In this episode Professor Steve Goldsmith interviews Carlo Ratti, professor of Urban Technologies and the founder of the groundbreaking Senseable City Lab at MIT.
In this episode Professor Goldsmith interviews Carlo Ratti, professor of Urban Technologies and the founder of the groundbreaking Senseable City Lab at MIT. They discuss new frontiers in urban data, edge computing, and rethinking the relationship between the environment, the urban environment and health. Ratti also explains why city leaders have to break from the past and why "best practices" are perpetuating old ideas.
Music credit: Summer-Man by Ketsa
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Betsy Gardner:
Hi, this is Betsy Gardner, senior editor at the Harvard Kennedy School and producer of The Data-Smart City Pod. Since we started this podcast, we've had great support from our listeners, and to make sure that you don't miss an episode, please find us under the new Data-Smart City Pod channel wherever you listen. Make sure to subscribe so you get each episode, and thanks for listening.
Steve Goldsmith:
This is Steve Goldsmith, professor of urban policy at the Harvard Kennedy School in Bloomberg Center for Cities. Today we have a terrific guest, Carlo Ratti, who's a professor of Urban Technologies at MIT, the founder of the Senseable City Lab, one of the country's...actually one of the world's leading advocates in thinking about the use of sensing devices and others to improve the way city spaces work.
So we're delighted to have you, Carlo. Thank you so much for your time.
Carlo Ratti:
Thank you very much for having me.
Steve Goldsmith:
How about a word or two about the lab itself? What was its original goal and how do you think about its work today?
Carlo Ratti:
We started almost 20 years ago now. And in the very beginning, we saw that basically we had a lot of data in cities, and we could use data in order to better understand cities, also use this understanding to transform design and ultimately the way we live in cities.
So that's what we've been doing. Some of the things, at the very beginning, we were among the first ones to use cell phone data to describe the city. This has kind of become mainstream now. We do a lot of work also with other types of data related to the environment, to sensing pollution.
One of the interesting things, going back to your question about today, I think we are getting even more data, even high resolution. So somehow now we can really get into questions that it was not possible to answer before, for instance, urban health, and we've been doing quite a bit of work on that recently. We're also getting into the architectural scape. At the very beginning, 20 years ago, we didn't have the data. Today, the data is so fine grain that we can actually look at dimensions that were impossible just a few years ago.
Steve Goldsmith:
We have a project we've been working on concerning hyper-local environmental data and how it connects to public health. As you think in your work about collecting data, what are some examples of that? The sensing devices you use, whether it's water or air, what things are collectible and how could you connect those to action?
Carlo Ratti:
We can collect data. Collecting data is getting cheaper, faster, higher resolution. So for instance, you were asking about the environment. Today, many cities still look at collecting air quality data using fixed monitoring stations and those stations are quite expensive. There's only few of them in the city. They require a lot of maintenance. And the first thing we started doing is actually using mobile sensors. If you use mobile sensors and if you maybe calibrate mobile sensors with just a couple of fixed stations, then you can get much more data at a lower cost. And also with increasing resolution, we know that air quality can change a lot between just one street and the neighboring street. And again, by using mobile sensors, you can quantify that.
But then there's something else. You don't just want to know about air quality in the city. You want to know how it affects people. And in order to do that, some of the work we did in recent years is about combining both data from air quality with the movement of people. What you want to look at is individual exposure. If you got an air quality problem in a neighborhood when there's nobody, then that's not an issue. The issue is about individual exposure. And again, today we can do that using, for instance, cell from data, mobility data or anonymized, aggregated and so on. And I think that's the beginning of rethinking the relationship between the environment, the urban environment and health.
Steve Goldsmith:
What would be the effect of edge computing in solving problems such as you suggested that many cities are actively involved with maybe arguments with 5G cell providers, but how do you think about the advantages of edge and the senseable city?
Carlo Ratti:
Well, thanks for asking that. Edge is very interesting, but edge is more about how we can make the whole process more efficient, how we can share data in a different way. We can optimize the data sharing process. But really the important thing is looking at what we can do with the data and ultimately really it's about citizens. It's about better understanding a lot of the issues we got in cities, starting with traffic, pollution and many others.
