Data grows more intertwined with the everyday and more involved in important decisions. However, data is biased in many ways from collection, to analysis, and the conclusions, which is a problem when it is often intended to provide an objective point of view. In their recently released manuscript for Data Feminism, Catherine D’Ignazio and Lauren Klein discuss the importance of varied points of view:

The double-edged sword of data shows just how important it is to understand how structures of power and privilege operate in the world. The questions we might ask about these structures can relate to issues of gender in the workplace, as in the case of Christine Darden and her wrongly delayed promotion. Or they can relate to issues of broader social inequality, as in the case of predictive policing described just above. So one thing you will notice throughout this book is that not all of our examples are about women–and deliberately so. This is because data feminism is about more than women. It’s is about more than gender. Put simply: Data Feminism is a book about power in data science. Because feminism, ultimately, is about power too. It is about who has power and who doesn’t, about the consequences of those power differentials, and how those power differentials can be challenged and changed.

In the interest of making the published work as complete as possible, D’Ignazio and Klein made the manuscript public and are ready for feedback.

xkcd referenced the ever-so-loved forecasting needle. I’m so not gonna look at it this year. Maybe.

A meme that cried “jobs not mobs” began modestly, but a couple of weeks later it found its way into a slogan used by the President of the United States. Keith Collins and Kevin Roose for The New York Times traced the spread of the meme through social media using a beeswarm chart. Blue represents activity on Twitter, yellow represents Facebook, and orange represents Reddit. Circles are sized by retweets, likes, and upvotes. The notes for key activities move the story forward.

The Economist built an election model that treats demographic variables like blocks that output a probability of voting Republican or Democrat:

Our model adds up the impact of each variable, like a set of building blocks. As a result, a group of weak predictors that point in the same direction can cancel out a single strong one. In theory, the model could identify a black voter as a Republican leaner, or a white evangelical as a probable Democrat—though it would require quite an unusual profile.

Remember when most people paid little attention to midterm elections and result forecasting was not really a thing? Yeah, me neither.

Be sure to check out the small interactive on the same page that lets you “build a voter” and get the model’s probability output. I’m a fan of the demographic-field-dropdowns-in-a-sentence format.

As the midterm elections loom, the ads focusing on key issues are running in full force. Using data from Nielsen, Bloomberg mapped the issues talked about across the country.

Bloomberg News analyzed more than 3 million election ads for 2018 congressional and gubernatorial races to get a sense of the most commonly discussed issue in 210 local television markets, as defined by the Nielsen Company. Across the U.S., 16 different topics are mentioned more than anything else during midterm TV ads.

The map above shows the most common per Nielsen market, but read the full article for the national breakdowns of the major issues.

Health care has been huge in my area. For the past few weeks, every YouTube video I watch is preceded by an ad, and my mailbox keeps getting filled with ads for and against a certain proposition, often on the same day.

As one might expect, many women, people of color, and L.G.B.T. candidates are running in this year’s midterms. It’ll be one of the most diverse elections in U.S. history. The New York Times provides a scrolly breakdown with 410 cutout faces floating around on your screen.

Randall Munroe, Kelsey Harris, and Max Goodman for xkcd mapped all the challengers for the the upcoming midterm elections. Names are colored by political party. They are sized by the level of office a candidate is running for and the chances of success. (I’m not totally sure how that scale works though.) Interact with the map to focus on regions, and click on names, which directs you to the candidate’s election site.

Wow.

I really like what The New York Times has been doing with augmented reality lately. What usually feels gimmicky is used as a tool to provide scale and detail and to invite closer observation. In their most recent, the Times got in the Halloween spirit and showed the “monsters that live on you.” You can view it in the browser, but it doesn’t quite compare to seeing a human-sized cockroach sitting your living room.

Over the next few months, I’ll be looking more closely at the available visualization apps to see what works and what doesn’t. In this issue, I start with Flourish.

