How Math Is Helping Us to Reduce Crimes

Math is helping us to safeguard houses that are more vulnerable to burglary.

It tells us that houses that are burglared once are likely to be burglared again.

It is unintuitive as you might think that burglars don’t have the audacity to return to the place where people will be most attentive to them after they made them undertake loss.

The rationale behind it is that burglars will be more familiar with the whereabouts of the place—which door to enter the house from and where the costly stuff is—and the position of the nearby police.

The paper Mathematical model offers new strategies for urban burglary prevention by Society for Industrial and Applied Mathematics reads:

Big cities also allow burglars to maintain anonymity and evade authority while offering ample opportunities for discreet disposal of stolen property. Burglars observe their target cities with the careful attention of urban planners, taking note of public spaces, roadways, building architecture, behavior patterns, and tenant schedules. Although law enforcement is making concerted efforts to address and prevent burglary, frequent offenses in major metropolises continue to unsettle city-dwellers.

This suggests that if your neighbour has become a victim, the next target could be you.

It tells us that urban places are likely to be burglared more than the rural places do because of the accumulation of more wealth.

“Our model focuses on the burglars’ dynamics: their propensity to strike, their preference to act in groups, and different strategies to choose targets,” Saldaña said. “All of these aspects are linked to the age of a burglar in our formulation. This allows us to implement different behavioral theories and use particular information obtained directly from offenders, like the so-called ‘individual offending frequency’ considered in criminology.”

It’s also easier to maintain anonymity in metropolitan cities—to disappear from the spot quicker—in comparison to the places in confinement.

We came to know it by analyzing the past episodes of houses that were the victim of burglary like we analyze weather and predict it.

It works like this:

A burglar’s age is the amount of time since his most recent offense, while a house’s age is the amount of time since it was last burglarized. The likelihood of robbery acts as a function of a burglar’s age, and a house’s susceptibility is a function of that house’s age. When a burglar commits a crime, the ages of both the house and the burglar reset to zero. These details add a level of heterogeneity to the populations of houses and burglars.

Ahmad Khan

I have no blood in my veins. I have ink.

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