hans koenderink - the national police of the netherlands - predictive policing

Rutger Rienks

Predictive PolicingTaking a Chance for a Safer Future

Politie - Landelijke Eenheid - DLIO

Predictive Policing

Predictive Policing

Taking a Chance for a Safer Future

-Making our society even better-

Rutger Rienks

1st edition, July 2015Version for the English translation 21-4-2015

The illustrations used in this publication were provided by the Action Plan for Streamlining Information within the Police Service.The author specifically allows the free dissemination and reproduction of this publication.

Production: Korpsmedia, PDC

Foreword

Crime will always be with us, at least that’s what I always thought. When I applied to join the police, another five years would pass before the film ‘Minority Report’ would be released. A film about a story dating from 19561 in which three clairvoyants could predict the future and the pre-crime department of justice could prevent offences before they had even been committed. For me this was a boyhood dream worthy of purs-uit. A safe society without crime, I was going to make sure it happened. Three years later, in 2005, perhaps partly inspired by this film, the Police in Development report (Board of Chief Commissioners, 2005) was publis-hed, with ten points determining the strategic course the Netherlands police would follow. This laid down the foundations for realising the first pre-crime department in the Netherlands.

The message was, among other things, that the police were no longer satisfied with a passive implementing role, but want to be authoritative trainers and facilitators as well. They want to be able to alert and ad-vise the Public Prosecution Service, the administration and partners, in order to make the Netherlands safer. Furthermore, information should have a greater role in combating crime and forming relevant policy, and Information-led Policing (IGP) should be elaborated upon. One year later, in 2006, this development continued. The strategic vision on the provision of police information and technology, ‘Winking Perspective’ (Board of Chief Commissioners, 2006), stated that strategy would focus on increasing the capacity of police officers to identify criminal behavi-our by means of generic alert principles and retrieving knowledge and signals from (police) data. For the first time we were actually reading about predictive analyses, provided in the context of determining who may be involved in a future event.By December 2014 the Netherlands police were on the eve of a breakthrough in predictive policing. Over the next few years predictive

1 The Minority Report is a short science-fiction story written by Philip Dick and first published in 1956 in the American science-fiction magazine, Fantastic Universe

methods and techniques will become common knowledge on an even-larger scale and within a growing number of different police domains. A bold forecast perhaps? Perhaps, but I see a number of important factors that will play a definitive role here.

In the first place, initial national and international experiments show that police performance can make enormous progress with the aid of predictive techniques and methods (Schakel et al., 2012; Willems & Doeleman, 2014). Necessary techniques such as data mining and pat-tern recognition have been on the market for some time now2. We can do more with fewer resources, we are in the right place more frequently, catch the most important criminal more frequently and we really can anticipate some types of incidents. Secondly, the economic situation is forcing us to become more efficient; innovation is fed by scarcity. Thirdly, the decision to create the National Police Service means that, since 1 January 2013, our forces are bundled and volumes are being compiled that facilitate speed and specialisation. Lastly, a fourth driver, the way in which the police are provided with information, is being thoroughly re-vised by the National Police Treatment Programme. As a result, old and variegated systems dating from the days of the regional police forces are making way for a new national and standardised functionality capable of making data available faster. The BVI3 development is an example of this. Rapidly available data, in conjunction with human experience and knowledge, are converted into action and results, so the police are finally able to take more effective, faster and preventive action.

People sometimes ask me whether I think predictive policing is a good development. ‘Bet your life I do!’ is my reply. Who could be opposed to the police machinery doing its work better? After all, every victim of no matter what crime wants nothing more than the prevention of the crime to which he or she was exposed. Of course, an important role is also played by discussions about ethical matters and the right to privacy of citizens versus the breach of privacy by the government. Just consider the fact that laws are being introduced prescribing what is and what is not allowable. The influence this has on the efficiency of the police is

2 The first international scientific conference on knowledge discovery and data mining (KDD-95) was in August 1995 in Montreal, Canada

3 BVI stands for Basic Information Provision, the data warehouse of the Dutch National Police

immediate. On the other hand, police tasks do not really change. Acting in accordance with current regulations and with subservience to the competent authority ensures the actual enforcement of public order and safety, and that the provision of help for those who need it4 remains intact. Because of progress in predictive policing, the police arrive at the scene faster and they may even take action before a contingency has occurred. The fact that in some cases this may have less pleasant consequences than desired could be the subject of necessary new policy and legislation. To my mind, the field of tension between what we are prepared to pay for safety and the privacy that our society is willing to sacrifice for the sake of safety is subject to a democratic process. It is within these agreements that the police operate, and they must do everything possible to work as effectively and efficiently as possible.

The world of policing is a fascinating and exciting world. No two days are the same and there is always a demand for new and creative ideas. After 7 years spent working with the police, I felt it was time to put my ideas to paper, because of increasing rumours surrounding the phenomenon of predictive policing. The police service is a fantastic employer, with wonderful colleagues who want the best for society. At the same time I see opportunities for improvement and for more effective and efficient policing. I hope to make a modest contribution with this booklet.

4 Article 3 of the Police Act 2012

Contents

1. Introduction 15

Technology and Big data for improved police performance 16

Predictive policing to date 18

Structure of the book 25

2. Recognising criminal behaviour 29

Observing using senses and sensors 31

Observing crime 35

Indicators of crime 37

About recognising indicators and using indicators for recognition 40

Recognising the future 45

3. From recognition to appropriate action 53

From encoding to (re)action 56

Combating and preventing crime 61

4. Knowledge and historical data for the future 69

The usefulness of sources and information 73

Obtaining knowledge from data 80

Obtaining knowledge from people 87

Predictive values and predictive systems 93

5. Useful predictions for the police 99

Predicting organisation operations 106

Predicting crime 111

Predictions on police performance 118

6. An ethical consideration 123

Are we becoming dependent on machines? 125

Objectivity and bias in data 128

Craving for optimisation and collectivity 132

7. The future of predictive policing in the Netherlands 137

What is needed in order to really get started 138

What else can we expect? 143

Epilogue 147

Index 149

Sources 151

Introduction

14 Predictive Policing

1. Introduction

What would it be like if you knew in advance what was going to happen? For ages people have been trying to predict events. For instance, where the most fertile soils can be found or what tomorrow will be like. Just consider the weather, for example. Wouldn’t it be great to know in advance that the weather will be nice later in the week, so you could plan a weekend away and not have to worry about showers or thunderstorms?

Predictions enable you to take into account what you think will happen in the future, such as earthquakes, changes on the stock market or the showers I mentioned earlier. You can sometimes make predictions be-cause of the existence of a degree of regularity or because ‘phenomena’ can be incorporated into mathematical models. For instance, we know precisely when the next eclipse of the sun will take place. Sometimes, how ever, It is difficult, particularly when you know very little about a topic.

Predictions can help us to see the possible consequences or results of our own activities or the activities of others. They provide an image of a moment in the future. Anticipating like this can increase your chance of success. Success in the form of a longer life, making fewer errors or making the right assessments. The trouble people will go to in order to obtain a good prediction depends on the added value of what is being predicted. Consider, for example, predictions on share prices. This is enormously complex, but because of the large potential margin for profit, millions are invested in order to obtain insight in developments in the value of shares and bonds.

How safe would we be if the police knew everything in advance and could prevent all forms of crime? Society would be completely different. This book is about a complex form of prediction: predictive policing, or in simple terms: police work based on predictions. Is it possible to predict crime? Can crime be recognised at an early stage? What are effective


police interventions? Isn’t it just a question of having more policemen and women out on the streets? Which instruments are most useful? Who are the best partners for collaboration, and the removal of which cause of crime would have the biggest effect on reducing crime? Can sensible predictions and well-informed decisions be made about these matters? Can the police access the right information, and pass it on to the right person at the right time?

This book tries to provide answers to this type of question. It shows the advantages that can be gained from predicting criminal behaviour, but also the disadvantages that are involved. Is the crystal ball of the police re-liable (enough)? What technology is available? And which ethical issues play a role? Initial exploratory thoughts on an emerging phenomenon.

The goal is more effective deployment of the police (Versteegh et al., 2013), also in respect of predictive policing. In order to become more effective, perhaps solutions should actually be sought in forging links with other partners, more effective deployment of information and pre-venting crime by removing their causes. The perspective of better police performance is tantalisingly close!

Technology and Big data for improved police performance

Rapid technological developments make it possible to make predic-tions with the aid of modern technological discoveries. Criminal beha-viours can be discovered via automated means. From speed offences to credit card fraud, and from malicious misrepresentations to smuggling goods across borders. It is possible to develop knowledge on how to pre-dict crime, now that data on crime are available on a broader scale. Using knowledge in model form in technological facilities will make it pos-sible to recognise and predict crime. Advances in smart mathematical algorithms enable us to actually establish connections between data and crime. This is creating opportunities for making large-scale predictions based on, for example, reference data of past events. Predictions about when and where certain types of crime will occur. This could be about li-quidations in the underworld or about the radicalisation process among individual citizens.

171. Introduction

Technological developments have given rise to entirely new forms of crime and criminals can now commit offences long-distance. On the other hand, criminals also leave their traces in this digital environment. The same technological development is also providing the police with new opportunities. Using all sorts of newly created sources such as so-cial media, a picture can rapidly be formed of a local situation, of groups of perpetrators and also, for example, of the mood of the population (De Vries & Smilda, 2014). Furthermore, data are not only much more omni-present than in the past, in the sense of quantity, the form that data take is also much more varied. Nowadays videos are stored as if they were e-mails and streaming services transmit zettabytes of data all over the world. In today’s society we can no longer imagine life without digital television or file-sharing services.

As time passes we learn better ways of dealing with these new pos-sibilities. In the past a bag snatching would be settled at a tribunal (= antiquated law court) with a few handwritten documents and an item that may or may not have been secured, while nowadays digital evidence has already made its debut in the court of law. Visual evidence, such as photographs and videos are used to convince a law court, and three-dimensional reconstructions (which enable you to walk through them using a pair of virtual reality glasses), based on evidence gathered, are presented before a law court. Nowadays, a geo-profiler – who used to make a calculation based on a dozen crimes in order to discover the most probable domicile of a criminal – has access to many more types of sources and models. All this enables us to make much better and more accurate statements and predictions (Rossmo, 1999).

New possibilities create new opportunities. More data are available with today’s technology, and extracting value from data is a lot simpler than it used to be. Data storage and data processing capacity are still increasing exponentially5. These developments are creating new possibilities for predictive policing. Not only is it simpler to store data from the past, it can also be retrieved and projected onto what is happening today. The dissection of knowledge is also becoming increasingly easy and faster. That is to say knowledge that can be used to calculate which future risks are increasing and which forms of intervention will be most effective.

5 Known as Moore’s Law since 1975


Which available intervention is best suited to a situation? This depends in part on familiarity with the problem, but on the other hand, also on the degree to which an actor is equipped to ‘neutralise’ a situation. It would be easy if all units had exactly the same characteristics: the first one who can get to the scene should be dispatched first. The sooner the problem will be solved, or so you might expect. Often, however, these choices are far more complex. Should an officer on a mountain bike go to the scene of a brawl, or a member of the mounted police, or should you send a pair of officers? Are you more likely to send units to a situa-tion that are closer and whose work can be interrupted, or units that are available, but further afield?

These are just some of the assessments that are made in situations re-quiring the police to find a solution to an acute problem. And this is only about providing emergency aid in one specific case. The police are confronted with many cases simultaneously, and assessments have to be made on many different fronts. Which criminal phenomena or crimes are given priority above others? How are such things determined? And how best to deploy your - generally limited - capacity?

When you can only respond reactively, which means you are literally always one step behind, it is a question of making the smartest choice. After all, in this case the harm has already been done. Retrospective justice makes sure that a criminal gets what he or she deserves. The deployment of police capacity as a proactive presence could possibly prevent a crime from taking place. The mere presence of an officer in a shop may be enough to stop a potential thief from stealing. Clearly, however, it would be impossible to place an officer in every shop. Having more police officers out on the streets may reduce crime slightly, but it is highly unlikely that ‘more of the same’ will be sufficient (Homel, 1994). In concrete situations it is the domain of intelligence that proposes the most suitable intervention.

Predictive policing to date

The term predictive policing was introduced in 2008 by William Bratton, chief of police with the Los Angeles Police Department (Perry, 2013). He was the first person brave enough to model crime in his police district

191. Introduction

using modern mathematical formulas and who managed and planned the deployment of his officers based on predictions. While the focus in the nineteen-sixties was on random surveillance and rapid response times, in the nineteen-nineties this shifted to community-led policing with the focus on problem-solving, proximity and realising partnership. Later, in the mid-nineteen-nineties, the measurement of police perfor-mance started and for the first time attention went into describing crime in terms of geography, via hotspot-maps. This new form, also referred to as Intelligence-Led Policing (ILP), was also used in an attempt to improve management of the police by means of visualising crime in-tensity in geographic areas, and thus increasing their performance. The introduction of predictive policing seems to be the next step, a step that could bring police performance up to a significantly higher level than in the past.

Predictive policing is defined as the science that calculates risks in rela-tion to crime using (computer) models and relevant (police) data. This then forms a basis for proposing police activities to reduce these risks. In the past ILP often used hard statistics on incidents from the past as a basis, while predictive policing adds a new dimension: that of the future. The aim of predictive policing is to explicitly relate the future to criminal behaviour or its generators, based on technology and models. In addi-tion, predictive policing also tries to predict intervention success and to fill in missing elements of criminal processes.

Hot-spot maps generally use not much more than incidents in a location over a period of time in the past to determine criminal intensity in a location. For determining a future risk, in addition to the number of incidents in the past, a predictive policing model generally also includes many other factors. For instance, a change in the number of incidents in a location over time, or other crime-increasing or crime-reducing factors, such as the presence of a police station. In effect, this makes predictive policing an extension to ILP. To start with, we can manage police per-formance better, because relevant factors for the future are taken into account. In addition, the deployable arsenal can be put to more effective and more efficient use, which also increases safety on the streets.

The chance of a safer future is also what legitimises this book. The power of the police will be enhanced by focussing on predictive policing. This


is an opportunity that we, the police, must not allow to pass by, and one that, as I see it, should be particularly encouraged. Opportunities should be grasped, in particular during this initial phase, in order to experiment and obtain experience without enormous consequences. It represents a challenge for the police to become adept at using this attractive resource, with the right guarantees and at the right pace. This demands not only the introduction of new people and new technology, as there are also ethical and organisational challenges that deserve our attention. These aspects are also discussed in this book, which is an initial attempt to place the topic of predictive policing in a Dutch context while also un-derlining its importance. In order to increase returns for the future, it is important that we start sharing the knowledge that is available about predictive policing on as wide a scale as possible.

The comment should be made that increasing safety is under threat from more than just crime. It is affected by other factors such as environ-mental disasters, unemployment, and how we build houses and roads. Of course, this book focuses emphatically on crime. On the one hand because this may be where the police can exert most influence and on the other hand because to date this is where the main focus of predictive policing has been.

The first known predictive-policing system was PredPol6. This system can make predictions about future crimes based on historic data on a type of crime, plus place and time. Categories of crime that the system can predict include traffic accidents, drug-related incidents and theft. By imposing a grid with cells measuring 150 by 150 metres onto a map of a given area, e.g., a city, the individual probabilities of an incident oc-curring in the future can be calculated for the various cells. Studies in Los Angeles and Kent show that PredPol can estimate high-risk areas one-and-a-half to two times better than police analysts. Between 8 and 6% of incidents occurred in the cells designated by PredPol as high-risk areas, in comparison with 5 to 3% for the police analysts. Comparable systems are the Crime Anticipation System (CAS), develo-ped by the police in Amsterdam, and the PRECOBS7 system that is used

6 See: The Economist “Predictive Policing, don’t even think about it”, 20 July 20137 Software developed by the Institut für musterbasierte prognosetechnik (ifmPt)

http://www.ifmpt.de

211. Introduction

in Switzerland and Germany. CAS makes use of other input variables, in addition to historic crime data. For instance, CAS uses data on the closest motorway entry, known criminal companies in the area and other socio-demographic data on residents. By jointly processing these, the system is capable of correctly predicting 40% of house burglaries and 60% of muggings by colouring in 3% of all the cells (Willems & Doeleman, 2014).

A Predictive Policing Model aims at providing a clear indication of the risk by incorporating risk factors. However, a model like this can also propose measures for neutralising these risks as far as possible and as ef-ficiently as possible. Consider, for instance, estimates of the threat posed by potentially dangerous lone wolves. Or the risk of attacks and what the police can do to remove a threat at the earliest possible stage. In order to neutralise risks it is important to know what resources the police can deploy and what is the expected effect of these resources. Should a pair of officers monitor matters or should a dog or an officer of the motorcade be sent? In short: what is the ideal recipe for the anticipated situation? A predictive policing-model can also predict possible perpetrators after an incident has occurred, by establishing the working method. This is where predictive policing can make the difference. By determining the most efficient and effective deployment proposal at the earliest possible stage, the police can improve their performance significantly and com-bat crime more efficiently.


Aren’t people too challenging and their behaviour too unpredictable to permit the recognition of trends and patterns? Bratton responded to this by stating that: ‘Crime is just a physical process and if you can ex-plain how offenders move and how they mix with their victims, you can understand an incredible amount.’ Socio-psychological literature has a number of theories about human behaviour in relation to crime. These show in the case of criminals (Scott, 2000), that ratio, in combination with e.g., the presence of an opportunity (e.g., Cohen & Felson, 1979), results in patterns (see Brantingham, 1993). In other words, crime really can be incorporated into models capable of making predictions.

Even criminals make rational decisions. For instance, about the chance of success, which may depend in part on the location, the time and the chance of getting caught. By paying attention to the right aspects with the best predictive value, you can improve your predictions about crime, until they are finally ready to use and provide police practice with ad-ded value. Relationships between variables make it possible to predict criminal activities. In some cases a prediction will be extremely accurate, while in other cases it is very difficult to make a useful prediction and to score higher than a priori baseline.

If you examine the major processes of police work, such as emergency aid, enforcement, detection and safety & security, there is room for pre-dictions to bring added value to each of these processes.

For instance, in the case of emergency aid, you could look at the optimum (geographical) composition of a group of units in action. For instance, units that may be expected to assist victims in a given area. Predictive policing can make statements about which combination of units can best be deployed and what equipment these units should be given.

In respect of maintaining public order during events, predictions can be made about the risk of escalations or about patterns of movement of masses of people. Are certain groups of people expected who are in conflict with one another? What about motorcycle gangs or football sup-porters? During normal surveillance routines too, a briefing about recent events or about changes in the environment enable you to form a picture of the situation in advance.

231. Introduction

This means you are better prepared with regard to certain aspects, so beats and movements can be more specific. For example, models can provide a specific focus by sorting locations, networks or criminals in respect of one another. For instance, when you arrest juvenile suspects, you can look at their past history and their potential for eventually tur-ning into top criminals. The sooner you establish that a criminal career is developing, the more measures are possible. And the more specific these measures can be. For the top-600 approach in Amsterdam, for example, very specific protocols have been drawn up for moments when the police come into contact with one of them.

For investigations, it is possible to make predictions based on criminals’ specific method of working, or modus operandi. Knowledge about a mo-dus operandi can help determine who is the most probable perpetrator or group of perpetrators. You can also determine which intervention strategy has most effect on a criminal network, in combination with the lowest possible cost price for the police. Is it better to remove a facilitator, e.g., an arms supplier, from a criminal network, or would imprisoning the main suspect be more effective?

Police frustrate criminals with CAS.

By Dick WillemsIt’s no fun anymore, the way things are going, every time I want to start working, the police keep getting in my way,” says Gerrit A, professional burglar. “I have tried altering my territory, but every time I just keep on bumping into them. It’s as if they know what I have in mind, I can’t carry on like this.”

A. is not the the only one suffering under the new policing approach. For instance, Kees van Z., notorious mugger, is also being thwarted by law enforcement officers. “It’s just not fair. Last week they arrested my buddy, so now I have to manage on my own. I simply don’t get a chance to get my hands on any money.”

The police in Amsterdam seem to be always one step ahead of the criminals. This is because police registers are being meticulously analysed and the results are used to predict that the chance of crime is bigger in certain places and at certain times in Amsterdam.

This analysis process is called the Crime Anticipation System. It allows the focused deployment of the police, so that a lot of crime can be prevented.

Gerrit A.: “I always enjoyed working in this city, but if the police carry on like this, I’ll be packing my bags.”


The ideal solution is to put a criminal behind bars for as long as possible while searching for the most effective evidence. In some cases it may be more efficient to bring a case to trial earlier, than to allow a single case to get caught up in too much detail. Preference goes out to return for the team. In that case, the expected additional return of pursuing the same case is inferior to an extra case that the same team can get on with during that (in part) same period.

Predictive models predict whether an event will occur or what the chan-ces are that something will happen. This is independent of time and place. As a result, the chance of an event occurring is not necessarily in the future. Clearly, this is does not really stroke with our natural feeling that a prediction must involve an event in the future. However, the two questions – whether an event has already occurred or whether it will occur – are comparable. Particularly when you have no knowledge of whether something did actually take place. In all cases it is a matter of trying to find out whether something took place, is taking place or will take place. In fact, a prediction can be a suspicion that something will take place, is taking place or did take place.

In this sense, the objective of investigations, determining the truth wit-hout knowing it in advance, is comparable with making a prediction. Evidence in a case increases the suspicion that a crime has been com-mitted. And this is the same as what indicators do for predicting a future situation. In this sense, evidence is also an indicator or predictor. The better the evidence, the more well-founded is the suspicion or prediction. Where it involves a case that has been completed, you might say that the event must have actually taken place. The evidence is complete, and the truth has been brought to light. The arrival of predictive policing provides criminal investigators with a new little brother: ‘pre-investigation’, which focuses not on truth-finding in the past, but truth-finding in the future.

But when is a case actually regarded as completed? When is the risk that something occurred in the past equal to 1? After all, it is a court that decides whether the evidence supplied is convincing enough to convict a person. To do this, the court considers the evidence that has been gathered. This process, as mentioned earlier, can be equated with a good prediction. When can we say with sufficient certainty, on the basis

of indicators, that an event has taken place, is taking place or will take place? What is the subsequent role of predictive policing in all this, and should we, the police, be willing to take such decisions ourselves? We will examine this in more depth.

Structure of the book

The first chapter discusses what predictive policing is all about. How did it come about and why is its popularity growing? Is it possible to predict crime and what does this require? In the first place, in order to predict crime, it is important to be able to see and make observations in the neighbourhood. This is discussed in the second chapter. It des-cribes factors that enable us to recognise deviant behaviour and what makes that behaviour so unique that we can distinguish it from other forms of behaviour. It also discusses the analogy that exists between the reconstruction of crimes in an investigation and making observations and predictions. Crime can be discovered at an early stage by paying attention to characteristics that lead to crime (Mulder, 2014).

The idea is, with the help of knowledge about crime and how it can be re-cognised, to use the various types of input and observations to construct the best possible picture of a situation before taking action. Chapter three is about allocating significance, both manually and automatically, to the surroundings and converting this into effective actions. The allocation of meaning forms the basis of a good prediction about what is going on. The faster we can form a picture of a situation and the more accurate it is, the more customised the ensuing action or police deployment will be.

By making use of knowledge and experience of our personnel, and by smart ways of making use of a growing amount of – increasingly acces-sible – data, it will be possible to construct predictive systems, based on cause and effect, that are capable of supporting police work. The way this works is discussed in chapter four. Chapter five goes into more detail about various techniques that make predictions possible and demon-strates examples from the police domain. Based on examples in the field of company operations, crime and police performance, an explanation


is provided of how predictions can contribute to optimising the police service.

