The death toll amounts to 373,377 lost lives.

Max Galka of Metrocosm has made some lovely maps in 2015—tracking everything from obesity trends to property values to UFO sightings—but his latest effort may be the most powerful yet. Using data from the federal Fatality Analysis Reporting System, Galka maps every single U.S. road fatality from 2004 to 2013. The death toll amounts to 373,377 lost lives.

At the national level, Galka’s map almost looks like an electricity grid stretching across America’s road network—with bright orange clusters in metro areas connected via dim red threads across remote regions. Here’s a wide view of the whole country:

Max Galka / Metrocosm

As you zoom toward a more local viewpoint, the dots begin to take shape as women, men, and children. Different colors representing drivers (red), passengers (orange), pedestrians (yellow), cyclists (blue), and groups (purple). Here’s a closer view of the Los Angeles metro area:

Max Galka / Metrocosm

You can narrow in on your own local street grid via the zoom function or through a search. Here’s the closest available look at midtown Manhattan:

Max Galka / Metrocosm

At a certain zoom threshold the map lets users select one of three factors that contributed to the crash: alcohol, speeding, or distraction. By Galka’s calculations, at least one of these factors played a role in 58 percent of the deaths. Alcohol and speeding each had a part in 31 percent, while distraction occurred in 18 percent. (More than one factor can come into play.)

Here’s a look at the factors involved in Washington, D.C., road deaths; white circles represent alcohol-related fatalities, purple reflects speeding, and green distraction:

Max Galka / Metrocosm

Writes Galka:

These are not the only factors tracked by FARS, but I thought they would be the most interesting to include in the map. Each of them contributes to a large number of accidents every year, and assuming these things are under the control of the driver, many of these accidents should be preventable.

Indeed, these three factors in particular are precisely the type of human errors that would be eliminated or dramatically reduced with self-driving cars. They can’t get here soon enough.

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