So somehow edge is providing an opportunity to streamline some of those processes, but let's not forget the ultimate goal, and that's about how we can live in cities better.
Steve Goldsmith:
This may be a little bit more what we should be doing, but let me ask you about it. So one of the challenges, it seems, listening to you and what we have seen as well is that you're collecting data which allows an elected official or advocacy group to think about managing a system better, managing public health better, managing mobility better. But cities are kind of organized by agency, they're organized by activity, but you're sensing the results of multiple activities in a place. So how are you addressing imagination, if you will? So if we have technology that can provide these terrific solutions, how do we get officials to imagine what they can do with that data?
Carlo Ratti:
Well, Steven, I think the most important thing is really to engage many people and to engage citizens. The more people you engage, the more likely you are to think about different solutions, to get what you're saying, to get imagination.
Let me give you a small example. We started recently looking at visual images of cities. For instance, Google Street View collects, as we all know, a huge amount of visual imagery in cities all around the world. And then if you use deep learning, if you use different types of artificial intelligence, you can analyze Google Street View and do different type of maps. The first map we started doing was a map of trees. We called it Treepedia, trees in cities, to know exactly where trees are, also what type of trees and so on. Then we opened this up for citizens to look at this, and for us it was incredible. We started receiving hundreds, even thousand, of inquiries from people who were looking at the data, had ideas, came up with ideas and also used the data in order to promote advocacy.
So somehow I think the best way to achieve what you're saying is really to open up this as much as we can and create feedback loops with citizens. Citizens are the ones who are going to inject a lot of creativity into this process.
Just a small addition. I think in order to do this, however, it's very important we don't focus just on top scientific papers. Of course, that's what we want to do as a research lab at MIT, but we want to make the research accessible to broader public. So that's why we put a lot of effort as well into data visualization, into videos, sometimes into writing op-eds and so on in order to make all of this accessible to citizens so that they can react to it and hopefully help improve their cities.
Steve Goldsmith:
One of the things I thought was interesting on your website, Senseable City website, was the utilization of existing assets to gather data that will improve operations, a trash truck that is a sensor, maybe a streetlight.
So what other examples have you worked on that you think would be helpful to our listeners? So what could they do with existing assets that would allow them to secure more information to sense the environment better for purposes of planning?
Carlo Ratti:
I would say the first thing, usually the first thing we try to do is look at the existing data. Sometimes people call it in research opportunistic data. It's basically using data maybe collected for running something else, for running the cell phone network, and you can use it and actually use it and understand insights into the city, into mobility, into traffic and so on.
So the first thing usually is to look at opportunistic data sets because, again, they're easily accessible. Somebody else is already collecting them and always you need to look at the data from the point of view of anonym anonymization and aggregation. But somehow you got the data in order to start analytics in the city very quickly and to share the data with citizens. If that is not possible, then you can look at existing networks, as you are saying, in order to collect more data. And yes, so we develop, for instance, little chip on trash in order to see what happens to trash, what goes to the right place, what goes to the wrong place, how we can improve the city's waste management system. In that case, however, it takes longer. You need to develop the tags, you need to deploy them, you need to engage citizens in the process and so on.
So I would say, first of all, we look at existing data sets already maybe collected by private or public companies. I think actually the Bloomberg administration you were part of did amazing work in sharing a lot of the data collected by the city and that actually resulted in very exciting research. So that should always be the first step. And then if that doesn't work, then it's very important to look at how we can leverage other urban systems in order to deploy new type of sensors or, even better, to involve citizens into collecting their data and sharing with the community.
Steve Goldsmith:
Just a couple of more questions and we'll let you go. I could ask you questions for hours, but let me just ask two. I'm going to ask you a tactical question and kind of a visionary question. Tactical question is a lot of the basic infrastructure of a city, whether it's motor and engine, it's the doors on a bus, it's the vibrations in a pump, it's a bridge, is capable of producing information that can allow more efficient city services. So using maybe bridge monitors as an example, what do you think the best applications of some of that would be just in the tactical work of how a city operates?
