Google Maps Timeline: Interesting or Valuable?

I was at a dinner party last night where a friend was talking about his Google Maps Timeline year-end report. Another friend asked:

Do you find it interesting or valuable?

This is a very good question to ask about technology in general.

Personally, I find Google Maps Timeline interesting, but not valuable. It’s generally interesting as journal. I’d love to import a static copy of the data automatically into Day One or other journaling software. (Lazy Web, has anyone done this already?)

Using it to track movement is interesting and potentially valuable. In 2018, I traveled 27,711 miles total, about one time around the world. I walked a bit less than a mile a day averaging 2.25 miles / hour — not as much or as fast as I would have liked. Tracking this data more regularly might encourage me to move more, but fitness trackers provide this same data in less creepy ways.

More valuable is the transit information. In 2018, I spent over 21 days on some sort of ground transportation for a total of 9,575 miles, about 26 miles / day. This was surprising for someone who works from home at least twice a week and sobering from the perspective of someone who cares about carbon emissions. That said, I don’t know that trying to optimize these totals further is very valuable. I already take lots of public transportation, carpool when I can, and drive a Prius. I could lower my carbon footprint much more dramatically by changing my diet.

I’m not sure whether Google Maps Timeline will ever be valuable for me individually. However, I do think Google makes this data valuable for me in aggregate. For example, Google is able to tell me the average wait time at my favorite coffee shops and restaurants. I could imagine this data in aggregate could be very valuable for urban planning. I would like for it to be used this way, and would be happy to give this data to someone if I trusted they would use it in useful and also ethical ways.

And there’s the rub. We still don’t have good, trusted agreements between organizations and individuals. I don’t trust the government to handle my data competently — to anonymize it appropriately, to store it securely, etc. — or to put it to good use. I trust Google to put it to good use, but I am very skeptical that they will use it ethically. That said, I trust Google a lot more than most companies, which is also potentially misguided.

Way back in the day, I did some work on these kinds of issues in community with good folks like Phil Windley, Doc Searls, and Identity Woman. Aman Ahuja and his friends at the Data Guild have also done a lot of good thinking around ethical guidelines for using data. I’m heartened by the work they’re all doing. If you know of others doing great work in this space, please share in the comments below. Be specific — what is the problem they’re tackling, and how are they going about it?

Why Are We Afraid of Data?

My friend, Gbenga Ajilore, is an economics professor. Last month, he gave a great talk at AlterConf in Chicago entitled, “How can open data help facilitate police reform?” It concisely explains how data helps us overcome anecdotal bias.

I was particularly struck by his point about how we need police buy-in for this data to be truly useful, and I was left with a bit of despair. Why is buy-in about the importance of data so hard? This should be common sense, right?

Clearly, it’s not. Earlier this year, I expressed some disbelief about how, in professional sports, where there are hundreds of millions of dollars riding on outcomes, there is still strong resistance to data and analytics.

On the one hand, it’s incredible that this is still an issue in professional sports, 14 years after Moneyball was first published and several championships were won by analytics-driven franchises (including two “cursed” franchises, the Boston Red Sox and the Chicago Cubs, both led by data nerd Theo Epstein).

On the other hand, it’s a vivid reminder of how hard habits and groupthink are to break, even in a field where the incentives to be smarter than everyone else come in the form of hundreds of millions of dollars. If it’s this hard to shift mindsets in professional sports, I don’t even want to imagine how long it might take in journalism. It’s definitely helping me recalibrate my perspective about the mindsets I’m trying to shift in my own field.

The first time I started to understand the many social forces that cause us to resist data was right after college, when I worked as an editor at a technology magazine. One of my most memorable meetings was with a vendor that made a tool that analyzed source code to surface bugs. All software developers know that debugging is incredibly hard and time-consuming. Their tool easily and automatically identified tons and tons of bugs, just by feeding it your source code.

“This is one of the best demos I’ve ever seen!” I exclaimed to the vendor reps. “Why isn’t everyone knocking on your door to buy this?”

The two glanced at each other, then shrugged their shoulders. “Actually,” one explained, “we are having a lot of trouble selling this. When people see this demo, they are horrified, because they realize how buggy their code is, and they don’t have the time or resources to fix it. They would rather that nobody know.”

