Of Maps and Memes: A Bit of Cartographic Fun

Co-Authored by Zhaneille Green

We use maps to communicate all the time. Historically, they have been used to navigate the world and to stand as visual, physical manifestations of defined spaces and places. What do you think of when we say “map”: a topographic map1 a transportation map2 or a city map3?

You can use maps to represent just about anything you want to say, far beyond these typical examples. We wrote this blog to invite you to have a little cartographic fun of your own.

If you’re on any kind of social media, you’ve probably seen maps like the one below, highlighting anything from each state’s favorite kind of candy to what the continental US would look like if all of the states’ borders were drawn along rivers and mountain ranges. People definitely seem to enjoy sharing these maps, curious to see what grocery store most people shop at in their home state, or laughing about California’s lack of popularity with the states in the surrounding area.

Map of most popular halloween candy in each US state. View the interactive version on candystore.com

Try your hand at creating your own silly map by using our programs in the Scholarly Commons. Start a war by creating a map that ranks the Southern states with the best barbecue using Adobe Photoshop or Illustrator, or explore a personal hobby like creating a map of all the creatures Sam & Dean Winchester met through the 15 seasons of Supernatural using ArcGIS.

If you’re feeling a bit more serious, don’t fret! Even if these meme-like maps aren’t portraying the most critical information, they do demonstrate how maps can be a great tool for data visualization. In many ways, location can make data feel more personal, because we all have personal connections to place. Admit it: the first thing you checked on the favorite candy map was your home state. Maps also tend to be more visually engaging than a simple table with, for example, states in one column and favorite animal in the other.

Using geotagging data, each dot represents where a photo was taken: blue for locals, red for tourists, and yellow for unknown. Locals and Tourists #1 (GTWA #2): London. Erica Fischer, CC BY-SA 2.0 via Flickr.

Regardless of what you want to map, the Scholarly Commons has the tools to help bring your vision to life. Learn about software access on our website, and check out these LinkedIn Learning resources for an introduction to ArcGIS Online or Photoshop, which are available with University of Illinois login credentials. If you need more assistance, feel free to ask us questions. Go forth and meme!

You can’t analyze data if you ain’t cute: Data Visualization

Meme from Reno 911 with the original text stating "You can't fight crime if you ain't cute" but the "fight crime" is crossed out and above is written "analyze Data"

Humans are highly visual creatures, even more so in our hyper-graphic world of ultra-filtered images and short aesthetic videos. Great ideas are ignored into oblivion in favor of shiny graphics and slick illustrations, so even data analysts need to be aware of how they present their findings. A well-designed infographic will be much more impactful, widely shared, and remembered than columns and rows of numbers. Even a simple graph can help people better come to conclusions and absorb information than they ever would with just numbers alone. People who can not only crunch numbers but also create stunning communications about those numbers are a real asset on the job market, so it behooves any hopeful data analyst to at least learn the basics of visualization.

LinkedIn Learning 

  1. Learning Data Visualization 
    1. This course clocks in at just under two hours and aims to give learners the scaffolding for a strong understanding of data visualization. Geared towards true beginners, this course challenges learners to think about their data, audience, and goals to create visuals that maximize impact. Learners will also learn about visual perception and chart selection strategies, which in turn can set users up for a deep understanding of visualization. 
  1. Data Visualization: Best Practices 
    1. A poorly designed visualization can be criminally misleading, causing viewers to come to biased and inaccurate conclusions that can negatively affect everything from their investment choices to their health practices. This 98-minute course will give learners the tools to avoid common visualization missteps and the tricks to make their visualizations better fit their data, audience, and goals. This course uses Adobe Illustrator, so those who are unfamiliar with the program should first check out this quick start introduction to the program on LinkedIn Learning. Remember, UIUC students have free access many Adobe products, including Adobe Illustrator!  
  2. Excel Data Visualization: Mastering 20+ Charts and Graphs 
    1. Once again, we will focus on this data skillset within the context of a familiar software, Excel. While it is not the first software that comes to mind when thinking about visualization, Excel has surprisingly powerful visualization functions that will certainly come in handy when analyzing data. This course covers the humble pie chart to the complex geospatial heat maps and 3D power maps. In just two hours, learners will be able to quickly take their data from tables to graphics.  

O’Reilly Books and Videos

Make sure you are logged into O’Reilly before clicking these links. The best way to login is to go to the library catalog’s record for a book offered through O’Reilly (Like this book on Python) and then follow the instructions on this Libguide to log in.