Shirley Wu used a tree metaphor to represent the interactions of five individuals with an SFMOMA texting service:

Last June, SFMOMA launched Send Me SFMOMA, a service where individuals could text a variety of requests – “send me love”, “send me hope”, “send me smiles” – and SFMOMA would respond with an artwork that best matched the request. They received over 5 million texts from hundreds of thousands of individuals over the course of a year.

And they’ve asked me to do something fun with that data.

Each tree represents a day, and each leaf or flower represents something that the service sent back.

It’s Halloween. Joshua Stevens mapped all the graveyards:

Right away I was struck by the geography. The pattern, however, makes a great deal of sense in the context of American history. Some of the deadliest battles of the Revolutionary and Civil Wars took place in Georgia, Kentucky, Mississippi, New York, South Carolina, Tennessee, and Virginia.

Get the print version here.

In a time we commit less to memory and rely more on technology supplements, Nicky Case provides an interactive comic to teach the science of spaced repetition, which can be used to “remember anything forever-ish.” My memory is horrible, and it only gets worse with time. I needed this.

Jen Christiansen spoke about her extensive experience as a graphics editor for Scientific American. Her talk notes span a wide range of topics from the “rules”, the spectrum of visualization, and collaboration:

[S]ome of my favorite recent Scientific American graphics are the result of bringing together different artists—plucking experts from each of those groups and matching them up to create a final image that draws upon all of their strengths, not forcing one artist to excel in all areas. For example, I love to take an artist who can develop spot illustrations with a stylus or pen, and pair them up with an artist who can custom code data visualization solutions, as in this example by Moritz Stefaner and Jillian Walters.

From Evogeneao:

This Tree of Life diagram is based primarily on the evolutionary relationships so wonderfully related in Dr. Richard Dawkins’ The Ancestor’s Tale, and timetree.org. The smallest branches are purely illustrative. They are intended to suggest the effect of mass extinctions on diversity, and changes in diversity through time. This diagram is NOT intended to be a scholarly reference tool! It is intended to be an easy-to-understand illustration of the core evolution principle; we are related not only to every living thing, but also to everything that has ever lived on Earth.

Design-wise, there are many things that could’ve made the graphic more readable, but something about it makes me like it just the way it is.

Most people interested in visualization have made a chart with Microsoft Excel. For your basic charts, it’s really easy, and it works well for what it was intended for. The process of visualizing data with methods beyond the standard chart types can be more challenging at times.

Based on data from Dave Leip’s Atlas of U.S. Presidential Elections, The Washington Post mapped voter turnout on a diverging color scale. Orange represents lower than average turnout in 2016 and purple represents higher than average.

Not to diminish the meaning of the map, but the most shocking part might be the placement of Hawaii.

High school seniors, in the Political Statistics class at Montgomery Blair High School in Silver Spring, Maryland, built a prediction model for the upcoming elections:

Under the guidance of Mr. David Stein, this model (which we named the Overall Results of an Analytical Consideration of the Looming Elections a.k.a. ORACLE of Blair) was developed by a group of around 70 high school seniors, working diligently since the start of September. Apart from the youth and enthusiasm that went into making it, the advantage our model has over professionally developed models is transparency. Unlike professionals, we need not have any secrets in regards to how our predictions are generated. In fact, the sections that follow attempt to detail exactly how we come up with all of the numbers involved in our model.

I’m so glad this exists and that young people are learning how to make things like this. My high school self is jealous, because the only statistics he got to learn was punched into a TI-83 calculator.

Accurat, in partnership with the Google News Initiative, built an augmented reality app to build statues of hope:

We live in a world awash with information. Every time we walk the street holding our phones, every time we perform a research online or buy a product with our credit card data is created and often time communicated to us. How can we make people care about a specific dataset? How can we form our own opinions and points of view on what matters to us? With Building Hopes we wanted people to take a stance on what they are hopeful for, even in a historical moment that many define as hopeless and bleak, and have them look at Google search data through this framework of their own creation.

There’s a web version, but be sure to check out the AR version if you can. You walk around your area picking stones, each representing something to be hopeful for, and the app points to you to statues nearby that others built.