When we ask our departments what their biggest crime headaches are, we almost always hear about the rise in catalytic converter thefts. The growth in this very specific crime type has been dramatic over the last few years, from 1,300 reported in 2018 to an estimated 156,000 in 2022, according to Carfax.
Catalytic converters are attractive to thieves because they’re simple to steal and can fetch high prices on the black market. An experienced thief with the right tools can remove one in under a minute and resell it for prices ranging from $200 to over $1,000. That’s because converters contain valuable metals such as platinum, palladium, and rhodium. Replacing a converter isn’t cheap either – parts and labor can cost over $3,000.
Converter thefts can occur in almost every neighborhood around the country, but there are ways that law enforcement agencies help deter them. A large percentage of these thefts appear to be the product of groups of people who work together and develop behaviors that can be tracked and anticipated by Geolitica’s machine learning models.
When converter thefts started surging a couple of years ago, our customers wanted to take action. The problem was that in most record management systems (RMSs) these were tracked in a more general category, like “theft from auto” or “theft of vehicle parts.” Some of our customers agreed to include RMS narrative fields and make that information indexed and searchable. As a result, these departments can search for terms like “theft” + “catalytic converter” to extract and highlight these exact crimes. This gives them immediate insight into the times and locations of converter thefts rather than digging them out from all their “theft from auto” crime records.
The next step was to set up a mission to deter catalytic converter thefts. Geolitica loaded and analyzed historical converter theft data and then displayed, on a Google Maps interface, the places most likely to be targeted. These highest-risk locations were uniquely generated every day for each shift, giving patrol officers very specific places to target. Geolitica’s targeting is more accurate than traditional approaches such as heatmaps because we use machine learning to identify behavioral patterns by time of day, day of week, and season of year. Officers patrolling these locations can drive down converter thefts by double-digit margins within a matter of months.
We have another customer who used Geolitica to identify the buyer of stolen converters, replacing a process they had previously done manually. They used our search capabilities to display all converter thefts associated with a particular MO (based on a targeted vehicle type) and found that they formed a circle around a known chop shop. After staking out some of their Geolitica-identified theft sites, they gathered sufficient evidence to connect the thieves to the chop shop’s location. The resulting arrests rolled up a prolific converter theft operation that had plagued the city and surrounding communities for many months.
These are some of the key ways Geolitica can help you with this growing and highly visible problem:
- Quickly identify and map out converter thefts, locations, and times
- Use machine learning to identify highest-risk locations and patrol them to deter additional thefts
- Give your analysts tools to conduct a deeper search of crime records by location, time, and MO to identify clusters of related activities.