  1. Fundamentals of Data Visualization 
    1. This handy book goes deep into the technical aspects of data visualizations. Learners will learn basic concepts like color theory along side more complex practices like redundant coding. This eBook also provides a helpful directory of visualizations so users can quickly find visualizations that fit their needs.
  2. The Data Visualization Lifecycle 
    1. This 4-hour course covers the basics of data visualization but looks at the actual process of professional data visualization that the other resources on this list do not address. Learners will gain technical skills in building visualization and a broader understanding of data visualization as a collaborative process based on external and internal stakeholders and audiences. This course teaches users how to interact with different data cultures, collaborate with colleagues, and how to treat visualization as a product.
  3. Interactive Data Visualization for the Web 
    1. Interactive data visualization is a trending skill in almost all fields that rely on data analysis and visualization of any kind. Allowing others to interact with your data and its visualization can make the data more accessible and memorable than ever before. This book gives users the skills to make interactive visuals with the fundamental concepts and methods of D3, the most powerful JavaScript library for expressing data visually in a web browser. Even those who are new to web programming will learn the basics of HTML, CSS, JavaScript, and SVG alongside the data visualization skills.

In the Catalog 

  1. #MakeoverMonday : improving how we visualize and analyze data, one chart at a time by Andrew Michael Kriebel and Eva Katharina Murray 
    1. Hashtags can be the start of beautiful movements, as those in the data analysis field learned as their #MakeoverMonday tag sparked a complete reimagining of how professionals approach data visualization. Readers will learn concepts of data visualization while viewing the real-life results of these concepts as shown by the hashtag-inspired graphics. #MakeoverMonday shows readers the “many ways to walk the line between simple reporting and design artistry to create exactly the visualization the situation requires”.  
  2. The functional art : an introduction to information graphics and visualization by Alberto Cairo 
    1. If there are data visualization celebrities, then Alberto Cairo is an A-lister. Known for his visualization journalism, he is a self-described information designer who has become famous for his gripping visualizations that stand as both formal art and excellent communication of data. This book allows users to learn the ins and outs of design all while strolling through a gallery of amazing visualization examples. This resource leans heavily on the theory of art and design, which makes it stand out from the other resources on this list. Alberto Cairo’s other works, The Truthful Art: data, charts, and maps for communication and How Charts Lie : getting smarter about visual information  are also worthwhile and insightful reads!  
  3. Data visualisation : a handbook for data driven design by Andy Kirk 
    1. Pivoting back to the more practical side of things, this handbook offers clear and useful processes for data driven designing. Readers will learn more about the visualization workflow, formulating briefs, working with data in the context of visualization, representing data accurately, integrating interactivity, and visualization literacy. 

And that’s it, folks!

With these visualization resources, the Winter Break Data Analysis series is ending on a pretty note. Hopefully, you have been able to keep your mind sharp and develop a new skill over the last month, but even if the timing was off, these resources and many more are available to students all year long! Did you enjoy one of these resources or posts? Do you have questions about any of these topics or suggestions for future series? Please tell us about it at sc@library.illinois.edu or on twitter at @ScholCommons. Thank you for joining this series and happy analyzing!  

Making Infographics in Canva: a Guide and Review

Introduction

If you’ve ever had to design a poster for class, you’re probably familiar with Canva. This online and app-based graphic design tool, with free and subscription-based versions, features a large selection of templates and stock graphics that make it pretty easy to create decent-looking infographics. While it is far from perfect, the ease of use makes Canva worth trying out if you want to add a bit of color and fun to your data presentation.

Getting Started

Starting with a blank document can be intimidating, especially for someone without any graphic design experience. Luckily, Canva has a bunch of templates to help you get started.

Canva infographic templates

I recommend picking a template based on the color scheme and general aesthetic. It’s unlikely you’ll find a template that looks exactly how you want, so you can think of a template as a selection of colors, fonts, and graphics to use in your design, rather than something to just copy and paste things into. For example, see the image below – I recently used the template on the left to create the infographic on the right.

An infographic template compared to the resulting infographic

General Design Principles

Before you get started on your infographic, it’s important to remember some general design guidelines:

  1. Contrast. High levels of contrast between your background and foreground help keep everything legible.
  2. Simplicity. Too many different colors and fonts can be an eyesore. Stick to no more than two fonts at a time.
  3. Space. Leave whitespace to keep things from looking cluttered.
  4. Alignment and balance. People generally enjoy looking at things that are lined up neatly and don’t have too much visual weight on one side or another.
An exaggerated example of a design that ignores the above advice.