Predictive policing, sharing knowledge and automatic observation and assessment of the outside world also involve aspects that give rise to risks that need to be managed. Chapter six considers a number of ethical dilemmas that have come to light. Why do we crave optimisation and aren’t we becoming too dependent on machines? Is there the threat of a Big brother scenario? Lastly, chapter 7 discusses the possible future of predictive policing for the Netherlands. What is needed in order to really do it and what ancillary effects or hurdles can we expect as we move towards a broader introduction. People, processes and technology will have to merge with judicial contexts that have yet to be developed to form a more effective instrument capable of providing added value and contributing responsibly to safety in our country.

Recognising criminal

behaviour

2. Recognising criminal behaviour

During my traineeship in the centre of Amsterdam, after carrying out what appeared to me to be a regular prostitution check, an experi-enced colleague suddenly concluded that this could be a case of human trafficking. I clearly remember wondering how this could possibly be a case of enforced exploitation. At first glance, the woman just appeared to be standing there, ‘happily’ doing her job. During our little chat, I had no suspicions whatsoever that pointed in that direction. Clearly, this was all uncharted territory for me, but the picture I had of enforced prostitu-tion did not stroke with what we had just encountered. I imagined girls covered in bruises, girls sitting listlessly behind their window. I expected the first time such girls ever came into contact with the police, they would pour out their heart and soul to us and ask us to do everything to help them escape from this harsh, inhuman situation. I couldn’t have been further from the truth.

“Didn’t you see the three telephones lying there then?” was the first ar-gument in response to the frown that had apparently developed on my forehead. “What’s more, she had the name of a known pimp tattooed on her leg and in this street that usually means something’s wrong.” These three arguments taught me that there were indicators that caused my experienced colleague to suspect human trafficking. It never occurred to me to cast so much as a glance at the number of telephones. How could I know that things were often badly wrong in this street, and of course, I didn’t know the name of the pimp either. The antennae of the colleague with whom I was sharing the beat were capable of discovering human trafficking in a way that was far superior to mine. Because of his experi-ence, he could recognise this phenomenon from typical characteristics that took on a signalling role for him. The result of the overall picture was that he suspected human trafficking. What he saw differed slightly


from what I had seen. Even though we were both exposed to the same situation.

What do you need to focus on in order to recognise this and other forms of crime: this is the pivotal question to predictive policing. In fact, the arousal of suspicions forms the basis for a statement about the future. ‘It is just possible that ...’ is a possible conclusion based on indicators or signs with a predictive effect in respect of a given situation or (criminal) phenomenon. In this connection, Mulder (2014) speaks of predictive profiling. ‘If we see this, then it is also possible that ...’ Or to put it more succinctly: ‘If we see A and also B and C, then it is almost certain that …’ In fact, this is what happened with my experienced colleague. He totted the signs up until the moment came that he was certain enough to draw up an official report. He exceeded a threshold value and formed a conclusion based on the puzzle pieces he had seen. With his knowledge, he could see and recognise the phenomenon, even though it wasn’t there according to my perception. Being able to recognise something by paying attention to certain signals applies not only to human trafficking. By dissecting crime and looking at, for example, the various steps in the process that lead to a criminal of-fence, you can make a prediction about whether or not it will take place. This chapter examines our ability to recognise crime. What do you need to focus on in order to discover a crime? The faster you can predict, the more time you have to do something about it, and the better you will be able to take appropriate measures.

Initially this chapter discusses in general the ability to make observations with our senses and sensors: resources that help us to observe crime. It goes on to describe how aspects of crime can be observed that may point to its existence. The next few paragraphs are about how these ‘indicators’ are not an automatic fact of life and how they do not always prove to be useful. The last two paragraphs discuss the ability to observe crime using indicators and how this enables us to make predictions.


Observing using senses and sensors

People observe by using their senses: visual or auditive signs, as well as via pressure on our body, and receptors for smell and taste. Things that we see are usually signs that help us to understand the world better. As a result we function better as human beings. After all, lacking eyesight is considerably debilitating. Animals too have senses. The senses of some animals are much stronger or more accurate than those of people, a talent that can benefit us. For instance, dogs have a highly developed sense of smell that is 10,000 times better than that of people. This is why the police often use dogs to search out certain smells that enable us to uncover crime. For instance, specially trained sniffer dogs recognise the smell of drugs, money and dead bodies. Nowadays, similar experiments are taking place using rats.

In addition to dogs and people, artificial senses or sensors are also used to detect signals. Sensors are ‘antennae’ that make observations. Count-less sensors exist that are capable of making exceedingly complicated observations. Cameras can perform visual observations, microphones can perform auditive observations, to name just two. Whether humi-dity, proximity, temperature, weight, movement, distance or height are involved, these are all physical quantities that can be measured by sensors. The diversity of observations by sensors far surpasses human observations. Furthermore, just like some animals, sensors observe more accurately than people. An additional advantage is that sensors are often relatively cheap, they are becoming increasingly compact and they often continue to make observations for longer than people. We construct entire networks of sensors capable of observing an enormous area. Consider, for examples, the camera networks in cities that observe the movements of persons or vehicles over a large distance.

Sensors and senses are capable of intercepting and transmitting specific signals. They actually filter the environment for signals. It is to this end that they were programmed or constructed. Depending on the measured signal value, they emit an output signal to indicate that something has been observed. This transforms the measured value into a puzzle piece. This puzzle piece is often presented as a datum, or - if you know how to interpret it - as information. It creates, as it were, an opportunity to form


a ‘picture’. For example, large telescopes that pick up signals in outer space receive enormous amounts of data. We are capable of understan-ding only a very tiny proportion of these. Only then can one speak of information.

In other words, sensors and senses create data or output signals that are processed by systems. These systems help us with further transforma-tion or interpretation. More complex observations can be obtained by combining senses or sensors. Combining an image with sound results in a more complete picture of a situation. Simply duplicating senses such as our eyes and ears allows people to observe not only the signals themselves but also the dimensions of ‘depth’ and ‘direction’. Two sen-sors observing a vehicle from a fixed distance are capable of measuring speed.

A growing proportion of human observation and interpretation can be transformed into technology. Microphones, for example, can be equipped with a piece of technology that is capable of automatically recognising ‘gun shots’ or ‘human screams’ (Valenzise et al., 2007). Automatically recognising human emotions, for example (Cowie et al., 2001), can help to improve our estimation of what is going on in situations. The more combinations of sensors or senses, the more complex the observation. More information is available from these observations. They enhance the picture we have of a situation and increase the uniqueness of an observation.

In addition to simple observations, machines are also increasingly ca-pable of observing so-called ‘higher-order phenomena’. These are phe-nomena that cannot be measured directly, but which can be deduced by combining a number of measurable aspects. As long as you know what you want to recognise and how it can be recognised. An example: by looking at patterns of movements of vehicles, it is possible to deduce whether their behaviour is normal or abnormal (Barria & Thajchayapong, 2011). The same can be said of people in certain situations, e.g., in public transport (Arsic et al., 2007) or in public places (Mehran et al., 2009). In this way, crime will in the end also be recognised automatically. An example of this is the automatic recognition of electricity fraud by


examining a person’s consumption within the perspective of his or her environment (Jiang et al., 2002).

During my graduation in 2002, I developed a system with cameras that was capable of recognising higher-order phenomena based on people’s patterns of movement in the corridors of buildings. The system could say something about whether a person in a corridor was searching or simply walking there. The system distinguished these two classes of movement patterns based on walking speed in combination with the degree to which a person moved in a straight line or, on the contrary, in a meandering fashion. A person who moved swiftly in a straight line was labelled a resident, and a person who moved slowly and in a meandering fashion was classified as someone who was searching.

It is a very challenging prospect if you want to observe a higher-order phenomenon such as theft. Slightly easier is when ‘someone takes so-mething away’. This is probably even simpler for a human being than it is for a machine. However, are human beings actually capable of recog-nising, flawlessly, an ‘intention to unlawfully appropriate’ (the removed goods)? This clearly requires more knowledge and information. For example, who the owner is at the moment that the item is removed. Other aspects are whether it took place `illegally’ and ‘with the object of’. You must know these facts before you can conclude ‘theft’. In other words, the phenomenon ‘theft’ cannot be automatically observed using only senses and sensors.

When machines observe something, they try to compile the best possible picture of a situation based on measured values or other known proper-ties. In fact, just as people do. Whether it is about recognising emotions or recognising deviant behaviour in a parking lot. You look at which ‘known’ situations fit best with the situation observed by comparing the compiled pictures with reference material. This could be compared to a game whereby you put your hand into an otherwise closed bag and ‘feel’ its contents. You make a statement (prediction) about the bag’s contents based on the characteristics of the objects – which you can only feel. A fork can be recognised from its shape in combination with four sharp points at the end. This is a form of observation whereby human beings –


just like a machine – opt for recognition as soon as the values measured are sufficiently in keeping with a known situation.

A human being receives data from the environment via his/her senses. You can convert these data into information, because you are capable of allocating them with significance (interpretation). This is what ena-bles you to distinguish between good and evil, based on characteristics that you have learnt over the course of time. During your life you have accumulated an enormous reference database, which you can use to compare signals. This enables you to determine whether you encounte-red the same situation previously – thus recognising it – and what you think about it. To ‘re-cognise’ something means, literally, to see some-thing again. You saw it once before and you formed an idea about it. Sometimes situations ‘remind’ you of something, but you can’t quite remember what. You know there is an association with a past event, but you are unable to recall which event at the moment.

The police sometimes use the term ‘gut feeling’ to describe this non-ex-plicit knowledge. A police officer is often not immediately able to clearly state exactly what he feels. But he has developed antennae for crime as a result of years of exposure to many forms of crime. You could also call it a form of professionalism or conditioning, whereby cause-and-effect relationships are formed by experience.

Vice versa, it is also the case that you can start searching for phenomena, while focusing your senses on observations that fit in with the phenome-non. When forensic investigative experts arrive at the scene of a crime, they know exactly where to look in order to discover as many clues as possible. They are trained in making specific observations. This is simi-lar to undercover officers who develop a nose for pick-pockets, or officers at a football stadium who can immediately spot the hooligans. In fact, you can form a picture of a situation from various observations. The same applies to a criminal situation. The converse also applies: if you know that a criminal situation is involved, you can try to learn or find out which observations are relevant. These two forms of observation are also referred to as ‘data-driven’ and ‘model-driven’. The first form, data-driven, asks what you can see with the help of all the data that are


available to us. The other form, model-driven, assumes that you are sear-ching for a phenomenon and asks which data are needed into order to find it. Recall, for example, the observations of my experienced colleague who concluded that human trafficking was involved.

Observing crime

It was in the early 19th century that people started to measure relations-hips between social and criminological factors in search of explanations for the occurrence of social phenomena such as crime. Francis Galton and Adolphe Quetelet were among the first to do this with the help of mathematical techniques (Wright, 2009). Over the course of the years, crime has been connected with physical, psychological and social factors, to name a few. Cesare Lambroso claimed that certain facial characteris-tics were related to criminal behaviour. With the right experience, you could literally see whether a person was a criminal. Shaw and McKay (1969) contended that criminal behaviour in individuals was more likely to be caused by the environment, e.g., the district in which they grew up. Sutherland (1924) argued that criminal behaviour was acquired and Cohen (1955) sought explanations in the social group in which a person grew up.

Even nowadays, some of this knowledge is used to predict crime, in a generic sense, for example, by comparing circumstances in neighbour-hoods and districts. But also in a more specific sense, the chance of a criminal career is examined on an individual level.

In order to recognise crime at an early stage you have to examine not only the perpetrator and the environment, but also the characteristic ac-tivities that end in crime. For example, activities that could indicate ‘pre-parative actions’ for terrorist attacks (Mulder, 2014) or meetings between important links in criminal networks. An example is a situation in which a group of persons, almost all in the same location, all turn off their mobile phones simultaneously. Or when people who are ‘known’ to the police rent a deserted warehouse and pay for it with cash in large deno-minations. It is particularly this knowledge about crime, and the process


leading up to it, that can help to recognise crime as early as possible, and as a result be able to take timely action. In the example of the experienced policeman, it is clear that he recog-nised human trafficking because he possessed knowledge about the phenomenon. Using this knowledge enabled him to recognise the phe-nomenon. Clearly a case of, in the language of Johan Cruijff, ‘You don’t see it until you get it’. Human trafficking can clearly be recognised from more – and different – properties than those I had assumed, involving a listless, bruised woman staring out her window. In other words you must be able to observe the indicators once you know which indicators signify a criminal phenomenon. Recognising crime can, in my opinion, definitely be regarded as professionalism. It is professionalism, because not everyone has the gift of such insight. Being able to say which vehicle you should check or on which corner of the street you want to stop and keep a look-out for pick-pockets. Experienced officers and detectives are renowned for their ‘nose for crime’. The challenge is to be able to share these antennae and the knowledge of officers, and make them indepen-dent of individuals. Making this knowledge explicit and retrieving it from the heads of officers and detectives not only increases the range of this knowledge, as the possibility of making predictions also increases proportionately. It allows members of the police organisation to learn from one another and innovation will be accelerated (Mascitelli, 2000).

The art of recognising the characteristics of crime demands knowledge or experience that can be learned by exposure in practice. Moreover, transferring images, words or written documents gives others an oppor-tunity to learn to recognise the forms of crime we want to trace. In this way, stereotypical indicators, scenarios, behaviours and profiles that indi-cate crime can be used to support others in the task of recognition. The ideal profile is one that has been reduced to a minimum discerning set of characteristics of a phenomenon8. After all, the fewer aspects that need to be recognised, the easier it becomes. If the driver of a vehicle with two young ladies in the back seats has the passports of these women and the women in question do not know their final destination, this could indicate human trafficking. The combination ‘expensive car and young

8 This principle is also referred to as Ockham’s razor in knowledge theory


driver’ is also fairly good for recognising money laundering. Particularly when it turns out that the driver does not even have an income. Training courses can also teach you which deviant behaviour could sig-nify criminal intentions. The ‘Search Detect and React’ (SDR) training of the Israeli International Security and Counter Terrorism Academy is an example of this. They teach you which behaviour is outside the normal pattern and thus noticeable, e.g., a person who spends too long in the vicinity of cash-dispensing machines.

In addition, deviant behaviour can also be magnified or incited. For example by deploying an officer on a raised platform alongside a busy footpath. People who notice the officer and have something to hide dis-play a different walking behaviour to those who have nothing to hide. Anticipating the incited altered pattern of walking will increase the suc-cess rate of catching a criminal. Another example is where long queues are consciously created at some airports. Customs officers walk along these queues accompanied by a dog, and stroke a cloth-covered stick over the luggage of people standing in the queue. Every now and again they get the dog to smell the cloth. The aim is to give people the idea that they will be caught if they are carrying illegal substances. Anyone who has something to hide becomes increasingly nervous, perspires more and has a different way of looking around him.

Indicators of crime

In order to recognise crime, it helps if you know what causes crime: knowledge about characteristic activities and indicators in relation to persons, locations or other forms of activities. In principle, all activities that take place when a crime is committed provide landmarks and en-try points for early recognition and intervention. Specific knowledge is required about the processes that lead to a crime in order to prevent specific crimes.

Highly complicated and philosophical discussions have taken place about exactly what knowledge that is and how it can be accessed. Plato claimed, for instance, that man already has all possible knowledge before he is born and that the trick is to be able to access it again during his life.


Aristoteles claimed, on the contrary, that knowledge is created based on perceptions. Thomas van Aquino elaborated on this and claimed that our active intellect can grasp concepts based on data provided via our senses. The implication is that a concept, such as a form of crime, is not observed until the essential properties for that type of concept have been recognised. Each of these essential properties is identical to what I refer to as indicators.

Indicators come in all shapes and sizes. Depending on an indicator and its value, you will be able to observe or predict the phenomenon you are seeking to a greater or lesser degree. The more indicators you observe, the greater the chance that a phenomenon will actually occur. Generally, each indicator has its own weight. The cohesion of these weights deter-mines the eventual predictive power.

Indicators can be subdivided into certain groups. For instance, some indicators recognise values in the form of numbers and others recog-nise values in the form of categories. There are countless possibilities for categorisation. Indicators used in forensic psychiatry for estimating the risks of individuals committing repeat infringements (Canton et al., 2003), are on the one hand subdivided into unalterable historic or static indicators. This includes such matters as current age and age at first contact with the police. On the other hand one can speak of dynamic indicators that can be influenced, such as environmental factors and clinical factors (Moerings, 2003).

Discovering which indicators exist and which combinations of obser-vations can be done in order to recognise phenomena with sufficient reliability is a profession in itself. Keeping to the example of human traf-ficking, it may be noticeable if several men in succession go to a hotel room and clean towels are requested by telephone. But is this sufficient to be able to presume that human trafficking is involved? What is de-viating in one situation, could be entirely normal in another situation. This is what makes it so complicated. A man wearing a winter coat on a summer’s day, standing among the public during a speech by the pre-sident, is probably more noticeable than at a pop-star’s concert. Or will the latter also be regarded as suspicious? After all, something may be hidden beneath his overcoat. Attention is drawn to anything different,


while normal matters often go unnoticed. In general, you could say that people have certain normative ideas for situations about what is normal and what is abnormal.

Being able to recognise what is different often depends on your expe-rience of something. Psychologists use the term ‘priming’ for this. In interrogation situations, for example, suspects often use certain types of arguments or ‘excuses’. They want to convince their interrogators that, far from being guilty, they are actually the victim. Once you know this, you are more likely to notice and recognise these stories.

A question often encountered when drawing up indicators and profiles is whether one really can generalise? Is there such a thing as typical be-haviour for a serial rapist or for a radicalising jihadist? Can all criminals simply be lumped together or compared with one another? Shouldn’t each situation be assessed individually? In fact, isn’t the interpretation of every observation subjective? An officer might label a man running through the street as a thief, while an athlete simply sees a jogger. And is a person who purchases a lot of enormous lamps really a potential cul-tivator of marijuana? Indicators should not be too specific. This causes you to miss some forms of the same type of crime, those that cannot be intercepted using that specific characteristic. Nor should an indicator be too generic, as this runs the risk of finding more cases than those you want to find.


The trick is to determine the right indicators (whether or not jointly) that will enable you to predict a certain phenomenon. You could compare this to choosing the right fishing net for the police. Depending on the type of net you use, you will catch a certain type of fish (i.e. criminal). A number of considerations play a role here. With combinations of observed indi-cators, it is sometimes important to be absolutely certain that you have found what you were looking for (which may mean missing some cases), while in other cases a few errors are of little consequence. This could depend on the action that is linked to the observation.

About recognising indicators and using indicators for recognition

The right senses or sensors (technology) for being able to observe or detect indicators will not be available in all situations. Sometimes people may be able to observe indicators for which there is no sensor, or vice versa. Sometimes a sensor is not available or cannot be placed, etc., etc. In addition, wide gaps may exist between theory and practice. You could compare it to an investigative team that is unable to come up with con-clusive evidence in the form of legal evidence, or realising conclusive evidence would cost too much effort. Sometimes there are situations in which you are certain who the perpetrator is, but the decision is made not to take the matter to court.

How are you supposed to distinguish a green spoon from a blue spoon in a closed bag if you can only insert your hand to feel them? Simply guessing has about a 50% chance of success. Extra information could help, for instance, that the green spoon is slightly larger. Particularly if the spoons are in the bag together. The information about relative size would enable you to recognise successfully. Your sense of touch enables you to ‘observe’ the distinguishing property based on the extra informa-tion. You then convert relative size into colour.

Recognition based on indicators is simpler in some cases than in others. This often depends on the distinction you have to make. Compare it to a situation in which you have to point out a suspect. You can choose between three or between 30 individuals. Distinguishing between three people is generally simpler than between 30. It is also simpler if one


person has white hair while all the others, except for one, have dark hair. The distinguishing characteristic is good in both cases, but the scope for solutions is greater with 30 people. After all, there are more from which to choose. Where the scope for solutions is larger, it results almost automatically in the chance that good distinguishing characteristics be-comes scarcer relative to others. As a result you will have to work with a combination of characteristics more frequently in order to still be able to identify the person as unique.

In some cases you will be able to simplify recognition of the best in-dicators or evidence. For example, by specifically labelling objects and persons. Jewellers, for instance, use DNA-spray to cover the perpetrators of robberies with a substance that is difficult to remove. Recognising this substance can subsequently place the perpetrator at the scene of the crime. Shops sometimes install detection portals for RFID-labels in order to recognise theft. The labels attached to goods are disabled after payment for the goods has been received. In this way, detection portals near a shop’s exit can suddenly make ‘illegal appropriation’ plausible. The ability to observe can be extended by adapting technology to specific situations.

The crux in the automatic observation of higher-order phenomena such as crime is having one or more distinguishing characteristics that are practical and which can be observed independently9. Do sensors or cir-cuits exist that actually make the observations you want? The purpose of which is, in the end, to have access to those distinguishing charac-teristics that enable to you realise recognition with sufficient certainty. Take stolen cars for instance. These can be automatically recognised if you can compare passing vehicles with a list of stolen vehicles. With the help of ANPR (Automatic Number Plate Recognition), a roadside camera can read the number plate of a passing vehicle. Comparing this number plate to the file with stolen vehicles creates a possibility for observing stolen vehicles.

Systems, like people, are getting better at observing indicators and distinguishing between what is normal and what is deviant. This is the basis to the automatic recognition of crime. Clearly, people provided

9 In addition to sensors, other sources are often needed, such as databases with reference data


knowledge about how to recognise criminal behaviour, and the techno-logy was specially developed for this. Even systems can make errors. The more complex the situation being recognised, the greater the chance of an error. In fact, observations by machines are comparable to those of people. Things sometimes go wrong. This is not always disastrous, but it can be annoying if it turns out that they are not reliable in all cases. Consider the example of a judge who forms a picture of an event based on the evidence supplied and who wrongly convicts someone. From the perspective of society, wrongly convicting a person is unacceptable. In such matters, all uncertainties must be removed. Convincing evidence of a crime must exist. This means that in this specific situation the chance of errors must be kept to a minimum. The challenge is, particularly with predictions on de future, to achieve this adequately.

The same can be said of predictive policing. This too is about forming a picture in order to be able to describe a situation, comparing it with known situations and making predictions. Predictions are not always correct. Just as a murder will not always be solved or an attack will not always be prevented. Preferably, a prediction should be as good and as pure as possible. This is all about chance and probability. At the same time, not everything is amenable to being predicted, and predictions cannot always be made in the same way. After all, predicting a murder is slightly different from predicting a speeding offence. Does ordering clean towels for a hotel room that is frequented by a lot of men always indicate human trafficking? In all cases the objective is to make the best possible prediction and minimise the risk of possible errors.

Indicators and profiles can help because they help to simplify realities that are often complex. However, this also results in a risk of so-called false positives. These are persons, situations or objects that fulfil the characteristics of the indicator or profile, but which subsequently are not representative of the compilation you sought. Relying on systems that carry out automatic observations involves the risk of false positives. For instance, the young driver of an expensive auto may not be a criminal but a professional footballer. Theoretically, profiles must contain preci-sely those distinguishing elements that are specific to the type of crime indicated by the profile.


An example from everyday practice: In Driebergen, in 2011, such a system was developed that could detect drug runners who transported heroin over land (Rienks & Jongmans, 2011; Schakel et al., 2012). The system could recognise a drug runner based on two observations. On the one hand, the pattern of movements of a vehicle being driven back and forth between two cities within a given (short) space of time. On the other hand, the fact that the vehicle under observation had been con-nected with drug-related crimes in the past. Several roadside cameras could recognise vehicles with the aid of a reference file of vehicles that were previously involved in drugs. This took place in the same way as when recognising a stolen vehicle. In fact, this system presented us with vehicles ‘worthy of monitoring’ on a silver platter. By getting the monito-ring team to focus on vehicles designated by the system, the number of grams of heroin found per vehicle investigated increased from 5 to 1027 gram. Moreover, the number of officers required was decimated. Prior monitoring indicated in advance how many vehicles the police could expect on a given evening. Deployment was scheduled accordingly.

