Carlo Ratti of MIT’s SENSEable City Lab talks about how technology can be employed in cities to address problems of pollution, congestion, and efficiency.

Ratti: So I wanted to start with something that’s, you know, everybody is talking about these days. It’s about big data.

Now, big data is about this incredible amount of information we produce today. We are now, from Eric Schmidt, quote, All the information was produced from the beginning of humanity to 2003, well, this is more or less what we produced yesterday and today over the past couple of days.

The definition of big data I heard recently by Mike Batti in Singapore a few weeks ago was, well, big data is also all water. You cannot put in an Excel spreadsheet.

But this big data has big consequences in cities. And that’s what I would like to talk to you about today. Call it big urban data.

Now, our cities over the past few years have been layered with many different types of digital information. Because of that, the way we understand them and the way we can respond to them we believe is changing dramatically. We can collect a lot of information from our cities. And then we can process the information and respond to that information.

I just wanted to make an example of something that has been happening in other fields like Formula 1. Well, if you think about Formula 1, 10, 15 years ago, if you wanted to win a race, you needed a good car and a good driver. You needed physical, solid things. But actually today if you want to win a race, you also need something like this. A system made of thousands and thousands of computers, made of thousands and thousands of sensors connected to those computers. A lot of information is analyzed, it is collected from the race, it is processed, and then decisions are made in realtime.

And that’s a little bit the same that’s happening today in our cities. If you are an engineer, you would call this a realtime control system, a system that’s basically made of two components, sensing component and an actuating component.

Sensing, you collect information, and then actuating, you respond to that information. And sensing and actuating is really what every dynamic system and what every living system does. When we meet each other, we sense each other, we collect information, we look at each other, we touch each other, we smell each other, and then we respond to that information.

Now, the amazing thing is we believe it’s almost like every atom out there in our cities is becoming like a sensor and then actuator. And we only have a few minutes today, but I want to share with you the capital projects that show these two dimensions.

The first project started by looking at something we think is a big issue today. And it’s about the computer. If you take the computer, you know everything about it. Every chip in the computer, you know where it was produced, how it moves across the planet, how it became the machine and came to your desk.

In other terms, the global supply chain is very well understood today. However, a few years from now, when we throw away the computer — and sometimes this is actually what happens to it. So what we decided to do recently was what if you put little chips onto electronic waste and other types of waste? What if we then track trash? So put little chips to follow trash and see what happens to it.

We did a first deployment that you see here in Seattle. You know, involving really 500 people and the 3,000 Smart tags.

There’s a little bit of sound, if you can have it.

And, you know, every possible type of object. And then following this, we started tracing and tracking the waste.

Should be a video. Yes.

So 3,000 objects is Seattle. And the day of deployment, you see the city. After a few days, you’ll see some of the main landfills. Next in Seattle, and then big surprise how far things have traveled. That was a big surprise because today everybody knows a small piece of the chain. But this was actually one of the first times where we could actually follow the whole and trace the whole chain from the beginning to the end after one month or two months.

And a lot of the waste actually traveling all across the country, sometimes in clumsy ways. Look at the trace it went from Seattle to Chicago and then down to Baja, California, for thousands and thousands of kilometers, certainly in a non-optimized way.

Well, you know, what did we learn from this project? Well, a couple of things. One is if we have all this information from our cities, in this case, about waste and, you know, what happens to things we throw away, then we can probably redesign as engineers, optimize such systems so we can reduce a lot of inefficiencies that you saw in those traces.

Then another thing we thought was quite important is that all of this big data, if it gives us information about what happens out there, then it can promote behavioral change.

And actually one of the most telling things, following the project, was somebody who told us: Well, you know, I used to drink water in plastic bottles and put plastic bottles outside of my door every day and think they would disappear. But actually, following the project, they know don’t disappear. They actually go a few miles from home and stay there forever. And now because of this, they stopped drinking water in plastic bottles.

Now, there’s a third thing we actually discovered that was more recently. And that was quite unexpected. That’s when a burglar came to our lab at MIT and stole a lot of stuff, including some of the tags that tell you where they go.

And here is a video about it. If you can still to fix the sound, it might be useful. Might be more fun.


Anyway, that was one of the machines. That’s what happened.

Thank you. So while this was just a quick example about sensing, I wanted to share with you another example about when you put more data streams together, that’s what they are doing in Singapore. So this was using sensors onto trash.

But imagine you can collect much more information and get to almost like a living city, where you know in realtime all what’s happening around yourself.

So here is data in Singapore from all of the networks. It tells you how the city is behaving by using all this digital communication networks, Smart, and also how much energy you are consuming and how that is related to temperature increase, as you see here.

How the city behaves during special events. You see the Formula 1 racing in Singapore and actually how people moved there and the city changes behavior.

Even simple things like it makes taxis in the rain. In Singapore, it’s impossible to find a taxi when it rains. But actually when you get this realtime information, taxi drivers and citizens can get a better match.

And how the city expands and shrinks because of traffic during the day. And then how well this information from the island, from Singapore as a city and as an island actually connects with the global flows. The global flows of people coming to Singapore and going out of Singapore, goods coming in the city and out of the city. So how all of this realtime, almost like a realtime web that has all this information about the city and helps you understand better what’s going on.

Now, in the last couple of minutes, I wanted to share with you a final project. And that’s about more the actuation side. So we see here sensing and the collection information, but how this actually can change our buildings, our cities, and the objects we use in the cities.

And just very briefly, a quick video about a project in Copenhagen. The mayor came to us a few years ago with a very precise question that was, how can all of this data, all of this technology help us to change — make the city more sustainable and change the way we move?

Now, the incredible thing, if you go to Copenhagen, is that traffic in the city looks like this. Copenhagen is a city where you had a lot of cars in the city center a few decades ago. It’s a city where now actually you have 30 to 50 percent of all trips every day on their bicycle.

So basically, we develop this bicycle idea that you can see in the video.


Now, I don’t know if you can put the audio. Anyway, the idea is imagine a wheel that you can retrofit to any bike. And then when you brake, so the wheel will get your energy and it gives it back to you. So by just changing the wheel, you will convert your bike almost into something similar to a Toyota Prius, when you brake, you save your energy, it gives it back to you, you control it through your cell phone, through your smartphone. And then when you do that, you actually can do a number of other things. So you can monitor what you’re doing. You can collect information about air quality, like in this case it tells you what’s the best way to move inside the city. That’s air quality data collected for Copenhagen.

And all of these things you can share with your friends. You can just put it on Facebook with other cyclists and create a community there. Or something that the mayor thought was a very good way to increase even more the number of cyclists in Copenhagen, do something like a frequent flyer scheme but based on green miles. So instead of collecting air miles, you collect green miles, and it becomes a way to incentivize the use of the bike in the city.

So that’s the video. That was the initial prototype. Now we have the third round of engineering, like in cars. In the car industry, it takes, as you know, a few years to go from the prototype to the final product. But we’re getting very close to it. And hopefully, it will be on sale next year.