I wanted to have some kind of gift of thanks to give to our most helpful customers, who worked closely with us, helping us develop our hurricane product. To be honest, it felt like we all weathered a storm together during that hectic week in August. We brainstormed on sending out shirts, or bags, or some other standard corporate gear, but none of it really felt like “us”. So I came up with a more unique gift that I think captures our culture.

This is a 3D model of Hurricane Irene. The height of the model represents the wind speed at that location. You can see there are 3 bands of different wind speeds. The outer band represents where wind speeds hit 39 mph, the next band represents 58 mph, and the third band represents 74 mph (hurricane force winds). Then running through the middle we have the path of the eye of the storm, and the height of that track represents the exact speed at that point in time (Irene got up to 120 mph).

I created the model by taking the GIS data straight from NOAA and using that to build up the 3D model by hand. Then I sent the 3D model off to Shapeways for printing. The printed version you see in the photos is made out of alumide, which is sort of a composite aluminum material.

For our customers who were working with us while Irene passed through, we hope this will be a nice reminder of the work we did. It’s just a little paperweight to sit on your desk, but for those who were watching Irene as it developed and keeping a very close eye on the footprint of the storm, I think it’s a nice memento.

A hurricane can be a difficult concept to understand. For those affected in its path, it’s an incredibly tangible, visceral thing. But for those watching from afar (like me, sitting in California), it’s less “real”. We hear the overly-dramatic news reports and the doom-and-gloom predictions, but it’s a purely theoretical experience. Having a little paperweight of the storm on my desk doesn’t really help me understand the true impact Irene had on all those folks along the east coast, but at least I can touch it.

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.

In 1990 I was an eight year-old kid. And like most eight year-olds I spent a lot of time in front of my TV. But the summer of 1990 was different. Instead of cartoons I was watching the first Gulf War.

The television media coverage of the war was everywhere. Except these weren’t the gruesome images of the Vietnam era. These were images that looked more like videogames. We had cameras attached to bombs that used night vision and targeting scopes as they dove into buildings. All the images were a bit fuzzy, a bit grainy, either tones of gray or green, and overall void of emotion.

But we were watching people die.

The disconnect between the emotionless images shown on TV and the reality that they represented has always stuck with me. The fact that we could (and still do) present something so horrible in such a clinical, disconnected way makes my head spin.

WikiLeaks Iraq data
I’ve been experimenting with mapping the recently released data from WIkileaks that documents deaths in Iraq. All told the data documents 108,365 deaths, which we assume are just a fraction of the true casualty count from this war. Of those deaths, 65,641 were civilians.

I’ve used SpatialKey to produce some heatmaps of these deaths by recreating the aesthetic of the night vision images we’ve grown so used to seeing. I downloaded the data from the compiled spreadsheet published by the Guardian. Each image has a high resolution version available (2,474 pixels by 1,419 pixels).

A view of the entire country

High resolution version

A closer look at the area of Baghdad

High resolution version

More details of Baghdad

High resolution version

Why?
These images are meant to be a bit provocative. Every tiny blurred dot represents someone dying. And yet it’s all presented in a way that everyone is comfortable with. When you glance at these images you don’t immediately think of killing. We’re so used to seeing emotionless, blurry images of rockets exploding and precision bombs targeting buildings that we disconnect the image from the reality. These are images of death. And the fact that we’re comfortable looking at them should give us pause.

This keynote was a bit different. I went out on a limb a bit and talked about the experimental projects that I’ve been working on, and my belief in the importance of pursuing fun experiments to stay invigorated and passionate about our work. It covers a number of mapping and data visualization projects I’ve been playing with, but the point was really that we all need to pursue what we’re passionate about. For me that happens to be maps right now, but everyone has their own unique areas of interest.

If you’re interested in mapping work then the projects I talk about should be right up your alley. But even if you’re not a map geek, I think the presentation is still interesting and (I hope!) inspirational.

You can also see the slide deck on its own, but I think the video gives much better context to the slides.

Here’s the video:

As I was flying back home into San Francisco airport I was watching the city lights out the window and got struck by a bit of inspiration. I find cities beautiful, from the graffiti to the neon signs to the line of headlights on the highway. A city viewed from above at night is captivating. I wanted to try to recreate that same look, but by visualizing data (in one sense you can say that the real view of a city from above is already a visualization of population data).

I started searching for images of cities at night, and found these amazing images from NASA. All those images were taken from a space shuttle orbiting the earth. These images tell you a lot about the city, the layout, urban density, planning (or lack thereof). I wanted to take other meaningful data and create similar images.