Adding Graphs and Graphics

Now that you have a template in hand and graphic design principles in mind, you can start actually creating your infographic. Under “Elements,” Canva includes several types of basic charts. Once you’ve added a chart to your graphic, you can edit the data associated with the chart directly in the provided spreadsheet, by uploading a csv file, or by linking to a google spreadsheet.

Canva interface for creating charts

The settings tab allows you to decide whether you want the chart to include a legend or labels. The options bar at the top allows for further customization of colors and bar or dot appearance. Finally, adding a few simple graphics from Canva’s library such as shapes and icons can make your infographic more interesting. 

Examples of charts available in Canva, with a variety of customizations.

Limitations and Frustrations

The main downsides to Canva are the number of features locked behind a paywall and the inability to see only the free options. Elements cannot be filtered by price and it seems that more and more graphics are being claimed by Canva Pro, so searching for graphics can be frustrating. Templates can be filtered, but it will still bring up results where the template itself is free, but there are paid elements within the template. So, you might choose a template based on a graphic that you really like, only to find out that you need a Canva Pro subscription to include that graphic.

The charts in Canva also have limitations. Pie charts do not allow for the selection of colors for each individual slice; you have to pick one color, and Canva will generate the rest. However, if you want to have more control over your charts, or wish to include more complicated data representations, you can upload charts to Canva, which even supports transparency.

Conclusion

As mentioned above, Canva has its downsides. However, Canva’s templates, graphics, and charts still make it a super useful tool for creating infographics that are visually appealing. Try it out the next time you need to present some data!

A Different Kind of Data Cleaning: Making Your Data Visualizations Accessible

Introduction: Why Does Accessibility Matter?

Data visualizations are a fast and effective manner for communicating information and are increasingly becoming a more popular way for researchers to share their data with a broad audience. Because of this rising importance, it is also necessary to ensure that data visualizations are accessible to everyone. Accessible data visualizations not only help an audience who may require a screen reader or other accessible tool to read a document but are also helpful to the creators of the data visualization as it brings their data to a much wider audience than through a non-accessible data visualization. This post will offer three tips on how you can make your visualization accessible!

TIP #1: Color Selection

One of the most important choices when making a data visualization are the colors used in the chart. One suggestion would be to use a color blindness simulator to check the colors in the data visualization and experiment to find the right amount of contrast between colors. Look at the example regarding the top ice cream flavors:

A data visualization about the top flavors of ice cream. Chocolate was the top flavor (40%) followed by Vanilla (30%), Strawberry (20%), and Other (10%).

At first glance, these colors may seem acceptable to use for this kind of data. But when ran through the colorblindness simulator, one of the results creates an accessibility concern:

This is the same pie chart above, but placed under a tritanopia color blindness lens. The colors used for strawberry and vanilla now look the exact same and blend into one another because of this, making it harder to discern the amount of space they take in the pie chart.

Although the colors contrasted well enough in the normal view, the color palettes used for the strawberry and vanilla categories look the same for those with tritanopia color blindness. The result is that these sections blend into one another and make it more difficult to distinguish their values. Most color palettes incorporated in current data visualization software are already designed to ensure the colors do not contrast, but it is still a good practice to check to ensure the colors do not blend in with one another!

TIP #2: Adding Alt Text

Since most data visualizations often appear as images in either published work or reports, alt text is a crucial need for accessibility purposes. Take the visualization below. If there was no alt text provided, then the visualization is meaningless to those who rely on alt text to read a given document. Alt text should be short and summarize the key takeaways from the data (there is no need to describe each individual point, but it should provide enough information to describe the trends occurring in the data).

This is a chart showing the population size of each town in a given county. Towns are labeled A-E and continue to grow in population size as they go down the alphabet (town A has 1,000 people while town E has 100,000 people).

TIP #3: Clearly Labeling Your Data

A simple but crucial component of any visualization is having clear labels on your data. Let’s look at two examples to see what makes having labels a vital aspect of any data visualization:

This is a chart for how much money was earned/spent at a lemonade stand by month. There is no y-axis labels to describe how much money is earned/spent and no key to discern the two lines that represent the money made and the money spent.

