5 Must-See TED Talks on Data Visualization!

Data visualization is crucial in understanding data and identifying hidden connections that matter. Below are 5 TED talks on data visualization you don’t want to miss!

1. Hans Rosling: The best stats you’ve ever seen

Han Rosling, cofounder of the Gapminder Foundation, developed the Trendalyzer software that converts international statistics – such as life expectancy and child mortality rate – into innovative, interactive graphics. The statistics guru is a strong advocate for public access to data and the development of tools that make it accessible and usable for all.  In this classic talk, Rosling highlights the importance of data in debunking myths about the gap between developed countries and the so-called “developing world.” Even though the talk was filmed 10 years ago, it still carries very important and relevant messages.

Watch more of Rosling’s TED talks here.

2. David McCandless: The beauty of data visualization

In this visually captivating talk, data journalist David McCandless suggests that data visualization is a quick solution to our current problem of information overload. Visualizations allow us to see the hidden patterns, identify connections that matter, and tell stories with data. To McCandless, “even when the information is terrible, the visual can be quite beautiful”; this is a controversial claim, however, since the main goal of data visualization should be to communicate information effectively through graphical means.

3. Dave Troy: Social maps that reveal a city’s intersections – and separations

A serial entrepreneur and data-viz fan, Dave Troy takes a people-focused approach to data visualization. Troy has been mapping tweets among city dwellers, revealing what connects communities and what separates them – above and beyond demographic factors such as race or ethnicity. He compares a city to a “giant high school cafeteria” and suggests that we see “how everybody arranged themselves in a seating chart”, arguing that “maybe it’s time to shake up the seating chart a little bit” to reshape our cities.

4. Eric Berlow & Sean Gourley: Mapping ideas worth spreading

An ecologist and a physicist, Eric Berlow and Sean Gourley, collaborate in this presentation to create stunning 3D visualizations demonstrating the interconnectedness of ideas. Taking 4,000 TEDx talks from 147 countries representing 50 languages, they explore their “meme-omes” – the mathematical structures that underlie the ideas behind these talks – and discover similarities between seemingly unconnected topics. Berlow and Gourley also broke down complex themes into multiple more specific ones, seeing what topics resonated with viewers and what kind of audience looked at what topic. To Gourley, mapping ideas in this way will help us “to see what’s being said, to see what’s not being said, and to be a little bit more human and, hopefully, a little smarter.”

5. Manuel Lima: A visual history of human knowledge

Founder of VisualComplexity.com Manuel Lima, described by Wired Magazine as “the man who turns data into art,” explains the visual metaphor shift from the tree to the network as “a new lens to understand the world around us.” Lima argues that the tree – an important tool to map everything from genealogy to systems of law to Darwin’s “Tree of Life” – is being replaced by a new metaphor – the network. Rigid structures are evolving into interdependent systems, and networks emerge to embody the nonlinearity, decentralization, interconnectedness, and multiplicity of ideas and knowledge. The shift in visual metaphor also represents a new way of thinking – one that is critical for us to solve many complex problems we are facing.

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Historical Data Requires Historical Finesse


Utilizing contemporary tools to analyze historical data provides a unique way to approach historical research, but can prove to be an arduous process as modern tools may not be compatible with historical data. This summer, I have been working with Professor Sarah Purcell to create maps for her book on spectacle funerals of key figures during the U.S. Civil War and Reconstruction. Most commonly, famous bodily remains traveled from city to city on railroads, in some cases on a special funeral train, though they also traveled on rivers and in one case, across the Atlantic Ocean. Nearly every historical figure discussed in the book has an accompanying map which charts their extended funeral processional route. Using GIS technology, we are able to juxtapose census and election data with the geographic routes in highly analytical maps.

In order to layer election data onto the map for Col. Elmer Ellsworth (died 1861), I gathered county-level election data from the Interuniversity Consortium for Political and Social Research (ICPSR) and county-level census data from the National Historical Geographic Information System (NHGIS). I then needed to combine the ICPSR election data and the NHGIS census data in a joined spreadsheet before importing the data into ArcGIS software to link the data to its county location.

At first, I thought we could link the data using something called a “FIPS code.” In an effort to standardize big data and allow for easy joining of tables by location, the Federal Information Processing Standard assigned each county in the United States during the 1990s with a unique five-digit code, more commonly known as a FIPS code. The first two digits are the state FIPS code and the last three are the county code within the state. For example, the FIPS code for Poweshiek County, Iowa is 19157. This code is assigned to the current borders of Poweshiek County. Yet the data I was analyzing is from 1860. Poweshiek County in 2015 represents a different land area than Poweshiek County in 1860. Thus, joining ICPSR and NHGIS data from the 19th century could not be completed using FIPS codes without introducing historical inaccuracy in the maps.

