Exploring Data Visualization #8

Note from Megan Ozeran, Data Analytics & Visualization Librarian: It’s been a pleasure sharing data visualization news with you over the last seven months. Now, I am excited to announce that one of our awesome Graduate Assistants, Xena Becker, will oversee the Exploring Data Visualization series. Take it away, Xena!

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Bar graph of energy consumption

From Chartable

1) The amount of energy required to make electronic devices and information centers run on a daily basis is significant—but just how much energy is used worldwide? Lisa Charlotte Rost used the principles from Alberto Cairo’s the truthful art to design and explain the choices behind a chart showing worldwide IT energy consumption.

Line graph showing income inequality over time

Junk Charts breaks down what works and what doesn’t in this graphic

2) Crazy Rich Asians was a box office hit this summer, gaining attention for its opulent set design and for being the first film to feature Asians and Asian Americans in most of the leading, directing, and other production roles since the 1990s. The New York Times used the opening of the film to write a report on Asian immigration and wealth disparity in the United States. Junkcharts wrote up a breakdown of the data visualizations used in the report, noting what the NYTimes did well and what areas could be improved in their representations.

A portion of a graphic that uses colored bars to indicate whether Brett Kavanaugh and Dr. Christine Blasey Ford answered the questions they were asked during the Senate confirmation hearing for Brett Kavanaugh.

The graphic incorporates the transcript to indicate what questions were answered or left unanswered.

3) The news cycle has been dominated by the confirmation hearing of Supreme Court nominee Brett Kavanaugh, who has been accused to sexual assault by Dr. Christine Blasey Ford. Vox created a simple but impactful chart that shows every time Ford or Kavanaugh answered (or did not answer) the question they had been asked.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email the Scholarly Commons.

Analyze and Visualize Your Humanities Data with Palladio

How do you make sense of hundreds of years of handwritten scholarly correspondence? Humanists at Stanford University had the same question, and developed the project Mapping the Republic of Letters to answer it. The project maps scholarly social networks in a time when exchanging ideas meant waiting months for a letter to arrive from across the Atlantic, not mere seconds for a tweet to show up in your feed. The tools used in this project inspired the Humanities + Design lab at Stanford University to create a set of free tools specifically designed for historical data, which can be multi-dimensional and not suitable for analysis with statistical software. Enter Palladio!

To start mapping connections in Palladio, you first need some structured, tabular data. An Excel spreadsheet in CSV format with data that is categorized and sorted is sufficient. Once you have your data, just upload it and get analyzing. Palladio likes data about two types of things: people and places. The sample data Palladio provides is information about influential people who visited or were otherwise connected with the itty bitty country of Monaco. Read on for some cool things you can do with historical data.

Mapping

Use the Map feature to mark coordinates and connections between them. Using the sample data that HD Lab provided, I created the map below, which shows birthplaces and arrival points. Hovering over the connection shows you the direction of the move. By default, you can change the map itself to be standard maps like satellite or terrain, or even just land masses with no human-created geography, like roads or place names.

Map of Mediterranean sea and surrounding lands of Europe, red lines across map show movement, all end in Monaco

One person in our dataset was born in Galicia, and later arrived in Monaco.

But, what if you want to combine this new-fangled spatial analysis with something actually historic? You’re in luck! Palladio allows you to use other maps as bases, provided that the map has been georeferenced (assigned coordinates based on locations represented on the image). The New York Public Library’s Map Warper is a collection of some georeferenced maps. Now you can show movement on a map that’s actually from the time period you’re studying!

Same red lines across map as above, but image of map itself is a historical map

The same birthplace to arrival point data, but now with an older map!

Network Graphs

Perhaps the connections you want to see don’t make sense to be on a map, like those between people. This is where the Graph feature comes in. Graph allows you to create network visualizations based on different facets of your data. In general, network graphs display relationships between entities, and work best if all your nodes (dots) are the same type of information. They are especially useful to show connections between people, but our sample data doesn’t have that information. Instead, we can visualize our peoples’ occupation by gender.

network graph shows connections between peoples' occupations and their gender

Most occupations have both males and females, but only males are Monegasque, Author, Gambler, or Journalist, and only females are Aristocracy or Spouse.

