This recent study used geotagged tweets to measure social connectedness within American cities. There are two measures: (1) concentrated mobility and (2) equitable mobility. The first measures the extent to which social connections (geotagged tweets) are concentrated in a set of places within the city. And the second looks at the degree in which people move between neighborhoods in roughly similar proportions. These measures are the y-axis and the x-axis, respectively, in this graph:

So how do you read this chart?

Well if you look at New York, you'll see that it is relatively high in concentrated mobility, but the lowest in terms of equitable mobility. This means that social connections are highly concentrated and that there's low connectedness to other neighborhoods within the city. Miami, on the other hand, is the opposite. It's also an outlier. Few hubs. But its social connections appear to cross neighborhoods and spread across the city.

Perhaps not surprisingly, the study found that the size of a city seems to have the biggest impact on social connectedness. Which makes sense -- it becomes harder to get around and so people start to localize. I am reminded of this whenever my friends in Los Angeles tell me they never go to the beach because it's simply too difficult and too time consuming to get across the city.

This also became clear to me after I started playing around with the Moves App back in 2015. The app no longer exists, but it was an activity tracker that allowed you to map where you, well, moved. And the more time you spent in one place, the more concentrated the activity would become. They depicted this through larger and larger circles. Example maps, here. My maps revealed that I need to branch out into different neighborhoods more often.

To download a full copy of the study, click here.

Chart: CityLab

Yesterday my friend Sachin Monga published a really great article on Medium called, 2014: My Year in Review. It was broken down into a few sections that included everything from his favorite blog posts of the year to all of the images he posted on Instagram. He called it “a stream of personal observations, data, and highlights for the year.”

And it put my end of the year blog post to shame.

One section that really stood out for me though was Places & Transit. Using a mobile app called Moves, Sachin extracted an incredible data set for where he physically spent his time and how he got around in 2014. I can’t believe I haven’t heard of this app yet – it’s totally in my wheelhouse. But I’m clearly late to the party. Facebook bought them in the first half of last year.

The data set included how many hours he spent at home and at work. His top 3 most visited coffee shops. His top 5 most visited friends. How many nights he stayed in a hotel. His average daily commute time. And his total distance walked and cycled, among many other things. It was fascinating. I love data – especially when it was previously impossible or difficult to collect it.

He was also able to translate his data into a set of beautiful maps, showing where he spent his time and how he got around. Here is his personal map for Toronto. The larger the circle, the more often he was there. Blue lines are cycling. And green lines are walking.

And here’s San Francisco (where he now lives):

After reading his post, I immediately downloaded Moves. And I can’t wait to see how my personal map of Toronto will look like in a few weeks and months. Once I have enough data points, I’ll be sure to share it with you all here.

In the interim, do you have any ideas for what this kind of data might be used for? I can certainly think of many. Let us know in the comment section below.

This recent study used geotagged tweets to measure social connectedness within American cities. There are two measures: (1) concentrated mobility and (2) equitable mobility. The first measures the extent to which social connections (geotagged tweets) are concentrated in a set of places within the city. And the second looks at the degree in which people move between neighborhoods in roughly similar proportions. These measures are the y-axis and the x-axis, respectively, in this graph:

So how do you read this chart?

Well if you look at New York, you'll see that it is relatively high in concentrated mobility, but the lowest in terms of equitable mobility. This means that social connections are highly concentrated and that there's low connectedness to other neighborhoods within the city. Miami, on the other hand, is the opposite. It's also an outlier. Few hubs. But its social connections appear to cross neighborhoods and spread across the city.

Perhaps not surprisingly, the study found that the size of a city seems to have the biggest impact on social connectedness. Which makes sense -- it becomes harder to get around and so people start to localize. I am reminded of this whenever my friends in Los Angeles tell me they never go to the beach because it's simply too difficult and too time consuming to get across the city.

This also became clear to me after I started playing around with the Moves App back in 2015. The app no longer exists, but it was an activity tracker that allowed you to map where you, well, moved. And the more time you spent in one place, the more concentrated the activity would become. They depicted this through larger and larger circles. Example maps, here. My maps revealed that I need to branch out into different neighborhoods more often.

To download a full copy of the study, click here.

Chart: CityLab

Yesterday my friend Sachin Monga published a really great article on Medium called, 2014: My Year in Review. It was broken down into a few sections that included everything from his favorite blog posts of the year to all of the images he posted on Instagram. He called it “a stream of personal observations, data, and highlights for the year.”

And it put my end of the year blog post to shame.

One section that really stood out for me though was Places & Transit. Using a mobile app called Moves, Sachin extracted an incredible data set for where he physically spent his time and how he got around in 2014. I can’t believe I haven’t heard of this app yet – it’s totally in my wheelhouse. But I’m clearly late to the party. Facebook bought them in the first half of last year.

The data set included how many hours he spent at home and at work. His top 3 most visited coffee shops. His top 5 most visited friends. How many nights he stayed in a hotel. His average daily commute time. And his total distance walked and cycled, among many other things. It was fascinating. I love data – especially when it was previously impossible or difficult to collect it.

He was also able to translate his data into a set of beautiful maps, showing where he spent his time and how he got around. Here is his personal map for Toronto. The larger the circle, the more often he was there. Blue lines are cycling. And green lines are walking.

And here’s San Francisco (where he now lives):

After reading his post, I immediately downloaded Moves. And I can’t wait to see how my personal map of Toronto will look like in a few weeks and months. Once I have enough data points, I’ll be sure to share it with you all here.

In the interim, do you have any ideas for what this kind of data might be used for? I can certainly think of many. Let us know in the comment section below.