The Not-So-Mystifying Power of Groupthink and Habits

My friend, Greg, recently sent me this excellent and troubling Nate Silver article, “There Really Was A Liberal Media Bubble,” on the 2016 presidential election. Silver references James Surowiecki’s book, The Wisdom of Crowds and suggests that political journalists fail the first three of Surowiecki’s four conditions for wise crowds.

  1. Diversity of opinion (fail)
  2. Independence (fail)
  3. Decentralization (fail)
  4. Aggregation (succeed)

Many of Silver’s points hit very close to home, not just because I believe very strongly in the importance of a strong, independent media, but because improving our collective wisdom is my business, and many fields I work with — including my own — suffer from these exact same problems.

On diversity, for example, Silver points out that newsrooms are not only not diverse along race, gender, or political lines, but in how people think:

Although it’s harder to measure, I’d also argue that there’s a lack of diversity when it comes to skill sets and methods of thinking in political journalism. Publications such as Buzzfeed or (the now defunct) get a lot of shade from traditional journalists when they do things that challenge conventional journalistic paradigms. But a lot of traditional journalistic practices are done by rote or out of habit, such as routinely granting anonymity to staffers to discuss campaign strategy even when there isn’t much journalistic merit in it. Meanwhile, speaking from personal experience, I’ve found the reception of “data journalists” by traditional journalists to be unfriendly, although there have been exceptions.

On independence, Silver describes how the way journalism is practiced — particularly in this social media age — ends up acting as a massive echo chamber:

Crowds can be wise when people do a lot of thinking for themselves before coming together to exchange their views. But since at least the days of “The Boys on the Bus,” political journalism has suffered from a pack mentality. Events such as conventions and debates literally gather thousands of journalists together in the same room; attend one of these events, and you can almost smell the conventional wisdom being manufactured in real time. (Consider how a consensus formed that Romney won the first debate in 2012 when it had barely even started, for instance.) Social media — Twitter in particular — can amplify these information cascades, with a single tweet receiving hundreds of thousands of impressions and shaping the way entire issues are framed. As a result, it can be largely arbitrary which storylines gain traction and which ones don’t. What seems like a multiplicity of perspectives might just be one or two, duplicated many times over.

Of the three conditions where political journalism falls short, Silver thinks that independence may be the best starting point for improvement:

In some ways the best hope for a short-term fix might come from an attitudinal adjustment: Journalists should recalibrate themselves to be more skeptical of the consensus of their peers. That’s because a position that seems to have deep backing from the evidence may really just be a reflection from the echo chamber. You should be looking toward how much evidence there is for a particular position as opposed to how many people hold that position: Having 20 independent pieces of evidence that mostly point in the same direction might indeed reflect a powerful consensus, while having 20 like-minded people citing the same warmed-over evidence is much less powerful. Obviously this can be taken too far and in most fields, it’s foolish (and annoying) to constantly doubt the market or consensus view. But in a case like politics where the conventional wisdom can congeal so quickly — and yet has so often been wrong — a certain amount of contrarianism can go a long way.

Maybe he’s right. All I know is that “attitudinal adjustments” — shifting mindsets — is really hard. I was reminded of this by this article about Paul DePodesta and the ongoing challenge to get professional sports teams to take data seriously.

Basis of what DePodesta and Browns are attempting not new. Majority of NFL teams begrudgingly use analytics without fully embracing concept. Besides scouting and drafting, teams employ analytics to weigh trades, allot practice time, call plays (example: evolving mindset regarding fourth downs) and manage clock. What will differentiate DePodesta and Cleveland is extent to which Browns use data science to influence decision-making. DePodesta would like decisions to be informed by 60 percent data, 40 percent scouting. Present-day NFL is more 70 percent scouting and 30 percent data. DePodesta won’t just ponder scouts’ performance but question their very existence. Will likewise flip burden of proof on all football processes, models and systems. Objective data regarding, say, a player’s size and his performance metrics — example: Defensive ends must have arm length of at least 33 inches — will dictate decision-making. Football staff will then have to produce overwhelming subjective argument to overrule or disprove analytics. “It’s usually the other way around,” states member of AFC team’s analytics staff.