Errors did nevertheless occur. The system designated as a drug runner a lady who was going to the vet to get an injection for her little poodle in her newly purchased second-hand car. The retrospective bouquet of flowers was entirely justified in this case, in view of the fact that the police are not particularly friendly when stopping such vehicles.

Another complicating aspect in relation to drawing up indicators and profiles is the fact that crime changes. Criminals change their supply routes and alter their processes. Particularly when they know they are being monitored by the police (Bovenkerk, 2009). This means that pro-files for recognition have to be similarly altered. It is important that the police keep an eye on the modus operandi of criminals without remai-ning one step behind. This will enable them to recognise and interrupt crime at the earliest possible stage. For instance, in the past burglars tended to use the ‘flipper method’ to force doors open, while nowadays the ‘Bulgarian’ method is more popular. The preparatory processes cri-minals use can also alter. The chance of increased revenue, new types of locks or greater notoriety of the new method are all reasons for making changes. This leads to a sort of cat-and-mouse game.


Our mission remains to seek out the best measurable, distinguishing – and therefore predictive – indicators for recognising a phenomenon for predictions in the past and the future. The more characteristic indi-cators you can specify, the greater the chance the predicted crime will take place. Every characteristic makes some contribution. By collecting, storing and sharing these characteristics and knowledge rules, the police are increasingly able to automate recognition. The following chapter is about preventing crime and determining appropriate actions.

iTrechter sees stolen car; owner as yet unaware of anything

By Reinier RuissenLast night, following instructions of iTrechter, colleagues of the traffic police picked up a stolen car and returned it to its rightful owner. The owner was fast asleep and didn’t even know about the theft.

These colleagues responded to a hit by iTrechter which was processing a profile capable of detecting probable cases of car theft. The profile does not even require that the car theft has been reported.

iTrechter employees got an idea for a promising profile during discussions with detectives. After analysing several car thefts, these detectives had established an M.O., one that was amenable to automated observation.

It seems that ‘car thieves’ often set to work as follows: They work in pairs, at night, using a car from the car thief’s home address.

They drive to a location some dozens of kilometres away, where the car they plan to steal is stationed.

The passenger steals the vehicle, after which the thieves drive both

vehicles back to where they came from.iTrechter receives all recordings from ANPR-cameras above motorways, which means it also see vehicle movements of possible car thieves.

In this case iTrechter saw the red Corsa of a known car thief travelling over the A15, away from the direction of his home address. This was at about 02:40 hours. It was quiet on the road; there were no vehicles in front of the Corsa nor behind it.

About 50 minutes later iTrechter saw the Corsa return, followed closely by a fairly new BMW X5. iTrechter knows that this type of car is in the top 10 of stolen cars.

Based on the ownership of this BMW, iTrechter knew that it was being driven away from the owner’s address.i-Trechter recognised this as the M.O. that the detectives had described.

This is why iTrechter issued a hit and the officers set to work. The results of which we all know.

The thieves were, needless to say, arrested.

Recognising the future

Recognising crime or criminal preparations is an activity that mainly takes place in the present and which tries to form the most reliable pic-ture of current reality. This section shows that it is already possible to make predictions not only for the future, but also for the past.

Predictions about situations in the past often make use of data from and about the past. In fact, similarly to the investigative process, which tries to reconstruct the truth of what took place in the past. This pro-cess, which is also referred to as ‘truth-finding’, can also be used for the present and for the future. Sensors are often used, whether or not in combination with reference data, for observations in the present. For predictions in the future you have to search for predictive (process) cha-racteristics, which enable you to deduct that a phenomenon will occur in the future. All three types of predictions can only be made with the help of the right knowledge and the right set of observations and indicators. The indicators are based on the one hand on the phenomenon (model-driven), or on the other hand on data (data-driven).

Take, for example, theft. Being guilty of theft is described in Article 310 of the Code of Criminal Procedure. “He that takes away any good wholly or partly, with the intention of illegally appropriating it, is guilty of theft and liable to a term of imprisonment not exceeding four years or a fourth-category fine.” For the uninitiated, this is a complex sentence with many words. What is meant exactly by ‘intention’, by ‘any good’ or by ‘illegally appropriating’? For this example it is sufficient to understand that the case must at least involve ‘any good’ in order to be able to speak of theft. This good cannot belong to the person who is taking it away. Moreover, the suspect must have the objective of appropriation, without the legal owner having granted permission for this.

During their basic training detectives learn that, in order to obtain con-clusive evidence for a court to be able to prosecute, they must focus on all so-called elements or components that make up a crime. This means that before one can speak of theft, it is important that a number of mat-ters have been examined. Whereby, preferably, you have observed the full set of components in order to realise the greatest possible chance that the court will prosecute. All components together form, as it were, the


conditions for penalization and thus, simultaneously, the legal characte-ristics of a crime. The moment that an officer has sufficient suspicion to make an arrest is the moment that the picture is sufficiently complete to conclude that theft has been committed. For the officer, that is, not as yet for a court. In fact, the officer is making a form of prediction based on his observation: a prediction about the actual occurrence of the crime, in the supposition that at a later stage it can be proved and the court will draw the same conclusion.

Notice here the analogy with the observation of the experienced colleague who was able to recognise human trafficking. With theft, it is the com-ponents that make it explicit that something is going on. This is a rule that has been agreed and has been incorporated into a section of the law. A rule that prescribes what things we must consider in order to be able to prove theft. These types of rules are also known as knowledge rules. They form the framework that you must examine in order to ‘conclude’ theft in this specific case. The components determine, in cohesion, the conclusion. The fact that a person has three telephones does not, in itself, say much, nor does the fact that someone takes something away from somewhere. It is the sum of the observed components which, in cohesion, lead to a certain probability that a phenomenon is taking place. Or that you can draw a conclusion.

Something similar takes place during investigations. The use of hypo-theses and scenarios enables us to form pictures of what might have hap-pened. For instance, in order to arrive at – via a motive – a perpetrator. In fact, you are making predictions about the past that you try to prove by collecting clues or other points of reference that correspond with the picture. A certain method of work or modus operandi used during a burglary can result in a picture of a possible perpetrator. The addition of extra relevant information makes it easier for you to determine whether the suspect really is the perpetrator. The objective of prosecution is truth-finding. In fact the focus is only on reconstructing the past, whereby the police act reactively. This reduces the chance that a suspect did not do something to a minimum. However, in the case of pre-construction, or truth-finding for the future, reducing the chance of incorrect decisions to a minimum is just as important, particularly if the police act proactively and preventively.


Reliability

In fact, a prediction itself is a chance, namely the chance that an event will occur. The probability that a situation such as the above-mentioned burglary will occur is always between 0 and 100 per cent. If there is a 10% probability of a burglary, then this is a small probability. The expectation is that about 1 out of 10 cases will actually involve a burglary. In the theory of probability, this is referred to as the chance of an event occurring. This is always between 0 and 1. If the chance is 1, then the expectation is 100% that something will be the case and when the chance is 0, this is 0 per cent. The reliability of a predicted chance says how much confidence we can have in a prediction. Reliability says something about the preclusion of errors or about the stability of a system. The fewer errors there are, the better. This stability can be measured, for example, by carrying out tests. The more often a prediction is good, the more stable it is. This too can be measured, in terms of robustness and ‘performance’. These too often lie somewhere between 0 and 1. If reliability is 0, then the results of a prediction are completely unreliable. If it is 1, then the outcome of the prediction will always correlate with reality.

By formulating various pictures – hypotheses – in advance, you can fo-cus your search for clues in order to reconstruct or prove a presumed situation. Clues from, e.g., an on-site investigation or interrogations can be used to ‘compile a picture’ of what happened. The picture can be completed in more detail as evidence is collected. This is a form of truth-finding whose purpose is eventually to be able to ‘establish’, with sufficient certainty or evidence, what happened in the past. Hypotheses for which most supportive material has been found are more likely to be accepted as the truth. Analogies can be found in hospitals, with the triage-system in the casualty department. Urgency – as well as the route through a hospital – is determined based on a number of characteristics. A similar process can be seen with prenatal research. This involves ma-king predictions based on specific values for the blood and the amniotic fluid, e.g., about the viability of a foetus. This is not about what hap-pened, but rather it is an attempt to estimate how matters stand in order to be able to act (or treat) as effectively as possible in the future.

In a more general sense, the term ‘suspicion’ is actually a prediction. A crime has been committed, but you cannot be absolutely certain whether anyone will actually be convicted. A suspicion always involves some form of prediction. There is a real chance. But when is that chance big enough for a court to be able to declare the crime proven? In a legal sense, there is greater certainty that a crime has been committed in a case involving ‘serious objections’ than in a case of a suspicion. Where is the boundary between suspicion and evidence? This is a relevant question here. Pos-


sibly by collecting as many clues and as much evidence as possible. Or by aiming to realise maximum variation in this respect. The court has more to go on if more clues (or evidence) are available. But does this also increase the plausibility of whether a crime was committed or not? Article 388 of the Code of Criminal Procedure speaks of ‘sustaining a belief.. due to the content of legal evidence’. Instead of evidence, before a court one also speaks of documentary evidence.

Fortunately, not all evidence can simply be introduced. For instance, evidence must have been obtained legally and must have been collec-ted using allowable means. Furthermore, not all evidence carries the same weight in convincing a court. For the police organisation, it is particularly important to focus on highly persuasive evidence. A relevant article10 applicable to a court within this context says that one piece of evidence is the equivalent of no evidence. No more than the statement of one witness, for example, is not enough for a court to be able to decide conclusively. After all, a statement may have been concocted just to get a suspect behind bars. You must have supporting evidence in order to fulfil what is known as the minimum evidence requirement. If anything, supplying more evidence may even increase the chance of a conclusion. This is a method of reducing uncertainty.

Making predictions about the future is based on a similar principle. Pre-construction, for example, leads to a statement about a future situation by collecting ingredients that increase the probability that it will occur.

An example of a reconstruction that can also serve as a prediction is declaring persons not responsible for their actions (Simon & Shuman, 2008). Nowadays, you check a person and his characteristics to see whether he/she was suffering from ‘a defective development or mental disorder at the time of committing a crime11.’ You collect the right infor-mation in an attempt to make a statement about a situation in the past. Renowned institutes process information or signals that were collected in the past which can show that persons were not responsible for their actions. They consolidate these into conclusions that can have enormous

10 This is also referred to as the `Unus testis, nullus testis’ principle, see article 342, para. 2 of the Code of Criminal Procedure

11 Article 39 of the Penal Code


consequences for the future penalisation of individuals, in respect of crimes that were committed in the past.

Pre-construction focuses not only on the past, but uses observations and indicators from the present in order to say something about the future. You could say that here too a crime must be ‘proved’ as far as possible, but now in advance and not retrospectively. Sufficient suspicion must at least have arisen in order to allocate a value to such a prediction. This means that pre-construction forms the basis to predictive policing. The challenge is in being able to predict a crime, or an element of a crime, with the greatest possible certainty. Important aspects are place and time, in combination with the perpetrator and any victims. The focus for being able to anticipate and predict crimes in the future should be on clues and other ‘evidence’ that form an indication for their future occurrence.

Some clues or indications make it more likely that a crime such as theft will occur in the future. By way of illustration, if you want to predict a burglary, you know there is a chance of being burgled if you go shop-ping without locking your door. It is more than likely that the chance of being burgled will be reduced if you do lock the door. The chance of being burgled will be greater if you leave your door wide open. Scientific research shows that the presence of broken windows (Kelling & Coles, 1998) or even the presence of rubbish on streets (Keizer, 2008) also


increases the chance of antisocial behaviour, such as burglaries. More-over, if a burglary has already been committed once, there is a bigger chance that burglars will strike again in the same location (Townsley et al., 2003). All these aspects have their own predictive value and each of them contributes its own weight to the prediction of an event. The chance that my home will be burgled is also greater if a known burglar has just been released, or if someone in the neighbourhood was burgled recently. The more of this type of predictive indicators there are, the big-ger the chance of a crime, in this case, theft.

Sometimes indicators are clearly obvious for people and sometimes, as in the case of recognising human trafficking, relevant knowledge is required. Indicators that help to predict crime, however, can always be found in the process that leads to a crime. This is inevitable, as the crime has not yet been committed. The focus of predictive policing is to recog-nise patterns and indicators that are related with processes that indicate future crimes. The next steps are making a risk-estimate and taking pos-sible measures to prevent a crime. This is the subject of the next chapter.

From recognition to

appropriate action

3. From recognition to appropriate action

The police act in response to signals that indicate a possible crime. This is how it has always been and perhaps it always will. These signals can be observed as indicators, processed and converted – or not – into action. Signals can also come in via partner organisations and via citi-zens who report a crime or call the emergency telephone line. Predictive policing is also involved in this. An officer or ‘the police’ use these sig-nals and indicators to form a picture of a situation and assess whether action is needed and what form it should take. Will it be settled by means of a mutation in an information system such as the BVH12, or will an investigation be started or will some other intervention be chosen? Determining the right intervention starts, as described in the previous chapter, with assessing the situation correctly. Police performance can influence this step directly.

As also mentioned in the previous chapter, the important thing is kno-wing what to look for when observing. Some signals are more important than others, so it is on these that an observer should focus. Yet other signals are transient and cannot be reproduced. Making sure they are properly stored and recorded is important for possible future use. If several persons witnessed the same situation, then everyone will have formed his or her own picture of the situation based on his or her own observations. How can we arrive at an objective and preferably validated opinion of what actually took place? Or is this not really necessary when deciding to take action? To what extent can we, the police, make decisi-ons when uncertainty exists and what consequences will this have?

Observations can be done from various locations with different perspec-tives, based on one’s own frames of reference, and will not always result in the same picture of a given situation. For instance, when interviewing witnesses it is extremely important to know whether a source is reliable

12 BVH stands for National Law Enforcement Database, a registering information system in which officers record encounters and draw up official reports of crimes


and whether a witness actually has any added value. How long ago was it that the incident occurred, have conversations about the situation taken place with others in the meantime, and was the witness really able to observe the situation? Each and every one of these aspects must be exa-mined in order to make an assessment of whether obtaining an objective picture of a situation is possible.

Unlike situations in which the police respond mainly to signals and reports of wrongdoings that have already taken place, predictive policing is mainly about taking action before wrongdoings have taken place. This requires the police to be more alert to different forms of signals and re-ports. With predictive policing a picture does not have to be constructed in order to determine the truth; instead the clearest and most reliable possible picture has to be constructed in order to arrive at estimated threats and risk assessments of a future situation.

Whether the police takes action depends on the picture they have formed of a situation. They go to the location, start an investigation or initiate another type of intervention. The action they take is closely linked to their assessment of the situation. Whether it involves reported domestic violence via 112 (emergency number) or the future release of a known sex offender, they will try to form a picture of the situation and the ac-companying risks and weigh up the different intervention scenarios against one another. Is this a situation that was expected or not? Can it be regarded as a standard situation? Is a standard solution available? Or should we, the police, quickly concoct a prescription, similarly to a doctor who determines a diagnosis in the casualty department of a hospital? The type of intervention subsequently chosen depends not only on the picture that has been formed of the situation, but also on the resources available for deployment. On holiday in Jerusalem when in my early twenties, I still remember my amazement as I stood watching a sort of frogman marching across a large plain towards a small traveller’s back-pack in front of the holy Wailing Wall. As I saw it, this was entirely out of all proportion. The whole plain was evacuated and hundreds of people stood watching from behind the barrier tapes. In view of the situation, this was either so threatening that such a measure was deemed neces-sary, or they had no other instrument that could be deployed. Another


possible scenario, i.e., that they were putting on a show for political rea-sons, for example, is in my opinion less probable.

This chapter is about appropriate ways of combating crime, how to prevent it and how to respond to a given sets of findings. Once again, the police can increasingly take advantage of modern technology. A growing armamentarium is available for observing, recording, sharing and converting signals into indicators that can support decision-making processes. Technology is increasing possibilities and enabling the police to make more accurate observations, interpret them faster and respond in more appropriate and varied ways. As a result predictive policing is rapidly becoming reality.


From encoding to (re)action

The step from observation to action or reaction by a system such as the police organisation is closely linked with the purpose for which it was designed. Systems are often developed for different purposes13, so they respond differently to the same observations in their surroundings. People can also respond differently to similar observations. This could depend on previous experiences in similar situations or on a person’s frame of mind at a certain moment. Because different people may have different experiences in similar situations, there is also the chance that their responses will differ. Furthermore, people in similar situations may have different objectives. This often makes it more difficult to pre-dict what a given response will be to a given situation.

An officer has access to a number of weapons. A handgun, handcuffs, a pepper spray and sometimes a truncheon. The situation and the frame of mind of the officer will determine whether or not a certain weapon will be used. Using a truncheon in a train compartment would probably be ineffective in view of the risk of entanglement in a luggage rack. It would also probably be unwise to draw a handgun in a room with many bystanders. Quite apart from the fact that a purpose must be served by any violence used and it must be in proportion with the situation, some officers will be more likely to use a weapon while others will not. Some officers may call in assistance. Personal preferences can play a role here, in combination with past experiences. But the behaviour of other col-leagues nearby can also cause an officer to make a different choice than the one he would ‘normally’ make.

Whether those involved retain positive or negative thoughts on a situa-tion depends on the success achieved with the action. In future situati-ons that are (more or less) comparable, they will be able to recall their previous experience as reference material in order to make a more well-thought out choice in the new situation. This is learning for the future on the basis of experience. Knowledge is being accumulated about what works and what does not work as well. If deploying the chosen type of weapon had the right results, it is more likely that they will choose for

13 In multi-agent technology, one also speaks of ‘beliefs’ and ‘desires’ which co-determine the reaction of a system

the same type of weapon in subsequent comparable situations. Just like people, systems also learn to adjust their behaviour, depending on the degree of success, and to carry out different actions in similar situations (e.g., Sutton, 1998). For instance, adversaries in computer games can adapt their behaviour according to your style of play, the objective being to offer more resistance.

A question that arises is whether we can also predict which action is the best in a given situation? During officer training courses, a number of standard situations are covered in order to teach them the most effective action to take based on experience from the past. For example, how to arrest someone, or how you should move when working as a pair in a catering establishment. Naturally, innumerable other possible situations exist for which no training is possible. You could compare it with a chess match. This has standard opening moves for which you can train, after which innumerable combinations of moves are possible so that training for all situations would not be feasible. The funny thing is, however, that the chess world has started compiling an enormous reference database14 that currently contains millions of chess matches. This makes it possible to calculate the most effective next move in many more situations than the standard opening moves. You could say that, depending on the situation in the criminal outside world, the challenge for the police is to choose from the arsenal of re-sources available within their own internal world and then to establish - whether or not in collaboration with partners - the most effective and ef-ficient action to neutralise the situation in the outside world. The ability to do this properly requires in the first place a thorough knowledge of criminal processes and how these come about. By subsequently varying methods of anticipating these processes, we can learn which are the best methods. Storing these experiences and interventions makes it possible to transform knowledge on combating crime into a collective legacy of ideas. Constructing such reference databases enables us to make better and more ‘informed’ predictions about effective interventions.

14 http://chess-db.com


The violence risk assessment instrument A Personalised Approach to Predictive Policing

By Remco van der HoornThe police have developed a national, uniform, personalised approach with their project Persoonsgerichte Aanpak (PGA, personalised approach). Knowledge on the most effective interventions and an optimum information position can reduce the chance of recidivism. The police want to transform the mainly incident-oriented method into a personalised approach. This will also make the approach of the police independent of target groups.

The PGA process uses data to work backwards from a (safety) problem to the persons causing the problem. This involves an important planning criterion, the violence risk-taxation instrument (RTI-violence). We know, based on scientific studies, which characteristics increase the chance of criminal behaviour.

In the past the number of crimes a person had committed determined

whether he was ‘at the top of the list’, while nowadays it is whether he demonstrates the greatest risk of recidivism. The RTI-violence predicts the risk of future violence for each person who is known to the police and who is registered in the Law Enforcement Database.

This makes it possible to draw up plans based on predicted future risk (predictive policing). These plans provide us with a valid and reliable list of persons who are eligible for a personalised approach. This list forms the point of departure for discussions with the Public Prosecution Service and municipalities about safety in a basic team, a district, a unit or the entire country.

The RTI-violence forms the ‘real-time, predictive’ basis for the personalised approach in every unit.

Such reference databases help us not only to compare interventions with one another in order to see which intervention was most successful in a comparable situation, but also to systematically collect crimes so they can be compared with one another and possibly correlated to one another. After all, some crimes may resemble others because they were set up in a certain way, e.g., by a similar group of perpetrators, because they take place in a similar region or because certain other elements can be correlated with one another. Keeping efficient records of various fixed elements of crimes creates a chance to make predictions about the missing elements of crimes about which we do not yet know everything. If two crimes are similar, then a statement can be made about the pos-sibility that they were committed by the same perpetrator. For instance, if the perpetrator of one of the crimes is known and the modus operandi of the other crime is similar, it may turn out to be improbable – based on


the other crimes in the reference database – that another perpetrator is involved. Attacks, for example, can be modelled according to a number of fixed ingredients (de Kock, 2014). Every attack can be constructed from 12 elementary scenario elements. Describing past attacks according to this model reveals patterns because certain groups of perpetrators will have developed preferences for certain (elements of) scenarios.

A reference database can even be used to reason backwards and deter-mine, for example, how certain knowledge was disseminated geograp-hically. You actually follow the geographical trail that a certain modus operandi leaves behind until you arrive at the source. Take the example of the chemical composition of certain drugs and the degree to which they are cut. In the case of smash-and-grab raids and ATM gas attacks, it turned out that – based on its modus operandi – a certain group could be linked with a number of unsolved ATM gas attacks. The modus operandi was always the same, namely, using a ram car and a getaway car and exploding cash-dispensing machines by filling them with a certain type of gas. After this, one car was used to smash through the ATM’s access door, and the other car was used for a quick getaway. Following similar crimes over the course of time results in a geographical pattern from which expectations can be inferred about a possible new crime.

There is also another reason why reference databases are useful. That is: knowing the various forms that a crime can take and which types are most probable. This is also referred to as scripting crime. Scripting is increasingly used to unravel the criminal process. This involves descri-bing the essential stages that took place before a crime is committed. It provides insight into a criminal’s working method and the preconditions that are needed in order to carry out a criminal act. Scripting criminal ac-tivities is used, for example, in cases of child abuse (Leclerc et al., 2011), stealing consumer electronic goods (Ekblom & Sidebottom, 2008) and the illegal dumping of waste (Tompson & Chainey, 2011).

It is subsequently possible to examine ways of making each step of a cri-minal process more difficult for a criminal (Hancock & Laycock, 2010). It is as though we were erecting barriers for a criminal, and fighting crime in this way. For the human trafficking phenomenon, for example, various barriers can be developed, often in collusion with various partners, in the field of recruitment, transport, accommodation and setting victims


to work. Developing these types of barrier models to combat crime has become commonplace within the police organisation. For example, bar-riers erected against the production of synthetic drugs include the pro-hibition of importing certain raw materials or precursors. Furthermore, all glass blowers in the Netherlands deemed capable of making a special type of flask that is needed for the production process were paid a visit. They were kindly invited to desist from making them and to report any orders. The fact that such measures are effective is generally apparent from the fact that the criminal process alters. For instance, laboratories were subsequently found with different types of precursors that need an extra distillation step, and steel barrels were found that had taken on the role of the glass flasks in the production process.

