Meet Jack. Or, What The Government Could Do With All That Location Data

By Jay Stanley
Dec. 5, 2013
 

We now know that the NSA is collecting location information en masse. As we’ve long said, location data is an extremely powerful set of information about people. To flesh out why that is true, here is the kind of future memo that we fear may someday soon be uncovered:

UPDATE: Watch the video version of this piece here.

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Dear commissioner:

 

Now that we have finalized our systems for the acquisition and processing of Americans’ location data (using data from cell phone and license plate readers as well as other sources), I wanted to give you a quick taste of our new system’s capabilities in the domestic policing context.

As you can see in this screen shot from our new application, an individual by the name of Jack R. Benjamin yesterday was flagged as a potential DUI risk:

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First, before we look into that alert, the system lets us quickly access a variety of background information about our subject. To start with of course, we can easily see Mr. Benjamin’s movements throughout the course of the day:

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A sense of the subject’s movements over time can also be graphically displayed:

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Or as a heat map:

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With another click of the mouse, we can see where Mr. Benjamin spends all his time:

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Looks like he spends a lot of time at someone else’s private home. Naturally more details are available at the click of a mouse:

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Looks like he spends the night there regularly – must be a lover. (Notice, though, that on 2/2/13 the subject seemed to have left at 3:50 in the morning, and didn’t visit again until almost 6 weeks later – looks like a temporary breakup!) Looks like the home belongs to a woman named Mary Smith:

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Just out of curiosity, who else has been visiting Mary Smith’s house?

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Looks like Mary has a few close friends. Wonder if Mr. Benjamin is aware of this Bill Montgomery character who spent a few nights with her?

Going back to the main screen, looks like Mr. Benjamin is quite a union activist. Perhaps we should notify George over at BigCorp (he serves at the Fusion Center with us). Just in case our man has been involved in the trouble they’ve been having over there.

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We can easily look at all the other vehicles that have been parked at the union hall:

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As well as all the specific times that Mr. Benjamin was there:

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We can click on a certain time and day, and find out who was there with him at that time:

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And look at a graphical correlation of which vehicles tend to be at the union hall at the same time as each other:

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Looks like Mr. Benjamin and three others are really the heart of the operation.

Going back to Mr. Benjamin’s lifestyle, we can take a look at where he tends to be, say, between the hours of 2:00 and 5:00 AM:

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And, what other people tend to be in close proximity to him during those nighttime hours:

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There’s his lover on the top line, but who are those other gentlemen? From the average yardage separation, it looks like they didn’t share a bedroom – perhaps just friends whose house he’s crashed at after a party. Except for this Mr. Narushima. Maybe he’s a camping buddy or something since the night they spent 4 yards apart was at neither of their homes. We could click through and see where they were, but let’s move on.

We can also get a visual graphic of Mr. Benjamin’s proximity correlates – the people who are most often physically close to him:

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Okay, so far we’ve just been poking around. Let’s go back to that DUI Pattern Alert that came up yesterday for Mr. Benjamin:

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Perhaps you’re curious how and why the system decided that Mr. Benjamin was at risk of a DUI. If you click on “Factor diagnosis,” the system will show you how it datamined that conclusion. It shows that there were four factors :

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We can click to get the details of each of the alert’s contributing factors. First, there was a social gathering in process, often a key precursor of a DUI:

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Second, look at the attendees’ location trails, several of which involved stops at liquor stores:

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Third, one of the attendees has a prior:

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Finally, our live-monitoring algorithms identified a movement pattern by another attendee who already left the gathering highly suggestive of DUI:

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No officer was able to reach this suspect in time to administer a test but his tag has been watchlisted for heightened scrutiny in the future.

For Mr. Benjamin, the system will use its predictive algorithms to guide your officers to the most likely interdiction location:

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Of course, the DUI flag is just one of many possible alerts that are generated by the system. Here is another alert we received yesterday: three subjects on the government’s watchlists congregated at a single location:

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In this case, upon investigation it was determined to be coincidence.

Another alert that we saw yesterday was this one for movement patterns suggesting possible criminal behavior:

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Upon investigation the suspect was determined to be a real estate agent.

That is just a taste of what this powerful new system is capable of. We look forward to working with your department for many years to come in our mutual efforts to keep America a safe and controlled place where no one, no matter where they are, can commit wrongdoing.

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