by Hannah Fry
This algorithm had certainly been proved effective. And it brought other positives, too. As it only prioritizes your existing list of suspects, it doesn’t suffer from bias of the kind we met in the ‘Justice’ chapter. Also, it can’t override good detective work, only make an investigation more efficient; so there’s little chance of people putting too much trust in it.
It is also incredibly flexible. Since Operation Lynx, it has been used by more than 350 crime-fighting agencies around the world, including the US Federal Bureau of Investigation and the Royal Canadian Mounted Police. And the insights it offers extend beyond crime: the algorithm has been used to identify stagnant water pools that mosquitoes use as breeding grounds, based on the locations of cases of malaria in Egypt.14 A PhD student of mine at University College London is currently using the algorithm in an attempt to predict the sites of bomb factories on the basis of the locations where improvised explosive devices are used. And one group of mathematicians in London have even used it to try to track down Banksy,15 the reclusive street artist, on the basis of where his paintings have been found.
The kinds of crimes for which geoprofiling works best – serial rapes, murders and violent attacks – are, fortunately, rare. In reality the vast majority of infractions don’t warrant the kind of man-hunt that the Clive Barwell case demanded. If algorithms were to make a difference in tackling crime beyond these extreme cases, they’d need a different geographical pattern to go on. One that could be applied to a city as a whole. One that could capture the patterns and rhythms of a street or a corner that every beat officer knows instinctively. Thankfully, Jack Maple had just the thing.
Charts of the Future
A lot of people would think twice about riding the New York City subway in the 1980s. It wasn’t a nice place. Graffiti covered every surface, the cars stank of stale urine, and the platforms were rife with drug use, theft and robbery. Around 20 innocent people were murdered below ground every year, making it practically as dangerous a place as any in the world.
It was against this backdrop that Jack Maple worked as a police officer. He had recently earned himself a promotion to transit lieutenant, and in his years in the force he’d grown tired of only ever responding to crime, rather than fighting to reduce it. Out of that frustration was born a brilliant idea.
‘On 55 feet of wall space, I mapped every train station in New York City and every train,’ Maple told an interviewer in 1999. ‘Then I used crayons to mark every violent crime, robbery and grand larceny that occurred. I mapped the solved versus the unsolved.’16
They might not sound like much, but his maps, scrawled out on brown paper with crayons, became known as the ‘Charts of the Future’ and were, at the time, revolutionary. It had never occurred to anyone to look at crimes in that way before. But the moment Maple stepped back to view an entire city’s worth of crime all at once, he realized he was seeing everything from a completely new perspective.
‘It poses the question, Why?’ he said. ‘What are the underlying causes of why there is a certain cluster of crime in a particular place?’ The problem was, at the time, every call to the police was being treated as an isolated incident. If you rang to report an aggressive group of drug dealers who were hanging out on a corner, but they ducked out of sight as soon as the cops arrived, nothing would be recorded that could link your complaint to other emergency calls made once the gang retook their positions. By contrast, Maple’s maps meant he could pinpoint precisely where crime was a chronic problem, and that meant he could start to pick apart the causes. ‘Is there a shopping centre here? Is that why we have a lot of pickpockets and robberies? Is there a school here? Is that why we have a problem at three o’clock? Is there an abandoned house nearby? Is that why there is crack-dealing on the corner?’17
Being able to answer these questions was the first step towards tackling the city’s problems. So in 1990, when the open-minded Bill Bratton became head of the New York Transit Police, Maple showed him his Charts of the Future. Together they used them to try to make the subway a safer place for everyone.18
Bratton had a clever idea of his own. He knew that people begging, urinating and jumping the turnstiles were a big problem on the subway. He decided to focus police attention on addressing those minor misdemeanours rather than the much more serious crimes of robbery and murder that were also at epidemic levels below ground.
The logic was twofold. First, by being tough on any anti-social behaviour at the crime hotspots, you could send a strong signal that criminal activity was not acceptable in any form, and so hopefully start changing what people saw as ‘normal’. Second, the people evading fares were disproportionately likely to be criminals who would then go on to commit bigger crimes. If they were arrested for fare evasion they wouldn’t have that chance. ‘By cracking down on fare evasion, we have been able to stop serious criminals carrying weapons at the turnstiles before they get on the subways and wreak havoc,’ Bratton told Newsday in 1991.19
The strategy worked. As the policing got smarter, the subways got safer. Between 1990 and 1992, Maple’s maps and Bratton’s tactics cut felonies on the subway by 27 per cent and robberies by a third.20
When Bratton became Police Commissioner for the New York Police Department, he decided to bring Maple’s Charts of the Future with him. There, they were developed and refined to become CompStat, a data-tracking tool that is now used by many police departments, both in the United States and abroad. At its core remains Jack Maple’s simple principle – record where crimes have taken place to highlight where the worst hotspots are in a city.