One way of developing interventions is by making use of so-called inter-vention matrices (Rienks & de Wit, 2012). An intervention matrix is a framework for drawing up interventions, based on a systematic approach in which several interventions achieve results in conjunction with one another (Farrington, 2000). This involves setting out the possibilities of the police and their partners (vertically) against a number of elements from a criminal process (horizontal). Examples of possibilities of the police are appropriate enforcement, large and small investigations, con-structing an information position and communication (strategy) when combating crime. Examples of elements of crime are individuals, the criminal network, the production process and the distribution market. By setting these two dimensions off against one another, you get areas that can be filled in for a specific case. Effective interventions that can be designed via the framework of an intervention matrix depend on, for example, level of knowledge about the phenomenon, the capacity provi-ded by the organisation and partners with whom you are collaborating. Some examples of opportunities that can be proposed and tested are: obtaining an information position on the distribution market and better-targeted enforcement in relation to the criminal network or the extensive investigation of an individual.

Storing the chosen activities per situation in combination with the results provides us with valuable reference material about how certain forms of crime were dealt with. In fact, it results in a collective memory in the form of a reference database. By using experiences of similar situations


from the past, predictions can be made about the chance of successful future interventions. It is rather like constructing the chess database. Based on the accumulated knowledge we can combat criminal activities more effectively once we know how to recognise them at an early stage. And that is precisely what predictive policing is all about. Depending on a criminal’s chosen scenario, an intervention proposal with proven efficacy can be chosen from a reference database, so that we may even be able to prevent the crime.

Combating and preventing crime

Citizens expect calamities if certain elements occur at once in their neighbourhood. You might say they have ‘developed a nose’ for recog-nising situations involving the threat of crime. Sometimes we decide not to lock our bike, while in other situations we make sure it is locked. In general, people are good at anticipating the risk of crime. They try to avoid becoming a victim. Examples of this are avoiding certain parts of a city after dark or locking our front door when we go out.


There are several ways of combating crime. Goldstein (1979) was one of the first to suggest that the police should change their role from a reac-tive one to a more pro-active role. He also said that not only did we have to collect knowledge about crime but also knowledge about the best and most relevant methods of combating crime. In order to become more effective, the police should focus in particular on dealing with the un-derlying problems that lead to crime. Environmental design and social development, e.g., creating jobs and combating poverty, would do much more to combat crime (Schneider, 2015).

Government crime prevention programmes focussed therefore on, e.g., psychological and social causes, by providing individuals and high risk groups with courses or sport programmes. These focus on keeping people ‘off the street’, teaching them standards and offering more op-portunities for participating in society. The expectation is that tackling poverty will make people less likely to take up a life of crime. These types of interventions reduce the motives for committing a criminal act.

An alternative to social development exists that is known as situational crime prevention or the theory of opportunity. This is a more short-term form of prevention and is based partly on the rational choice theory (Scott, 2000), whereby a criminal weighs up the costs and benefits of an opportunity (Becker, 1968). The idea is to make the opportunity less at-tractive and the costs higher than the benefits. By reducing the opportu-nity or the criminogenic conditions in an environment, the opportunity for a criminal becomes less attractive in the sense that either the ‘costs’ increase, or the ‘benefits’ are reduced.

Making the opportunity less attractive creates a chance of ancillary ef-fects occurring. The so-called waterbed effect could serve as an example of this. This effect is where crime occurs less frequently in certain places or at certain times, while in other places it actually increases. This results in a sort of cat-and-mouse game in which the police force a criminal to make changes. Determining the extent to which this effect will actually occur is difficult and depends on factors relating to, e.g., the speed of introducing an intervention, the chosen intervention mix or the focus that may or may not already have been given to certain forms of crime. In tackling skimming, for instance, whereby bank cards are copied and PIN codes are observed at cash dispensers, a number of interventions


carried out together with partners were so successful that this phenome-non hardly ever occurs nowadays. Whether the criminal has been curbed in undertaking criminal activities is an entirely different question. And this question may actually be much more important. After all, these criminals may have shifted their domain to entirely different forms of mobile banditry, such as pickpocketing (Siegel, 2013).

It seems that limiting the opportunity does reduce the total amount of crime. This is despite the fact that some of the criminals do seek out a different opportunity or a different type of crime (Clarke, 1997). An explanation of the latter could be that criminals must adjust their beha-viour in order to continue to be successful. Continual adjustment costs a criminal energy, and excessive energy coupled with too high a ‘cost price’ eventually result in exhaustion. Clarke (1982) referred to this as social and physical forms of crime prevention.

Physical and mental crime prevention focuses on the opportunity. ‘Costs’ are allowed to increase so that committing a criminal act becomes less attractive. This could be because getting hold of the loot demands greater physical effort and the chance of being caught increases, but also be-cause, for instance, committing a speeding offence is more difficult with bumps in the road. The government prevents crime by erecting barriers. An example is the introduction of laws and legislation on aspects that can contribute to crime, such as limiting trade in weapons or regulating the raw materials for synthetic drugs. Benefits are reduced by reducing potential profits.

Laws in the Penal Code also have a preventive effect as the prospect of prison sentences and fines also help to prevent crime. Punishments are not only to retaliate for inflicted suffering, but also to discourage criminals from straying again from the straight and narrow (Wartna et al., 2013). The graver the crime, the greater the negative prospects for the criminal. This boosts the mental costs. In the so-called what works approach (Andrews & Bonta, 1998), empirical research is helping us examine the conditions under which certain measures and punishments offer perspective on prevention and preventing recidivism. Research by Wartna et al. (2013) shows on the one hand that interventions focussing on adults are more successful in reducing the chance of recidivism than those for other age groups. On the other hand, it seems that rehabilita-


tion is more effective as a deterrent than the imposition of punishments. Making use of this and other knowledge that reduces the risk of reci-divism enables us to search for the optimum mix of measures (Tonry & Farrington, 1995) in order to reduce the chance of recidivism and prevent crime.

Funnily enough, where a measure focuses on preventing crime, this in itself often also acts as a possible cause and also therefore as a predictor of crime. After all, measures are taken in order to reduce crime. When the mayor of New York announced a measure involving the repair of all broken windows as quickly as possible in his city, crime in the city was reduced considerably. The broken windows reduced a person’s threshold for demonstrating criminal behaviour. Altering this reduced the tempta-tion to behave criminally. In fact, the presence of anormative elements actually create an opportunity for abnormal behaviour. At least behaviour that our democratic country has agreed is undesirable.

The recognition of elements that are tackled in order to prevent crime can also be used to predict crime. Other predictors can be discovered by, for example, looking at distinguishing characteristics that are typical for groups of persons who are guilty of criminal behaviour. For instance, a distinction can be drawn between ‘habitual offenders’ and persons who demonstrate incidental criminal behaviour. A characteristic of habitual offenders is, for example, getting involved in crime at an earlier age than non-habitual offenders, still being active in crime at an advanced age and, moreover, committing increasingly serious crimes during their criminal career (Task Force Crime Prevention, 1993). If the deployment of scarce police capacity were equal to the number of criminals that can be reached as a result, then it would be worthwhile focussing on these ‘habitual offenders’ in particular.

A number of factors that can be correlated with habitual offenders seem to be connected with the degree to which they are involved in society or in a social network. Habitual offenders are often confronted at an early age with family circumstances in which poverty, violence and neglect play a role. Their neighbourhood can be described as a bad living en-vironment. Habitual offenders often have difficulties at school and are often faced with juvenile unemployment, which alienates them from society (Sampson & Groves 1989). Crime becomes a means of survival.


Needless to say, although the factors mentioned do contribute to a big-ger chance of criminal behaviour, this does not necessarily mean that a person who grows up in a broken home which is in a ‘bad’ living envi-ronment and who also drops out of school is automatically an habitual offender. Indeed: an examination of the above-mentioned studies shows that, in general, men aged between 18 and 35 years, both habitual of-fenders and non-habitual offenders, are over-represented in displaying criminal behaviour. Once again, this does not mean that all men in this age category are automatically criminals. So how does it work then? Which of these aspects would you most like to know, and which of the factors that I mentioned has the greatest direct link with crime? In other words, what is the weight of the factor and is this affected or not by the presence of other factors? Making a good prediction and making a success of predictive policing depends on this. But how do we actually find these factors and is it always the case that the combined presence of several factors can make a prediction even stronger? The next chapter discusses this in more detail.

Right mix of interventions makes basic teams more effective

By Marleen RibbensHow effectively are the police deployed in tackling High Impact Crime? The EffectAllocator helps the police reply to this question more effectively.

Using the computer model, basic teams can simulate the expected effect of an intervention against High Impact Crime (HIC). This helps the police and their partners realise the focussed deployment of people and resources, and an optimum division of tasks in tackling HIC.

The computer model uses a smart database that makes connections between interventions, expected effects and contextual data that affect them. Examples are type of perpetrators, weather conditions, secure housing trustmark, access roads, etc.

Initial responses came from Zaanstad: “We are finally able to measure which

intervention makes sense and which does not.

For instance, we can measure how effective our dynamic roadside checks are in reducing home burglaries.”

The objective of the EffectAllocator is to learn from the interventions deployed by structurally testing them in practice.

This will automatically enrich the database with open and closed sources on daily police deployment. More than a thousand police interventions have by now been tested for their effectiveness in practice.

The effect of an intervention depends very much on the context in which it is carried out. It turns out that some interventions are much more effective in one area than in another, irrespective of the capacity deployed.

Knowledge and historical

data for the future

4. Knowledge and historical data for the future

Similarly to sensors, our senses can receive and pass on signals. They supply our brains or systems with input and function as a source of data. A source that receives signals and then transmits them in a given form. Senses pass stimuli on to our brain, where they are interpreted as a fee-ling, sound, image, or some other dimension. This is similar to how sensors work. Cameras, microphones and other sensors receive signals and transmit them to another part of an ‘automated system’. They trans-mit the signals received in the form of data that are processed elsewhere, externally to the sensor.

Receiving information and data is an important aspect of the work of many police departments. We sometimes find out much later whether the information is of any use. Take the example of recording confidential communication between two suspects, or clues that are collected by fo-rensic detectives. Volatile signals are, unlike some clues, extremely tran-sient. Signals are converted into data when stored. In some cases simply retrieving data can provide added value. For example, for interpretation or recognition at a later date, such as sound recordings of a conversation in a foreign language. After storage, a situation can be retrieved repea-tedly for investigation purposes, which may result in more aspects being noticed, and thus greater understanding. A precondition to predictive policing is that the police can access and process data, information and knowledge. This chapter shows just why this is.

Recording data means they can be shared and makes it possible to pro-cess them at a later date. This started with ancient civilisations who left drawings behind in caves and scratched symbols onto stones. In today’s digital, networked society (Castells, 2000), messages can be sent all over the world – electronically and via modern means of communication – at the drop of a hat, so that exchanging, storing and processing data is much simpler than it used to be. By storing data received via signals that


would otherwise have been lost, we can use them later when investiga-ting crimes, just as we do, for example, when seizing goods.

The data represent signals received and are often nothing more than actual observations of a situation at a given moment. Subjecting data to early filtering or refinement reduces the amount of data. Often we want to retain the more relevant data, thereby increasing the chance of it being valuable. Transforming data makes them easier to process. These are all steps in transforming data into knowledge that enables individuals or organisations to perform better.

Many different definitions of data exist and they are regularly confused with such concepts as information and knowledge (Liew, 2007). In this book, data is used only to mean data. Data form the building bricks of communication. They represent unusable facts until they have been or-ganised or an interpretation model has become available. Data generally come in the form of stored symbols such as numbers, words, diagram-mes or images. Data can also be the final product of signals received.

Generally, collecting data haphazardly is not an objective in itself. Col-lecting data objectively and selectively makes it simpler to find the infor-mation one seeks. Indeed, just as the police cannot simply seize goods, neither can data simply be collected or stored. Relevant terms in this respect are proportionality and purpose limitation. Data and information that are to be used as evidence must have been collected legally. Recor-ding confidential communication, for example, is regulated in article 126l of the Code of Criminal Procedure, and article 126m applies if data are collected by ‘automatic data processing’ and not by a person. The provision of data is also bound by rules. For example, the Police Data Act (WPG, Wet Politiegegevens) allows certain forms of processing and providing police data (whether or not automatically), thereby stipulating relevant terms and conditions. The WPG also contains information about accuracy, completeness and security requirements in respect of these data.

For the rest, article 1a of the WPG defines police data as being limited to personal data that can be processed within the framework of carrying out the police task. These are data relating to a person or based on which


a person’s identity can be deduced. Data – other than personal data that can be processed within the framework of carrying out the police task – can also be designated as police data, but these are beyond the scope of this law. For example, data about police-deployment or crime statistics. Nor does the WPG say anything about the provision of knowledge.

Incidentally, seizing data, in a legal sense, is still problematic. This is because, according to article 94 of the Code of Criminal Procedure, only objects (including those capable of ‘ascertaining the truth’) are eligible for seizure. Data are, in this context, not an object15. Data are an abstract concept. Data carriers, on the other hand, can be seized. Therefore, when suspicion arises that data recorded on a data carrier could be used for truth-finding, only the carrier is eligible for seizure. Interpreting data can result in information. For example, if they are presented in a usable context, in a way that is newsworthy and which helps to reduce uncertainty. Information is a message with a meaning, intended to enable us to make decisions, create opportunities or solve problems. This means that if you know the purpose for which available data will be used, they can become information. The reverse is also the case: if you know which information you want to supply, you can think about which data you will need.

Finally, by knowledge we mean what we know, believe and expect. Know-ledge is described as meaningful information. It has also been described as a map of the world that we compile based on information received. It is our human reference database that we call experience or ‘tacit know-ledge’, and which individuals accumulate throughout their lives. In this way knowledge creates value because it results in understanding and has the capacity to recognise, reason and assess how to act and to learn. Knowledge is often linked to ‘knowing’. Knowing how or knowing why something happens, for example. It is about the possibility of being able to reason about cause and effect.

Data, information and knowledge can be stored, processed and handed over. With the help of modern technology, data processing can take place

15 Article 94a, para. 6 of the Code of Criminal Procedure says that the term objects refers to all matters and proprietary rights


on the other side of the world. Furthermore, several different types of data-producing systems can be connected with the same data-processing system. In this context, one often speaks of data manufacturers and data consumers. Data is manufactured by data manufacturers by generating and dispatching data. Data consumers consume and process data. Data can be processed by ignoring, storing, interpreting or filtering them, or by transforming them. Ignoring data can make them disappear and never return. Storing data on a data carrier ensures that they can be sub-sequently retrieved. Getting man and machine jointly involved in this enhances the entire process.

Knowledge can be created from stored data. Data that are ‘interesting’ for the police can reveal patterns in time or place. Knowledge can be ex-tracted from these patterns. For instance, based on a series of incidents in the past, a prediction can be made about an event that has not yet take place. You could say that a pattern creates an expectation value for the future. For example, over a period of several weeks, the accumulated statistics from an active ANPR sensor in combination with a list of stolen vehicles can tell us on which days and at which times, on average, most stolen vehicles pass the ANPR location. With these insights we can easily predict when we will have the greatest chance of success if we want to deploy a fixed team to find as many stolen cars as possible during a con-trol action. This knowledge creates value for the organisation because it can increase the performance of the police while reducing any costs involved.


An important ingredient for predictive policing is knowledge about crime. It is needed in order to make predictions, but also to know how to recognise crime and what the weak links are in criminal processes. On the other hand, knowledge about the police is also necessary in order to know how to combat crime, how the police can act and what its bounda-ries are. Harmonising these two knowledge domains is the work of in-telligence. Whether it is knowledge inside people’s heads or knowledge embedded within data, the challenge is to broaden the degree to which this knowledge is available, and thus broaden the (earlier) recognition of crime. In a broader sense, the police can benefit from unlocking this knowledge. This chapter goes on to discuss where this knowledge can be found, how it can be clarified and how it can facilitate the realisation of predictive systems.

The usefulness of sources and information

In circa 3500 BC the written word developed in several places in the world at once (Man, 2001). Drawings became symbols and several symbols formed languages for combining data into information. Using language helps us to record and exchange information. People learn from one another by passing on information and knowledge to one ano-ther via language. Learning from one another makes us smarter, more effective and more adaptable. This applies not only to individuals, but also to an organisation like the police. Knowledge is retained, people feel connected and synchronicity is promoted when the written or spoken word is used to share experiences with one another about, for example, crimes we have encountered, or success stories about deployment or ac-tions. This is why it is important for a group of people or a company that collaborates on achieving an objective to disclose and exchange as far as possible the knowledge and experience gained. This results in collective knowledge and people can complement and stand in for one another where possible. The total performance becomes greater than the sum of the separate parts. You could say that it results in synergy, thereby enhancing the performance of a team or company.

Information about one’s own experiences or the experiences of others, or the knowledge gained as a result, provides insight into what others have


encountered and about their response to a certain situation. An example is people recounting narratives, also known as storytelling, while sitting enjoying a cup of coffee together (Koren et al., 2010). People often feel a degree of curiosity about new knowledge or the current state of affairs. People enjoy obtaining information about relevant factors that could affect them. Just think of the desire to see the news on the television, and to attend the morning briefing at the police station. This is known as information-pull. People can use someone else’s knowledge to their advantage and create opportunities to solve problems.

From the perspective of groups or a company, in addition to the need to receive information, there is also a need to supply information. This means, among other things, that the police benefits immediately from sharing information efficiently and from a good briefing. It ensures that employees are kept informed about the latest news, in the expectation that they will perform better. The latter is also referred to as information-push. A top-down information-push ensures synchronisation within an organisation. It ensures convergence of viewpoints so that decisions get pushed through.

The judicial authorities establish jurisprudence so that similar cases re-ceive the same treatment. It is also to guarantee uniformity of decision-making across the board. Police staff keep records of incidents so that others can do something about them later and so information is retained about the steps taken and actions carried out. This ensures that everyone starts from the same position and will act similarly in similar cases. The better informed people are about each other’s work, the more they can work along the same lines and the more information and knowledge is available to be able to make the right decisions. In the past everything was recorded manually in registers and card index boxes, and libraries were built for physically storing knowledge, while nowadays data, information and even knowledge are generally available in digital form. People can take advantage of all sorts of services and sub-scriptions via databases or the internet. The internet is a source that is available throughout the world, capable of linking companies with other databases, and also with one another. Naturally, in addition to this elec-tronic highway, other isolated networks still exist over which digital data,


information and knowledge can be exchanged and unlocked. Examples are the intranets of organisations, or operational networks over which sensor data are transmitted.

For the rest, a database can be a source of information, if one knows how to interpret it, but does this make it an information source? In everyday language these terms are frequently used interchangeably. A language purist might say that information has a sender while data has a source. The distinction we use is not as strict, so all sources that could possibly contain information are designated as information sources.

Different types of sources exist. Sources, in addition to being either in-ternal or external, i.e., from inside or outside the organisation, can also be either freely or not freely accessible, stable or unstable and they can contain reliable or unreliable data. They can also contain many or few data or (much or little) information. Sources can be accessed directly or indirectly, available or unavailable, and may be either people or ma-chines. For example, depending on the type of source and its expected added value within the context of making predictions, different sources can be used differently and they are either more or less useful. Who owns the data or the information and how can we communicate with this source? Does a specific protocol exist? Are the data up-to-date, or do they come from a historic source?

In the case of human sources, police informants can be divided into arrestees who tell their story, people who contact the police themselves to tell a story and people who were approached in order to obtain infor-mation, whether or not in exchange for a reward. A few comments can be made if we want to predict the reliability of these types of sources. In general, someone who has been arrested had no intention of voluntarily crossing paths with the police. Unlike a person who voluntarily approa-ches the police in order to tell their story. Persons who receive payment for providing the police with information may be the most motivated, although they probably would not have provided this information vo-luntarily. What motivates a person to provide information voluntarily? Extra suspicion could even be aroused if a person cooperates in full after being arrested. Someone may intentionally try to side-track the police by spreading disinformation in order to keep the police from finding out


about their friends, or even falsely put the blame on a criminal enemy. All these aspects should be taken into account before such information is processed internally. In other words, the predicted added value of a source also depends on the reliability of the source. This reliability may be independent of any value of the data and/or information that could be obtained as a result.

A lot can also be said about the reliability of, for example, witnesses’ state-ments. Apart from the fact that people make errors, whether consciously or not, our brains can also fill in missing information and memories be-come blurred with time. People may also be influenced by other people’s stories about the same situation (Wright et al., 2009). When checking the reliability of a statement, it is important to search for relevant clues that could be of help. How much time has passed since the incident took place, what was the exact position of the witness, does his story have any inconsistencies, does it tally with the stories of other witnesses and has he or she been in contact with them? Did the witness make an earlier statement and what do we know about that? A prediction can be made about the reliability of the source depending on the outcomes of these questions.

Apart from being sufficiently reliable, sources should also provide suf-ficient added value in order to be usable. Sources must contain useful data, information or knowledge. Making good predictions about such matters can be very difficult. Sometimes information only proves to be relevant retrospectively, contrary to initial expectations. Nevertheless, here too certain guidelines can be drawn up. For instance, did the source prove useful in the past or can the source be expected to supply relevant information at all? You could compare this, for example, to the useful-ness of clues. Some clues can quickly be linked to a perpetrator or a crime, while others can’t. Collecting too many clues from a crime-scene could be too time-consuming for a clue-processing authority, while col-lecting too few clues could minimize the chance of added value in an investigation. In this context, this is referred to as the evidential value or distinguishing capacity of a clue or information source. The more distinctive the information source is, the more added value it often sup-plies. Moreover, a source’s added value often increases if an authority or a different source can validate the information.


But which data are not only useful to the police but also available? Which sources are needed in order to maintain law and order when decisions about the latter need to be taken. What does predictive policing have to offer in this context? An enormous amount of data are available within the police organisation itself. Not only our own data, but those of others too. Just think of all the incidents that have been recorded in police systems. These represent events recorded by officers according to their perceptions and these often also include what action was taken and whether this resulted in an arrest or another action.

The police have sufficient sources with access to information about the people and resources that can be deployed and about any action that was taken in certain situations. Examples are administrative data on officers, when they can be deployed and what powers they have. But we also have information about which vehicles are available and which police stations are open. When a control centre receives a report about an incident, it helps to have a list of available units as a basis for making a decision on which unit to send to the situation. Without this information it is really difficult to make any decision, let alone the right decision, for maintai-ning law and order.

Whether it is about truth-finding or about maintaining law and order, it is important to be able to retrieve, record and process data and infor-mation. Even if only to be able to guarantee process continuity when employment contracts end. In the past most data were obtained by of-ficers or detectives out on the streets (pull). But citizens can contribute information too, for instance, by reporting a crime (push). The arrival of the internet in the middle of the nineteen-nineties made it possible to send photographs and video recordings to the police, and nowadays there are systems that can transmit real-time camera images from an alarm centre to the police, for instance in the event of a raid16. By moni-toring internet services such as web fora and social media services such as Twitter, we can obtain not only information and data from the real world, but also images from the digital world, and here also law can be enforced and crime detected.

16 http://www.police.nl/onderwerpen/live-view.html


Unlocking relevant sources can prove to be a formidable task. For ins-tance, seizing a data carrier is completely different from gaining access to the deep-web, a closed section of the internet that cannot be accessed using search engines. We sometimes have to resort to using software in the form of ‘webcrawlers’ or ‘spiderbots’ in order to access sources auto-matically and over distance. Moreover, browsing through such sources demands advanced techniques, whereby in some cases even the security of the data or the data carrier itself has to be breached. Clearly, accessing the data and information one seeks is not always an easy task.

It has become essential that the police are able to cope with enormous quantities of digital data and information in order to continue to fulfil its tasks. Not only do they need to access the data, they also need to be able to process them in order to convert them into information or use them to generate evidence. In the case of data carriers that have been seized, for example, there is every chance that they contain terabytes of data. This makes it increasingly difficult to gather the right information or data. Just where is that one little piece of evidence, or what will it take to reconstruct a time-line of activities? The police cannot afford to lose sight due to the amount of data. They have to wade through the data and still be able to see the information and knowledge. The ability to cope with big-data, to succeed in managing it, is in itself a specialised task.

