What’s the global data journalism community tweeting about this week? Our NodeXL #ddj mapping from July 22 to 28 finds The New York Times analyzing the catalyst behind Hong Kong’s recent protests, National Geographic visualizing human migration in the past 50 years, Ellery Studio’s fun and informative renewable energy coloring book, and The Economist’s findings that Hillary Clinton could have won the 2016 US election if all Americans had turned up to vote.
Fuel Behind Hong Kong’s Protests
Anger over the increasing hold of mainland China on Hong Kong’s political sphere has triggered hundreds of thousands of Hong Kong residents to protest in the streets. The New York Times writes that underlying all that rage is the people’s deep anxiety over miserable economic fortunes, housing in-affordability, and major income inequality.
Behind political anger that triggered the massive #protests in Hong Kong, tiny apartments the size of parking space and punishing work hours underscore the #economic roots of the territory's growing frustration. https://t.co/soO8RNRrwm #ExtraditionBill #HongKong #protests
— Jodi Xu Klein (@jodixu) July 23, 2019
“Three percent of humanity worldwide are migrants, and this figure has held for 50 years.” National Geographic visualized the ebb and flow of people across borders over the past five decades to help us understand why people leave their countries.
What does 50 years of human migration look like? The percentage of migrants worldwide has actually held steady at 3% in that time span. For @NatGeo, I worked on how to visualize this in a graphic. THREAD (1/18). https://t.co/0iAaSHduts pic.twitter.com/fouNexfygV
— Alberto Lucas López (@aLucasLopez) July 23, 2019
Google News Lab data editor Simon Rogers and Knight Chair in Visual Journalism at the University of Miami Alberto Cairo have collaborated with data visualization experts over the past three years to push the boundaries of the field. These collaborations not only created creative visualizations but also resulted in the launch of freemium data viz tool Flourish and data sonification tool TwoTone, with more open-source tools in the pipeline. Also, look out for a new massive open online course this fall.
Here is a great summary by @smfrogers and @AlbertoCairo about their ongoing collaboration – which has produced really cool #dataviz projects over the past 3 years by some of the most creative minds in this field: https://t.co/NKLEHmWxbF pic.twitter.com/a3v6pKJ1hV
— Sandra Rendgen (@srendgen) July 24, 2019
Infographics Coloring Book on Climate
The Infographic Energy Transition Coloring Book (IETCB) is an activity book that uses infographics to engage and educate people of all ages on climate change and renewable energy. The team behind the award-winning book launched a Kickstarter campaign to fund a second edition, which will have updated facts and figures. Also mentioned on Fast Company and by Alberto Cairo on his blog.
We've got a copy of the 2017 energy transition colouring book in the office and everyone talks about how gorgeous it is. They've got a kickstarter for an updated one https://t.co/pj3QjFstjJ (ht @mrchrisadams)
— Alice Bell (@alicebell) July 18, 2019
What if all Americans had turned out to vote in the 2016 US elections? Would Hillary Clinton be president? The Economist sought to answer this question using a statistical method, popular among leading quantitative social scientists, called “multi-level regression and post-stratification.” G. Elliott Morris explains the team’s methodology in this Medium post and Twitter thread. Data on Github.
Truly fascinating use of data crunching to find out ‘if all USA had voted, would Trump be President’ – “In the end, we needed to estimate how demographic variables, like race and education, interact with geography and voting behav…https://t.co/uepGr3CyR3 https://t.co/nXoOL0MhRj
— Nicole Yershon (@nicoleyershon) July 5, 2019
Facebook’s Ad Library
Here’s an attempt by Roland Schmidt to analyze political campaign data from Facebook’s ad library using R. He explains how he retrieved the data for selected Austrian electoral candidates who ran campaigns on the social network platform, and described the limitations of the data and how he tried to address some of these shortcomings.
~first kind of #rstats blog – had a look at Facebook's political ad library and ads run by Austrian candidates in #EP2019 elections. A few suprises, at least to me.https://t.co/3rP7Pbrlpg
ty in particular to @hrbrmstr @sharon000 @brodriguesco pic.twitter.com/eq396i5Vuz
— Roland Schmidt (@zoowalk) July 26, 2019
Interactive Election Game
Ahead of Ukraine’s extraordinary elections of people’s deputies on July 21, the Nikolaev Center for Investigative Journalism produced an interactive game based on facts and data to test the public’s knowledge on the electoral candidates and its readiness to vote. (In Russian.)
🇺🇦 Предвыборный #мультимедиа-проект в Николаеве: журналисты @InvestigatorNik накануне выборов в Верховную Раду рассказывали о кандидатах в форме интерактивной #игры, основанной на реальных фактах и данных #ddj.
— GIJN in Russian (@gijnRu) July 25, 2019
Stunted Growth in Pakistan
Datastories.pk visualized data from Pakistan’s 2018 National Nutrition Survey. The results reveal alarming statistics: The national average for the prevalence of stunting in children under 5 years old is 40.2%.
40 percent of the #children under five years of age are stunted in Pakistan. #ddj #datajournalism https://t.co/DdpE7bjmzq
— Data Stories (@datastoriespk1) July 29, 2019
What Makes a Chart ‘Bad’?
Business analytics and data visualization expert Kaiser Fung expands his thoughts on what makes a bad chart, following Conversations with Data’s newsletter on bad charts. He divides bad charts into two categories: misleading and misinterpreted, and offers a checkup method to help determine that data graphics sync with the intended message.
What makes a chart 'bad'? @junkcharts explores this question by applying the Trifecta Checkup framework to the #charts featured in our latest edition of Conversations with Data: https://t.co/P8ar8cglPs #ddj #dataviz #datajournalism
— DataJournalism.com (@datajournalism) July 24, 2019
European Data Journalism Survey
The European Data Journalism Network (EDJNet) is gathering information about the practices of newsrooms, data teams, and individual journalists with regard to the use of data techniques in their reporting, in particular when covering European and cross-border issues. Fill in the survey here.
🇪🇺Are you a EU correspondent willing to learn more about data?
📈Are you a data journalist keen on EU topics?
🗞️Are you a European media interested in collaborative initiatives?
Go to https://t.co/5aUnyO9Uay and help us find out: pic.twitter.com/R8bIMNvAqG
— European Data Journalism Network (@EdjNet) July 8, 2019
Thanks, once again, to Marc Smith of Connected Action for gathering the links and graphing them. The Top Ten #ddj list is curated weekly.
Eunice Au is GIJN’s program coordinator. Previously, she was a Malaysia correspondent for Singapore’s The Straits Times, and a journalist at the New Straits Times. She has also written for The Sun, Malaysian Today and Madam Chair.
For a look at Marc Smith’s mapping on #ddj on Twitter, check out this map.