There is nothing in this graph that provides any useful information regarding the money earned or spent at the lemonade stand. How much money was earned or spent each month? What do these two lines represent? Now, look at a more clearly labeled version of the same data:

This is a cleaned version of the previous visualization regarding how much money was earned/spent at a lemonade stand. The addition of a Y-axis and key now show that more money was spent in January/February than earned, but then changes in March peaking in July, and then continuing to fall until December where more money is spent than earned again.

In adding a labeled Y-axis, we can now quantify the difference in distance between the two lines at any point and have a better idea of the money earned/spent in any given month. Furthermore, the addition of a key at the bottom of the visualization distinguishes the lines telling the audience what each represents. By clearly labeling the data, it is now in a position where audience members can interpret and analyze it properly.

Conclusion: Can My Data Still be Visually Appealing?

While it may appear that some of these recommendations detract from the creative designs of data visualizations, this is not the case at all. Designing a visually appealing data visualization is another crucial aspect of data visualization and should be heavily considered when creating one. Accessibility concerns, however, should have priority over the visual appeal of the data visualization. That said, accessibility in many respects encourages creativity in the design, as it makes the creator carefully consider how they want to present their data in a way that is both accessible and visually appealing. Thus, accessibility makes for a more creative and transmissive data visualization and will benefit everyone!

Halloween Data Visualizations!

It’s that time of year where everyone starts to enjoy all things spooky and scary – haunted houses, pumpkin picking, scary movies and…data visualizations! To celebrate Halloween, we have created a couple of data visualizations from a bunch of data sets. We hope you enjoy them!

Halloween Costumes

How do you decide what Halloween costume you wear? Halloween Costumes conducted a survey on this very topic. According to their data, the top way people choose their costume is based on what is easiest to make. Other inspirations include classic costumes, coordination with others, social media trends, and characters from either recent or classic movie or tv franchises.

Data on how people choose their Halloween Costumes. 39% of people base it on the easiest costume they can find, 21% on classic costumes (such as ghosts, witches, etc.), 14% on recent TV or movie characters, another 14% on couples/group/family coordination, 12% on older TV or movie characters, and 11% on social media trends.

The National Retail Federation also conducted a survey of the top costumes that adults were expected to wear in 2019 (there were no good data sets for 2020…). According to the survey, the most popular Halloween costume that year was a witch. Other classic costumes, such as vampires, zombies, and ghosts, ranked high too. Superheroes were also a popular costume choice, with many people dressing up as Spider-man or another Avengers character.

 

Data on the top 10 costumes of 2019. The top choice was dressing up as a witch, followed by a vampire, superhero, pirate, zombie, ghost, avengers character, princess, cat, and Spider-man.

 

Halloween Spending and Production

According to the National Retail Federation, Halloween spending has significantly increased between 2005 to this year, with the expected spending this year surpassing 10 billion dollars! That is up from fifteen years ago when the estimated Halloween spending averaged around 5 billion dollars.

 

This is data on expected Halloween spending between 2005 and 2021. In 2005, the expected spending was 3.3 Billion dollars. In 2006, it was 5 billion dollars. In 2007, it was 5.1 billion dollars. In 2008, it was 5.8 billion dollars. In 2009, it was 4.7 billion dollars. In 2010, it was 5.8 billion dollars again. In 2011, it was 6.9 billion dollars. In 2012, it was 8 billion dollars. In 2013, it was 7 billion dollars. In 2014, it was 7.4 billion dollars. In 2015, it was 6.9 billion dollars. In 2016, it was 8.4 billion dollars. In 2017, it was 9.1 billion dollars. In 2018, it was 9 billion dollars. In 2020, it was 8 billion dollars. Finally, in 2021, it is expected to be 10.1 billion dollars.

With much spending invested in Halloween, it would make sense that the production of Halloween-related items would likely grow too to meet this demand. The U.S. Department of Agriculture records each year the number of pumpkins produced in the United States. Besides one dip taken in 2015, it appears that pumpkin production has almost doubled in the past twenty years on average.