Officers enter information about suspect situations, incidents and others forms of activities into the same registration system, the Natio-nal Law Enforcement Database (BVH). Investigators working on cases record their actives in a different system (SummIT). This is resulting in a collective organisation-wide information position that allows colle-agues throughout the entire country to access information – practically real-time – that was entered elsewhere. In the future the entire police organisation will have a single system for recording activities and experi-ences. It is true that all sorts of authorisation limitations apply regarding personal data within the context of the WPG, but policy is ‘share, un-less...’. The objective of which is to keep the information position of the organisation in synch.


This centralised information position can be used to obtain knowledge. Although the goal may be registration in order to carry out police proces-ses. You could say that this information position is a source for com-piling knowledge, just as police employees and stored sensor data are a source of knowledge. Whether it is a relevant source depends on the question posed or the knowledge one is seeking. For instance, the BVH will probably provide the necessary knowledge about how to recognise a criminal in action, but whether it can also help us learn how to predict domestic violence may not be immediately evident.

Within the framework of predictive policing, it is important to know how we can retrieve knowledge from such sources that helps us to make use-ful predictions. After all, having access to information and knowledge about how criminals work could also provide us with an indication of how to recognise it in the future. So-called knowledge banks or shared workspaces can help here. These are, in fact, digital locations on the in-ternet or intranet where we can deposit knowledge and information for others who also have access to this location. This is how communities are created that can exchange knowledge and experiences over distance.

For the rest, people often incorrectly suppose that knowledge –- and pos-sible predictions – about criminal behaviour can always be found among our own police data. If this knowledge can be found at all, it is just as likely to be found from other data, from people or any other possible source. It is therefore essential to have access to a relevant set of sources, whether these are people or systems. But how do we know which sources are relevant?

The arrival of the digital world sets completely different security and storage requirements, and has resulted in many new possibilities for ex-change and processing. Computer systems can transmit messages over the whole world at the speed of light. Increasing amounts of data can be stored in increasingly smaller spaces and information-processing units such as processors are getting faster at automatically comparing datasets with one another. This is creating many new opportunities. Not only for obtaining knowledge from data, but also, in particular, for learning from the past for the future.


Obtaining knowledge from data

The total set of information, times and persons that the police can access has to be filtered as efficiently as possible so that only the best information remains: information for the person who at that moment can benefit most from it. It would not make sense to inform everyone about all information, and definitely not at every moment of the day. The quantity of available information should be filtered and weighed up in order to minimise the total set. Knowledge that has been collected about a situation that may be about to occur can help us to limit the mountains of available information.

Getting the available data and information to the right person at the right moment is a separate field of expertise. This field of expertise is known as business intelligence. Business intelligence tries to create an advan-tage for an organisation by ensuring that decision-making takes place based on superior information. The conversion of data into information leads to knowledge, which improves decision-making, and results in an advantage for an organisation. In this context, better decision-making means making efficient use of the knowledge that is available in the or-ganisation about similar problems, one’s own possibilities for action and the expected effects. In fact, a solution is found once you can properly identify a problem and you know which – applicable and available – armamentarium is most effective for tackling a problem.

Take a hypothetical case in which we know that a certain group plans to hold a demonstration on a given day. Clearly, the police want to be present so they can maintain law and order. However, we do not know where the demonstration will be held. This information is of no use to officers who are not on active service, as they will do nothing with it. It is important to hand over the information in good time. Too early is wrong, but so is too late. In addition, the information that is handed over must actually be newsworthy, or it will have little effect on any decision-making and actions that could be undertaken. Rumours about the possibility of a demonstration are too vague to be able to take concrete action. Being able to respond adequately demands more insight into the situation.


In order to obtain this insight and make the information more news-worthy, it is also important to examine which sources are relevant to the task that has to be carried out and any decisions this will involve. This phase is also referred to as data preparation. In this case a newspaper could provide added value, as also could recent stories from colleagues or ‘mutations’ entered into police systems. The source must be available and must contain reliable information.

As a result of the knowledge and experience people have obtained, eve-ryone has certain preferences for a certain type of source or knows about its existence. Other sources can be used to search for relevant sources. In fact, this is comparable to the process of searching for the right witness or for the most distinguishing information. Once a selection has been made and the source files are available, the data can be retrieved and integrated into a single set, whether or not in a different form. Compare this to drawing up a briefing prior to the demonstration itself. The in-formation and data could come from different sources and they will all be included in one way or another, depending on the briefing template. This results in uniformity and makes it possible to compare different items to one another (Dean et al., 2008).

Sometimes, the ideal form for the data may not be immediately acces-sible or extra information may be needed in order to provide specific details. This is known as pre-processing. In this phase, for example, text-mining algorithms17 are used to allocate extra labels to data so they can be compared to one another. For instance, it is helpful to introduce structure if data are only available in an unstructured form. Using entity recognition or another type of tagging engine, a sentence including the words ‘Dam 12’ could be allocated the extra information that this may involve a possible location. Developing such labellers or tagging engines, which is a science in itself, actually makes small predictions about the significance of certain data that fulfil a certain profile (Song et al., 2008). You could say that the information undergoes an excel-like transforma-tion after which additions can be made. People do this on social media, for example, by naming the people who appear on photographs so they

17 Text-mining can be used to recognise words and the group connected with this word can, for example, be added as a label. For instance, ‘dam 12’ could be labelled ‘location’ and ‘car’ could be labelled ‘vehicle’


can be found faster. This is what makes photographs, audio-recordings and even free text fields of police systems interpretable – to a degree – for a system (Bertin-Mahieux et al., 2011, Strohmaier et al., 2012), in the sense that filters can label these data as relevant, and subsequently include these enriched data when searching for replies to questions.

Simply adding data or labels to data does not result in knowledge. Let’s use a simple example to see how we can do this. Suppose that all demon-strations during the past year have been entered into a single file. For the moment we can ignore whether this took place with or without text-mining and whether it involved a lot of manual labour in advance. For all these demonstrations we have recorded the number of demonstrators, the location and the topic. We can now calculate a number of things. For example how many demonstrators showed up, on average, per demon-stration. We can also examine whether, on average, demonstrations were held more frequently in certain locations than in others. The more useful data we have, the more useful information – potentially – we can extract. The reliability of statements increases, moreover, with the number of de-monstrations included in the database. For example, we could examine, per topic, whether we can determine a favourite location and the average number of demonstrators. Not much can be learned from the fact that a single demonstration on freedom of speech was held at location X, involving Y demonstrators. If five demonstrations took place at location X with Y demonstrators, then if another demonstration about freedom of speech is announced, we could expect to say, with some conviction, that the location will probably be X and the number of demonstrators probably Y.

In order to maintain law and order it is important to know whether these demonstrations involve a risk of becoming derailed and getting out of control. The answer to this can be found on the basis of historical data from various sources. First we will have to add this new variable to our demonstrations file. By looking back in time we can now make state-ments about cases in which a demonstration did get out of control. Did this always happen with certain topics and in a specific location? Did things only get out of control if a minimum number of demonstrators were present? Based on demonstrations in the past, the demonstration file we have compiled helps us to learn for the future. It helps us deter-mine dependencies between the variables we have measured and which


have been allocated a value. This means we can predict a location with a degree of probability if we are familiar with the topic and we can predict whether there is a chance that a demonstration will get out of control.

In a certain sense, a prediction is the chance of a situation occurring, whereby the challenge is to maximise the reliability of this chance. If we infer a 100% chance – with a high reliability – of a demonstration getting out of control, then it would be wiser to take action than not. This could involve, for example, taking extra precautionary measures in the form of having an extra ME platoon as back-up or by providing additional visible supervision in the form of mounted police. Based on our current data we cannot say much about precisely what the best action is, other than en-tering into a discussion with the organisers or the mayor. For instance, about whether or not to postpone or relocate the event. Postponement would probably avoid a lot of turmoil. It this is not possible then, based on the data collected, we could make a statement about the possibility of a different location. After all, lessons from the past may show that we can reduce the risk of escalation by opting for a different location.

Naturally, this example is only about demonstrations, but we can easily extend this example to other events of types of crime. The question of whether we can predict the occurrence of certain events with sufficient certainty should become apparent from 1) whether sufficient quantities of the right data and information have been gathered, and 2) whether any links between certain variables are strong enough to form a basis for drawing conclusions and expectations. This is where the theory of probability and statistics comes in and makes statements about, e.g., the minimum number of demonstrations needed to be able to say with a degree of reliability18 whether a situation will actually get out of control or not.

In this case, the event we want to predict is whether the demonstration will get out of control; this is also referred to as the target variable or dependent variable. The topic of the demonstration, the location and the number of demonstrators are also referred to as independent variables. If there is a clear relationship between the dependent and the indepen-

18 A criterion for this could be, for example, the 95% reliability interval that is used to be able to make a certain assumption. The term used for this is the probability value of α


dent variables, then the independent variables are also referred to as predictors or indicators. In other words, an indicator is an indication that something else will occur. If it turns out that a given topic for a demonstration is related to its getting out of control, then the topic has become a predictor. Therefore, to a degree, an indicator contributes to being able to predict another event.

It is no easy task to be able to determine in advance which variables will be needed in order to be able to predict a certain target variable or event. If floor plans of a government building are found when premises are searched, can this be regarded as preparatory actions? This may seem a logical way of reasoning based on common sense, while predicting whether someone is pregnant or not19 based on the purchase of soap in combination with a number of other products is not as obvious. In fact, one cannot always say in advance which indicators have a higher predictive value in being able to predict an event.

In the case of our demonstration file, we have access to only three pos-sible indicators. In some cases a data collection could have as many as 1000 different variables, whereby it remains to be seen whether de-pendencies can be found and whether a target variable can actually be predicted. The case of demonstrations was about the chance of an event occurring or not and, analogously, techniques can be deployed if we want to determine who could be a possible perpetrator based on a description or a modus operandi.

The challenge is, of course, to keep the database as small and relevant as possible. This saves time either when collecting or when transfor-ming data. The difficulty is, however, that relationships between certain variables are not always apparent in advance and variables are not im-mediately available or measurable in all cases. A distinction can be made here between indicators with a qualitative value and indicators with a quantitative value. Sometimes, for example, extremely complicated text-mining is needed in order to determine from reams of text whether a person can be labelled as dangerous or not, insofar as this can be deter-mined with sufficient certainty. If we subsequently have access to these measurements, this may prove to be a useful indicator for predicting

19 http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html


whether a person does or does not have a high risk of recidivism. Recog-nising such relevant variables and predictive characteristics from data in relation to a target variable is also known as model-building or model induction. It can best be compared with a search for the best combina-tion of indicators which will – either jointly or separately and in a given sequence – result in the best prediction.

In the case of trying to predict a possible perpetrator, you could compare it with the Milton Bradley game ‘Guess Who’. For the sake of conveni-ence, let’s say ‘Who is the villain’. Two players receive a card showing a villain, and each of them have 24 possible suspects on the table in front of them. The players take turns to ask questions about characteristics of the villain. For instance, whether he/she wears glasses or not, has blond hair, etc. The idea is to be the fastest to discover which villain your opponent has in his hands. And you do this by taking turns to ask a single question, to which the answer can only be ‘Yes’ or ‘No’ After each turn, the players flip down the images of suspects who do not have the characteristic mentioned.

Several strategies can be used to win the game. If the plan is to play only one game, then really large risks can be taken by asking specific ques-tions that only relate to a single, unique person, for example, a yellow feather on a hat. The possible consequence of doing this is that after the player’s turn a large number of images remain upright, or he has already won. After entering all suspects’ characteristics into a file, it is just a matter of calculating in order to discover that certain characteristics will always lead to success if questions are asked in the right sequence and the outcome of the previous question is taken into account. In other words, maximising the chance of success. It is these indicators, the ones with the greatest distinguishing capacity, which help us make quick and pure predictions.

The models found can eventually be tested in order to see how well they perform. This can be done using training data that are already known to have predictive value and, for example, whether a burglary has taken place or not. The models are tested on 90% of these data, which means they are tested 10 times, each time using a different 10% as test-set. The average score of a model is then a standard for how well the model


is expected to perform in the future. This method of testing is known as cross-validation. Some of the indicators demanded by the models as input can be provided via manual observation, and some automati-cally. Once a model is automatically supplied with all indicator values, a system can make use of the model to send a signal after an interesting observation has been received. This could be an e-mail or a bell that rings in the control centre.

These types of models play a role in artificial intelligence. Based on se-lective observations from the environment, a system or machine takes action, possibly supported by our own information.

If two criminals get into a car and drive towards one another, this could mean they are about to hold a criminal meeting. The chance becomes greater as the cars approach one another. The chance becomes even gre-ater if a known meeting place exists where they are about to arrive almost simultaneously. A number of measurements enables you to predict such a meeting with a degree of certainty. The ANPR, taped senders or even wiretapping data can determine whether a car that can be linked to a criminal is on the move or not, as well as the direction in which the car is travelling. Incorporating these signals into the same system makes sampling possible in order to discover whether the sought combination of variables with the right readings is taking place. By coupling a list of known criminal meeting places to the system, in combination with a few mathematical formulae about approximately when and where the cars will meet, it becomes possible to predict these criminal meetings.

Depending on the chance that a meeting will take place, a threshold value can be determined before deciding to take action. The police have different ways of responding to the chance of an incident taking place and the degree of gravity or prioritisation involved. This may vary from doing nothing to units swinging into action with tyres squealing and blue lights flashing. A knowledge system is equally capable of coming up with a proposal for an intervention.

A model is capable of self-adjustment, depending on its success, for example, by only issuing the police with a warning at a later moment in the run-up to an imminent criminal meeting. You could say that the sys-


tem has a self-learning capacity. The technique behind this is known as reinforcement-learning. However, though this may increase the chance of an event occurring, as mentioned earlier, at the same time this situa-tion involves the chance of units arriving too late, so that the meeting can no longer be prevented.

Nevertheless, knowledge about this type of ‘criminal meetings’ and their precursors cannot simply be retrieved from data. Apart from a data-driven method, a model-driven method will also often be needed, whereby the knowledge of officers and detectives is transferred to a system that will attempt to take over this prediction. This will only succeed if the various perpetrators of this type of crime, i.e., the variables being measured, can actually be measured. If a vehicle does not carry a taped sender and we want to use its position to identify a meeting by distance, we will have to find another way of measuring this variable. If we can’t, then identifying a meeting by distance becomes extremely difficult.

Obtaining knowledge from people

Knowledge can be obtained not only from data. Knowledge can also be obtained from the minds of people. This is the field of knowledge management (Nonaka & Takeuchi, 1995). Human knowledge is defined here as the consolidated experience of an acting individual. This know-ledge is comprised of a representation or model of the world. Plato once said that people who have lived in a cave their whole life, and only see shadows reflected on the cave’s walls, come to regard these shadows as their reality. In other words, to be able to determine a person’s viewpoint, you have to know what that individual has been through and what expe-riences he has had. In other words, people have their own personal spec-tacles through which they interpret reality. This legitimises the question of whether an objective reality exists.

Similarly, human knowledge about things that generate crime can be translated into a set of relevant indicators that makes us suspect that a criminal event has taken place. This time not just based on data, but based on a mental model that has an idea of how the world works. This objective of this model-driven approach is to try to capture a mental


knowledge model of criminal behaviour in descriptions or diagrammes that can be communicated. Consider, for example, process descriptions or lists of indicators.

I distinctly recall how, after having spent a few months involved in monitoring heroin couriers, I wanted to examine every car if it had an occupant with a fur collar and a little scented tree hanging from the in-terior rear-view mirror. Experience had taught me that these were the distinctive characteristics for recognising a courier. A picture of the phe-nomenon had been formed inside my head based on positive examples. By compiling a list of these examples I was able to point out a number of characteristics that are specific for this criminal phenomenon. By using this knowledge and paying attention to these ‘predictive characteristics’, I too was suddenly able to recognise a criminal phenomenon. Remem-ber the example in chapter 2, of my colleague who recognised human trafficking.

The model of the criminal world that criminals and officers and detec-tives have compiled can – with the help of the right techniques and the necessary human cooperation – be transferred from minds onto paper or into systems. This is also referred to as making knowledge more explicit or codifying knowledge (Hansen et al., 1999). This is how knowledge is converted into information that can be shared. Information that can be shared can be transferred to other people and possibly also to systems


without needing the original source. People can also share knowledge with one another (Smith, 2004). An organisation becomes collectively more intelligent when knowledge is transferred, which gives it an edge over competitors. In the case of the police, you could say that the compe-tition we face is the criminal outside world.

Recording, sharing and using knowledge are individual steps, each with their own challenges. A number of techniques exist for obtaining knowledge from people with a view to recording it. Interviews and work-shops are perhaps the best known. The police academy, for example, has developed a special ‘toolbox’20 that ensures such workshops are properly structured and given in phases. Putting structured questions to knowledge-carriers or experts makes it possible to convert some of their knowledge to paper (pull). They can actually do this themselves, whether or not aided by motivation-supporting methods such as reward systems (push) or by mental guidance technology to structure their thinking. It can be useful to gather experts from the same field together and get them to talk to one another about, for instance, what they look for when identifying a criminal situation or when they ‘see one coming’.

Obtaining this knowledge via experience is often time-consuming and can be accelerated by sharing knowledge. The well-known buddy system was in fact perfectly tailored for the police. It was a way of transferring knowledge from old to young and was a permanent part of the educa-tion of young aspirants. Adopting a certain manner of deportment, for example, implicitly transfers knowledge without any actual discussion or exchanging information in any other way. With the buddy system, exchanging knowledge on how to act in certain situations was perhaps just as important as exchanging knowledge about being able to recognise criminal aspects in one’s surroundings. In this context, Aristoteles drew a distinction between a professional and a master. Professionals do their work, he said, without knowing exactly what they are doing and they often work routinely. Masters, on the other hand, know exactly what they are doing and are capable of passing on this professional knowledge.

20 https://www.policeacademie.nl/onderwijs/overdescholen/spl/overdespl/Pages/Book-of-Crime-een-methode-voor-problemgericht-werken--.aspx


With their programme ‘Knowledge Development In Models’ (Boelsma & Van der Kruijk, 2008), the police have done pioneering work in recor-ding knowledge with the objective of getting to grips with the criminal production process. The process of transferring waste products in Eu-rope, for example, was put onto paper and dissected in order to see what aspects could be given attention in order to be able to detect contraven-tions. Similarly, models have been developed for identifying the stages of radicalising, and to provide preparatory teams with information on things to look out for within the context of safeguarding and securing persons and objects. Recording what experienced people watch out for has made it possible to share knowledge. Experiences in this aspect have been laid down in the Modelling in Safety Manual (Van Wijnen, 2010) which was drawn up to support the development of knowledge models in police organisations.

Making certain types of knowledge explicit can be extremely difficult. Take, for example, the smell of Marijuana. You can recognise the smell once you know it, but it is really difficult to describe the smell. You could compare the smell of marijuana to the resin-like smell of pine trees. But is this description sufficient to expect a person who reads this and who is trying to assimilate this knowledge to actually be able to identify the smell of marijuana? Or is more information needed? Entire languages can be developed for describing certain characteristics that help to trans-fer knowledge. Describing tastes, e.g., for wines, is an example of this.

Knowledge systems or knowledge banks exist that employees in an orga-nisation can use as a form of reference manual for recording knowledge and exchanging it between people. For example, the police knowledge-net21, part of the police academy’s website. Employees can use this to search for topics about which knowledge is available within the organisa-tion. If the knowledge is not available within the system, but employees have contributed their own knowledge domains, you may be able to find a contact in the organisation who does have the knowledge you seek. The success of such systems in organisations varies and – without social in-teraction – will not automatically come into existence (Pan & Scarbrough,

21 https://policeknowledgenet.policeacademie.nl


1998). On the other hand, computer systems can play a supporting role not only in extracting, and recording knowledge, but also in sharing it (Alavi & Leidner, 2001). Whether such systems take off depends on the motivation of individuals and the culture within a company. It is also the case that people accumulate knowledge in different ways, so that the choice is often made to provide knowledge via different carriers. Applying or making use of knowledge leads to what we might call intel-ligent behaviour. If we have knowledge about a situation, e.g., because a preparatory survey has already been carried out, then we will want to use this knowledge when taking action, such as entering premises, perhaps in order to enter as surreptitiously as possible, and possibly taking extra safety measures relating to risks as yet unknown. For instance, a resident who may be armed and dangerous. It is not without reason that special units are sent to deal with special situations.

As I already said, not all knowledge will and can be used for every si-tuation. Either because the knowledge is not available, or due to the consideration of inducing different behaviour, possibly because different objectives may apply in a similar situation. In other words, having know-ledge does not automatically mean it will be used. Reading a manual on how to set up a service, for example, does not mean a person actually understands the manual and is skilled in the activities. Let alone that a


person will always use the knowledge in the right context and will ‘go by the book’ as it were. Considerations may apply that require a person to ignore the manual. Just as some colleagues, despite having trained in situations, nevertheless deport themselves differently.

Accumulating knowledge by remembering its significance does not mean that a person will or can make use of that knowledge. Some things require a lot of practice and you have to do them repeatedly before be-coming competent. Repeated practice in applying knowledge leads to skills and processing actions becomes automatic. Walking and cycling are examples of this. Other things you may never learn, such as speaking another language fluently, despite the required knowledge being avai-lable. Regular practice makes processes automatic, so that it becomes easier to convert knowledge into the right action.

Consider, for example, the knowledge deployed by a detective during an effective interrogation of a witness or suspect. Rapid switches take place between what is known in communication science as involvement and content. Understandably, this is referred to as interrogation skills. A skill is not quite the same as simply having knowledge. Without practice, people are often unsuccessful at making use of knowledge about inter-rogation methods, such as the funnel model, the ‘duck decoy’ and how to build up tension.

It is difficult to predict what will happen during an interrogation. Without introducing structure, it could go in one of many different directions. Furthermore, a suspect or witness may decide to cease cooperating and make no further disclosures. It is only by active participation that these skills can be developed. What a skill actually amounts to is being able to apply knowledge in practice: not only knowing when you need to act, but doing it in such a way that few errors are made.

In this sense, being good at predicting criminal behaviour can be des-cribed as a skill that needs to be practised until it is mastered. The four-stroke system is sometimes used to describe the process of obtaining skills: from unconscious incompetence, via conscious incompetence and conscious competence to unconscious competence. The knowledge is actively used at the moment that a person is aware of his or her own


actions. It is simpler to make knowledge explicit during these phases than in phases in which a person is unaware of his or her actions.

Predictive values and predictive systems

A good prediction is the same as an expectation that is fulfilled. You could say that a person has been proven right because the presumed situation actually occurred. Whether an expectation will be fulfilled depends on how good the prediction is. The quality of a prediction that proves to be correct in 10 out of 10 cases could be described as rather good. If it is only correct in 5 out of 10 cases, then its quality could be described as reasonable and a prediction that is always wrong could be worthless. The greater the chance of a successful prediction, the greater the value of the predictive system.

Without going any deeper into the subject, it is important to remember that when a prediction – from a person or a machine – proves right, this does not necessarily mean that the next prediction will also be good. However, if 100 predictions in succession were right, then the chance is greater that the next prediction will also be right, as long as a comparable prediction is involved, based on more or less the same input. Even after 100 good predictions, there is still the chance that an error will be made the next time. This is why it helps to carry out the best possible reliability tests on systems that make predictions.