On the one hand, it’s incredible that this is still an issue in professional sports, 14 years after Moneyball was first published and several championships were won by analytics-driven franchises (including two “cursed” franchises, the Boston Red Sox and the Chicago Cubs, both led by data nerd Theo Epstein).

On the other hand, it’s a vivid reminder of how hard habits and groupthink are to break, even in a field where the incentives to be smarter than everyone else come in the form of hundreds of millions of dollars. If it’s this hard to shift mindsets in professional sports, I don’t even want to imagine how long it might take in journalism. It’s definitely helping me recalibrate my perspective about the mindsets I’m trying to shift in my own field.

365 Photos Project: By the Numbers

New Year's Eve Selfie

I made and shared one photograph every day last year. It was an amazing experience, and at some point, I’d like to share what I learned and what it all meant. For now, here are some numbers from the project.

I made 365 photographs.

70% were candids.

47% were made outdoors.

50% had people in them.

180 people I knew made at least one appearance. 50 of those people appeared twice or more. The person who appeared the most? Me at 20 appearances, ranging from straight-up selfies to body parts to shadows.

The photos were made in 43 cities across six different states (California, Ohio, New Mexico, Michigan, Massachusetts, and Maryland) and D.C.

84% were made in the Bay Area. 78% of my Bay Area shots were made in San Francisco. 36% of my San Francisco shots were made at home or my office.

89% were shared on the same day. There were an average of 20 social media (Flickr and Facebook) interactions (likes, favorites, and comments) per photo.

Here is the breakdown of my photos by time made:

2015 365 Photo Project by Hour Taken

I made the vast majority of my photos after 12pm, with most of them shot between 5-8pm. Ten were made after 11pm. This very much reflects my personal rhythms as well as my story focus. I’m an early riser, so taking photos in the morning when the light was good wouldn’t have been a problem. However, I often spend my mornings in solitude focusing on my work, and the story of the day usually doesn’t start to unfold until the afternoon.

74% were horizontal in orientation.

52% were made with the equivalent of a 50mm lens. I shot 22 photos with a borrowed Fuji X-T1 while my Olympus OM-D E-M5 was in the shop. I shot 16 photos with my Moto X cell phone, and one with a borrowed iPhone.

95% were shot with natural light, but I really had fun playing with the other 5%, including light paintings and HDR.

56 photos prominently featured food or cooking, including three when I was sick with a stomach bug. No surprises here. I love to eat.

52 photos were made during work (i.e. project-related meetings, meetups that I organized, or work-related artifacts). Most of these were related to my Collaboration Muscles & Mindsets program and my DIY Strategy / Culture Toolkits, my primary experiments of the past few years.

24 photos had a computer in it.

10 photos were basketball-related.

At least six photos were used in other people’s articles or blog posts, including one in the Washington Post.

Three photos appeared in Flickr Explore — days 227, 250, and 293. (A fourth that was not originally part of my Photo of the Day project became part when I included it as a screenshot.) I’ve been a Flickr member since 2005, and up until this year, I had never had a photo appear in Explore, so this was a huge thrill.

Speaking of screenshots, I also posted one photo not taken by me. (It was taken by my friend, Dana Reynolds.) On both of these days, I did take photos (in one case, really good ones), I just felt compelled to make exceptions.

Finally, I took about 20,000 photos overall in 2015. (This is an estimate based on my Lightroom numbers, which are under-reported, because I do a rough cull as soon as I start processing.) This is about the same as 2013 (when I started taking photography seriously) and 2014.

Of these 20,000 photos, I marked about 500 them as “good.” Many of my photos from my 365 project did not make the cut.

In other words, for every 100 photos I took in 2015, I considered two or three of them good. From what I’ve heard from other photographers, this is a pretty typical yield.

Super Bowl Collaboration Lessons: Data, Preparation, and Accountability

The New England Patriots won its fourth Super Bowl last night, beating the Seattle Seahawks 28-24 in a thrilling contest and a crazy finish. I normally root against all Boston sports teams, but I made an exception this year, because Tom Brady is of my generation, and I can’t be mad when an old guy wins.

(If you’re not interested in football, skip ahead to my takeaways.)

For those of you who didn’t see the game, the ending was exciting and… surprising. New England was up by four points with about two minutes left, when Seahawks quarterback, Russell Wilson, threw a long pass to unheralded receiver Jermaine Kearse. The Patriots undrafted, rookie cornerback, Malcolm Butler, made an unbelievable play on the ball that should have effectively ended the game.