 

This is data on the number of pumpkins produced in the United States every year. In 2001, it was 8,460,000 pumpkins produced. In 2002, 8,509,000 Pumpkins were produced. In 2003, 8,085,000 pumpkins were produced. In 2004, 10,135,000 pumpkins were produced. In 2005, 10,756,000 pumpkins were produced. In 2006, 10,484,000 pumpkins were produced, in 2007, 11,458,000 pumpkins were produced. In 2008, 10,663,000 pumpkins were prodcued. In 2009, 9,311,000 pumpkins were produced. In 2010, 10,748,000 pumpkins were produced. In 2011, 10,705,000 pumpkins were produced. In 2012, 12,036,000 pumpkins were produced. In 2013, 11,221,000 pumpkins were prodcued. In 2014m 13,143,000 pumpkins were produced. In 2015, 7,538,000 pumpkins were prodcued. In 2016, 17,096,500 pumpkins were produced. In 2017, 15,600,600 pumpkins were produced. In 2018, 15,406,900 pumpkins were produced. In 2019, 13,450,900 pumpkins were produced. Finally, in 2020,, 13,751,500 pumpkins were produced.

Halloween Activities by Demographics

Finally, here are two statistics taken from the National Retail Federation again regarding how people celebrate activities based on age and region. As the data shows, younger people seem more likely to dress in costumes, visit haunted houses, or throw parties on Halloween. Meanwhile, older individuals are more likely to decorate their homes or hand out candy.

This is data about how people celebrate different Halloween activities by age. Those 65 and older are only 31% likely to carve a pumpkin (31%) as opposed to the 43-50% likelihood of other age groups. Those 55-64 are the most likely to decorate their homes/yard (58%) while 18-24 are the least likely (47%). Those 18-24 years old, however, are the most likely to dress in costume (69%) while only 18% of those 65 and older will dress in costumes. Those 25-34 are the most likely to dress their pets up at 30% with only 8% of those 65 and older doing the same. Those 65 and older are 81% likely to hand out candy, however, while only 51% of people 18-24 years of age will pass out candy. Those at ages 35-44 are 38% likely to take their children trick-or-treating, while only 13% of those 65 and older do so. The 18-24 year old demographic are the most likely to throw or attend a party (43%), while 11% of those 65 and older do the same. Similarly, 18-24 demographic are the most likely to attend a haunted house at 32% while only 3% of those in the 65 and older range do the same.

At the same time, there seems to be not too huge of a difference in celebrating by region, apart from those living on the west coast being more likely to dress up or those living in the northeast more likely to hand out candy. Other than those two differences, it seems that most regions celebrate the same Halloween activities in the same proportions.

This is data about how people celebrate different Halloween activities by region. 42-46% of people carve a pumpkin (with those in the Midwest on the higher end and the South on the lower end). 50-54% of people decorate their home or yard with the Midwest and Northeast on the higher end and the South on the lower end. 41-52% of people dress in costume with those living in the West on the higher end and the Midwest on the lower end. 19-22% of people dress their pets with those living in the West on the higher end and the Midwest on the lower end. 64-70% of people hand out candy with the Northeast on the higher end and the West and South tied on the lower end. 22-26% of people take their children trick-or treating with those living in the Midwest and South on the higher end and the West on the lower end. 25% of people throw or attend a party equally across regions. 17-19% of people visit a haunted house with the Midwest and South on the higher end and the West on the lower end.

 

We hope these data visualizations got you in the mood for spooky, Halloween fun! From all of us at the Scholarly Commons, Happy Halloween!

Introductions: What is Digital Scholarship, anyways?

This is the beginning of a new series where we introduce you to the various topics that we cover in the Scholarly Commons. Maybe you’re new to the field or you’re just to the point where you’re just too afraid to ask… Fear not! We are here to take it back to the basics!

What is digital scholarship, anyways?

Digital scholarship is an all-encompassing term and it can be used very broadly. Digital scholarship refers to the use of digital tools, methods, evidence, or any other digital materials to complete a scholarly project. So, if you are using digital means to construct, analyze, or present your research, you’re doing digital scholarship!

It seems really basic to say that digital scholarship is any project that uses digital means because nowadays, isn’t that every project? Yes and No. We use the term digital quite liberally…If you used Microsoft Word to just write your essay about a lab you did during class – that is not digital scholarship however if you used specialized software to analyze the results from a survey you used to gather data then you wrote about it in an essay that you then typed in Microsoft Word, then that is digital scholarship! If you then wanted to get this essay published and hosted in an online repository so that other researchers can find your essay, then that is digital scholarship too!

Many higher education institutions have digital scholarship centers at their campus that focus on providing specialized support for these types of projects. The Scholarly Commons is a digital scholarship space in the University Main Library! Digital scholarship centers are often pushing for new and innovative means of discovery. They have access to specialized software and hardware and provide a space for collaboration and consultations with subject experts that can help you achieve your project goals.