The network graph makes it especially visible that there are some slight inconsistencies in the data; at least one person has “Aristocracy” as an occupation, while others have “Aristocrat.” Cleaning and standardizing your data is key! That sounds like a job for…OpenRefine!

Timelines

All of the tools in Palladio have the same Timeline functionality. This basically allows you to filter the data used in your visualization by a date, whether that’s birthdate, date of death, publication date, or whatever timey wimey stuff you have in your dataset. Other types of data can be filtered using the Facet function, right next to the Timeline. Play around with filtering, and watch your visualization change.

Try Palladio today! If you need more direction, check out this step-by-step tutorial by Miriam Posner. The tutorial is a few years old so the interface has changed slightly, so don’t panic if the buttons look different!

Did you create something cool in Palladio? Post a comment below, or tell us about it on Twitter!

 

Exploring Data Visualization #7

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

A collection of six different radar charts, each showing one student's test scores in multiple subjects

From “The Radar Chart and its Caveats” by Yan Holtz

1) Data analyst Yan Holtz and designer Conor Healy have helpfully compiled a list of visualization caveats at their site From Data to Viz. Among the common pitfalls in data visualization they discuss the use of radar charts, as in the image above.

Two elementary school floor plans generated by computer modeling, optimized to minimize traffic flow between classes and material usage. The floor plans look biological, with the hallways branching to smaller hallways and the rooms shaped as all sorts of polygons instead of rectangular.

From “Evolving Floorplans,” created by Joel Simon

2) Bioinformaticist Joel Simon “grew” an elementary school floor plan using advanced computer science methods. As he points out, “The results were biological in appearance, intriguing in character and wildly irrational in practice.” The project certainly demonstrates that computer models are only as good as the data that humans give them (in this case, there were no constraints based on architecture or engineering rules). On the other hand, imagine your school was laid out like this! Read all about the project at Simon’s website.

A demonstration of a chart makeover. The before chart shows two pie charts. Each slice of the pie chart is the percentage of U.S. population within an age group. The first pie chart is 2010, the second is 2013. The makeover, or "after" chart, is a slope graph that shows the change in millions of people within each age group, which are each represented by a line.

Chart makeover created by Patricia Manasan for Storytelling With Data

3) Want to feel inspired? Dozens of people submitted data visualization makeovers to Storytelling With Data. Take a look at what people changed for ideas about how to make your own visualizations better.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.

Lightning Review: the truthful art by Alberto Cairo

Image of the truthful art

Hailed by one of our librarians as a brilliant and seminal text to understanding data visualization, the truthful art is a text that can serve both novices and masters in the field of visualization.

Packed with detailed descriptions, explanations, and images of just how Cairo wants readers to understand and engage with knowledge and data. Nearly every page of this work, in fact, is packed with examples of the methods Cairo is trying to connect his readers to.

Cairo’s work not only teaches readers how to best design their own visualizations, but goes into the process of explaining how to *read* data visualizations themselves. Portions of chapters are devoted to the necessity of ‘truthful’ visualizations, not only because “if someone hides data from you, they probably have something to hide” (Cairo, 2016, p. 49). The exact same data, when presented in different ways, can completely change the audience’s perspective on what the ‘truth’ of the matter is.

The most I read through the truthful art, the harder time I had putting it down. Cairo’s presentations of data, how vastly they could differ depending upon the medium through which they were visualized. It was amazing how Cairo could instantly pick apart a bad visualization, replacing it with one that was simultaneously more truthful and more beautiful.

There is specific portion of Chapter 2 where Cairo gives a very interesting visualization of “How Chicago Changed the Course of Its Rivers”. It’s detailed, informative, and very much a classic data visualization.

Then he compared it to a fountain.

