Inside the Mind of Mobile Data Expert Mark Forster

AirSage takes the data constantly pinging from our smart phone and apps, adds it to other data sources, and crunches out remarkable insights on how we move — and what we do — in our cities. Mark Forster, Designated CEO for AirSage, tells us how this technology works, why it won’t be Big Brother and why data can help fight the current and future pandemics.

CityAge: Smartphones are ubiquitous and so is data. Tell us what AirSage is doing?

Mark: What we acquire is billions of location signals which come from smartphones. There are two main technologies: One uses cell phone data that comes from carriers such as AT&T, Verizon, Deutsche Telekom, etc. The other comes from the apps on smartphones in the form of GPS signals — typically from mobility apps, dating apps, news apps, and other types of apps which transmit a location signal.  We then supplement that data with other data sources such as vehicle tracking and Census information, and then analyze and process it to project population movements.

What we provide are insights into human movement, understanding patterns of human movement.

CA: So, you know where and why we are moving around in the city?

Mark: That’s what we do for smart city projects — understand how are people moving, over space and time, and therefore, how can you optimize the service in a city in terms of transportation routes, in terms of traffic optimization, in terms of garbage collection, which roads to close, when you have to renovate a building and therefore you need to close down the (street) lane, where can you afford to do that and where not?  We can even tell you where drones should be allowed to fly based on people traffic patterns.

CA: You do that by drawing what you call “polygons” around an area — a kind of geofence. Tell us how that works.

Mark: We’re trying to understand who are the people in this defined location, either a particular point of interest or custom shaped polygon. What are they doing and where are they from? So understanding, for instance, what is their trip behavior? Is this just a stop for two hours and before and after they’re somewhere else or do they stay all day or even spend the night in the area for several days? We’re trying to understand that kind of behavior.

Typically, if you look at the available data which we have out there we can zoom down to block size. So, it’s pretty granular. From a GPS standpoint, just from a signal standpoint, we’re looking at probably 15 feet of accuracy, and that’s pretty, accurate. That could be a particular store, or a bus stop, or a park entrance.

CA: Wow, that is accurate. What could you do with that at, say, at New York Jets game?

Mark: Okay. If you define a polygon for a Jets game, such as around the stadium for example, it means that we look at the data which is happening there on site. And we can also look at, ‘What is the origin of that smart device? Where do those devices/people come from?’ That tells us a lot, down to block-level reading.

We can look further by examining associated census data. For instance, you can break down the demographic data on the people attending the game. What kind of population is there, the composition of people attending the Jets game by income for example? Once fans are allowed back into stadiums, it can inform traffic patterns for tailgating, the pattern of when people arrive, and how long stay after the game is over.

CA: That seems to offer a great advantage in planning transportation for a city.

Mark: Correct. If you have different stadiums or different locations, different venues, if you wish — it could also be concerts, festivals, parades — you’ll have people choosing different modes of transportation to get to the location. Our data can give you insight into these options historically, and we can go further and show you how your newly enacted infrastructure and policy changes affected these existing conditions.

You will also see what the densities are in those locations. So, if you wish, what is the count at the festival? What is the density of people along a parade route? Which people are located in these places? You’ll see the travel times for them to get to the event. You’ll see the peak hours and you’ll see the mode of transportation, which is very important. Are people using the highway? Are they using a secondary road? Are they using a different mode of transportation, like transit, or are they cycling or are they going on foot?

In once recent project, a client wanted to know how many people coming from a major transit center were walking to a stadium to determine if it was worth building a multi-million-dollar walkway.  Our data showed only a small % of traffic came directly from the transit center during game times, and as a result it probably wasn’t worth the investment.

CA: Can this technology help us during the pandemic?

Mark: Well, you can observe a Jets game and you can analyze the data and look at what are the densities, what are the movements around certain venues   . . .  are there actually more people or less people there?