At the Scholarly Commons, we support a wide array of topics that support digital and data-driven scholarship that this series will cover in the future. We have established partners throughout the library and across the wider University campus to support students, staff, and faculty in their digital scholarship endeavors.

Here is a list of the digital scholarship service points we support:

You can find a list of all the software the Scholarly Commons has to support digital scholarship here and a list of the Scholarly Commons hardware here. If you’re interested in learning more about the foundations of digital scholarship follow along to our Introductions series as we got back to the basics.

As always, if you’re interested in learning more about digital scholarship and how to  support your own projects you can fill out a consultation request form, attend a Savvy Researcher Workshop, Live Chat with us on Ask a Librarian, or send us an email. We are always happy to help!

Simple NetInt: A New Data Visualization Tool from Illinois Assistant Professor, Juan Salamanca

Juan Salamanca Ph.D, Assistant Professor in the School of Art and Design at the University of Illinois Urbana-Champaign recently created a new data visualization tool called Simple NetInt. Though developed from a tool he created a few years ago, this tool brings entirely new opportunities to digital scholarship! This week we had the chance to talk to Juan about this new tool in data visualization. Here’s what he said…

Simple NetInt is a JavaScript version of NetInt, a Java-based node-link visualization prototype designed to support the visual discovery of patterns across large dataset by displaying disjoint clusters of vertices that could be filtered, zoomed in or drilled down interactively. The visualization strategy used in Simple NetInt is to place clustered nodes in independent 3D spaces and draw links between nodes across multiple spaces. The result is a simple graphic user interface that enables visual depth as an intuitive dimension for data exploration.

Simple NetInt InterfaceCheck out the Simple NetInt tool here!

In collaboration with Professor Eric Benson, Salamanca tested a prototype of Simple NetInt with a dataset about academic publications, episodes, and story locations of the Sci-Fi TV series Firefly. The tool shows a network of research relationships between these three sets of entities similar to a citation map but on a timeline following the episodes chronology.

What inspired you to create this new tool?

This tool is an extension of a prototype I built five years ago for the visualization of financial transactions between bank clients. It is a software to visualize networks based on the representation of entities and their relationships and nodes and edges. This new version is used for the visualization of a totally different dataset:  scholarly work published in papers, episodes of a TV Series, and the narrative of the series itself. So, the network representation portrays relationships between journal articles, episode scripts, and fictional characters. I am also using it to design a large mural for the Siebel Center for Design.

What are your hopes for the future use of this project?

The final goal of this project is to develop an augmented reality visualization of networks to be used in the field of digital humanities. This proof of concept shows that scholars in the humanities come across datasets with different dimensional systems that might not be compatible across them. For instance, a timeline of scholarly publications may encompass 10 or 15 years, but the content of what is been discussed in that body of work may encompass centuries of history. Therefore, these two different temporal dimensions need to be represented in such a way that helps scholars in their interpretations. I believe that an immersive visualization may drive new questions for researchers or convey new findings to the public.

What were the major challenges that came with creating this tool?

The major challenge was to find a way to represent three different systems of coordinates in the same space. The tool has a universal space that contains relative subspaces for each dataset loaded. So, the nodes instantiated from each dataset are positioned in their own coordinate system, which could be a timeline, a position relative to a map, or just clusters by proximities. But the edges that connect nodes jump from one coordinate system to the other. This creates the idea of a system of nested spaces that works well with few subspaces, but I am still figuring out what is the most intuitive way to navigate larger multidimensional spaces.

What are your own research interests and how does this project support those?

My research focuses on understanding how designed artifacts affect the viscosity of social action. What I do is to investigate how the design of artifacts facilitates or hinders the cooperation of collaboration between people. I use visual analytics methods to conduct my research so the analysis of networks is an essential tool. I have built several custom-made tools for the observation of the interaction between people and things, and this is one of them.

If you would like to learn more about Simple NetInt you can find contact information for Professor Juan Salamanca here and more information on his research!

If you’re interested in learning more about data visualizations for your own projects, check out our guide on visualizing your data, attend a Savvy Researcher Workshop, Live Chat with us on Ask a Librarian, or send us an email. We are always happy to help!

Holiday Data Visualizations

The fall 2020 semester is almost over, which means that it is the holiday season again! We would especially like to wish everyone in the Jewish community a happy first night of Hanukkah tonight.