The fountain was beautiful, and designed in a way to tell the same story as the maps Cairo had created. It was fascinating to see data presented in such a way, and I hadn’t fully considered that data could be represented in such a unique way.

the truthful art is here on our shelves in the Scholarly Commons, and we hope you’ll stop and give it a read! It’s certainly worthwhile one!

Exploring Data Visualization #6

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

U.S. immigration represented by concentric rings like a tree, where outermost ring is the most recent, with colors denoting immigrants' origin primarily by continent

from National Geographic, “200 Years of U.S. Immigration Looks Like the Rings of a Tree”

1) Two Northeastern University professors visualized immigration data for National Geographic by creating a fascinating chart that looks a lot like the growth rings of a tree. They write, “Like countries, trees can be hundreds, even thousands, of years old. Cells grow slowly, and the pattern of growth influences the shape of the trunk. Just as these cells leave an informational mark in the tree, so too do incoming immigrants contribute to the country’s shape.”

two line graphs, one with a legend and one with direct line labeling, demonstrating the advantage of the latter

from StorytellingWithData, “Accessible data viz is better data viz”

2) Accessibility is important in all kinds of communication, and data visualization is no exception. But it’s not always obvious how to make visualizations more accessible. You can find several tips for improving your visualization in “Accessible data viz is better data viz.”

Polar histograms of the streets in major cities across the U.S.

by Geoff Boeing, “Comparing City Street Orientations”

3) Urban planning postdoc Geoff Boeing used open map data to create a series of polar histograms that demonstrate how the streets in various U.S. cities do or don’t follow a neat grid. It’s a great example of a visualization that looks intriguing and also packs a lot of information. Learn more about it in his blog post, Comparing City Street Orientations.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.

Exploring Data Visualization #5 – R edition

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

This month, I wanted to share some resources specifically for learning to visualize data using R.

1) R is a free, open source programming language that is heavily used for statistical analysis, but has also expanded to encompass nearly any kind of data analysis you would want to do. In the Scholarly Commons, we have R and RStudio (a user-friendly R development environment) installed on all of our lab computers. RStudio’s website provides links to a lot of ways for you to get started with R.

2) R guru Hadley Wickham gave a public lecture at the University of Notre Dame last August. (Note that his talk starts about 37 minutes into the video.) In the lecture, he walks through a simple example of the iterative process of data visualization in R, and gives additional related advice for doing data science. You can learn from his lecture without knowing any R, but you will find it easier to understand if you have basic experience with programming in general.

3) If you want a book to help you learn more in depth, Wickham and a colleague wrote R for data science: Import, tidy, transform, visualize, and model data. You can read R for data science online, or you can come in to the Scholarly Commons to read the physical book while practicing on one of our lab computers.

4) You can also find a number of specific R courses at Lynda.com, such as “Data Visualization in R with ggplot2.” Just make sure to log in with your U of I credentials so you can access the courses for free.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.

Exploring Data Visualization #4

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Welcome back to this blog series! Here are some of the things I read in May:

a cartoon image of a few buildings, above two cartoon characters, one who is pointing and saying "We missed people here," while the other character shrugs and says "We can't do anything about it"

from Alvin Chang at Vox, “How Republicans are undermining the 2020 census, explained with a cartoon”

1) Alvin Chang, Senior Graphics Reporter at Vox, “covers policy by making explainers with charts and cartoons.” This month he explained the precarious state of the upcoming 2020 U.S. Census.

a dual-axis line chart overlaid with a stick figure drawing of a confused person misreading the chart's data

from Lisa Charlotte Rost at Uncharted, “Why not to use two axes, and what to use instead”

2) Lisa Charlotte Rost, a designer for Datawrapper, explains why dual-axis charts are almost always terrible, and what you can use instead.

text saying "The Wisdom and/or Madness of Crowds," surrounded by a cartoon rendering of a network graph

“The Wisdom and/or Madness of Crowds,” a game created by Nicky Case

3) Play this cute game! Nicky Case combines the logic of network graphs with the science of crowds in an “explorable” that shows why some crowds generate wisdom, while others create madness.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.

