More friendships between rich and poor might mean less poverty

Recently published in Nature, research by Chetty, R., Jackson, M.O., Kuchler, T. et al. suggests that economic connectedness, or friendships between rich and poor, could improve economic mobility. The researchers used Facebook connection data from 70.3 million users, along with demographic and income data. NYT’s The Upshot explains the relationships with a collection of maps and charts.

You can find an anonymized, aggregated version of the data through the Social Capital Atlas. Also, I am very much into this socially-focused use of social media data.

Tags: , , , ,

A long distance relationship between a temperature difference

Everyone’s story is a little different. Alyssa Fowers tracked her long-distance relationship in the context of the temperature between two locations and the travel to and from.

Tags: ,

Texting history after the first swipe

Speaking of relationship timelines, Chris Lewis used texting history with his girlfriend after the first swipe on Bumble as the backdrop of their own story. A few 21k messages later, they’re engaged and live together. [Thanks, Chris]

Tags: ,

Probability you will break up with your partner

Rosenfeld, et al. from Stanford University ran a survey in 2009 for a study on How Couples Meet and Stay Together. Dan Kopf and Youyou Zhou for Quartz used this dataset to estimate the probability that you will break up with your partner, given a few bits of information about your current relationship.

The Stanford data page says a 2017 release is on the way. I’m curious how, if anything, has changed in relationships between 2009 and now.

Tags: , ,

Closeness in relationships over time, illustrated with a couple of lines

Cartoonist Olivia de Recat illustrated the closeness over time for various relationships. Charming. Unfortunately, the print is sold out. Sad trombone.

Tags: , ,

Following your gut, following the data

The Wall Street Journal highlighted a disagreement between data and business at Netflix. Ultimately, the business side “won.” However, maybe that’s the wrong framing. Roger Peng describes the differences between analysis and the full truth:

There’s no evidence in the reporting that the content team didn’t believe the data or the analysis. It’s just that their fear of damaging a relationship with an actor overruled whatever desire they might have had to maximize clicks or views. The logic was probably along the lines of “We may take a hit in the short-run but we will benefit from this relationship in the long-run.” Whether that’s true or not is unclear, but it’s a tricky question to answer with data. It’s not even clear to me how you would formulate that question.

Data often pitches itself as the path to definitive answers, but most of the time it gives you possibilities and weighted suggestions. Follow blindly, and you end up with creepy, algorithmically-generated YouTube videos.

Tags: , ,

People relationships in data analysis

Roger Peng discusses the importance of managing the relationships between people — analyst, patron, subject matter expert, and audience — for a successful analysis:

Human relationships are unstable, unpredictable, and inconsistent. Algorithms and statistical tools are predictable and in some cases, optimal. But for whatever reason, we have not yet been able to completely characterize all of the elements that make a successful data analysis in a “machine readable” format. We haven’t developed the “institutions” of data analysis that can operate without needing the involvement of specific individuals. Therefore, because we have not yet figured out a perfect model for human behavior, data analysis will have to be done by humans for just a bit longer.

Whenever someone touts a tool for “automatic insights”, whether it be in analysis or chart generation, something like this comes to mind.

Tags: , ,

Tracking her boyfriend on Strava

Elizabeth Barber was in a long-distance relationship, and Strava was a way for her to connect with him. It became a point of anxiety when her boyfriend cycled with someone else more and more often.

I was curious, and Strava is a joyless data bank for the insecure. When The Washington Post reported in January that US military bases are visible in the GPS shadows of uniformed Stravites, I was not shocked. I had performed equally fastidious forensics on the cyclist’s Strava maps. Tracing her routes on that anxious morning and days to come, I could see where she lived, where she drank beer and got coffee. I knew how many calories she burned working out, and how often. I knew when and where and with whom she spent time (increasingly, my boyfriend).

Data without much context: enough to drive anyone a little nutty.

Tags: , ,

Who marries who, by profession

Marriage and jobs

People with certain professions tend to marry others with a given profession. Adam Pearce and Dorothy Gambrell for Bloomberg Business were curious.

When it comes to falling in love, it’s not just fate that brings people together—sometimes it’s their jobs. We scanned data from the U.S. Census Bureau’s 2014 American Community Survey—which covers 3.5 million households—to find out how people are pairing up.

You get a matrix of professions organized by more male to more female, left to right. Mouse over any profession or use the search box and lines project out to the five most common professions that the one of focus tends to marry to. The pink and blue color gradients indicate the sexes of the two spouses.

So for each profession, you get a quick view of who people marry, whether it be outside their own or within. I like how when you mouse over the far left or the far right, you see lines jut across to the opposite side. I wonder what the tendencies are in total for male-dominant to marry female-dominant professions and vice versa.

Tags: , ,