Featured Resource: QGIS, a Free, Open Source Mapping Platform

This week, geographers around the globe took some time to celebrate the software that allows them to analyze, well, that very same globe. November 13th marked the 20th annual GIS Day,  an “international celebration of geographic information systems,” as the official GIS Day website puts it.

the words "GIS day" in a stylized font appear below a graphic of a globe with features including buildings, trees, and water

But while GIS technology has revolutionized the way we analyze and visualize maps over the past two decades, the high cost of ArcGIS products, long recognized as the gold standard for cartographic analysis tools, is enough to deter many people from using it. At the University of Illinois and other colleges and universities, access to ArcGIS can be taken for granted, but many of us will not remain in the academic world forever. Luckily, there’s a high-quality alternative to ArcGIS for those who want the benefits of mapping software without the pricetag!

the QGIS logo

QGIS is a free, open source mapping software that has most of the same functionality as ArcGIS. While some more advanced features included in ArcGIS do not have analogues in QGIS, developers are continually updating the software and new features are always being added. As it stands now, though, QGIS includes everything that the casual GIS practitioner could want, along with almost everything more advanced users need.

As is often the case with open source software alternatives, QGIS has a large, vibrant community of supporters, and its developers have put together tons of documentation on how to use the program, such as this user guide. Generally speaking, if you have any experience with ArcGIS it’s very easy to learn QGIS—for a picture of the learning curve, think somewhere along the lines of switching from Microsoft Word to Google Docs. And if you don’t have experience, the community is there to help! There are many guides to getting started, including the one listed in the above link, and more forum posts of users working through questions together than anyone could read in a lifetime. 

For more help, stop by to take a look at one of the QGIS guidebooks in our reference collection, or send us an email at sc@library.illinois.edu!

Have you made an interesting map in QGIS? Send us pictures of your creations on Twitter @ScholCommons!

 

Exploring Data Visualization #16

Daylight Saving Time Gripe Assistant Tool

Clocks fell back this weekend, which means the internet returns once again to the debate of whether or not we still need Daylight Saving Time. Andy Woodruff, a cartographer for Axis Maps, created a handy tool for determining how much you can complain about the time change. You input your ideal sunset and sunrise times, select whether the sunset or sunrise time you chose is more important, and the tool generates a map that shows whether DST should be gotten rid of, used year-round, or if no changes need to be made based on where you live. The difference a half hour makes is surprising for some of the maps, making this a fun data viz to play around with and examine your own gripes with DST.

A map of the United States with different regions shaded in different colors to represent if they should keep (gray) or get rid of (gold) changing the clocks for Daylight Saving Time. Blue represents areas that should always use Daylight Saving Time.

This shows an ideal sunrise of 7:00 am and an ideal sunset of 6:00 pm.

Laughing Online

Conveying tone through text can be stressful—finding the right balance of friendly and assertive in a text is a delicate operation that involves word choice and punctuation equally. Often, we make our text more friendly through exclamations points! Or by adding a quick laugh, haha. The Pudding took note of how varied our use of text-based laughs can be and put together a visual essay on how often we use different laughs and whether all of them actually mean we are “laughing out loud.” The most common laugh on Reddit is “lol,” while “hehe,” “jaja,” and “i’m laughing” are much less popular expressions of mirth.

A proportional area chart showing which text laughs are most used on Reddit.

“ha” is the expression most likely to be used to indicate fake laughter or hostility

how to do it in Excel: a shaded range

Here’s a quick tip for making more complex graphs using Excel! Storytelling with Data’s Elizabeth Ricks put together a great how-to article on making Excel show a shaded range on a graph. This method involves some “brute force” to make Excel’s functions work in your favor, but results in a clean chart that shows a shaded range rather than a cluster of multiple lines.

A shaded area chart in Excel

Pixelation to represent endangered species counts

On Imgur, user JJSmooth44 created a photo series to demonstrate the current status of endangered species using pixilation. The number of squares represent the approximate number of that species that remains in the world. The more pixelated the image, the fewer there are left.

A pixelated image of an African Wild Dog. The pixelation represents approximately how many of this endangered species remain in the wild (estimated between 3000 and 5500). The Wild Dog is still distinguishable, but is not clearly visible due to the pixelation.

The African Wild Dog is one of the images in which the animal is still mostly recognizable.

Scary Research to Share in the Dark: A Halloween-Themed Roundup

If you’re anything like us here in the Scholarly Commons, the day you’ve been waiting for is finally here. It’s time to put on a costume, eat too much candy, and celebrate all things spooky. That’s right, folks. It’s Halloween and we couldn’t be happier!