Carlo Ratti:
You're absolutely right. What we can say is that today we are a little by little building a digital twin of our cities. And the advantage of the digital twin is that when you got all the data about the physical city in the digital twin, then you can apply different type of analytics. You can apply artificial intelligence in order to better understand what goes on and improve the city, hopefully doing all of that in a very open, transparent way so the citizens can play a role in the analytics.
Looking in more detail about what you mentioned about the project about monitoring vibration of bridges and, more generally, monitoring the road infrastructure of a country, of a city, using data is something we started recently. Now today, if you want to monitor a bridge, that can cost you in a traditional way which means putting on the bridge fixed sensors and collecting all the data through wires and analyzing data, that can cost you around half a million dollars per year.
Now, what we try to do is try to collect the data in a cheaper and more pervasive way and the data you collect is data about vibration of the bridge. And in every smartphone you got the ability to measure vibrations. You've got accelerometers, you've got gyroscopes. And so we started doing it and saying can we do this in a way to monitor vibrations on bridges and more generally about the health of the road infrastructure in a city or in a country. And it was very surprising to us. We published recently a paper in Nature Communications Engineering just a few months ago about this that it turns out the results are quite good. And then when you look at this, we will never be able to monitor all the bridges in a country using traditional systems. It would be too expensive, even if it's very important because a lot of the infrastructure, for instance, in the United States was built after World War II and is now getting to the end of its design life.
So we should monitor more and more of. But if you use this system based on app data, data collected from smartphones, then it looks like we can get very good results. Yes, maybe not exactly the same as what we can get with fixed sensors, but still we get an early warning, we get something similar to what we did during COVID. During COVID, we had triage. You do a first scan and then if you got a red flag you can do additional tests, you can be sent to do additional testing and so on. So we can start implementing that in cities and countries, not only for the broad infrastructure but for many other critical systems.
Steve Goldsmith:
Let me close with the following. Our audience is mostly state and local officials and nonprofits. So if you're going to suggest to them over the next one to five years how they could use the capacities and the data to create a senseable city, what would you recommend they do? What actions would you recommend they take based on the capacity to sense things better?
Carlo Ratti:
Steven, first of all, the job you're doing at Harvard is terrific. You're really helping a lot many cities in the US and beyond.
Let me tell you something a bit more general. I think sometimes cities, mayor, city officials, they think in terms of best practices. And I think that it's a problem. And the reason is that if you think about best practices, what you're doing, you're looking at what another city did maybe they started the process 20 years before, the designing, they implemented, they saw it was successful. And what you do, you're just looking at that and perpetuating it. So best practices are a way to lock our future into the past. I think what we need to do more, what mayors and city officials should do more, is take risks, try new things, see what works, what doesn't work.
I think we saw a lot of that during COVID. During COVID, we didn't have best practices and so many cities had to innovate, to try something. But some of those things actually help accelerate a lot innovation in cities. And we must accelerate innovation in cities if we want to achieve some of the ambitious targets we have. Most cities, C40 cities for instance, have committed to become zero carbon by 2040 or 2050. And to do that, I think we need new models, we need to try more, to experiment more, to use the city as a living lab. And again, don't look at best practice and don't look at the past but try to innovate, team up and try new things. And yes, something will fail but that will open the way to a lot of successful new innovation. I think that's what cities, mayors, city officials should do more and more.
Steve Goldsmith:
This is Steven Goldsmith, professor of Urban Policy at Harvard's Kennedy School talking with Carlo Ratti, professor at MIT, director of the Senseable City Lab. And yes, so appreciative of his time. And also I thought that closing statement which is leaders, mayors, county executives, presidents, governors can do better than best practices. Innovation means excelling in meeting current conditions. Thank you so much, Carlo, for your time.
Carlo Ratti:
Thank you very much, Steven.
Betsy Gardner:
If you like this podcast, please visit us at datasmartcities.org or follow us @DataSmartCities on Twitter. And remember to subscribe at the new Data-Smart City Pod channel on Spotify, Apple Podcasts, or wherever you listen. This podcast was produced by me, Betsy Gardner, and hosted by Professor Steve Goldsmith. We're proud to be the central resource for cities interested in the intersection of government, data, and innovation. Thanks for listening.