Serial killers should fear this algorithm

Bloomberg.com tells us of Thomas Hargrove, who is “building software to identify trends in unsolved murders using data nobody’s bothered with before.” Sounds interesting, hmmm? Here’s a bit from the article: Serial Killers Should Fear This Algorithm.

He spent months trying to develop an algorithm that would identify unsolved cases with enough commonalities to suggest the same murderer. Eventually, he decided to reverse-engineer the algorithm by testing his ideas against one well-known case, that of Gary Ridgway, the so-called Green River Killer, who confessed to killing 48 women over two decades in the Seattle area. Hargrove thought that if he could devise an algorithm that turned up the Green River Killer’s victims, he’d know he was on the right track.

“We found a hundred things that didn’t work,” he recalls. Finally, he settled on four characteristics for what’s called a cluster analysis: geography, sex, age group, and method of killing. For gender, he stuck with women, since they make up the vast majority of multiple-murder victims who aren’t connected to gang-related activity. When he used women between the ages of 20 and 50—the cohort most commonly targeted by serial killers—the algorithm lit up like a slot machine. “It became clear that this thing was working,” he says. “In fact, it was working too well.”

The Green River Killer came up right away in this algorithm. That was good news. Hargrove’s algorithm also pulled up 77 unsolved murders in Los Angeles, which he learned were attributed to several different killers the police were pursuing (including the so-called Southside Slayer and, most recently, the Grim Sleeper), and 64 unsolved murders of women in Phoenix.

Then there was a second group of possible serial killers, those unrecognized by local police. “The whole point of the algorithm was to find the low-hanging fruit, the obvious clusters,” Hargrove says. “But there were dozens and dozens of them all over the country.” [continue]

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