All the visualizations below have been created with SpatialKey. However, this is some experimental work I’ve been playing with to generate the “night light” images, so it’s not released (and might not ever be). Basically this is a peak behind some of the R&D work I do for fun (yes, for a dataviz dork like me making fake “cities at night” images is my idea of fun).

Crime in San Francisco
This image is all crime in San Francisco for a 3-month period. You can see some of the same features that you can see in the NASA space image, such as Golgen Gate Park and the Presidio (the area on the north-west edge of the city). All in all it’s interesting how similar the crime image looks compared to the NASA image. Downtown is the brightest spot in both images, which means that it’s literally the brightest area of the city (the most streetlights), and also has the most crime.

And here are breakdowns for a few different crime types. Notice how different the distributions are. Narcotics crimes are heavily clustered and can be found downtown (in the Tenderloin), in the Mission (near the 16th St BART station), and along Haight Street near Golden Gate Park. Whereas vehicle theft is scattered fairly evenly throughout the city.

Narcotics

Theft

Vehicle Theft

Burglary

Graffiti Reports in San Francisco and New York
Both San Francisco and New York publish their 311 data, which is when citizens call for city services. One category of 311 calls is to report graffiti. Graffiti is interesting in that it often follows specific city streets. When we look at the graffiti data for both cities we see specific streets that have far more graffiti than others. I love these images (particularly the one of SF) because they really look like a view of street lights from a plane.

Trees planted in San Francisco
Another one of my favorites of this set is data for all the trees that the city of San Francisco has planted since 1990 (all this SF data is available at datasf.org). You can see the heavy planting along Market St (which cuts diagonally through downtown), as well as along streets like Sunset Blvd (the street running north/south on the western side of the city).

Street lights (or SF as a giant lite-brite)
One final image of San Francisco we have is the locations of every street light in the city. I liked this image because it reminded me of playing with a Lite-Brite when I was a kid. It almost makes city planning feel light a grown-up version of playing with little plastic lights.

Check out the video below and read the full article on the SpatialKey blog.

Read the whole article on the SpatialKey blog to see how we put this together and learn more about the SFPD’s “Operation Safe Schools.” You can also watch the full resolution video on YouTube

As a brief disclaimer in case I slur any words near the end: I was in Denver for a short trip and we squeezed in a time to meet with Jon and James right before I had to head to the airport to fly home. The only problem was that we had to meet at about 10am in the morning. And since the show is called Drunk on Software we obviously had to be drinking. So by the time I got on my flight I was probably 6 beers down

A big thanks to Jon and James for making the time to have us over (that’s the living room of Jon’s house). And thanks for the beer guys!

To really launch this product in style, we’ve put together this video to give an overview of the SpatialKey system. Even after watching it a dozen times I get blown away every time I watch it. Make sure to check it out in all its HD full-screen glory!

We’ve also got a whole series of videos that go into much more detail of all the different features. If you spend a few minutes watching some of those we’re betting you’ll start brainstorming ideas about how you can use SpatialKey with your own data.

This private beta period means that you fill out the form requesting access and we will be adding accounts on a first come first served basis over the next few weeks. We will add accounts as fast as we can over the following weeks and then after this private beta period we will open the system up to a full public beta, at which point anyone will be able to sign up for a trial account. SpatialKey will be a software as a service product that you’ll pay for with a monthly subscription based on your usage and data needs (pricing details will be coming very shortly, for now all accounts are a free trial with a cap of 10,000 rows per dataset).

I’ll be posting much more about SpatialKey and what you can do with the software, but until then go check it out and let us know what you think!

SpatialKey has been a long time coming and it wouldn’t be possible without the fantastic work of everyone involved. A personal thank you (in no particular order) goes out to every talented member of the SpatialKey family: Darron, Ben, Andy, Zach, Reggie, John, Tom G, Anthony, Francisco, Robert, Mike, Brandon, and Tom L.

Here’s an excerpt from the article talking about SpatialKey:

Today, the officers are using the new BI tools to perform geographic profiling of crimes and analysis of police data “in seconds,” he says. Before, it could take days for the department’s single crime analyst to fulfill a report request. An added bonus is that experienced police officers with extensive street experience are now able to apply their firsthand knowledge to crime analysis.

“You have practitioners asking the what-if questions, which has changed the way we police,” Greiner says.

And here’s the cover of this month’s print edition of the magazine:

That’s our heatmap! Yeah!