Those hotspots tend to be quite focused. In Boston, for instance, a study that took place over a 28-year period found that 66 per cent of all street robberies happened on just 8 per cent of streets.21 Another study mapped 300,000 emergency calls to police in Minneapolis: half of them came from just 3 per cent of the city area.22
But those hotspots don’t stay the same over time. They constantly move around, subtly shifting and morphing like drops of oil on water, even from one day to the next. And when Bratton moved to Los Angeles in 2002, he began to wonder if there were other patterns that could tell you when crime was going to occur as well as where. Was there a way to look beyond crimes that had already happened? Rather than just responding to crime, the source of Maple’s frustration, or fighting it as it happened, could you also predict it?
The flag and the boost
When it comes to predicting crime, burglary is a good place to start. Because burglaries take place at an address, you know precisely where they happened – unlike pickpocketing, say. After all, it’s quite possible that a victim wouldn’t notice their phone missing until they got home. Most people will report if they’ve been burgled, too, so we have really good, rich datasets, which are much harder to gather for drug-related offences, for instance. Plus, people often have a good idea of when their homes were burgled (perhaps when they were at work, or out for the evening), information you won’t have for crimes such as vandalism.
Burglars also have something in common with the serial murderers and rapists that Rossmo studied: they tend to prefer sticking to areas they’re familiar with. We now know you’re more likely to be burgled if you live on a street that a burglar regularly uses, say on their way to work or school.23 Wefn1 also know that there’s a sweet spot for how busy burglars like a street to be: they tend to avoid roads with lots of traffic, yet home in on streets with lots of carefree non-locals walking around to act as cover (as long as there aren’t also lots of nosey locals hanging about acting as guardians).24
But that’s only the first of two components to the appeal of your home to a burglar. Yes, there are factors that don’t change over time, like where you live or how busy your road is, that will ‘flag’ the steady appeal of your property as a target. But before you rush off to sell your house and move to a quiet cul-de-sac with a great Neighbourhood Watch scheme, you should also be aware that crime hotspots don’t stay still. The second component of yo
ur home’s appeal is arguably the more important. This factor depends on what exactly is going on right now in your immediate local neighbourhood. It’s known as the ‘boost’.
If you’ve ever been broken into twice within a short space of time, you’ll be all too familiar with the boost effect. As police will tell you after you’ve first been victimized, criminals tend to repeatedly target the same location – and that means that no matter where you live, you’re most at risk in the days right after you’ve just been burgled. In fact, your chances of being targeted can increase twelvefold at this time.25
There are a few reasons why burglars might decide to return to your house. Perhaps they’ve got to know its layout, or where you keep your valuables (things like TVs and computers are often replaced pretty quickly, too), or the locks on your doors, or the local escape routes; or it might be that they spotted a big-ticket item they couldn’t carry the first time round. Whatever the reason, this boost effect doesn’t just apply to you. Researchers have found that the chances of your neighbours being burgled immediately after you will also be boosted, as will those of your neighbours’ neighbours, and your neighbours’ neighbours’ neighbours, and so on all the way down the street.
You can imagine these boosts springing up and igniting across the city like a fireworks display. As you get further away from the original spark, the boost gets fainter and fainter; and it fades away over time, too, until after two months – unless a new crime re-boosts the same street – it will have disappeared entirely.26
The flag and the boost in crime actually have a cousin in a natural phenomenon: earthquakes. True, you can’t precisely predict where and when the first quake is going to hit (although you know that some places are more prone to them than others). But as soon as the first tremors start, you can talk quite sensibly about where and how often you expect the aftershocks to occur, with a risk that is highest at the site of the original quake, lessens as you get further away, and fades as time passes.
It was under Bratton’s direction that the connection between earthquake patterns and burglary was first made. Keen to find a way to forecast crimes, the Los Angeles Police Department set up a partnership with a group of mathematicians at the University of California, Los Angeles, and let them dig through all the data the cops could lay their hands on: 13 million crime incidents over 80 years. Although criminologists had known about the flag and the boost for a few years by this point, in searching through the patterns in the data the UCLA group became the first to realize that the mathematical equations which so beautifully predicted the risk of seismic shocks and aftershocks could also be used to predict crimes and ‘aftercrimes’. And it didn’t just work for burglary. Here was a way to forecast everything from car theft to violence and vandalism.
The implications were indeed seismic. Rather than being able to say that a recently victimized area of the city was vaguely ‘more at risk’, with these equations, you could quantify exactly what that risk was, down to the level of a single street. And knowing, probabilistically speaking, that a particular area of the city would be the focus of burglars on a given night, it was easy to write an algorithm that could tell police where to target their attention.
And so PredPol (or PREDictive POLicing) was born.
The crime tipster
You might well have come across PredPol already. It’s been the subject of thousands of news articles since its launch in 2011, usually under a headline referencing the Tom Cruise film Minority Report. It’s become like the Kim Kardashian of algorithms: extremely famous, heavily criticized in the media, but without anyone really understanding what it does.