The same thing can be used for pandemics. You can understand, what are the densities down to the block level? Do we have an issue with social distancing? Do we have an issue with spread of a virus, for instance, because you see a high-density area of a virus and you see how those people spread over time? Furthermore, we can provide data showing you how traffic patterns have changed from before the pandemic to now here near the end of the year. More people are telecommuting, going to different places than they did before the pandemic, etc.

I do see tremendous opportunity going forward.

CA: Do you get asked whether this is Big Brother Watching or too intrusive, such as Cambridge Analytica once was?

Mark: We do not break out individual people. All the data we source is anonymized and therefore we’re unable to actually map it back to an individual person. We use data from anonymous devices, not individual people.

We’re not interested in knowing the individual person. We want all the data anonymized. We’re not interested in understanding ‘What is Mark doing on a Saturday afternoon?’ We’re trying to understand what are the people on this defined location [polygon] doing and where are they from? Understanding, for instance, what is their trip behavior? Is this just a stop for two hours and before and after they’re somewhere else? We’re trying to understand that kind of behavior.

The other part [regarding privacy] is obviously from a legal perspective. We adhere to all of the strictest laws. Cambridge Analytica was unethical. We’re not part of the spooky business. We’re not in the advertising targeting area, retargeting when you look up product A and it follows you for the rest of your life. We’re providing insights which allows us to improve cities, improve transportation. We make statements about human – the entire population in that area – behavior. It is never about individuals.

CA: Back to transportation. What’s a real-life example where AirSage worked?

At the end of day, it’s about understanding human movements and deriving insights from that. Now, obviously you can use it in transportation for optimization of routes A to B. Take for example of one of the studies, which we just published, around Lake Tahoe.

For Lake Tahoe, they (city officials) were estimating, across all the agencies, eight to 10 million people visiting annually. Whereas in reality, using our data, it was about 25 million. So that was the first shocker to them.

As a city, you need to understand what are the routes and the distances people are actually traveling, not just how many people are there at one point, but actually what point connections do they need? What are the intersections, etc? Those are movement insights, which we provide.

You can use it for transportation to optimize public transportation. And if I recall correctly, Lake Tahoe had an optimization of, I think, roughly 20% better utilization of public transportation (after  the study), which is massive. Those are the insights you can use by understanding things which you normally just have to assume. Our data is more accurate and because it is more accurate you can make better decisions and therefore improve the service.

CA: But can AirSage go deeper, to give us more understanding about human behavior and how we interact with infrastructure?

Mark:  If you look at movement and then try to understand what is happening in terms of mobility, and actual behavior, then you go deeper down from the data perspective. Which is understanding to what so-called points of interest someone is moving.

Which geofences are those people going to and what are those geofences? Are those restaurants? Are those concert venues? Are those parking lots?

So, you can, in that sense, define any kind of polygon or any kind of point of interest, which you want to observe. And therefore, you can say that 10% of people attending this game, afterwards, pass by, or go or stay at this restaurant. So, you can make that kind of analysis. You can drive that insight from the data.

With the pandemic, human behavior in terms of shopping has changed dramatically, and our data illustrates how people are going (or not going) to malls, shopping centers, tourist attractions and other venues vs before COVID-19.  These insights are valuable to a whole range of different industries in terms of driving investor decisions.

CA: Can you give me the blue-sky future for this smart city technology?

Mark: In a certain way data is becoming the new infrastructure, especially data about human movement and ultimately, indirectly, human behavior. We’re focusing on the movement parts. The reason for that is we all know from the large cities, that they’re congested, they’re inefficient. They are not built for the scale and the size we have nowadays or, in a lot of cases, the reliance on private automobiles.

For city planners and engineers to improve our cities, they need insightful data, and not assumptions. They need to understand how people are actually moving, what people are doing, and when are they doing it.  We can provide that piece of the answer. In a certain way, location or movement insights are a basic infrastructure upon which you can build and improve, because at the end of the day, a city should serve the population.

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