The Hound of the Data Points

The Hound of the Data Points

Geographic profiling pioneer Kim Rossmo has been likened to Sherlock Holmes; his Watson in the hunt for serial killers is a digital sidekick — an algorithm he calls Rigel.

from POPULAR SCIENCE, March 2003

Until he was called in on the Beltway Sniper investigation, Detective Kim Rossmo’s most confounding case was the South Side Rapist. For almost a decade, an unknown assailant,
his face bandit-wrapped in a scarf, had been stalking women in quiet Lafayette, Louisiana, and then assaulting them in their homes. He remained at large in 1998 when Rossmo, then a detective inspector with the Vancouver Police Department in Canada, was called in to help. The police were under pressure. The town was hungry for an arrest. There was a glut of raw information. But after a couple of thousand tips and close to a thousand suspects — numbers that would be dwarfed by the 15,000 tips a day that the sniper case would generate, but a sea of data all the same — investigators were no further ahead.

Rossmo’s job was to help direct the manhunt. If he couldn’t find the needle, he hoped at least to radically thin the haystack. And he would do so through the careful application of that most powerful of investigative tools: a mathematics equation.

Rossmo, 47, is the inventor and most zealous proponent of criminal geographic targeting (CGT), more commonly known as geographic profiling. He uses CGT to hunt society’s most dangerous game: violent serial criminals — arsonists, rapists and murderers whose taste for carnage seems only to sharpen with time, and who tend to programmatically continue their offenses until they are caught. There’s no mistaking Rossmo for the FBI profilers down in Quantico’s Behavioral Assessment Unit, the ones that movies like The Silence of the Lambs have turned into celebrities. He can’t tell what kind of offender is terrorizing the town, how old or what race, whether he has delusions of grandeur or issues with Dad — nor does Rossmo particularly care about those things. His interest is in the most neglected of the Five W’s: Where did the offender strike? From this Rossmo can usually calculate where, most likely, he lived.

In Lafayette, Rossmo and lead investigator McCullan “Mac” Gallien walked the city’s streets for three straight days, revisiting the crime sites. Then Rossmo produced a computer-
generated printout that resembled a tie-dyed shirt; its bands of color — from cool violet to hot yellow — told police, essentially, where to look first. That narrowed the hunting area to half a square mile, and reduced the pool to a dozen suspects who lived in that zone. Investigators were buoyed. But the bubble burst when, one by one, each of the suspects was cleared based on DNA evidence.

Then Gallien received an anonymous tip that he almost dismissed as a joke. The man the informer named was someone Gallien knew personally — another cop — Randy Comeaux, a pleasant-mannered Stephen King lookalike who was a
sheriff’s deputy in a department just outside of town. Idly curious, Gallien checked Comeaux’s address and compared it to Rossmo’s probability map. Not even close.

To be complete, though, Gallien fished out Comeaux’s personnel file. At the time of the rapes, he discovered, Comeaux had resided someplace else. Gallien checked that address against Rossmo’s profile and drew in a breath. The house fell right into Rossmo’s “hot zone.”

Read the whole article here:

www.popsci.com/scitech/article/2003-03/hound-data-points

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