So before you fill your mind with images of seers lying in pools of water screaming out their premonitions, let me just manage your expectations slightly. PredPol doesn’t track down people before they commit crimes. It can’t target individual people at all, only geography. And I know I’ve been throwing the word ‘prediction’ around, but the algorithm can’t actually tell the future. It’s not a crystal ball. It can only predict the risk of future events, not the events themselves – and that’s a subtle but important difference.
Think of the algorithm as something like a bookie. If a big group of police officers are crowded around a map of the city, placing bets on where crime will happen that night, PredPol calculates the odds. It acts like a tipster, highlighting the streets and areas that are that evening’s ‘favourites’ in the form of little red squares on a map.
The key question is whether following the tipster’s favourite can pay off. To test the algorithm’s prowess,27 it was pitted against the very best expert human crime analysts, in two separate experiments; one in Kent in southern England, the other in the Southwest Division of Los Angeles. The test was a straight head-to-head challenge. All the algorithm and the expert had to do was place 20 squares, each representing an area of 150 square metres, on a map to indicate where they thought most crime would happen in the next 12 hours.
Before we get to the results, it’s important to emphasize just how tricky this is. If you or I were given the same task, assuming we don’t have extensive knowledge of the Kentish or Californian crime landscapes, we’d probably do no better than chucking the squares on to the map at random. They’d cover pathetically little of it, mind – just one-thousandth of the total area in the case of Kent28 – and every 12 hours you’d have to clear your previous guesses and start all over again. With this random scattering, we could expect to successfully ‘predict’ less than one in a hundred crimes.29
The experts did a lot better than that. In LA, the analyst managed to correctly predict where 2.1 per cent of crime would happen,30 and the UK experts did better still with an average of 5.4 per cent,31 a score that was especially impressive when you consider that their map was roughly ten times the size of the LA one.
But the algorithm eclipsed everyone. In LA it correctly forecast more than double the number of crimes that the humans had managed to predict, and at one stage in the UK test, almost one in five crimes occurred within the red squares laid down by the mathematics.32 PredPol isn’t a crystal ball, but nothing in history has been able to see into the future of crime so successfully.
Putting predictions into practice
But there’s a problem. While the algorithm is relatively great at predicting where crime will happen over the next 12 hours, the police themselves have a subtly different objective: reducing crime over the next 12 hours. Once the algorithm has given you its predictions, it’s not entirely clear what should be done next.
There are a few options, of course. In the case of burglary, you could set up CCTV cameras or undercover police officers and catch your criminals in the act. But perhaps it would be better for everyone if your efforts went into preventing the crime before it happened. After all, what would you prefer? Being the victim of a crime where the perpetrator gets caught? Or not being the victim of a crime in the first place?
You could warn local residents that their properties are at risk, maybe offer to improve the locks on their doors, maybe install burglar alarms or timers on their light switches to trick any dodgy people passing by into thinking there’s someone at home. That’s what one study did in Manchester in 2012,33 where they managed to reduce the number of burglaries by more than a quarter. Small downside, though: the researchers calculated that this tactic of so-called ‘target hardening’ costs about £3,925 per burglary it prevents.34 Try selling that to the Los Angeles Police Department, which deals with over 15,000 burglaries a year.35
Another option, which deviates as little as possible from traditional policing, is a tactic known as ‘cops on the dots’.
‘In the olden days,’ Steyve Colgan, a retired Metropolitan Police officer, told me, ‘[patrols] were just geographic, you got the map, cut it up into chunks, and divvyed it up. You’re on that beat, you’re on that beat. As simple as that.’ Problem was, as one UK study calculated, a police officer patrolling their randomly assigned beat on foot could expect to come within a h
undred yards of a burglary just once in every eight years.36
With cops-on-the-dots, you simply send your patrols to the hotspots highlighted by the algorithm instead. (It should be called cops-on-the-hotspots, really. I guess that wasn’t as catchy.) The idea being, of course, that if the police are as visible as possible and in the right place at the right time, they’re more likely to stop crime from happening, or at least respond quickly in the immediate aftermath.
This is exactly what happened in Kent. During the second phase of the study, as the evening shift started, the sergeant printed off the maps with red squares highlighting the at-risk areas for that evening. Whenever the police on patrol had a quieter moment, they were to go to the nearest red box, get out of their cars and have a walk around.
On one particular evening, in an area they would never normally go to, police found an east European woman and her child in the street. It turned out that the woman was in an abusive relationship and, just minutes before, the child had been sexually assaulted. The sergeant on duty that night confirmed that ‘they found these people because they were in a PredPol box’.37 The suspect was arrested nearby later that night.
That mother and her child weren’t the only people helped by the algorithm during the cops-on-the-dots trial: crime in Kent overall went down by 4 per cent. Similar studies in the United States (conducted by PredPol itself) report even bigger drops in crime. In the Foothill area of Los Angeles, crime dropped by 13 per cent in the first four months of using the algorithm, despite an increase of 0.4 per cent in the rest of the city, where they were relying on more traditional policing methods. And Alhambra, a city in California not far from LA, reported an extraordinary 32 per cent drop in burglaries and a 20 per cent drop in vehicle theft after deploying the algorithm in January 2013.38