To celebrate the end of this semester, here are some fun Christmas and Hanukkah-related data visualizations to explore.

Popular Christmas Songs

First up, in 2018 data journalist Jon Keegan analyzed a dataset of 122 hours of airtime from a New York radio station in early December. He was particularly interested in discovering if there was a particular “golden age” of Christmas music, since nowadays it seems that most artists who release Christmas albums simply cover the same popular songs instead of writing a new song. This is a graph of what he discovered:

Based on this dataset, 65% of popular Christmas songs were originally released in the 1940s, 50s, and 60s. Despite the notable exception of Mariah Carey’s “All I Want for Christmas is You” from the 90s, most of the beloved “Holiday Hits” come from the mid-20th century.

As for why this is the case, the popular webcomic XKCD claims that every year American culture tries to “carefully recreate the Christmases of Baby Boomers’ childhoods.” Regardless of whether Christmas music reflects the enduring impact of the postwar generation on America, Keegan’s dataset is available online to download for further exploration.

Christmas Trees

Last year, Washington Post reporters Tim Meko and Lauren Tierney wrote an article about where Americans get their live Christmas trees from. The article includes this map:

The green areas are forests primarily composed of evergreen Christmas trees, and purple dots represent Choose-and-cut Christmas tree farms. 98% of Christmas trees in America are grown on farms, whether it’s a choose-and-cut farm where Americans come to select themselves or a farm that ships trees to stores and lots.

This next map shows which counties produce the most Christmas trees:

As you can see, the biggest Christmas tree producing areas are New England, the Appalachians, the Upper Midwest, and the Pacific Northwest, though there are farms throughout the country.

The First Night of Hanukkah

This year, Hanukkah starts tonight, December 10, but its start date varies every year. However, this is not the case on the primarily lunar-based Hebrew Calendar, in which Hanukkah starts on the 25th night of the month of Kislev. As a result, the days of Hanukkah vary year-to-year on other calendars, particularly the solar-based Gregorian calendar. It can occur as early as November 28 and as late as December 26.

In 2016, Hannukah began on December 24, Christmas Eve, so Vox author Zachary Crockett created this graphic to show the varying dates on which the first night of Hannukah has taken place from 1900 to 2016:

The Spelling of Hanukkah

Hanukkah is a Hebrew word, so as a result there is no definitive spelling of the word in the Latin alphabet I am using to write this blog post. In Hebrew it is written as חנוכה and pronounced hɑːnəkə in the phonetic alphabet.

According to Encyclopædia Britannica, when transliterating the pronounced word into English writing, the first letter ח, for example, is pronounced like the ch in loch. As a result, 17th century transliterations spell the holiday as Chanukah. However, ח does not sounds like the way ch does when its at the start of an English word, such as in chew, so in the 18th century the spelling Hanukkah became common. However, the H on its own is not quite correct either. More than twenty other spelling variations have been recorded due to various other transliteration issues.

It’s become pretty common to use Google Trends to discover which spellings are most common, and various journalists have explored this in past years. Here is the most recent Google search data comparing the two most commons spellings, Hanukkah and Chanukah going back to 2004:

You can also click this link if you are reading this article after December 2020 and want even more recent data.

As you would expect, the terms are more common every December. It warrants further analysis, but it appears that Chanukah is becoming less common in favor of Hanukkah, possibly reflecting some standardization going on. At some point, the latter may be considered the standard term.

You can also use Google Trends to see what the data looks like for Google searches in Israel:

Again, here is a link to see the most recent version of this data.

In Israel, it also appears as though the Hanukkah spelling is also becoming increasingly common, though early on there were years in which Chanukah was the more popular spelling.


I hope you’ve enjoyed seeing these brief explorations into data analysis related to Christmas and Hanukkah and the quick discoveries we made with them. But more importantly, I hope you have a happy and relaxing holiday season!

Exploring Data Visualization #18

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.

Painting the World with Water

Creating weather predictions is a complex tasks that requires global collaboration and advanced scientific technologies. Most people know very little about how a weather prediction is put together and what is required to make it possible. NASA gives us a little glimpse into the complexities of finding out just how we know if it’s going to rain or snow anywhere in the world.

Continue reading

Exploring Data Visualization #17

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.

The unspoken rules of visualization

Title header of essay "The unspoken rules of data visualization" by Kaiser Fung. White text on a black background with green and red patches Continue reading