Data Visualization, SpatialKey

Ethics and the use of DUI data

I do a lot of work with San Francisco crime data, and one of the things that I’ve been struggling with is one particular dataset: the locations of all the driving under the influence (DUI) arrests in the city. Just yesterday there was an article about US Senators asking Apple to remove DUI checkpoint applications from the app store.

San Francisco publishes a huge amount of crime data, going all the way back to 2003. You can grab a single CSV file with all the data. Over a million crimes. It’s beautiful.

If you look at just the DUI records you start seeing patterns. Here’s about a thousand DUIs over the past 2 years (2009-2010). Click any of these images for larger versions of the maps.

If we look at a density map individual streets start lighting up. Specific intersections stand out.

Here’s a representation that assigns the number of DUIs to the street segment they occurred on and colors the data like a typical traffic map.

And finally just for fun, here’s a 3D rendering of the same 2 years of data:

It’s compelling data, and fairly easy to tell an interesting story. But is there an ethical issue around visualizing or using this data? There’s a lot that you can do with the data, obviously visualizations like this are just scratching the surface.

An idea that crosses the line

Following one train of thought to its logical conclusion leads me to a mobile app idea. It’s a simple app, essentially just a routing application. You type in where you’re going and you can get directions from your current location, just like any other mapping or GPS routing application. Except we can give you directions that avoid known DUI hotspots. In a very simplified sense, routing algorithms basically give streets a score, usually determined based on factors like speed limit, road size, distance, etc. The path with the lowest score wins, and that’s what you end up getting for your directions. All you’d have to do to route around common DUI locations is make the number of historical DUIs along a street segment count in the routing algorithm’s calculation. Streets with lots of historical DUIs would be avoided in favor of side streets with fewer arrests. You’d avoid Geary Blvd and intersections like 16th St and Mission St.

It’s an easy app and the data is there for the taking. I’ll leave aside the question of whether the idea would work in terms of being effective at making drunk drivers avoid actual arrest. For argument’s sake, let’s assume that it would work, or that some other similar type of app could. It’s not an app I’d build, and I assume pretty much everyone understands the moral objection.

I don’t have any big moral takeaway or conclusion. On the one hand there are arguments that data and knowledge can never inherently be bad. Then there are arguments that this particular data (or at least specifically a DUI-avoiding directions app) would only be used to encourage drunk driving. I’m not going to make the DUI-avoiding mobile app, that goes way too far down the path of encouraging bad behavior. But it brings up a lot of interesting questions we need to think about as we’re working with data like this.



Related:

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  • I've been playing with different ways of representing data (see my previous night lights example) and I decided to venture into 3D representations. I've used a full year of crime data for San Francisco from 2009 to create these maps. The full dataset can be download from the city's DataSF…
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8 thoughts on “Ethics and the use of DUI data

  1. But this data isn’t terribly useful without information about traffic density right? I don’t know SF to know whether or not those areas where more DUIs happen just have more traffic, but it seems like it’s not a huge problem to point out where there are a lot of DUI arrests if there is also a lot of traffic there.

    =Ryan

  2. Yeah, traffic volume clearly accounts for a lot of the pattern. It would certainly be interesting to combine the arrest data with traffic data to find places where the ratio is higher than average.

    But even without the traffic data this can probably produce some actionable intelligence. A lot of times you have alternative street options that are nearly just as good. Taking California St as a substitute for Geary Blvd, or Guerrero instead of Valencia or Mission St, for example.

    So yeah, maybe all the maps show is which streets are more trafficked (well, that and where all the strip clubs are in North Beach).

  3. I have a great idea. Let’s assume you have a relative who was seriously injured or even killed by a drunk driver. You would like very much to avoid the places where they hang out. You’ve never taken a drink in your life, you’re a big time paid up member of MADD, and think the legal drinking age should be raised further.

    Can you build an application to help this poor person?

    Of course. It works exactly the same way as your “immoral” DUI checkpoint avoider. But wait, how is that immoral for this application?

    The morality or immorality is not in the data or the application, it’s in the person’s use or misuse. Tell the US Senators to go back to making useful laws and not abridging our freedoms.

    Sincerely,

    BW

  4. Brian says:

    @Bob Warfield – Good points. I’d add that such a “DUI Avoider” app would be helpful in a couple other scenarios as well.

    1. You never drink, and the delay from congestion around DUI checkpoints annoys you. This app enables you to avoid that hassle.
    2. You have a new car, and you’re worried that a drunk driver will slam into it in its parking space. The DUI avoider app gives you a better idea of where to park (even just within a few blocks).

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  6. plumpy says:

    I didn’t really understand the out-of-place political diatribe at the end of Bob’s post, but I agree: when you were describing it, at first I assumed /avoiding/ drunk drivers was what you were getting at, not a way for drunk drivers to avoid cops.

  7. Bart says:

    Coldfusionpaul: I suspect you have a cartoonish notion of a drunk. The average heavy drinker has learned a great many adaptive tricks to allow herself to continue to drink; masking, avoiding, denying, escaping, subverting pleading… An iPhone app that tells her if/where there are checkpoints? No problem! Keep in mind that the vast majority of drunks who DUI/kill innocent people are functional drunks who may seem just a little tipsy – yet are easiy 2x the legal limit of blood alcohol. The falling down drunk DUI usually only happens in college/high school students. The vast majority can operate an iPhone app and car – but neither very well to the sober..

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