In this context of reliability, the forensic investigation department speaks of the rarity of traces. For example, about the chance that these came from someone else. If this chance is less than 1 in a billion, it is extremely probable that the traces came from this person, though one can never be absolutely sure of this. Such evidence can alter the relationship between chances that several predictions have the same probability. Think back for a moment to the chance of a burglary after leaving the door wide open. Leaving the door open may make the chance of the phenomenon burglary occurring greater than it would have been if the door had been closed. You could say that the open door was an invitation to commit a burglary. Therefore, leaving your front door wide open influences the chance between a burglary taking place/not taking place. The same ap-plies to a court case. Here a judge assesses whether a suspect can or


cannot be found guilty based on the evidence provided. The challenge is to provide such evidence that a judge regards the evidence of the pu-blic prosecutor more or less probable (guilty) than the evidence for the defence (innocent). Clearly, the choice of scales as a symbol of jurispru-dence was fairly apt.

In a mathematical sense, certain aspects must have been dealt with in relation to evidence that is put before the court. Opponents in this respect feel that “Mathematics should not be allowed to cast their spell over the court” and particularly warn against exaggerating mathematical aspects (Tribe, 1971). On the other hand, mathematicians generally op-pose allowing evidence that was derived in any other way than purely mathematically22. After all, evidence based on a single example, a horror story or an appeal to humanity, may be convincing without having any mathematical value whatsoever. Compare this, for example, with the way in which arguments are introduced into a court in order to convince a judge. A lawyer or public prosecutor who is known to the court, or who is a good talker, may carry more judicial weight without the involvement of any evidence at all.

Nevertheless, it is relevant to do something about this if we want to make predictive models. We benefit most from objective statements about whether events did or did not take place, based on measured variables whose predictive power has been demonstrated. This is why frequently used techniques from machine learning and artificial intelligence are based on what is known as Bayesian statistics. This could make it possi-ble for machines to become even more independent than courts in asses-sing situations and whether or not they occurred. Without mathematics we would be completely at the mercy of arbitrariness.

Predictive models from the field of artificial intelligence can help to sup-port human decision-making or these decisions can be left entirely to machines (Negnevitsky, 2005). Naturally, the question is to what extent does our organisation want this, and whether sufficient confidence can be developed to allow decision-making to depend on machines. Predic-tive models inevitably lead to a debate about decision-making based on data, information and explicit knowledge, versus decision-making based

22 http://staffhome.ecm.uwa.edu.au/~00043886/humour/invalid.proofs.html


on intuition. Furthermore, predictive models cannot be developed for all possible situations; constructing them is no easy task and they depend directly on high-quality data. Sufficient data must be available, moreover, to be able to make statistically reliable predictions.

For the acceptance of predictive models – and this applies much more than for systems that people deploy for more general activities – it is not only the final usability and ease of use that are important (Davis, 1989). For instance, with decision-supporting systems, it helps to have insight into the how the model being used works and to have confidence in the knowledge used by the model (Shibl et al., 2013). This means everything can be explained and users understand what is involved and how the results can be used.

Artificial intelligence, machine learning, data-mining and other techni-ques from the fields of mathematics and informatics make it possible to sift through enormous quantities of data automatically, to discover patterns and to be able to make predictions (Nath, 2006). Nowadays this is also referred to as data science and analytics. These are different terms for a field that is intended to retrieve knowledge from data and to com-municate about that knowledge. Analytics tries to retrieve knowledge from data and, aided by mathematical models, make predications based on relevant input. Using these techniques, statements can be made about, for example, the class or category of crime to which a certain set of observations or indicators belongs.

Speech recognition, for example, makes use of such techniques. Speech recognition unravels and examines sound signals, searching for unique characteristics that can be traced back to a single individual, similarly to an examination of the characteristic minutiae of fingerprints. With speech recognition, however, these unique characteristics can only be identified after complex data handling and this requires specialist know-ledge. Nevertheless, also in this case the challenge is to search for the distinguishing characteristics associated with a specific target variable, in this case, a certain voice. In other words, it depends on which obser-vation has to be made or which indicator value has to be measured. In order to achieve this, data-processing techniques start doing their work in databases, on plain text, audio-recordings, images and even on video-recordings.


These models that have been developed, or intelligent systems, can also become a part of a much larger entity. These are also referred to as multi-agent systems (Wooldridge, 2002). For example, these systems can make a prediction independently, based on their observations. A total system can subsequently make a statement or carry out an action, based on a combination of individual predictions. You could compare this to a group of people cooperating on something. In addition to a group com-prised of similar units (e.g., an ME platoon), it could be comprised of a combination of different units and resources. For example, a search in which a helicopter, a number of vehicles and a number of police officers cooperate on arresting a suspect. Multi-agent systems are often deployed if no agreement exists for finding a solution and if realising an objective is a matter of trial and error. Feedback can help these systems to self-adjust, the objective being to improve their performance.

The IRIS model; mathematics for security guards

By Jeroen van der KammenIn our country, administrators and prominent government functionaries should be able to speak and act freely despite their position, which at times may be precarious. Particularly in the times in which we live, when misinterpreting a cartoon or an unfortunate choice of words can lead to enormous social unrest (and as a result to personal feelings of insecurity), a model that offers insight into the risks incurred and subsequent interventions that can be taken is an absolute must.

For some time now use has been made of a mathematical knowledge model (IRIS) to form a detailed picture of all the risks that can arise. These risksresult in a customised range of measures that can be taken in order to reduce or take away these risks.

The model was developed many years ago by an employee involved in the project Knowledge Development in Models (KiM) who, with a forward-

looking view, wanted to realise better, more systematic insight into the risks that have to be identified.

In particular, providing suitable and systematic answersto these risks was pioneering work within the field of surveillance and security. The resulting model permits a much broader field of application than surveillance and security alone, and in the future it will be introduced and improved even further.

As a result, we have left far behind us the days in which we started with an action in response to an event. Knowledge helps everyone right at the start of the process and not when it is too late. The model makes use of attacks that occurred in the past, to be able to offer solutions at the start, which may mean that these attacks could havebeen avoided altogether.

Preventing attacks is not done using a crystal ball; in fact it looks at what can happen and how to remove that “can” as far as possible by early intervention.

Useful predictions

for the police

5. Useful predictions for the police

Some years after joining the police, I came across the air fleet. The air fleet of the former National Police Services Agency (KLPD) had purchased type 135 Eurocopters from the Airbus company on the advice of a report from an external agency. A calculation was made based on using half the helicopter’s action radius to determine the distance a sin-gle helicopter flight could span. This would enable the helicopter to reach every place within a circle around its starting position and subsequently return to its start location without having to refuel. With a little creativity in placing these circles on the map, just five circles were needed to cover the whole of the Netherlands. One extra helicopter would also be needed to replace a helicopter in need of a check-up or maintenance work. All in all, based on a proportionate distribution, six police helicopters would be sufficient for the whole of the Netherlands. I still remember having my doubts about that proportionate distribution. Moreover, the feeling that there must be a better way grew even stronger when it emerged that the helicopters were not deployed as proposed in the report but, for purposes of economy, were all stationed at the same location.

Fortunately, a lot has changed since then. In cooperation with the Uni-versity of Twente, the police have worked hard on optimising how the air fleet is deployed. In addition to optimising the home base locations (Buiteveld, 2010, Van Urk et al., 2013), the routes and planning are now determined based on maximum expected returns (Vromans, 2014). Crime incident patterns are used as input, as also are possible take-off and landing sites and the number of annual flight hours available. The latter aspect of optimisation alone resulted in a 20 to 50% improvement, with helicopters arriving on time more frequently in order to support ground-based officers during home burglaries and street robbery.

Retrospectively, I realised why my gut feeling had played up when I dis-covered that all the helicopters operated from the same home base. My motivation in respect of this resource was particularly based on making maximum use of it, unaware as I was of any financial considerations.


After reading the report, I assumed that apparently no information was available on patterns of incidents in the Netherlands which could be important for deploying a helicopter and that this was why they had opted for a homogeneous distribution over the country. Perhaps it was an attempt to realise a sort of ‘fairness’, I thought, the idea that every municipality should have an equal right to air support. When it subse-quently became evident that all units were stationed in one location, I felt certain I could reliably predict that at that moment deployment of the resource was not particularly optimal.

The following is an explanation, based on the above and a number of other aspects of patterns in data, of techniques that exist for being able to make certain types of predictions. Afterwards, based on these different types, examples are described of the criminal outside world, police busi-ness operations and the interaction between police business operations and this criminal outside world, i.e., police performance.

Proportionate distribution was what I had in mind. I felt this would result in deploying the resource more effectively than having all the helicopters in the same location, and it would be even better – to my mind – to coor-dinate this resource according to the pattern of incidents in the outside world. Proportional distribution makes resources equally available every-where. Just suppose, for instance, that on average one officer is available for every 500 Dutch citizens, this would permit only limited variations in this 500 per municipality. An example of homogeneous distribution is when every municipality achieves the same average of 1 per 500. If it turns out that certain municipalities do not keep to this, then these have either more or less than 1 officer per 500 residents. Based on the assumption that every officer is equally effective and that the occurrence of crime is equally spread (though of course this is not the case in both instances), an expectation can be pronounced about the effectiveness of the resource. By obtaining experience in the effectiveness of indivi-dual resources and in how crimes are perpetrated, deployment can be improved much more than by means of proportionate distribution. For example, if it turns out that more crime occurs in densely populated city regions (Regoeczi, 2002), then it might be a sensible idea to switch more capacity to these regions. Based on this type of information, a prediction can be made about how effectively a resource is being deployed.

Geographic and temporal properties are characteristics of data based on which a different type of prediction can be made. By discovering patterns in crime in a geographical or temporal sense, it is possible to predict where and when the next incident can be expected (Brantingham & Brantingham, 1984). Plotting the times of criminal events, e.g., theft, over time can allow certain patterns to emerge. At certain times, certain forms of crime will occur to a greater or lesser degree. Clear differences in whether incidents do or do not occur can be seen between day and night, in the weekend or on weekdays, on pay-day, or at other specific moments. The more data there are available and the longer the period over which data have been collected, the better a prediction will be on the expected number of crimes in a certain place or at a certain time. I remember a case involving a person who dug holes in the woods with the danger of intentionally wounding other people. By geographically plotting the locations of these traps over time, we were able to predict where the next hole would be dug. The suspect could quickly be detained by focussing the available capacity.

Another example of predictions is discovering locations where perpetra-tors may possibly be found, calculating from the scene of an incident. If one knows at which time the crime was committed, it is possible to calculate the maximum distance the perpetrator can have travelled. This involves a simple model assuming a vehicle’s maximum speed, resulting in a circle around the location of the crime. A more complex model also takes into account, e.g., the transport network at the scene of the crime and the average maximum speed that can be achieved due to traffic. This does not result in a circle-shaped area in which the perpetrator could be located, but gives a much more accurate indication of the possible location of the perpetrator.

Regression is another method of predicting. This involves the existence of a proven relationship between two variables. One of the variables can be regarded as a predictor of the other variable. An example of this is that the number of hours an officer spends on the street can be expected to equal the number of arrests he or she makes. The precise relationship requires measurements capable of indicating whether the relationship is strong or weak, and whether another factor can be determined capable of indicating, e.g., the expected increased number of arrests after doubling


the number of hours spent on the street. This may be twice as many, but possibly even four times as many. So-called linear relationships exist whereby the increase in the target variable is directly proportional to the predictive variable, and quadratic relationships also exist, whereby the increase in the target variable outstrips that of the predictive variable.

For example, if the population increases, you can also expect crime to increase. For a number of types of crime this is indeed the case (Nolan III, 2001). Over the years, numerous studies have been carried out that have discovered the predictors of crime. This has resulted in useful and surprising insights that facilitate anticipation. For example, repairing broken windows reduced crime (Kelling & Coles, 1998), but even the ve-getation in an inner city can have an effect (Kuo & Sullivan, 2001). Such research into dependencies is typically the domain of criminologists and social-psychological scientists. Long-term, intensive research is often required in order to establish such relationships. The main question in this type of cases is what increases or decreases along with an increase or decrease in crime. Knowing this enables us to combat crime better and increase the focus of interventions. If it turns out that robberies and sui-cides have a stronger relationship with poverty and inequality of income than with rape and theft (Hsieh & Pugh, 1993), this knowledge can be put to immediate use as input for policy.

It is often the case that not one input variable but a combination of a number of input variables can be used to increase the accuracy of pre-dicting a target variable even further (Perry et al., 2013). It also turns out that some types of crime often recur in locations where they were com-mitted previously. As such, the occurrence of an event has itself become a predictor for the occurrence of a subsequent event (Townsley et al., 2003).

Classification is a form of prediction in which not one but several target variables are distinguished, such as several types of crime or several potential suspects. You want to make a statement based on a number of identified characteristics of these persons or objects, or indeed anything, by placing them in a certain category or class. In mathematical terms, you could say that you are trying to relate the input variables to one or more known target variables. Once again, the challenge is to seek out


indicators that are distinctive of a certain class. In any case, sufficiently distinctive to be able to distinguish between different classes or target groups. This could involve a number of types of crime or a number of types of suspects.

Actually, this is the same situation as in the ‘Who did it’ game, in which a model is made so that, on average, you play an optimum game and finish having asked as few questions as possible. In fact, the challenge is which question, relative to the previous question, results in most added value in order to achieve the best possible prediction. All sorts of search algorithms exist for this, such as greedy search or simulated annealing, which examine, based on the data, which joint combination of predictors has the best predictive effect, also in comparison with other categories. If the first question in the ̀ Who did it’ game tries to distinguish between men and women, it would not be very sensible to follow this with a ques-tion about whether the person sought wears lipstick or has long hair. In any case, the two target groups are so similar that this would not introduce any further distinguishing capacity. A better question may be to ask whether the person wears anything on their head or not. The most distinguishing indicator is subsequently added to the collection. This is mainly about the added value of one indicator compared to another, and we shall assume, for the sake of convenience, that the data are simply available so that the algorithm can get on with its work. Gathering data is often an enormously time-consuming business that takes much longer than the testing of models. Being able to classify certain combinations of observations enables us as it were to equip locations, persons or crimes with categorical labels.

In addition to certain types of crime, it is also possible to classify, e.g., reports made by citizens via the internet or tweets. A classification algo-rithm uses the data that are needed to be able to make a statement about the most probable class of the object being scrutinised. In the case of tweets, for example, one can examine the mood of the population (Jiang et al., 2011) regarding events in the outside world or regarding actions taken by the police. Sufficient useful information can be obtained by spe-cifically looking at various characteristics such as the number of words, but also language errors or specific words used. Meta data can also be included, such as data about the author of a message.


A classification system can be used for reports received from citizens that could serve as a filter for triage reports. Such techniques can be used to select reports that represent an enormous threat or which involve an enormous chance for the police to gain ground by carrying out a rapid intervention. This is also a way of classifying reports into certain risk categories. It is then the degree of threat or the organisational chance that determines whether a specific class will be chosen. This results in a so-called risk assessment instrument. This is an instrument capable of indicating, often with the aid of colour codes (e.g., green to red), the extent of the risk or chance involved in the input set being measured. Such instruments can be used to filter or weight criminal networks or potentially violent individuals and, depending on the outcome, subject them to a different process or type of treatment.

Particularly useful and frequently used sub-categories of classification systems for the police are identification and verification systems. Fin-gerprints can be taken, for example, using seven measuring points which experts regard as characteristic enough to be jointly sufficiently distinguishing to be able to proceed to making an identification. This is similar to an identification that is made based on DNA material. The combination of points or indicators observed must be so unique that they can be traced back to a single individual. In general it is more diffi-cult in relation to a modus operandi because several criminals often use similar systems. However, adding the locations does increase the chance of a unique match. One has to keep on adding extra distinguishing characteristics in order to introduce more uniqueness and thus be able to trace the possible set of solutions back, with sufficient certainty, to a single individual.

After all, a verification system is asked whether someone or something really is who or what he/she/it claims to be. The question is, in fact, whether the image the indicators presents of the target variable is actual-ly correct. What is important here is to examine the chance that, in view of the input variables, another target variable is not actually indicated. An answer to this can be found by examining the proximity of other possible persons or forms of crime, and by examining whether the characteristics measured are sufficiently distinctive.


FNA – all criminal networks automatically mapped out.

By Tobias de WitIt started with a simple request at the reporting centre in Driebergen. A number of persons in a car were called to a halt for control. In the past such a request meant that the reporting centre had to consult a number of systems in order to determine whether the persons were sought or in need of any other form of attention.

This has become much easier with a new overview, the Fluid Networks Approach (FNA). A simple overview showed that these persons were members of a number of networks that had in the past been involved in various drugs cases. Linked to this overview is a protocol on how colleagues out on the streets should deal with this information. They checked the persons based on the powers they had at that moment.

For the sake of certainty, in view of the information from the FNA system, these persons were followed for a while. They subsequently arrived at a parking lot behind a supermarket where a drugs-related transaction took place. These persons could be arrested and a few kilos of drugs were seized.

The persons themselves had no history in the field of drugs. It is a known fact, however, that criminals generally work with people they trust. This is why relationships are so important. Once all relationships have been extracted automatically, the Social Network Analysis algorithm can be used to weigh relationships and map all possible networks.

This also enables you to determine criminal behaviours in which persons without a history are probably involved. This is one of the points of departure of FNA. Furthermore, automatic extraction based on key words results in a summary of these behaviours. These can be classified, eventually resulting in a method that steers more effectively and efficiently in the direction of topics on which the police should focus.

Using this model to restructure the basic information of the Dutch police is an exact science that enables you to extract knowledge from data. Without reading a single letter, you ‘know’ what is in the systems. Creating these types of models and systems is the future for the Dutch police.

Clustering is the last form of predictive mathematical technology we shall discuss here. Clustering makes it possible to group observations without having defined certain classes in advance. This is a method for collecting data on, e.g., burglaries or smash-and-grabs, without actually knowing in advance whether a distinction has or has not been made between different types of smash-and-grabs. A subsequent examination can then reveal whether different modus operandi were used, or whether there are certain situational aspects in the environment that justify a certain distinction or not. In the case of clustering, there is every chance that at first glance an examination of the data reveals no difference what-soever or that there are simply too many data for a person to be able to process them manually. This technique can be used, for example, to


compare surveillance patterns with one another and to discover whether certain contributions to internet fora may come from the same person.

Predicting organisation operations

In order to optimise the organisation’s operations, opportunities exist on countless fronts for using predictive science or technology to obtain value from data in order to facilitate decision-making. This applies in fact to every organisation and the police is no exception. I will discuss this, however, because it is important that this too is used in combating crime. It is the interplay between optimum organisation operations and knowledge of the (criminal) outside world that boosts police perfor-mance. Supportive services within the organisation’s operations, such as the P&O department, the facilities department and even the financial department can also benefit directly from predictive systems. Without being exhaustive, I shall demonstrate a number of possibilities.

In general a lot of data are available within the P&O or HR department. Data about employees and their performance within the organisation. Increasing use is currently being made of predictive models and analy-tics in order to generate knowledge from these data. (Davenport, 2006). The tasks of a P&O department can be sub-divided into tasks relating to recruitment, promotion and outflow. The challenge with recruitment is to design it in such a way that members of staff can be deployed as effectively as possible during their careers.

In fact, attempts are made during intake procedures to obtain the best possible picture by indicating the characteristics sought in a candidate for a vacancy by drawing up a so-called function profile. In this case, the function profile of an applicant, but try comparing this with the function profile of a criminal. Based on the vacancy, offers are received from peo-ple, and letters are examined to see whether the organisation agrees with candidates’ thoughts regarding their suitability. This is, in fact, a second filter. Based on information from the letters and any enclosed curricula vitae, the chance of success is examined in the sense of a possible match between supply and demand. This involves looking at specific criteria such as motivation and level of education, but characteristics sought could also include such skills as ability to cooperate and assertiveness. It all depends on what an organisation is looking for.


Based on earlier experiences, recruiters may have developed positive or negative ideas about certain values of these characteristics. A preference for a certain education or university, for example, or for certain hobbies or previous employers could lead to a feeling based on which a decision is made on whether or not to go on to the next step: inviting a candidate to attend an interview. During this interview, and any subsequent in-terviews, interaction allows an examination of whether the candidate’s ideas about the function really are in keeping with the function profile. A third filter.

Knowledge can be compiled by recording in a database of experiences with candidates for the function profiles that are to be filled. Knowledge about which target group in the market external to the organisation can best be approached to achieve a high return. On the other hand, selection could also focus on that part of the market that is particularly known to have high-quality expectations so that later stages of the process will be faster. The objective is to optimise yield for the organisation, in terms of both speed and quality.

All considerations during this process are comparable to the deploy-ment of personnel during an action or making personnel available for a project. Here too attention is paid to the intended objective and the quality required of the personnel in order to be effective. Filtering as efficiently as possible at the start of the chain, i.e., during job applica-tions, maximises the chance of success later on in the process. Various aspects may be involved in this success. For instance, it could be about a match between a person and the job, but also between a person and the organisation. If someone consciously chooses for an organisation rather than for a function, the yield could be lower in the short term than in the long term. Whether this is desirable or not depends on the policy of the organisation in combination with market supply.

Useful predictions are also possible in respect of promotion. For ins-tance, in order to be promoted, is it better to follow a management de-velopment trajectory or individual customised training? Is it better for a person seeking promotion to have worked in many positions for a short period of time or in few positions for a longer period of time? What works better? For example, an average standard for speed of promotion can be calculated by comparing historical intake data with the current


positions or scales of staff within the hierarchy of an organisation. It is also possible to look at the average number of positions that a person held in an organisation during a given period of time. A comparison of individuals’ situations results in a measurable indicator for steering purposes, depending on the organisation’s objectives. Thus, if it takes on average 6 years to be promoted from police constable to Senior Consta-ble and certain individuals have still not achieved this after 12 years, then special provisions or safety nets could be developed for them.

With outflow, if a person’s age is known, one can calculate exactly how long it will be before they will start drawing their pension. Doing this for all employees results in a picture of the age spread of the entire workforce. An organisation is generally regarded as healthy if the different age ca-tegories are spread fairly homogeneously. This ensures on the one hand the retention of experience, while on the other hand the organisation benefits from sufficient fresh impulses from young people. Based on the predicted outflow, and depending on personnel policy, new employees can be recruited at a pace that can be predicted accurately. In addition, for a variety of reasons, a certain percentage of employees will leave the organisation prematurely. This percentage fluctuates and will partly re-sult from people’s deaths during their employment. This percentage can be estimated fairly accurately, particularly over a longer period of time. Other employees will find new work outside the organisation. Predicting this is more difficult and may depend on the employment climate out-side the organisation, as well as on the employment climate within the organisation. The employment climate outside the organisation could depend on, e.g., the demand for and the chance of a certain type of work, the remuneration offered and the economic climate. The employment climate within the organisation could depend on the atmosphere on the work-floor, opportunities for promotion or other chances of deve-lopment and perspectives. Measuring these or similar factors makes it possible to create a model that tries to determine the outflow percentage of employees who will not remain until retirement age. The accuracy and reliability of the model will become apparent from the necessary tests that have to be carried out. If the model is good enough, then the results can be used to improve anticipation via the desired intake.

One final element I want to mention within this context is absenteeism. Absenteeism affects the degree to which personnel are not available for


deployment. It is often an expensive cost item that one tries to avoid. Making sure registration systems provide a good overview of the level of absenteeism in an organisation enables one to measure the extent of current absenteeism. All too often it becomes a challenge to draw up full schedules for actions and to realise the obligatory staffing of 24-hour posts if absenteeism exceeds a given percentage. Predictive models can help make predictions over absenteeism. For example, based on histo-rical data, but also possibly based on national flu statistics or telephone calls to GPs. As a result, plans can be adjusted and the organisation’s effectiveness will increase. Measures can also be taken that are predicted to have an effect on reducing the percentage of absenteeism.

