13 July 2019 | New Scientist | 47This makes sense: if thieves get away with
a burglary and know the area, they might be
tempted to come back. So you can use weekly
reports of burglaries to predict future ones.
We developed algorithms based on this,
and showed in a 2006 trial that they would
out-predict hotspot policing if deployed weekly.
Similar algorithms to ours have been used
to create commercial predictive policing
products. One, called PredPol, is now widely
used by police in the US.Could predictions improve further?
I think so – and in two ways. First off, most
algorithms make predictions in the form
of squares on a map. But these bear no
relationship to the urban landscape – they
might be bisected by a train line. Working
with West Yorkshire Police on their PatrolWise
project, we wanted to try making predictions
at the level of street segments, meaning any
section of road between intersections. This is
meaningful urban geography, both for police
officers and the way that an offender mightnavigate. The idea is that offenders become
aware of a house and then forage around
the streets nearby for new targets.
Second, the high-risk areas we predict can
be all over the place, such as on different sides
of the city. So we developed our algorithm to
spit out four 2.5-kilometre-long patrol routes
that cover the highest-risk areas possible in
a continuous line. The trial isn’t finished yet,
but so far, police figures suggest that crime
has reduced more quickly in the areas that
are using PatrolWise than those that aren’t.A lot of people are worried about bias
in algorithms. Are you?
The big worry is that algorithms might
perpetuate bias in existing data sets.
We should definitely be worried about
this – and more worried the less
transparent the approaches used are.
But this doesn’t apply equally to all
algorithms. With the place-based crime
prediction that we do, the data that goes invoice-activated devices and so on – continue to
revolutionise our lives, but offer opportunities
for misuse. At the same time, it is important
not to be too alarmist – these technologies can
be used to help reduce or detect crime, too.
You also study evidence-based policing.
How does that work in practice?
It means asking whether things the police
do will have the desired effect. For example,
the City of London Police has recently been
running trials to gather evidence on whether
having officers wearing tasers increases the
number of violent incidents they are involved
in. During the trial period, it turned out that
officers carrying a taser on their chest were
assaulted 48 per cent more than unarmed
officers outside the trial period. You can test
things like this with trials.
More and more groups around the world,
including universities and some police forces,
are championing evidence-based policing and
working collaboratively to generate evidence.
But having evidence that a certain policing
method is better isn’t yet a requirement
for police, and I think there could be more
of a push in that direction. I hope future
generations of officers are exposed to it
right from the start of their careers.
You have done a lot of work in predictive policing.
It sounds a bit like sci-fi — does it work?
Forty years of research shows that, roughly
speaking, 80 per cent of urban crime occurs
in 20 per cent of places. That’s according to
both reported crime statistics and surveys
of victims, which capture crimes that aren’t
reported to the police. Given that we know
this, the question is how you direct limited
police resources to do the most good. One
solution is hotspot policing, where you send
police to the places with the most concentrated
reports of crime. Randomised controlled trials
show that it is effective: if officers patrol the
hotspots, it suppresses crime and it doesn’t
shift it elsewhere.
But you took it further?
Places of high crime are unlikely to be the same
tomorrow as they are today. One area might
generally have the most crime in it over the
course of a year, but on a daily basis, it is going
to move and temporarily flare up in other
places. When we started asking if we could
predict that, we discovered a phenomenon
we called “near repeats”: when a home is
burgled, that house and its neighbours are at
greater risk of repeat victimisation for a short
period, before the risk quickly fades away.
is crimes reported to the police. For things
like burglary and vehicle theft, we know from
victim surveys that most are reported, not
least because you need a crime number for
insurance purposes. So we have a good picture
of crime that is committed. That’s different
to when an algorithm might be working from
a data set that doesn’t include crimes against
certain demographics of the population.
What our algorithms don’t work on is data
on arrests. If they did, that would be a problem,
because arrests are a function of police activity,
which can, in theory, be biased, for example
because not all crime is detected.Besides sending out police on patrols, what can
you do to prevent the crime you predict?
In something we called Operation Swordfish,
we tried to see if we could intervene to prevent
burglaries in an easier and less expensive
way than sending patrols. In a randomised
controlled trial in the East Midlands, we gave
at-risk homes a “target hardening kit”, which
included things like a tiny LED that made it
look like your TV was on at night, and a door
alarm. The total cost was about £12. We told
people, “you’re at an elevated risk, it’s going to
go away – nothing scary – but here are some
things you can use to protect yourself.” For
every 1000 burglaries that were reported to the
police and prompted the delivery of the target-
hardening kit to nearby homes, around six or
seven burglaries were prevented per week.
Many people worried the approach would
have negative effects, increasing fear of crime,
for instance. But we tested it and found that’s
not what happens at all: it didn’t increase fear
of crime and people in these treatment areas
were more satisfied with the police. ❚“ Officers carrying
a taser were
assaulted more
than unarmed
officers”
Joshua Howgego is a features editor at New Scientist
specialising in physical sciencesShane Johnson is a
criminologist and director
of the Dawes Centre for
Future Crime at University
College London