: Better than a definition is an example. Let’s take a look at this table of Kenyan cities showing city name, county name, and city population. Take a moment to understand the structure of this data, because I’m about to ask you a few questions on it.
• #10: Better than a definition is an example. Let’s take a look at this table of Kenyan cities showing city name, county name, and city population. Take a moment to understand the structure of this data, because I’m about to ask you a few questions on it.
• #11: Now, let’s answer the same questions by using the visualization. What are the cognitive steps required? How easy or difficult is the process?
• #12: Now, let’s answer the same questions by using the visualization. What are the cognitive steps required? How easy or difficult is the process?
• #13: Now let’s ask an additional question we didn’t ask before.
• #14: We’ve learned that data visualization can be useful in telling us things about a set of data, making it easier to find information and answer questions. We’ve also learned that this usefulness depends both on the design of the visualization and the specific information we are looking for.
• #15: Let’s take a look at another example. This is a data set called Anscombe’s Quartet, named after the statistician who devised it. It consists of four separate sets of data, each of which is a list of ten pairs of numbers. So there are ten different X and Y values that are paired. To make this a bit more concrete, you can imagine that each data set describes ten people, X represents their height and Y represents their weight. The interesting thing is that all four of these data sets have exactly the same relationship between the X and Y numbers. All X values have the same average and standard deviation, and so do all Y values. Furthermore, the correlation between X and Y is the same for all sets. And except for the last one (which has a bunch of 8s), there’s not much we can do to distinguish them or describe them meaningfully by just looking at the numbers in the table. Now let’s see what happens when we plot them.
• #16: Here we’ve visualized the data in what’s known as a scatter plot. Each dot represents one of the ten pairs, located on the horizontal axis by X value and on the vertical axis by Y value. By visualizing the data, we see patterns, outliers, and relationships that were impossible to detect in the chart.
• #17: So we’ve learned that DV is important. It can help us resolve ambiguous data, locate outliers, and generally understand the structure and pattern of a data set.
• #20: Locations of geocoded tweets in Nairobi before the 2013 presidential elections, a collaborative between Ushahidi and Hivos.
• #21: Infographic of twitter activity in Africa in late 2013 produced by Portland Communications.
• #22: Interactive tool from the Gapminder Foundation animating the health and wealth of world countries over time. This screenshot shows the historical path of Kenya from 1800 to 2013. Note the number of data types (life expectancy, GDP, population per country and year) and variety of visual encodings (x- and y- position, size, color, time).
• #25: [1] Alexander Lex, Harvard CS171 (2015) [2] Bertin (1967) [3] Sneiderman et al. (1999) [4] John Stasko, CS7450 (2013)
• #27: Alright, let’s get started – what is data visualization?
• #28: Visualization by Tor Norretranders, referenced by David McCandless, demonstrating the data bandwidth of each of our senses. Note: this is not a very good visualization! (Is the size of the sight region measured by the blue shape only, or the full rectangle?)
• #29: This hierarchy persists. Also, at each level we have detections of different features and objects. Face-specific cells.
• #65: Readings in Information Visualization: Using Vision to Think (Ben Schneiderman et al, 1999)