Absenteeism can be tackled by examining the causes and generators of absenteeism and by opting for smart interventions. Examples of ‘measu-res’ that help to minimise absenteeism are supporting a healthy life-style by providing healthy food in the canteen and providing sports facilities for employees. Other possible measures could include keeping an eye on the health of staff, making sure that work pressure is not too high and that safety standards are met. This is analogue to combating crime by finding interventions to deal with or neutralise the generators of crime. An answer to the question as to which of these measures has most effect in relation to the costs incurred would definitely be of added value when making choices. This is no different when combating crime; this too involves deploying the intervention that is most effective in relation to the costs involved.

Where P&O or HR is about people and maximising their deployment, you could say that facilities management does the same for resources. The facilities department can also use the available knowledge and data to make certain predictions. For example, a direct relationship generally exists between the number of employees and the floor space an organi-sation needs to be able to provide its employees with a workplace. This dependency may vary per branch, but a linear relationship does genera-lly exist. For instance, although police officers spend more time outside than office staff, opportunities do exist in this respect. Just as they do with the number of cells in relation to the number of implementing employees in law enforcement. It really is possible to harmonise these. Based on historic data, predictions can be made about the number of expected arrests and containments per employee.


Another possibility for facilities management involves being able to make use of an equal amount of equipment. This is demonstrated by the example of the helicopters at the start of this chapter. Storing all capacity in a single location can have financial advantages or advantages of a dif-ferent administrative nature. For instance, in relation to maintenance. From an operational point of view, this is often less efficient. Just sup-pose there was only one police station in the whole country! This would be completely unacceptable from the perspective of proximity to citizens, travelling distance in the event of incidents and for many other reasons. Another option is to create a police station with 200 employees for every 100,000 residents. Although both models have their pros and cons, the latter model may seem fairer than a model in which all employees are deployed in a single location. On the other hand, with a population of 18 million, this requires 180 locations instead of 1. Analytics and other mathematical techniques help us to calculate and compare such scena-rios – which involve various solutions – with one another. This could involve weighting the pros and cons, so that the various advantages and disadvantages do not all carry the same weight. The aim in this situation is to predict what would be the ideal solution in practice.

The last example is about the fleet of vehicles, but in fact such con-siderations can be made for all resources or ‘assets’. Analogue to the recruitment of personnel, the fleet of vehicles is there for deployment, maintenance and, after a certain period of time, vehicles also reach an end of their life. When purchasing, input parameters can be included that reflect on predicted deployment, predicted maintenance and life expectancy. Appropriate selection at the start of the process will result in advantages later on down the chain. Costs per kilometre, the expected number of maintenance overhauls, users’ experience from the past and any other important indicators can be included in the model so that a well-balanced choice is made. In the case of pooled vehicles, for example, it is feasible to predict, based on estimated usage, the ideal number of vehicles in a pool. At a certain stage purchasing too many vehicles no longer benefits police performance and soon becomes too expensive. Purchasing too few vehicles will also lead to poorer police performance. Once again, a good prediction will help to reduce costs and increase the yield per vehicle. Compiling a relevant set of indicators demands the same skills that we saw earlier and can be obtained from employees or from data.


Predicting optimum maintenance is also a science that can be extremely lucrative (Dekker, 1996). If maintenance is minimal, defects will crop up and vehicles will be unavailable for use. If maintenance is carried out too often, vehicles will not be available as often as in an ideal situation. An assessment has to be made based on the prediction. This could be about the timing of maintenance, but it could also be about replacing parts, such as tyres or fuel filters. Replacing filters too often makes costs higher than in an ideal situation, replacing them too infrequently could result in other defects and eventually lead to increased costs. The main question with this type of model is often where are costs at a minimum. In order to make optimum use of vehicles, what you really want is that the vehicles in the fleet are always deployed in a situation that contribu-tes to the police task in such a way that as many incidents as possible can be served simultaneously, and – depending on scarcity – where possible, supplemented with a geographical honesty factor. This demands data on the past in order to harmonise deployment of the vehicles according to their expected use.

Countless possibilities exist where predictive models can be of added value in an organisation’s operations. The above is only the tip of the iceberg. Whether personnel planning is involved, or financial estimates, they all benefit from good predictions. Investing in these will optimise police performance not only in terms of cost efficiency, but also particu-larly in terms of the availability and suitability of people and resources, so that in the long term more criminals will be caught.

Predicting crime

If the police were extremely accurate in predicting crime, our work could look completely different. If we can predict crime for the future, then it can be seen in the present and re-discovered in the past. Predicting crime helps you to understand how it works, what generates it and how you can observe it. Several levels can be distinguished on which crime can be predicted: an operational level, a tactical level and a strategic level. On an operational level or ‘case level’, predictions are about who is about to strike, when and where, how and with what motive, etc. One level higher, the ‘supra-case’ or tactical level, is about predicting where most crimes will be committed, which criminal networks will develop in the


future and how the modi operandi alter per type of crime. At an even higher level, the strategic level, insight can be gained into, for example, the spread of types of crime relative to one another, in a quantitative and a geographical sense. Police performance could benefit enormously from this knowledge. Crime can be combated by effective and efficient anticipation of an expected situation.

Much is known about the characteristics and the causes of different types of crime. The same applies to the degree to which they occur. Throug-hout the world criminologists are carrying out research in order to get a better understanding of crime. Such knowledge has been recorded in books and scientific literature, but it can also be found in the minds of police officers and detectives. Knowledge can also be found in police sys-tems that store information on cases and incidents, and countless other sources are also available. These vary from information from insurers, travel organisations and the Tax and Customs Administration to internet data and social media feeds. All these sources, whether they come from humans or databases, can be used to make predictive models. Integra-ting these models into systems provides the police with triggers in the form of increased chances, hits or matches so they can come up with an appropriate response.

The systems mentioned, such as CAS and PREDPOL, are all operational and show that predicting crime is not science fiction. At the same time, both examples clearly show on a tactical level that not all crime is equally amenable to prediction and that for the moment it is an illusion to sup-pose that crime can be predicted with 100% certainty. It is also becoming clear that explicit evaluation methods need to be developed so that sys-tems can be compared with one another. After all, what does it mean exactly that a model is better at predicting than a police analyst? What does an 8% hit-chance say about the usefulness of the system? Where are the upper and lower limits and how many of the boxes are risk boxes? If we had gambled on a prediction, what chance of success would this have had? These are relevant questions that must be answered before we can say anything useful about the added value. Though here too the emphatic comment should be made that the success of such a system depends not only on its usefulness or performance, because – as I men-


tioned earlier – it will also have to be user-friendly, if the organisation is going to adopt it.

Below are a number of other examples of possible and useful predictions that could be of added value for a police operation and which relate to different levels and various moments in time in the future, the present and the past.

If we start with individuals, we can try to discover suspects from among a set of arbitrarily chosen individuals. We can try to ‘reason in the di-rection of the suspects’ by filtering the total set according to, e.g., clues from a police investigation. Which person fits best with the pattern of leads found or variables measured? Who fits in with the profile? In such models individuals can be allocated a bigger or smaller chance of being designated as a more or less probable suspect, based on, for example, the modus operandi, number of previous occasions a person has had dealings with the police, or places of incidents. None of this is new. The police have been using descriptions of perpetrators or fingerprints and DNA-traces for decades. In fact, these too are (parts of) profiles that can qualify or disqualify labelling someone a suspect. Comparing profiles, whether automatically or not, with a large collection of ‘possibilities’ makes it possible to pull out the one who fits in best with the profile. Obtaining biometric data on people has simplified their recognition. The uniqueness of the data obtained is so specific that a person can be designated with an extremely high level of reliability. Where such clues are lacking, the challenge is to combine other factors in order to achieve a similar capacity to distinguish.

A related possibility, in addition to searching for an identity or a unique individual, is making statements about whether the police already know of someone and whether different incidents can all be traced back to the same individual (Chen et al., 2004). This involves similar techniques and, based on the degree of uniqueness of the indicator values collec-ted, determines whether sufficient distinguishing capacity exists and whether a statement in one of these directions can be made with suf-ficient certainty. The biggest challenge in such cases is often not building the model. The challenge is more likely to be in collecting the relevant data and the availability of any reference files needed such as trace data


banks for making comparisons. A lot of detective work and craft is nee-ded, as well as any necessary pre-processing, in order to make sure the desired indicators, equipped with a value, are available in the same file.Predictions can also be made in respect of individuals. For instance, whether recidivism can be expected of a person (Wartna et al., 2013) and whether there is a chance that someone will commit another murder after being released. It seems that when historic data on people are used (e.g., age at first contact with the police or whether he or she has a history involving weapons, as well as current age, what a person earns), these factors are all of added value in increasing the correctness of a prediction (Berk et al., 2009).

Such predictions on people can be used to determine whether someone will naturally be driven to become a hardened criminal, whether a per-son will become radicalised or whether a person is capable of carrying out an attack or committing murder. A growth factor of the risk can be determined over time by measuring certain forms of behaviour, e.g., by means of analysing the text of statements made on internet fora or via social media. Examining word-clouds for contributions to discussions on fora over time may reveal that certain words are getting bigger while others are getting smaller. Adjustable threshold values for specific state-ments help to generate automatic triggers that reveal where thresholds have been exceeded.

Depending on the chance of an event occurring, chances can be grouped and allocated to certain categories determined in advance. An example of this is the traffic light model that attaches labels depending on the risk detected. Red often means a high risk and green a low risk. The orange label is often in-between, so a risk is involved but it is not seri-ous enough to warrant being labelled red. The criminal development of certain persons over time within a given context can help to categorise important players in a criminal network and possibly even to classify them as red, orange or green. For instance, an examination of crucial contacts in relation to the ability of a network to function or by using centrality measures from graph theory (Hamers, 2011) makes it possible to categorise the members of an organisation relative to one another according to ‘risk’ (to society) or according to key positions. The same can be done on a tactical level by comparing entire networks with one another (Rienks & de Wit, 2012).


Which categorising principle is used and which categories one wants to retain for subsequent procedures after the risk assessment depends on the envisaged objective. The number of categories or classes into which output is grouped may relate to the usefulness of those individual cate-gories or to the degree to which the chosen instrument or algorithm is capable of distinguishing between the risks with sufficient reliability. For example, in the event of a national threat, the Netherlands uses a five-point scale and Belgium uses a four-point scale. Choosing the number of levels is arbitrary though it may relate to the categories of interventions that you want to be able to carry out. The addition of scale levels or other categories generally reduces the strength of a prediction, if only because the more categories there are, the fewer data are available for training purposes.

Within forensic psychiatry a risk assessment instrument (HKT-30) is used that determines the chance of recidivism based on clinical and his-torical indicators. Getting different psychiatrists to complete the instru-ment for the same clients proved that the indicators could be assessed (manually) as sufficiently similar to one another. As a consequence, the differences between the psychiatrists’ assessments were small enough to permit use of the instrument (Canton et al., 2003). The consequences for individuals of an incorrect assessment based on an erroneous obser-vation can affect, for instance, their being committed to care by a court or being granted conditional release. Compare this with the consequences of false positives by classification systems.


Predicting events on locations is a different category of predictions. Sim-ply counting historical data on burglaries or stabbings may already have predictive value for a location in relation to the future. Heat maps or hot-spot maps provide a picture of the past that is often useful for the future too. This is what PredPol actually does. By carrying out extrapolation that is non-linear, e.g., by taking adjacent fields into account, a system can issue a predictive value for the future.

Geographic profiling is a field with a strong focus on geo-spatial data and events. Geographic profiling uses crimes belonging to a single series to attempt to determine, e.g., where a suspect lives (Rossmo, 1999). Calcu-lating the distances between different crime locations makes it possible to predict subsequent locations and events.

The same mechanism can also be used for the times of events. For example, experience based on data from the past teaches us that there is often a wave of burglaries in the autumn. Possibly because days are shorter than nights and criminals feel safer in the dark. In the case of in-ternet swindle, for example, it is at the weekend that most fake websites go on-line with complete copies of existing websites for conning custo-mers by accepting payments for products that will never be delivered. It depends on the type of crime whether it is committed with any degree of regularity over time. A pattern’s regularity may involve a seasonal repeat, but it could also be daily. Increased knowledge about this regularity helps the police to be more alert about certain types of crimes being committed at certain times.

Another form of geographical prediction is tracing an unknown location based on leads or indicators. This could be the origin or source of some wrongdoing, but also a location where certain stashes of money or we-apons are being stored. Twitter analyses are really good at showing how certain opinions or statements develop over time and how these spread geographically. Playing these back is fairly simple because the data of all tweets can be requested. The ability to trace heroin or other drug sam-ples back to a single source is different again. Parties can be traced back to a single source, for instance, by examining the molecular composition and the degree of cutting. This demands different reconstruction skills which eventually result in a chance or a prediction about answering the question of whether a certain party can be linked to a source.


Being able to anticipate events as early as possible often goes hand-in-hand with more uncertainty the further into the future an event is loca-ted. The same applies to planned activities. After all, there are still many factors that can result in activities failing to take place. The closer two vehicles approach one another, the greater the probability that a criminal meeting is about to take place. This also applies to planned activities as the distance over time diminishes. Although there is always a chance that planned activities will not take place, it is handy for the police to have a good overview of activities that are imminent and which may in-volve an increased risk varying from an attack to pick-pocketing or other disturbances of public order. The most accurate possible estimation of the risk can be done by men or machines, whether or not in collabora-tion. Here too, by learning from previous experience, we can find ways of perfecting this science in the future.

In most cases, predictions about events is far from a matter of course, and it is the small steps in parts of such processes that continually im-prove or simplify the realisation of predictions. An example of this is being able to automatically filter data that could provide relevant input for a prediction. In some cases simply too many data are available to be able to distil anything that is even remotely useful. Take, for example, data from telephone-tapped conversations. These are audio-recordings of discussions, thousands of which are stored for some investigations. Sometimes in foreign languages or dialects, which makes automatic translation or conversion from speech to text quite challenging. The in-formation retrieval discipline within informatics deals with being able to distinguish relevant information in relation to the enormous quantities of files or documents available.

Such systems, which use techniques based on mathematical formulas, can support people in filtering and carrying out pre-selection. This ena-bles us to classify automatically analysed telephone-tapped conversations according to presumed relevance, and indicators can be dissected from data that embody a more predictive element than other indicators in res-pect of the phenomenon being sought. These pre-selection techniques can also be used on reports received by the police with a view to sifting out more relevant reports and giving them a higher priority for further handling. It is important that an abundance of data are available and that


the question asked of the data can be properly defined about what output is relevant. Other examples of techniques in this field make it possible to automatically draw up summaries of text or audio-recordings, thus spee-ding up searches for people. For instance, pictures of child pornography can automatically be compared with a database in order to determine whether or not material is new.

Predictions on police performance

If the police did know in advance which crime was going to be com-mitted, where, in which location and by which perpetrator, it would subsequently be an enormous task to design internal operations around this. Being able to act or intervene as effectively and efficiently as pos-sible, in as many predicted cases as possible, is like putting together a jigsaw puzzle. The more time that is available, the better the police are able to prepare. People and resources can be released for deployment in locations that have been determined in advance with clearly defined objectives. The less time there is available, the more difficult it is to rea-lise a comparable response adequately. Capacity management and good planning are of the utmost importance. If only to be able to guarantee continual availability, to be there where you are needed and to be able to intensify efforts at the moment that this is deemed necessary based on the predictions.

By working with scripts and scenarios in relation to events, the orga-nisation can keep records and detailed plans on how to respond even before the events have occurred. Based on previous experience in the past, we can learn how to deal with and solve certain matters. What is needed to make sure a certain type of demonstration does not get out of control, and what security measures should be in place during a world congress? Depending on risk assessments about the outside world, and using available internal resources, whether or not supplemented by those of partners, partly based on experience from the past, we shall look for suitable measures to remove the risks or to be able to cope with them.The police can make life a lot simpler for itself by making use of patterns in the outside world. Use can be made not only of patterns in crime, but also patterns relating to how normal society functions. Detecting a departure from regular patterns is often interesting and newsworthy.


It differs from what we regard as normal and could form a reason for closer inspection. Examples of typical patterns are commuting traffic and biorhythm, but also, e.g., images of traffic jams or contact moments with government authorities, to name but a few. Exploring could reveal patterns and result in knowledge that can be used to predict, e.g., the presence or absence of people from certain places. People generally go to work in the morning and return home in the evening. The police can use this type of patterns for a focussed intervention. It makes little sense for a community policeman to keep calling on people who are not at home. Even the knowledge, for example, that a passport and a driving licence have to be renewed once every 10 years can make the police more ef-fective. For instance, investigations into missing persons could use such information – in consultation with the municipality – to take advantage of these moments in order to trace these persons.

Changes in the outside world are forcing the police to adjust and keep up with modern society (Rienks & Tuin, 2011). For example, the arrival of the internet made new forms of crime possible, to which the police had to find an answer. In this respect, you can only deal with something if you know how it works. This and other developments have resulted in a growing arsenal of weapons available to the police over the course of time. New resources create new opportunities not only for criminals but also for the police. The arrival of the data revolution and being able to store and retrieve transactions and events has given us the opportunity of making a growing number of predictions.

Knowledge can be exchanged collectively by using experience from the past and because results achieved are more accessible. The design of evaluations can also be improved because data are available more often. This makes it possible for an organisation to adjust more rapidly. Adap-tive systems are capable of learning from their own activities and try to develop in the direction of improved performance, in some cases even with a view to an improved chance of survival. Just think of the business community where, in the end, large companies that failed to adjust to a changing market simply had no future.

In their interaction with the outside world, the police continually have to weigh up the deployment of resources proportionately and alternatively, in the face of a predicted situation. In this sense, using an ME platoon


to deal with a domestic violence situation would not be fitting. Further-more, the police have an interest in the smallest possible deployment of people and resources to achieve the greatest possible change in the outside world. Often it is scarcity that forces us to make choices and opti-mise. Should we send an officer on a bike or a pair of officers to a home trespassing situation? Which villain should we remove first from the criminal network? Or should we deal with the other network first? These are typical questions that justify a clear assessment that takes experience from the past into account in order to predict expected performance in the future in relation to the resource deployed.

If we think back to how the helicopters were positioned and their en-visaged effectiveness, then what determines their deployment is the pat-tern of incidents of crimes for which the helicopter may have an added value in combating these crimes. This maximises the effectiveness of a resource in relation to the intended effect. Whether it is about optimisa-tion in the time spent on investigations, the places for special squads, determining locations for starting officers’ shifts, determining the pac-kage of interventions during combat activities, or trying to position law maintenance units cleverly. This is where a difference can be made both for people and for resources. An essential matter for increasing the ef-fectiveness and efficiency of the police.

An ethical consideration

6. An ethical consideration

Predictive policing is all about pro-active police action and the as-sumption, based on leads and indicators, that a situation is taking place or will take place somewhere. Different ideas about this exist within society. Just the word ‘police’ is often enough to start a discussion. Some people would rather have no police at all, while others are more than satisfied with them. Are people right to complain of feeling they are constantly being watched by cameras and other sensors that have been installed to increase our sense of safety? Does safety always come at the cost of privacy? Some people only want more ‘bobbies on the streets’, while others mainly want the police to be effective so they remain af-fordable.

Predictive policing brings many opportunities for making the police more effective. But it also introduces risks. For example, the risk that the police become too dependent on technology, or the risk that decisions are made inside a black box, the working mechanism of which is under-stood by no-one any more. Is this what we want? Some people are afraid of artificial intelligence and think that their lives will soon be regulated by robots. Another issue is how should we deal with false positives? Should we accept them as an inherent risk of more effective policing? Or should we only intervene if we are 100% certain and run the risk of missing out on other potential candidates? This chapter discusses such ethical aspects that the introduction of predictive policing involves. Well-considered decision-making, policy and legislation are unavoidable if we are to realise the full potential of these opportunities for society.

In our considerations, it is important to pay attention to these ethical and other relevant aspects that could influence the successful introduction of predictive policing. Not only because of its enormous impact on society, but also because misunderstandings exist about, e.g., how certain tech-nologies work. I remember a meeting in which a piece of technology was declared worthless because of the fact that it was not used. The person


who said this simply did not realise that more is required than the mere purchase of technology in order to deploy it successfully. The danger is that something that is unknown is often also unloved. On the other hand, there is the danger of making far-reaching statements about tech-niques that one knows little about. For instance, in an inaugural lecture a professor cited rather offhandedly research which claimed in general that risk models have about a 50% chance of erroneous results (Moe-rings, 2003). “In the meantime, it is claimed that risk models have im-proved to an extent”. Anyone in the field of machine learning or artificial intelligence would have been rather surprised by this. Making a general supposition about a classification or risk assessment algorithm for the entire criminal domain can be described as, to put it mildly, remarkable.

Some predictions are extremely simple, while others are more complex. A 100% score is often impossible, but naturally the goal is to get the scores as high as possible. The chance of a correct prediction is correla-ted with the power of the measured input variables, how the algorithm was determined and whether it has undergone sufficient training and testing. Only then can a statement be made about performance, whether the number of real positives is proportionate to false positives.

Different instruments can achieve completely different scores per type of crime. The term added value was also used earlier in this book. The choices an instrument makes must eventually supply sufficient added value in order to be used by the police. There may be good reason for ac-cepting a certain percentage of false positives (Stuart et al., 1993). Recall the lady with her poodle in Chapter 3 whose driving pattern resembled that of a drugs courier on the motorway; police cars used considerable intervention force to force her to come to a halt by hemming her in and forcing her to reduce her driving speed, while it eventually turned out that she had done nothing wrong. You may feel that this is going too far and that the police should only have intervened after a visual inspection confirmed the machine selection or that the police should only actu-ally intervene after a crime has been committed. Was this a situation in which respect for private life was shown as laid down in article 10 of the Constitution of the Netherlands?


Incidentally, a taxi was also stopped during the same action in which the lady with her poodle was mistaken for a drugs courier. Most officers would not have let this happen. It turned out that this taxi contained almost 2 kilos of hard drugs, something that the average officer would never have expected. The profile that had been created successfully de-signated the taxi as a possible candidate. You could say that this opened the eyes of these colleagues and new knowledge was formed on the spot by amending existing ideas. But was this an incident or was it a pattern and can you learn anything from a situation that is based on an incident or a single event? Where is the cut-off point at which you are allowed to generalise?

Are we becoming dependent on machines?

The usefulness or added value of systems for the police is not an ade-quately legitimate principle for justifying their deployment. Particularly not when a mere bouquet of flowers would be insufficient to rectify the consequences of false positives. Proportionality and subsidiarity are concepts that may play a role here. The police cannot simply take their pick from the entire available arsenal for every type of crime. This idea is reflected in terrorism legislation, which grants the police more po-wers and more far-reaching powers than when carrying out their regular tasks. In a case of shoplifting too, even without endangering their own life, the police are unlikely to shoot a fleeing suspect in the leg in order to realise an arrest.

A good idea would be to establish a limit, or agree on a process, so that not everyone runs the risk of being unjustly confronted with police brutality. In a legal sense, the limit is often determined by the word ‘sus-picion’. The police come into action when they suspect that an offence has been committed. Someone once explained to me that there must be at least a 75% chance that, in the professional’s estimation, the suspect actually is the perpetrator. Before some resources can be used, there must be a situation involving so-called ‘incriminating evidence’. This is where the chance that the suspect really is the perpetrator is between 85 and 90%. If these limits are acceptable for reliability assessments


for following up a person, where should the limits be for following up machine-supported follow-ups?

The question is, in fact, what exactly is the difference. Intervening before or after a crime committed by an unknown perpetrator will always result in making the best possible reconstruction or preconstruction of the si-tuation, whereby the objective is to realise a sufficient degree of certainty. In this sense, investigations and pre-investigations are comparable. Af-ter all, the wrong persons and objects are also sometimes the victims of arrests and raids. Though not really acceptable from the perspective of society, it is apparently unavoidable. It may simply be impossible to teach people or machines to make faultless predictions.