Man in all black with a jack o' lantern mask dancing in front of a green screen cemetery

If you’ve been keeping up with our Twitter (@ScholCommons) this month, you’ve noticed we’ve been sharing some ghoulish graphs and other scary scholarship. To keep the holiday spirit(s) high, I wanted to use this week’s blog post to gather up all our favorites.

First up, check out the most haunted cities in the US on The Next Web, which includes some graphs but also a heat map of the most haunted areas in the country. Which region do you think has the most ghosts?

If you’re more interested in what’s happening on across the pond, we’ve got you covered. Click on this project to see just how scary ArcGIS story maps can be.

https://twitter.com/ScholCommons/status/1187058855282462721

And while ghosts may be cool, we all know the best Halloween characters are all witches. Check out this fascinating project from The University of Edinburgh that explores real, historic witch hunts in Scotland.

The next project we want to show you might be one of the scariest. I was absolutely horrified to find out that Illinois’ most popular Halloween candy is Jolly Ranchers. If you’re expecting trick-or-treaters tonight, please think of the children and reconsider your candy offerings.

Now that we’ve share the most macabre maps around, let’s shift our focus to the future. Nathan Yau uses data to predict when your death will occur. And if this isn’t enough to terrify you, try his tool to predict how you’ll die.

Finally, if you’re looking for some cooking help from an AI or a Great Old One, check out this neural network dubbed “Cooking with Cthulhu.”

Do you have any favorite Halloween-themed research projects? If so, please share it with us here or on Twitter. And if you’re interested in doing your own deadly digital scholarship, feel free to reach out to the Scholarly Commons to learn how to get started or get help on your current work. Remember, in the words everyone’s favorite two-faced mayor…

A clip of the Mayor from Nightmare Before Christmas saying There's only 365 days left until next Halloween

Featured Resource: BTAA Geoportal

We at the University of Illinois are lucky to have a library that offers access to more journals and databases than any one person could ever hope to make their way though. The downside of this much access, however, is that it can be easy for resources to get lost in the weeds. For the typical student, once you are familiar with a few databases or methods of searching for information, you tend to not seek out more unless you absolutely need to.

This week, we wanted to fight back against that tendency just a little bit, by introducing you to a database which many readers may not have heard of before but contains a veritable treasure trove of useful geographical information, the Big 10 Academic Alliance Geoportal.

This resource is a compilation of geospatial content from the 12 universities that make up the BTAA. Types of content available include maps (many of which are historic), aerial imagery, and geospatial data. Researchers with a specific need for one of those can easily navigate from the Geoportal homepage to a more specific resource page by selecting the type of information they are looking for here:

A screenshot from the BTAA Geoportal, displaying icons to click on for "Geospatial Data," "Maps," and "Aerial Imagery."

Alternatively, if you don’t particularly care about the type of data you find but rather are looking for data in a particular region, you can use the map on the left side of the display to easily zoom in to a particular part of the world and see what maps and other resources are available.

A screenshot from the BTAA Geoportal showing a world map with numbers in orange, yellow, and green circles scattered around the map.

The numbers on the map represent the number of maps or other data in the Geoportal localized in each rough region of the world, for example, there are 310 maps for Europe, and 14 maps for the Atlantic Ocean. As you zoom in on the map, your options get more specific, and the numbers break down to smaller geographic regions: 

A close-up of Europe on the same map as above, showing that the one "310" circle on the world map is now divided into many smaller numbered circles around the continent.

When the map is zoomed in close enough that there is only one piece of data for a particular area, the circled numbers are replaced with a blue location icon, such as the ones displayed over Iceland, Sweden, and the Russia-Finland border above. Clicking on one of these icons will take you to a page with the specific image or data source represented on the map. For example, the icon over Iceland takes us to the following page:

A screenshot from the BTAA Geoportal showing a historic map of Iceland with some metadata below.

Information is provided about what type of resource you’re looking at, who created it, what time period it is from, as well as which BTAA member institution uploaded the map (in this case, the University of Minnesota). 

Other tools on the home page, including a search bar and lists of places and subjects represented in the Geoportal, mean that no matter what point you’re starting from you should have no problem finding the data you need!

The Geoportal also maintains a blog with news, featured items and more, so be sure to check it out and keep up-to-date on all things geospatial!

Do you have questions about using the Geoportal, or finding other geospatial data? Stop by the Scholarly Commons or shoot us an email at sc@library.illinois.edu, we’ll be happy to help you!

Exploring Data Visualization #15

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.

