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.

A few weeks ago I had coffee with a friend of mine who is a partner at a Toronto-based enterprise software company called Polyform Labs. Their products are geared towards the commercial real estate industry and, since I’m a big proponent of introducing more technology into the real estate space, I thought I would share a little bit about them with you all today.

The first of their 4 main products is called Lingo, which is a machine learning tool that helps companies quickly review legal documents and contracts. In the case of commercial real estate firms, the most obvious use case is leases. These documents are often hundreds of pages long and, if you have a big portfolio of properties, I’m sure you can imagine how quickly these pages add up.

What’s neat about Lingo is that you basically upload a lease and then the tool interprets and summarizes all of the clauses for you. It then tells you where you’re potentially exposed and where your risk factors are. And if by chance it gets it wrong, you can correct it and the system will actually get smarter – hence the machine learning part.

I’m not going to go through their entire product line, but I did also want to mention one more called Aura. They call it a “location-aware loyalty engine”. And you may have heard of similar products out there in the marketplace. What it does is use the MAC address on mobile phones (which is a unique, but anonymous, identifier) to track how people and crowds move through spaces.

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.

A few weeks ago I had coffee with a friend of mine who is a partner at a Toronto-based enterprise software company called Polyform Labs. Their products are geared towards the commercial real estate industry and, since I’m a big proponent of introducing more technology into the real estate space, I thought I would share a little bit about them with you all today.

The first of their 4 main products is called Lingo, which is a machine learning tool that helps companies quickly review legal documents and contracts. In the case of commercial real estate firms, the most obvious use case is leases. These documents are often hundreds of pages long and, if you have a big portfolio of properties, I’m sure you can imagine how quickly these pages add up.

What’s neat about Lingo is that you basically upload a lease and then the tool interprets and summarizes all of the clauses for you. It then tells you where you’re potentially exposed and where your risk factors are. And if by chance it gets it wrong, you can correct it and the system will actually get smarter – hence the machine learning part.

I’m not going to go through their entire product line, but I did also want to mention one more called Aura. They call it a “location-aware loyalty engine”. And you may have heard of similar products out there in the marketplace. What it does is use the MAC address on mobile phones (which is a unique, but anonymous, identifier) to track how people and crowds move through spaces.