In order to join two tables of data in any computer program, there must be a common column between them. From ICPSR, I had a table of county-level election data from 1860 and from NHGIS, I had a table of county-level 1860 census data. If I were to join data tables of current counties, the FIPS code would serve as my common column. However, instead of using FIPS codes to join the data, I created a common column using the name of the county and state. Creating a unique name for each county assures that I correctly joined the historic county data to the historic county borders. Poweshiek County’s unique identifier would be: “PoweshiekIowa.” I quickly discovered that joining data by this concatenated column was not without error. I went through each county individually to discover discrepancies, many of which resulted from spelling inconsistencies between the two databases.

After cleaning the data, the tables joined neatly. Using GIS, I then linked the combined election and census dataset to the geographic borders of the counties on the electronic map. I color coded the map by political party. The darker shade of each color show where the political party won the majority of votes in the county (greater than 50%), while the lighter shade of the color shows where the party won a plurality of the votes in the county. As you can see from the map’s multiple colors, unlike modern American politics, the 1860 presidential election involved more than two prominent political parties including Republicans, Northern and Southern Democrats, and the Constitutional Union Party. The political divide between North and South is clearly apparent along the Mason-Dixon Line between Pennsylvania and Maryland foreshadowing the sectional conflict of the American Civil War nearly six months after the election.

Mapping historical data is certainly a different process than mapping current data and can prove to be more time-consuming and complex. Though current tools (like FIPS codes) can help standardize mapping techniques, they may not be applicable in historical data settings and current tools may need to be discarded or updated. Historical FIPS codes, anyone?

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Gender Inequality Visualizations

In honor of International Women’s Day on March 8, we at DASIL have found some great visualizations on the web that speak to gender inequality. At DASIL, we do host a few visualizations that speak to economic inequality in the US, but we’d like to highlight some other areas of inequality here.

The New York Times found that more men named John are C.E.O.s than all female C.E.O.s combined. They also explore the breakdown of gender in Congress.

UN’s Global Pulse has a great map showing the number of tweets about various topics—including gender inequality, education, and discrimination. It’s a great way to look at global opinion on many issues.

The Food and Agriculture Organization of the United Nations put together an interactive map of various statistics from the Gender Landrights Database. The link here will show you the percent of female agricultural holders in various countries.

Finally, at DASIL we have several visualizations that point to other factors of gender inequality. Two striking ones are Mean Income by Age, Race, and Gender, and Hourly Wages by Education and Gender. Each of these interactive graphs will let you select which combinations of variables you’d like to compare.

Wages by Education and Gender comparing females with a high school education and females with no high school degree

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Visualizing Disease Outbreaks: A Question of Scale?

Vaccinations are a hot-button issue right now as measles outbreaks crop up throughout the United States. Measles, mumps, rubella, whooping cough, and polio are all deadly diseases that can be easily prevented with vaccines. Outbreaks of these diseases have been occurring worldwide for a long time, but outbreaks have been increasing in the U.S. while going down in other countries, according to the video below:

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Black Civil War Soldiers: A Data Exploration in History 214

In the Fall 2014 semester, students in History 214: The American Civil War and Reconstruction class got help from DASIL to explore data about black soldiers who enlisted in the U.S. Army during the Civil War.  Where did black soldiers come from?  What kinds of economic and social factors influenced their experiences?  Students read an article by the very creative economic historians Dora L. Costa and Matthew E. Kahn, “Forging A New Identity: The Costs and Benefits of Diversity in Civil War Combat Units for Black Slaves and Freemen” (2004),” which used a variety of census, enlistment, and pension data to examine some of the effects of serving in the military on the almost 200,000 black men who enlisted in the U.S. Army after 1862.  Students had also read several articles that used Geographic Information Systems to do spatial analysis, and I was interested in doing an in-class exercise to help them think critically about military data and to introduce them to using the GIS technology.

DASIL Director Kathy Kamp, Post-Baccalaureate Fellow Sara Sanders, and DASIL student Harry Maher worked with me to design an exercise that could help students to visualize data about black soldiers for themselves and to think about the effects of geography on black enlistment.  We used a dataset on “Union Army Recruits in Black Regiments in the United States Army 1862-1865” compiled by Jacob Metzger and Robert A. Margo available through the ICPSR as a starting point.

The DASIL staff created tables that contained the Metzger and Margo data, along with 1860 census information on population and agricultural production and some tables on black enlistment from Freedom’s Soldiers: The Black Military Experience in the Civil War  (Berlin, Reidy, and Rowland, 1998).  We worked together to create a GIS exercise based on the data that could whet the students’ appetites for working with spatial data in just one class period.

The in-class exercise had two points: to get the students thinking about the nature of the datasets and to have them use ArcGIS software to create maps that showed the relationship between the proportion of black soldiers enlisted in relation to the total population by state and cotton production by county.  The Metzger and Margo dataset is based on a judgment sample, so students were able to examine the dataset alongside census records to appreciate how judgment samples do not include comprehensive data.  Nor do they represent a random, representative sampling of black soldiers.  But used alongside the census data and the tables from Freedom’s Soldiers, the data were still able to help them form some useful conclusions.

Map showing Number of Black Soldiers and Cotton Production in the United States in 1860

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