Except that miraculously, the ball hit Kearse’s leg as he was falling on the ground, and Kearse somehow managed to catch the ball on his back. The Seahawks had a first down five yards away from the end zone, and, with the best power running back in the game, Marshawn Lynch, they seemed destined to steal this game from the Patriots. Sure enough, Lynch got the ball on the next play and rumbled his way to the one-yard line with a minute to spare.

To understand what happened next, you have to understand how clock management works in the NFL. The Seahawks had one minute and three chances to move the ball one yard and win the game. The clock would run down continuously unless one of two things happened: the Seahawks threw an incomplete pass or one of the teams called a timeout. (Technically, they could also stop the clock by running out of bounds, but that was unlikely scenario given their field position.) The Seahawks only had one timeout remaining, meaning that they could stop the clock with it once. The only other way it could stop the clock was to throw an incomplete pass.

At the same time, the Patriots had a very hard decision to make. If the Seahawks scored, then the Patriots would need enough time to score as well. With a minute left, they had a chance. Any less, and they were as good as done.

To summarize, the Seahawks top priority was to score. It’s second priority was to do so with as little time as possible left on the clock, so that the Patriots didn’t have a chance to answer. The Patriots either had to try to stop the Seahawks from scoring, or — if they believed that the odds of that happening were unlikely — they needed to let the Seahawks score as quickly as possible, so that they had a chance to answer.

That was the complicated version of the situation, at least. The joy of football is that it is both wildly complex and brutally simple. The simple version of the situation was this: One yard, three chances. Give ball to big, strong man known affectionately as “Beast mode.” Watch him rumble into end zone. Celebrate victory.

That was the version that most people — myself included — saw, so when the Seahawks decided to try to pass the ball, most everybody was shocked. The reason you don’t pass in that situation is that you risk throwing an interception. That’s exactly what happened. Malcolm Butler, the unlucky victim of Kearse’s lucky catch two plays before, anticipated the play, intercepted the ball, and preserved the Patriots victory.

The media — both regular and social — predictably erupted. How could they throw the ball on that play?!

(Okay, takeaways start here.)

I took three things away from watching the game and that play in particular.

First, I had assumed — like most of America — that Pete Carroll, Seattle’s head coach, had made a bungling error. But when he explained his reasoning afterward, there seemed to be at least an ounce of good sense behind his call.

With about 30 seconds remaining, in the worst case scenario, Carroll needed to stop the clock at least twice to give his team three chances to score. They only had one timeout. So, they would try a pass play first. If they scored, then they would very likely win the game, because the Patriots would not have enough time to score. If they threw an incomplete pass, the clock would stop, and the Seahawks would have two more chances to try to score, both times likely on the ground.

Still, it seemed like a net bad decision. It still seemed like handing the ball off the Lynch was a lower risk move with a higher probability of success in that situation.

It turns out that when you look at the actual numbers, it’s not clear that this is the case. (Hat tip to Bob Blakley for the pointer to the article.) In fact, the data suggests that Patriots head coach, Bill Belichik, made the more egregious error in not calling a timeout and stopping the clock. It may have even behooved him to let the Seahawks score, a strategy he employed in the Super Bowl four years earlier. Or, maybe by not calling timeout, Belichik was demonstrating a mastery of game theory.

The bottom line is that the actual data did not support most people’s intuitions. Both coaches — two of the best in the game — had clearly done their homework, regardless of whether or not their decisions were right or wrong.

Second, regardless of whether or not the decision was good or bad, I loved how multiple people on the Seahawks — Carroll, Wilson, and offensive coordinator, Darrell Bevell — insisted that they were solely responsible for the decision. There was no finger pointing, just a lot of leaders holding themselves accountable. Clearly, there is a very healthy team dynamic on the Seahawks.

Third, I loved Malcolm Butler’s post-game interview, not just for the raw emotion he displayed, but for what he actually said. He made an unbelievable play, which he attributed to his preparation. He recognized the formation from his film study, and he executed. All too often, we see remarkable plays like Butler’s, and we attribute it to incredible athleticism or talent, when in reality, practice and preparation are responsible.