Which milk has the smallest impact on the planet?

Climate change impacts each of us in direct and indirect ways. Mitigating your personal carbon footprint is an important way to address climate change for many people, but often people are unsure how to make choices that benefit the climate. Daniela Haake from Datawrapper took a close look at how her choice to have milk in her coffee was damaging or benefiting the planet and it turns out things aren’t looking great for café con leche. The chart Haake published, created using data from Dr. Joseph Poore, compares the carbon emissions, land use, and water use of milk and the top 4 milk alternatives.

A chart comparing the carbon emissions, land use, and water use of milk and the top 4 milk alternatives

Soy milk has the lowest overall impact on carbon emissions, land use, and water use.

Here’s Who Owns the Most Land in America

“The 100 largest owners of private property in the U.S., newcomers and old-timers together, have 40 million acres, or approximately 2% of the country’s land mass,” Bloomberg News reports. The people who own this land are the richest people in the country, and their wealth has grown significantly over the last 10 years. Bloomberg created a map that demonstrates where the land these people own is located. Compared to the rest of the country, the amount of land owned by these people looks relatively small—could Bloomberg have presented more information about why it is significant that these people own land in these areas? And about why they own so much land?

A map of the continental United States with the land owned by the 10 largest owners of private property highlighted

This image shows only the land owned by the top 10 landowners.

How to Get Better at Embracing Unknowns

Representing our uncertainty in data can be difficult to do clearly and well. In Scientific American this month, Jessica Hullman analyzed different methods of representing uncertainty for their clarity and effectiveness. While there may be no perfect way to represent uncertainty in your data viz, Hullman argues that “the least effective way to present uncertainty is to not show it at all.” Take a look at Hullman’s different ways to represent uncertainty and see if any might work for your next project!

A gif showing two methods of demonstrating uncertainty in data visualizations through animation

Animated charts make uncertainty impossible to ignore

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

Welcome Back!

Principal Skinner from Simpsons saying

Hello students, faculty, and everyone else who makes up the amazing community of the University of Illinois at Urbana-Champaign! We hope the beginning of this new academic year has been an exciting and only-mildly-hectic time. The Scholarly Commons, your central hub for qualitative and quantitative research assistance, has officially resumed our extended hours.

That’s right, for the entirety of this beautiful fall semester we will be open Monday-Friday, 8:30am-6:00pm!

In addition to our expansive software and numerous scanners, the Scholarly Commons is here to provide you with access to both brand new and continued services

Ted Mosby from How I Met Your Mother asking

New additions to the Scholarly Commons this semester include two, new, high-powered computers featuring: 6-core processors, NVidia 1080 video cards, 32GB RAM, and solid-state drives. 

For the first time, we’ll also be offering REDCap (Research Electronic Data Capture) consultations to help you with data collection and database needs. Drop-in hours are available during this fall on Tuesdays, 9:00-11:00am in the Scholarly Commons.

CITL Statistical Consulting is back to help you with all your research involving R, Stata, SPSS, SAS, and more. Consultations can be requested through this form.
Drop-in hours are available with CITL Consultants:
Monday: 10:00am-4:00pm
Tuesday: 10:00am-4:00pm
Wednesday: 10:00am-1:00pm, 2:00-5:00pm
Thursday: 10:00am-4:00pm
Friday: 10:00am-4:00pm

Billy Mays saying

Once again our wonderful Data Analytics and Visualization Librarian, Megan Ozeran, is offering office hours every other Monday, 2:00-4:00pm (next Office Hours will be held 9/9). Feel free to stop by with your questions about data visualization!

And speaking of data visualization, the Scholarly Commons will be hosting the Data Viz Competition this fall. Undergraduate and graduate student submissions will be judged separately, and there will be first and second place awards for each. All awards will be announced at the finale event on Tuesday, October 22nd. Check out last year’s entries.  

As always, please reach out to the Scholarly Commons with any questions at sc@library.illinois.edu and best of luck in all your research this upcoming year!

Exploring Data Visualization #14

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 day in the life of Americans: a data comic

A comic demonstrating the amount of time Americans spend sleeping, at work, free, or doing other activities from 4 a.m. to 3 p.m.

By illustrating the activity most Americans are doing at a given hour, Hong highlights what the average day looks like for an American worker.

Happy May, researchers! With the semester winding down and summer plans on the horizon, a lot of us are reflecting on what we’ve done in the past year. Sometimes it can be hard to determine what your daily routine looks like when you are the one doing it every day. Matt Hong created a cute and informative data comic about how we spend our time during the day, based on data from the Census Bureau. Check out Hong’s Medium page for more data comics.