Exploring Data Visualization #3

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Welcome back to this blog series! Here are some of the things I read in April:

a photograph of a knit pattern in a very strange shape, using green yarn

“Make Caows and Shapcho” pattern knit by MeganAnn (https://www.ravelry.com/projects/MeganAnn/skyknit-the-collection)

1) Janelle Shane, who has created a new kind of humor based on neural networks, trained a neural network to generate knitting patterns. Experienced knitters then attempted these patterns so we can see what the computer generated, ranging from reasonable to silly to downright creepy creations.

map showing that many areas of the United States get their first leaf earlier than in the past

from NASA Earth Observatory, “Spring is Arriving Earlier in National Parks”

2) Considering we had snowfall in April, you might not think spring began early this year (I know I don’t!). But broadly speaking, climate change has caused spring to begin earlier and earlier across the United States. The NASA Earth Observatory looked at data published in 2016 to create maps that visualize how climate change has changed the timing of spring.

3) If you want to learn a new tool but aren’t sure what to choose, have a look at Nathan Yau’s suggestions in his post What I Use to Visualize Data. He even divides his list into categories based on where he is in the process, such as initial data processing versus final visualizations.

Exploring Data Visualization #2

In this monthly series, I share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them. You can find previous posts by looking at the Exploring Data Visualization tag.

Welcome back to this blog series! Here are some of the things I read in March:

Chart showing that the sons of black families from the top 1 percent had about the same chance of being incarcerated on a given day as the sons of white families earning $36,000

From The New York Times, “Extensive Data Shows Punishing Reach of Racism for Black Boys”

1) The New York Times took data from a recent study about income inequality and designed a variety of compelling data visualizations. The article text and the visualizations complement each other to convey the pervasive insidiousness of racism, especially for black boys.

A chart legend with the categories

From Elijah Meeks, “Color Advice for Data Visualization with D3.js”

2) D3.js is an open JavaScript library that you can use to visualize data. A data visualization engineer at Netflix (what an interesting job!), Elijah Meeks provides some great advice when picking your colors in D3. More importantly, these tips are helpful no matter what visualization tool you use.

A demonstration of selecting bins for histograms, showing too few, too many, and just the right number

From Mikhail Popov, “Plotting the Course Through Charted Waters”

3) Want to learn some data visualization basics? Mikhail Popov from Wikimedia conducted a data visualization literacy workshop for Wikimedia Foundation’s All Hands 2018 staff conference, and he made the entire workshop available online.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.

Exploring Data Visualization

Hi everyone! As mentioned in an earlier post, I’m Megan Ozeran, the Data Analytics & Visualization Librarian in the Scholarly Commons. In this new monthly series, I will share a combination of cool data visualizations, useful tools and resources, and other visualization miscellany. The field of data visualization is full of experts who publish insights in books and on blogs, and I’ll be using this series to introduce you to a few of them.

To jump-start this series, here are a few items for February:

A Tableau dashboard analyzing baseball data with regard to African American players

Created by Yoshihito Kimura, “African American baseball players have consisitently [sic] contributed to win”

1) data.world hosted weekly data visualization events related to Black History Month. See the data and the visualizations that people have created by clicking on the dataset links on their Black History Month page. The visualization above was contributed to the Baseball Demographics project.

A movie passes the Lena Waithe Test if there's a black woman in the work, who's in a position of power, and she's in a healthy relationship.

From FiveThirtyEight, “The Next Bechdel Test”

2) FiveThirtyEight, known for telling data-rich stories with visualizations, has made it easier than ever to download their data. For instance, you can download the data behind their article “The Next Bechdel Test” and experiment with how you might visualize it differently.

An 8-bit graphic of a millennial with the caption, "Follow me as I make my way toward a stable financial future."

From HuffPost, “FML”

3) “Why millennials are facing the scariest financial future of any generation since the Great Depression.” This long, intense article combines writing and data visualization in a brand new way. I recommend viewing it in a computer browser because the mobile version may not be as easy to read.

I hope you enjoyed this data visualization news! If you have any data visualization questions, please feel free to email me and set up an appointment at the Scholarly Commons.