At the beginning of this year I wrote a post about a mobile tracking app called Moves that I had heard about through my friend Sachin Monga. He had just published a beautiful set maps showing where he physically spent his time in both Toronto and San Francisco.

His post spurred me to download the app and at the end of my post I promised to share my own set of maps once I had collected enough data points. It’s only been about 3 weeks, but already my maps are starting to fill out, so I thought I would do a release.

The orange lines represent transport of some sort (car, subway, streetcar, and so on) and the green lines represent walking. I don’t cycle very often in the winter (I know, I’m a fair-weather cyclist), so you won’t see any of those lines just yet. However if I posted a map from the summer, I know it would look completely different.

Here’s a first one showing a regional scale:

Here’s a second one showing the city of Toronto:

And here’s a third one showing mostly downtown:

What’s interesting about these maps is how much you can tell about me and the way I move around the city.

For one, there’s a good chance I ski or snowboard given that I’m driving up to Collingwood, Ontario in the winter. You can also see how heavily dependent I am on the Yonge subway line, which is the thickest orange line in the middle of downtown. It’s also interesting to see how localized I am within my neighborhood (St. Lawrence Market). I walk to get groceries. I walk to the gym. I walk to coffee. And the list goes on.

This is fairly typical for people living in urban neighborhoods, but it would be interesting to see where it applies in the city and where it begins to fall apart. I would also imagine that there’s a correlation to the area’s Walk Score, although this (Moves) might actually be a better measure since it’s usage data.

Either way, imagine what cities could do if they had this sort of data for every resident. They would be able to see precise resident flows and then determine exactly where transit and infrastructure investments should be made instead of politicking to determine where they should be made.

That time is coming.

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.

At the beginning of this year I wrote a post about a mobile tracking app called Moves that I had heard about through my friend Sachin Monga. He had just published a beautiful set maps showing where he physically spent his time in both Toronto and San Francisco.

His post spurred me to download the app and at the end of my post I promised to share my own set of maps once I had collected enough data points. It’s only been about 3 weeks, but already my maps are starting to fill out, so I thought I would do a release.

The orange lines represent transport of some sort (car, subway, streetcar, and so on) and the green lines represent walking. I don’t cycle very often in the winter (I know, I’m a fair-weather cyclist), so you won’t see any of those lines just yet. However if I posted a map from the summer, I know it would look completely different.

Here’s a first one showing a regional scale:

Here’s a second one showing the city of Toronto:

And here’s a third one showing mostly downtown:

What’s interesting about these maps is how much you can tell about me and the way I move around the city.

For one, there’s a good chance I ski or snowboard given that I’m driving up to Collingwood, Ontario in the winter. You can also see how heavily dependent I am on the Yonge subway line, which is the thickest orange line in the middle of downtown. It’s also interesting to see how localized I am within my neighborhood (St. Lawrence Market). I walk to get groceries. I walk to the gym. I walk to coffee. And the list goes on.

This is fairly typical for people living in urban neighborhoods, but it would be interesting to see where it applies in the city and where it begins to fall apart. I would also imagine that there’s a correlation to the area’s Walk Score, although this (Moves) might actually be a better measure since it’s usage data.

Either way, imagine what cities could do if they had this sort of data for every resident. They would be able to see precise resident flows and then determine exactly where transit and infrastructure investments should be made instead of politicking to determine where they should be made.

That time is coming.

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.