What Qualifies as Middle-Income in Each State

A bar chart that shows the range of incomes that qualify as "middle-income" for households made up of four people, organized by state.

The distribution of middle-income for households made up of four people.

Nathan Yau at Flowing Data created an interesting chart that shows the range of income that is considered “middle-income” in each state and the District of Columbia in the United States. The design of the chart itself is smooth and watching the transitions between income ranges based on number of people in the household is very enjoyable. It is also enlightening to see where states fall on the spectrum of what “middle-income” means, and this visualization could be a useful tool for researchers working on wage disparity.

When People Find a New Job

A frequency trail chart that shows peaks based on the age when people change jobs.

The bottom of the chart shows jobs that people transition into later in life.

The end of the semester also means a wave of new graduates entering the workforce. While we extend our congratulations to those people, we often inquire about what their upcoming plans are and where they will be working in the future. For some, that question is straightforward; for others, a change of pace may be on the horizon. Nathan Yau of Flowing Data also created a frequency trail chart that shows at what age many people change career paths. As Yau demonstrates in a bar chart that accompanies the frequency trail chart, the majority of job switches happen early and late in life, a phenomenon which he offers some suggestions for.

A bar chart showing the distribution of the age at which people switch jobs. 15-19 is the highest percent (above 30%) and 55-64 is the lowest (around 10%)

The peak at the “older” end of the chart indicates some changes post-retirement, but also makes you wonder why people are still finding new jobs at age 85 to 89.

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

Exploring Data Visualization #13

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.

One Way to Spot a Partisan Gerrymander

Even though it feels like it was 2016 yesterday, we are more than a quarter of the way through 2019 and the 2020 political cycle is starting to heat up. A common issue in the minds of voters and politicians is fraudulent and rigged elections—voters increasingly wonder if their votes really matter in the current political landscape. Last week, the Supreme Court heard two cases on partisan gerrymandering in North Carolina and Maryland. FiveThirtyEight made an elegant visualization about gerrymandering in North Carolina. The visualization demonstrates how actual election outcomes can be used to extrapolate what percentage of seats will go to each party.

A graph that shows the Average Democratic vote share in the U.S. House plotted against the actual outcome. A pink line represents the average outcome and since it does not pass through (0, 0), that indicates partisan bias in the House election being studied.

If there is no partisan bias in voting districts, the outcome should be 50/50.

As you scroll, the chart continues to develop and become more complicated. It adds results from past elections to contextualize the severity of the current problems with gerrymandering. It also provides an example of the outcomes of a redrawn district map in Pennsylvania.

Mistakes, we’ve drawn a few

Two different charts that both represent attitudes in the UK toward Britain voting to leave the EU. The chart on the left is a sine chart which looks erratic while the chart on the right shows the averages of plotted lines and demonstrates clear trends.

The change from line chart to plotted points better demonstrates the trend of the attitudes toward Brexit.

Sarah Leo from The Economist re-creates past visualizations from the publication that were misleading or poorly designed. The blog post calls out the mistakes made very effectively and offers redesigns, when possible. They also make their data available after each visualization.

Seeing two visualizations of the same data next to one another really helps drive home how data can be represented differently–and how that causes different impacts upon a reader.

FastCharts

The Financial Times has made an online version of their quick chart-making tool available for the public. Appropriately titled FastCharts, the site lets you upload your own data or play around with sample data they have provided. Because this tool is so simple, it seems like it would be useful for exploratory data, but maybe not for creating more complex explanations of your data.

The interface of FastCharts, showing a line chart of global temperature anomalies from 1850 to 2017.

FastCharts automatically selects which type of chart it thinks will work best for your data.

Play with the provided example data or use your own data to produce an interesting result! For a challenge, see if any of the data in our Numeric Data Library Guide can work for this tool.

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

Exploring Data Visualization #12

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.

American segregation, mapped day and night

Is segregation in the United States improving? And if it is, what race sees the most people of different races? And do the answers to these questions change based on the time of day? Vox sets out to answer some of these questions through a video essay and an interactive map about segregation in the United States cities at work and at home.

A map of Champaign County showing data peaks where the highest population of Black people live.

This map shows the population density of Black people living in Champaign-Urbana, IL. The brighter the pink, the higher the percentage of Black people living only near Black people.

A map showing the areas in Champaign County populated by white people.

This map shows the population density of white people living in Champaign-Urbana, IL. The brighter the pink, the higher the percentage of white people living only near white people.

