That's how it is in the new world of computerised "predictive policing", a high-tech crime-busting technique which Gomperz is helping to pioneer and which could revolutionise modern law enforcement.
The technique is based on a single mathematical algorithm, developed by the University of California, Los Angeles.
This complex equation can in theory predict, with pinpoint accuracy, where criminal offences are most likely to happen on any given day.
Though that sounds like the stuff of science-fiction novels, the principle is straightforward.
The formula mines several years' crime statistics to reveal underlying patterns. These are projected into the future to predict where cars might be stolen, or houses burgled.
The forecasts are startlingly specific, and that's where the policing comes in. Each morning, Gomperz and his colleagues in the Foothill area of northern Los Angeles, where the technology is being tested, are given maps identifying a handful of 125sq m "boxes" - the supposed crime hot-spots for that day.
The officers are instructed to visit them as often as time allows. Once inside the "box", they make themselves as visible as possible.
Computerised "predictive policing" is still in its infancy, but all the early signs are that it has a startling effect. In the city of Santa Cruz, California, where it was first tested last year, a 25 per cent drop in crime was recorded.
In Foothill, the number of non-violent crimes has dropped from about 50 a week to nearer 40 in the eight weeks since trials began. So striking have the results been that UCLA's system will this year be introduced throughout Los Angeles.
And it could soon be crossing the Atlantic. On January 23, Captain Sean Malinowski, an LAPD officer who helped develop the technology, will demonstrate it to delegates at the Defence Geospatial Intelligence conference in London.
"From the data we're seeing, it stops crime happening," Malinowski says, adding that in an era of declining resources, predictive policing can be a particularly valuable tool, since it isn't labour intensive.
"If a suspect turns up, say, to steal a car, and he sees a police officer, then maybe that's enough to stop him committing a crime that day.
"Making arrests is still important. It keeps officers motivated. And in this trial it has certainly been happening.
"But arresting people also takes up a huge amount of time. Booking one guy can take up most of a shift. So if we can reduce crime without doing that, so much the better."
Malinowski struck on the idea for computerised predictive policing several years ago, when he was asked to supply UCLA with crime data for a research project.
In conjunction with UCLA academics, he decided to create an equation which could model crime patterns. The algorithm they came up with uses three pieces of information about each crime - the time, date and location.
Jeff Brantingham, a UCLA anthropologist who helped to develop it, says the trial in Foothill is using a randomised control. On some days, the boxes officers are asked to focus on are randomly generated; on others, they use the algorithm.
"Comparing crime on the days we use the algorithm and the days we don't will give us a true gold-standard test as to whether it really works."
The concept of predictive policing is not without critics. Civil libertarians are concerned it might lead to life mirroring the Tom Cruise film Minority Report, in which police target people for crimes they might commit in the future.
It also has legal complications. Under the Fourth Amendment of the Constitution, police are forbidden to stop a suspect without "reasonable suspicion" that they are committing a crime. No one yet knows if being in a geographic box identified by a computer programme represents reasonable suspicion.
Independent