The map is interesting and effectively demonstrates the continued presence of segregation in communities across the United States. However, there is little detail on the map about the geographical features of the region being examined. This isn’t too much of a problem if you are familiar with the region you are looking at, but for more unfamiliar communities it leads to more questions than it answers.

NASA’s Opportunity Rover Dies on Mars

 

After 15 years on Mars, the Opportunity Rover Mission was officially declared finished on February 13th, 2019. The New York Times created a visualization that lets you follow Opportunity’s 28 mile path across the surface of Mars, which includes a bird’s eye view of Oppy’s path as well as images sent by the rover back to NASA. Opportunity was responsible for discovering evidence of drinkable water on Mars.

A map of the surface of mars with a yellow line showing the path of NASA's Opportunity rover. There is a small image in the corner of Santa Maria Crater taken by the rover.

The map of Opportunity’s path is accompanied by images from the rover and artists’ renderings of the surface of Mars.

The periodic table is a scatterplot. (Among others.)

 

The periodic table: a data visualization familiar to anyone who has ever set foot in a grade school science classroom. As Lisa Rost points out, the periodic table is actually just a simple scatter plot, with group as the x-axis and period as the y-axis. Or at least, that’s true of the Mendeleev periodic table, the one we are most familiar with. See some other examples of how to break down the periodic table on Rost’s post, which links to the Wikipedia article on alternative periodic tables. If you find a favorite, be sure to tweet it to us @ScholCommons! We are always curious to see what visualizations get people excited.

A visualization of the periodic table of the elements with the elements represented by different colored dots. The dot colors correspond to when in time the elements were discovered, which is coded in a key at the top of the chart. Yellow is before Mendeleev, blue is after Mendeleev, orange is BC, and black is since 2000.

A periodic table color coded by Lisa Rost to show when in time different elements where discovered.

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

Transformation in Digital Humanities

The opinions presented in this piece are solely the author’s and referenced authors. This is meant to serve as a synthesis of arguments made in DH regarding transformation.

How do data and algorithms affect our lives? How does technology affect our humanity? Scholars and researchers in the digital humanities (DH) ask questions about how we can use DH to enact social change by making observations of the world around us. This kind of work is often called “transformative DH.”

The idea of transformative DH is an ongoing conversation. As Moya Bailey wrote in 2011, scholars’ experiences and identities affect and inform their theories and practices, which allows them to make worthwhile observations in diverse areas of humanities scholarship. Just as there is strong conflict about how DH itself is defined, there is also conflict regarding whether or not DH needs to be “transformed.” The theme of the 2011 Annual DH Conference held at Stanford was “Big Tent Digital Humanities,” a phrase symbolizing the welcoming nature of the DH field as a space for interdisciplinary scholarship. Still, those on the fringes found themselves unwelcome, or at least unacknowledged.

This conversation around what DH is and what it could be exploded at the Modern Languages Association (MLA) Convention in 2011, which featured multiple digital humanities and digital pedagogy sessions aimed at defining the field and what “counts” as DH. During the convention Stephen Ramsay, in a talk boldly title “Who’s In and Who’s Out,” stated that all digital humanists must code in order to be considered a digital humanist (he later softened “code” to “build”). These comments resulted in ongoing conversations online about gatekeeping in DH, which refer to both what work counts as DH and who counts as a DHer or digital humanist. Moya Bailey also noted certain that scholars whose work focused on race, gender, or queerness and relationships with technology were “doing intersectional digital humanities work in all but name.” This work, however, was not acknowledged as digital humanities.

logo

Website Banner from transformdh.org

To address gatekeeping in the DH community more fully, the group #transformDH was formed in 2011, during this intense period of conversation and attempts at defining. The group self-describes as an “academic guerrilla movement” aimed at re-defining DH as a tool for transformative, social justice scholarship. Their primary objective is to create space in the DH world for projects that push beyond traditional humanities research with digital tools. To achieve this, they encourage and create projects that have the ability to enact social change and bring conversations on race, gender, sexuality, and class into both the academy and the public consciousness. An excellent example of this ideology is the Torn Apart/Separados project, a rapid response DH project completed in response to the United States enacting a “Zero Tolerance Policy” for immigrants attempting to cross the US/Mexico border. In order to visualize the reach and resources of ICE (those enforcing this policy), a cohort of scholars, programmers, and data scientists banded together and published this project in a matter of weeks. Projects such as these demonstrate the potential of DH as a tool for transformative scholarship and to enact social change. The potential becomes dangerously disregarded when we set limits on who counts as a digital humanist and what counts as digital humanities work.

For further, in-depth reading on this topic, check out the articles below.