A noticeable aspect about such discussions is that people want to keep a tight rein on assessments of what is ‘good’ or ‘bad’. Placing trust in the choices of machines is still quite limited, particularly where more com-plex forms of observation are involved in which errors could be made and people become dependent on such systems. For example, the ac-ceptance of self-driving metros is still being discussed 50 years after their introduction23. Another example is that of security gates at an airport. Humans, not machines, currently still decide whether a person or an object may or may not board an aeroplane, while we fully accept having our vegetables weighed automatically at the supermarket.

Robotisation has boomed since the industrial revolution and many pro-fessions have already become obsolete. This process will probably conti-nue. Portrait artists became obsolete in about 1850 after the arrival of the photographic camera and knocker-uppers (who used to rouse sleeping people) disappeared with the arrival of the alarm-clock, and nowadays technology supports a growing number of professions or has replaced them entirely. The livelihood of stock exchange dealers, teachers and health care workers is threatened by machines that are taking over their work. Robots are more accurate, faster and continue working 24 hours a day. In fact, exactly what the police need. Some predictions go as far as to state that half of all employees could be replaced by robots (Frey & Osborne, 2013). Is this a threat to the continued existence of today’s policeman or detective? Police work is people work (Immel, 1987), but

23 http://www.railway-technology.com/features/featuredriverless-train-technology/


for how much longer? What can we expect of predictive policing in this context?

People do fear robotisation and artificial intelligence. Perhaps because of such films as ‘2001: a Space Odyssey’ and ‘Terminator’, some people even predict the end of humanity24. It is not improbable that machines will indeed become smarter than people (Armstrong, 2014). Think of the chess computer Deep Blue that beat the chess world champion in 199725, or the Watson computer that managed to beat the best players of the te-levision programme ‘Jeopardy’26. Despite the fact that this also provides people with entirely new opportunities and challenges, it is important to take it seriously. If algorithms and software can re-write themselves and learn from the errors made, where will it all end and who will be in control?

Another issue involved here is that, in this context, the concept of ‘sus-pect’ also gets a different meaning. If mistakes are made by autonomous systems such as self-driving vehicles or other machines capable of reasoning, who bears the liability? Suppose a self-driving car causes an accident, can the system be designated as a suspect or as the guilty party? Or should we point our arrows at whoever designed the system? What would be an appropriate penalty in such a case? Should the system be taken off the market? Such topics will increasingly make an appearance on the political agenda.

24 http://phys.org/news/2014-12-hawking-ai-human.html#inlRlv25 http://www-03.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/26 http://www.ibm.com/smarterplanet/us/en/ibmwatson/


Objectivity and bias in data

One possible intervention for preventing crime is to make sure you are visibly present in time and assume that visible uniforms or a police car have a preventive effect. As soon as this presence is coupled with actions, such as preventive body searches before a crime has taken place, this represents an enormous intrusion of a person’s privacy (Van der Leun & Van der Woude, 2011). Whether preventive body searches are justified by the situation is determined by a mayor who declares a certain geographi-cal zone as having a possible safety risk for a given period of time. What can we expect in the future if models designate locations as risk areas? Could it be that the police will also be allowed to make use of different powers? Or will we still be limited to intensifying our surveillance?

From personal experience I know that with preventative body searches it is extremely difficult to choose people randomly from a stream that exceeds the processing capacity. Although guidelines do emphatically suggest that people should be selected randomly, it is the knowledge in your head that automatically starts filtering according to characteristics that you regard as high-risk. High-risk in the sense that I personally tend to filter out persons if I feel they have a bigger chance of involvement in a criminal offence as a possible perpetrator.

A comparable case could apply to traffic control27. Based on the road traffic act, cars can be forced to stop and drivers asked for their driving licence, without the involvement of any suspicion. The so-called weapon ruling28 allows an investigating officer who stumbles on facts and cir-cumstances that involve a reasonable suspicion of an offence to make use of investigative powers and he can, for example, search a vehicle29 if he suspects that drugs will be found.

You can imagine how proud I was after a weapon was found and con-fiscated from a car that I had pulled out during a control on the Rokin in Amsterdam. All I had seen was four troublesome teenagers wearing caps in a really expensive car. Did this have anything to do with the Road

27 Article 160 Road Traffic Act 199428 Supreme Court 02-12-1935, Dutch Law Reports 1936, 25029 Article 9 Opium Act 12 May 1928


Traffic Act? Was it a gut feeling? In a split second I commanded the vehicle to head towards the control post and bingo.

This was not based on any instrument that makes use of objectified knowledge, nor was there any intention of making selective choices, just as there aren’t during preventive body searches. Citizens are obliged by law to cooperate in a traffic control and a body search. If you have nothing to hide you might think that it doesn’t matter and that the time it costs you is an investment in a safer society. But how random is ran-dom? Wouldn’t it be much better if during a traffic control the police could make use of objective criteria that have been tested and approved in advance? This would avoid the risk of arbitrariness or selection based on a gut feeling.

Being selected and checked more frequently than average can result in feelings of inequality and even discrimination (Open society jus-tice initiative, 2009). This is not exactly what was meant by increasing feelings of safety. Various studies point out the risk of ethnic profiling (Çankaya, 2012; Amnesty international, 2013), whereby skin colour and ethnicity are used as distinguishing characteristics for selecting from a population. The idea is frequently voiced that this is a breach of the non-discrimination principle30. Unequal treatment based on personal characteristics such as age, religion, race, gender, beliefs, skin colour or origin is prohibited. Therefore, the fact that crime is mainly commit-ted by young men (Lykken, 1995) does not legitimise focussing mainly on young men during preventive body searches. Nor is it possible to randomly check ethnic minorities such as non-western immigrants who are over-represented in crime statistics (Centraal Bureau voor de Statis-tiek, 2013). If this is taken a step further, one might also query selective surveillance of deprived neighbourhoods on the basis of information. From a mathematical perspective, this is difficult to comprehend, be-cause you actually want to optimise by making use of distinguishing characteristics in relation to crime in the same way that a skipper choo-ses his nets. He chooses the mesh of the net meticulously according to the fish he wants to catch. Right there, wherever crime is taking place, is where the police want to be.

30 Article 2 Universal Declaration of Human Rights dated 10 December 1948


DiscriminationFrom a mathematical perspective, discriminating means drawing a distinction. An entirely different meaning from how it is used in everyday language. In this sense, distinguishing is no more than focussing or filtering data with the purpose of increasing its relevance. In a mathematical sense, working with profiles and paying selective attention is always discriminating. If the police achieve their best results by focusing on prioritised persons or crimes, they are making a distinction. Positioning all police helicopters in the Netherlands in Amsterdam is also discriminating, in a mathematical sense. People are quick to describe the acceptance of non-random police deployment as discriminating, in a non-mathematical sense. This is often linked to the idea or feeling that the police make distinctions based on ethnicity and race. If it is apparent that certain groups of the population are over-represented in the crime statistics, is it socially acceptable if extra attention is given to this? This is an interesting discussion about which the last words have not yet been spoken.

Relevant in this context is that, where people are involved in a preventive body search or a traffic control, there is no question of a suspect situation on the basis of which a control is being carried out. Where there is a sus-picious situation, then there is also a suspected criminal offence. Here this is not the case, which is why it is there is no justification for making use of knowledge and statistics on crime to carry out selective control.

But when can one speak of the suspicion of a criminal offence? How well does a predictive model for this purpose have to perform? Can a gut feeling be used or mathematical statistics permitted at all in respect of age, race, origin or colour of skin? Is the threshold value for suspicion, the 75% chance of being the perpetrator of a crime, always sufficient as a criterion? From the perspective of society, hidden here is the risk of false positives that our society has such difficulty in accepting. Over-representation can lead to stigmatisation and this entails the risk of discriminating against minorities. For instance, it seems that people of different ethnic origins receive different punishments for the same crime (Weenink, 2007). The police could or should take this into account as soon as a situation is declared suspicious. Another pertinent recom-mendation is that profiling should take place based mainly on behaviou-ral aspects instead of on personal characteristics when investigating a suspect situation (Cankaya, 2012). What do we, as citizens, think about the fact that more systematic surveillance takes place in one district than in another? Do we regard this as acceptable? In this context, Bovenkerk


(2009) rightly pointed out the risk of what he calls the contrary effect: getting entire population groups against you.

Distinguishing characteristics that are based on knowledge or data should be tested objectively by making use of, e.g., random sampling. A risk inherent in determining characteristics from knowledge is that ge-neralisations are made based on stigmatising and stereotyping, whereby some incidents are quickly converted into patterns that do not agree with reality. In the same way that one might arrive at a certain ‘bias’ due to one or two bad experiences. For example, during a trip to South Africa, I once had a negative experience in Durban, in which I almost got robbed. Ever since I dislike Durban, probably totally unrealistically, but that one incident suddenly made the entire city unattractive to me.

Police data do not necessarily provide an objective reflection of society or of crime. For example, because the police focus more on high-impact crime instead of on fraud or other forms of white-collar crime, it is only logical that more data are available in police systems on certain forms of crime. This makes comparing distinguishing characteristics for specific forms of crime less simple and corrections have to be carried out for such ‘biased sampling’.


Craving for optimisation and collectivity

The last topic I want to deal with here is the question of how far should we be willing to go in optimising how the police work? Often it is no sur-prise that everything can be done more effectively and more efficiently or that new technology becomes available. Something quite remarkable, as I see it, is that this chance of optimisation is often immediately grasped as a means of realising economies. Economies in order to save money because people think that the same work can be done with less manpo-wer. As a result, a large proportion of the expected added value is im-mediately wasted. This is because it undoes the extra villains who could have been caught or the extra resources that could have remained for spending on other things such as training, maintenance and innovation. A missed opportunity. In my opinion, this is the extra space that results in creativity that could speed up developments.

The university that I attended has introduced a new educational model because of ‘performance thinking’ based on economisation31. It seems that the graduation percentage has become more important than the academic education itself. Attendance at lectures is obligatory and the free choice of extracurricular activities and self-actualisation has been displaced by this desire to optimise. As I see it, it is arguable whether this will actually result in better students. I see the same thing currently hap-pening within the police force. Forming the National Police optimised and emaciated it into an implementing organisation that is no longer capable of self-reflection or adaptability. This influences the rate at which it can master new ideas, such as predictive policing, and benefit from them. The need of flexibility and adaptability is a continuous one. If only in order to keep up with developments in the outside world. The police is not a production plan suitable for standardisation.

A form of optimisation that I feel could be more useful is the opportunity principle32, as it is called in the Netherlands. This principle allows the Public Prosecution Service to waive prosecution on the grounds of the ‘general interest’. This means, for example, that there is always leeway to decide what will and will not be taken up as criminal cases. This means a

31 http://www.utwente.nl/onderwijs/bachelor/studeren-in-enschede/twents-onderwijsmodel/32 Articles 167 and 242 of the Code of Criminal Procedure


lot of discussion can go into deploying the available capacity, without any obligation to take matters any further.

Another relevant question in this respect is how powerful do we actually want the police to be? Do we really want to transform the strong arm of the law into some sort of omniscient powerhouse? Does our society want the police to act as a solid entity, to intervene collectively and to be organised in such a way that internal knowledge is explicitly available everywhere within the organisation? Wouldn’t a more fragmented situ-ation be more sensible, in view of the risk that all this knowledge could suddenly fall into the wrong hands? Just imagine criminals being able to gain access to all police knowledge and profiles that have been developed in the battle against crime. This would provide them with a powerful instrument that can use all the collective knowledge against our own organisation. Criminals would in any case no longer target places where police are deployed based on predictive models. Consider the popula-tion register, for example. During the second world war this was put to gruesome use by the German occupier to achieve an entirely different objective than that for which the information was collected. It should come as no surprise that two resistance fighters, notably disguised as police officers33, set fire to Amsterdam’s population register, then still a physical register.

Is there a risk in this era of digitisation that we are creating a sort of big brother, capable of recognising and comprehending everything at an early stage? Is this comparable with the doom scenario of a totalitarian regime for the western world (Orwell, 1949)? Crime, on the other hand, has also become a global issue nowadays, whereby knowledge is shared and exchanged throughout the world via the internet. People from near and far can enter into coalitions with the objective of undermining soci-ety. The question to ask is what would be an appropriate answer?

These are just a few questions that can result in discussions, and to which no simple answer can be given. Time will tell how we can manage to find an answer to these and other aspects that are associated with the arrival of predictive policing. Perhaps needless to say, but everyone

33 https://stadsarchief.amsterdam.nl/presentaties/amsterdamse_schatten/oproer/aanslag_be-volkingsregister/


should be aware of the fact that technology and the armamentarium being developed can also be used for other than their intended purposes (Van de Poel & Royakkers, 2011). By taking this into account at an early stage and incorporating the necessary safety guarantees into their own system, those who develop and commission such matters can spare so-ciety a lot of misery in advance.

The future of predictive

policing in the Netherlands

7. The future of predictive policing in the Netherlands

Opportunities to make a success of predictive policing in the Nether-lands are increasing rapidly. The arrival of the National Police and having centralised the provision of information in the field of data is making it possible to bundle capacities. A new era is heralded by the arrival of big-data processing applications and by making knowledge within the organisation collective. An era in which predictive policing will break through and in which an even bigger role will be given to the power of equipping operations with information. It has been transformed from a necessary evil, via process optimisation, into a core asset of the police. An asset that has immediate impact on their performance. This is the start of a new development. A development that requires an owner within the police force. Investments in learning will be necessary to improve our control of technology that is capable of transforming data to knowledge and of obtaining it from the minds of people. This knowledge then has to be converted into profiles so that people or machines can perform pre-dictive activities. These are all necessary steps to allow predictive policing to become a reality.

In the previous chapters I showed how obtaining knowledge about what generates crime is essential if one wants to make a prediction. Indicators have to be distilled from data or minds and then be made measurable in a system that uses an algorithm to make a choice about the probability that the event (that is being predicted) will or will not occur. Training and adjusting algorithms can improve them so that they make more precise observations with a higher degree of accuracy. Based on an estimate, which incidentally can also be done manually, decisions can be made and a set of measures can be proposed for intervention. These measures can also be optimised with the help of predictive techniques. Naturally, the goal being to achieve the most effective and efficient result possible in the outside world, with or without partners.


Perhaps the right credo for us is where there’s a will, there’s a way. Never-theless, implementation will not happen without a few setbacks. Quite a few myths already exist about predictive policing (Perry et al., 2010). For instance, that computers know the entire future and can do every-thing for you. And that predictive models are really expensive and good predictions can reduce crime enormously. These are all misconceptions that are comparable with blaming technology if people do not use it – or use it incorrectly – during an intervention. In order to avoid hindering its successful introduction, it is important to discredit such myths as soon as possible and to ensure that the real story gets told.

The challenge is in realising an effective introduction. The introduc-tion of information-led policing, for example, showed that a good story and a long-term approach are needed in order to allow this potentially promising domain to flourish. Every change deserves attention and its introduction should be properly supervised. By way of pre-sorting, the following tips serve as a means of anchoring predictive policing more solidly within the police domain. What is needed in order to really get started and what other challenges will the police face on the road to pre-dictive policing?

What is needed in order to really get started

To get predictive policing embedded within the organisation it is impor-tant to examine which facets within the organisation play a role in its introduction or implementation. Depending on the ambition, one could opt for a rapid transition or for a more conservative, safe scenario. This conservative scenario starts with small-scale experimentation in order to find out whether and where in the organisation the best performance can be realised. Another approach could be to opt for a structurally phased introduction, the objective of which is massive, rapid change. For every new technology different implementation strategies exist and everything depends on, e.g., the absorptive capacity of the organisation and the confidence in the potential that has been chosen (Rienks & Tuin, 2011). In my opinion, we will not need a separate ‘department of pre-crime’, as in the film Minority Report, in order to start experiencing the performance of predictive policing. In many cases we can simply seek alignment

with existing components of the police force, such as its information organisation. This will eventually be transformed into an intelligence organisation, by tailoring information products to fit the intervention. However, this demands knowledge: knowledge that is guaranteed and available. A great new task for the police academy perhaps?

People and company processes within the organisation will have to change before new technology can be introduced. The following is a de-tailed description of relevant aspects of the people, company processes and technology that are required in order to make predictive policing a reality.

People who can be deployed to make predictive policing successful can be divided into a number of groups. In the first place people will be nee-ded who can obtain knowledge from other people. This is about know-ledge of the criminal outside world, our own police world and knowledge about the interaction between them. In general, these are people who must be able to connect with experts in implementation; they must be able to circulate in this world and to ask critical questions. They must also be able to meticulously transfer their findings and convert them into specifications for model building. Secondly, people will be needed who retrieve knowledge from data. These are typically employees with a beta background, who know how to deal with enormous quantities of data. They understand analytics and data science. These people must be able to cope with big-data technology, to unlock data sources and combine data with one another. This demands programming knowledge and an understanding of tagging engines as well as a healthy dose of creativity and perseverance. This is in order to finally convert data into value for the police.

It is my opinion that both the domain that retrieves knowledge from data, and the domain that retrieves knowledge from minds should report to a Chief Knowledge Officer who supervises the meticulous processing of the knowledge available within the organisation. In this way, know-ledge will eventually be converted into a strategic asset for the police force (Bollinger & Smith, 2001). Knowledge that contributes to creating intelligence and thus also to improved police performance.


Then we need people who are able to make models based on the accu-mulated knowledge and patterns. This requires a translation from paper to technology in order to be able to use these models. These could be risk assessment models, profiles for automatic observations, or models that propose a set of interventions based on a given situation. Components within the organisation are also needed that, apart from developing the predictive models, will also be involved in their implementation, main-tenance and control. It would be undesirable to allow the developers of models to also become their controllers. Designing good processes for all this is often no simple matter.

The implementation of every new model, such as when a system output becomes operational, is a reform process. A project-based approach is sometimes required just for informing everyone about its added value. This has to be done by people who are skilled in change management and marketing. Only then will introductions succeed correctly and at the right pace.

Technicians will also be needed who can make the hardware and the platform operational, so that the data are gathered and for installing flexible software. Software capable of processing data and carrying out business intelligence. As this often involves specialised hardware and software, it is extremely important to develop the relevant knowledge in-house and minimise dependency on third parties.

Something will also have to be done with the output of the models and systems, i.e., with the prediction itself. Translating this part of the output into action could take place, in the event that immediate follow-up is necessary, in a part of the organisation that is comparable with the ope-rations room, the real-time intelligence centre or the reporting centre. Here too, people will have to be trained and educated. If follow-up can be delayed and there is still sufficient time to respond, then a preparation unit could, for example, make good use of the predictions produced by the models and systems. If this is not the case, then experts will have to provide instructions that can immediately interpret the warnings issued by systems and convert these into action. This depends on the type of decision-making for which the prediction was made. If tactical and stra-tegic decision-making is involved, no immediate follow-up is generally necessary.


Business processes in relation to predictive policing will have to be de-signed, bearing in mind the initial introduction phase of a predictive system as well as when it is being used. During the development phase, model development will probably be the most complex process. This is when room particularly has to be made available for experimentation and creativity. Extracting knowledge from minds and data is not an acti-vity that can be predicted in advance. Preferably these activities will take place in a central and independent part of the organisation. Standardi-sing the development process will focus particularly on phasing of the process. Estimating introduction times will vary per type of system being developed and in some cases it may prove to be too complex to ever reach the finishing line of production status.

When implementing or introducing a system that has been developed, it is a good idea to organise proper supervision in respect of its progress and to involve in the transition those parts of the organisation that are af-fected by the change. Sometimes pressure is exerted from outside, before a person has even realised whether a new instrument has added value for his or her own business. In other cases people simply contain their enormous expectations while impatiently awaiting the new resource. In both cases, it is important to manage these expectations and test and practice with the system in order to achieve maximum performance.

If predictive models are involved that focus on predictions to assist im-mediate intervention, then this involves a form of alarm, after which appropriate intervention is necessary. In cases of indirect follow-up, it may also be the case that, based on a prediction, a process stops or splits into different versions of follow-up. In cases of indirect intervention, during the implementation phase it is important to be clear about which existing company processes could influence the system’s output so that these can be suitably adjusted. In order to keep the change for the orga-nisation to a minimum, in most cases it will be necessary to organise the best possible embedding within what currently exists.

Technology that is necessary to be able to make predictions will play a role throughout, from automated observation to automated intervention. A distinction can be made here between hardware and software. Both are needed to enable a system to communicate with people. To convert physical input to digi-language, to process it and store it (or not as the


case may be). This is so that any output can subsequently be presented in the physical world. Technology is also needed to combine data, informa-tion and knowledge, whether or not remotely, and to ensure continued synchronicity within the organisation.

Predictive policing demands specific hardware and software, the tech-nology of which cannot be compared with applications from office auto-mation, particularly during the construction phase of predictive models. Big-data applications, for example, on which pattern recognition can take place, are typified by the enormous diversity of data, large volumes and the high speed with which these data have to be processed. For the deve-lopment of predictive policing applications, a lab environment is needed to be able to test and experiment with modern software and hardware, to see whether new ideas and possibilities can be discovered and translated into operational solutions in operational technology.

This operational technology will be an enormous network with sensors and actuators that are part of the total intervention armamentarium of the police, interpreted jointly by people and technology.

Legal aspects and legislation determine the frameworks within which the police have to operate. Much is still to be expected in respect of pre-dictive policing. For the moment legislation focuses mainly on classical forms of police action. Few frameworks are as yet in place for proactive and predictive activities. It is clear that not in all cases can all resources simply be deployed. For instance, the automatic, purposeless processing of police information is not permitted. Purpose limitation, proportiona-lity and subsidiarity will continue to play a role. But what shape will the frameworks take within which research can and will be possible (and allowable) into patterns in data that can lead to the automatic recogni-tion of a suspect? And when may patterns and profiles obtained from human knowledge or from data be translated into technology that can be deployed for a police operation? And though people may make errors, so can machines. Shaping the frameworks for this interaction will be a challenge for legislation in the near future. The mechanism that moni-tors these frameworks must fulfil an authoritative and supporting role as early as possible while this field of work is being developed.


What else can we expect?

The introduction of predictive policing as a new mantra for the police force, can enhance police performance even further. Because the police can anticipate crime at a much earlier stage, they will be better able to anticipate and to carry out more effective interventions. On the other hand, at the same time there is the risk of unreasonably high and unre-alistic expectations. It will demand a lot of the adaptability of the police if they are eventually to experience the advantages. The introduction of information-led policing, for example, is a process lasting many years and even now it is still a topic of discussion. This is why I feel that a big-bang introduction of predictive policing would not be wise. It would be much better to allow an orchestrated movement to take place and feed on the introduction of model-applications that embody the ideas. Free-riding on developments in information-led policing and introducing it as a sub-domain could help to speed up its acceptance and implementation.

One of the first reactions I ever received in response to research into radicalising extremists was along the lines of: “Yes, it’s all very nice, that insight of yours, but we are already so busy.” In other words: it is only when you invest in something that it results in knowledge that can actually be translated into action. This may raise capacity issues as a result. For a long time it was rather reassuring, in a way, that even we, the police, did not know everything, nor could we see everything. This has an enormous limiting effect on the capacity to respond to everything. The arrival of improved intelligence due to automatic observation and alerting instruments from the domain of predictive policing will change all that, which means different mechanisms of choice and appropriate policy will be necessary.

In some cases, as in the case of a ban on tolerating transit34, the police will actually have to act immediately. For instance, as soon as we ‘know about’ the possession of – or presence of – objects that are harmful to pu-blic health or a danger to society. But what is ‘knowing’ in this context. Is it somewhere in between a reasonable suspicion and absolute certainty? And what about the opportunity principle in relation to this and what might the role of predictive applications be in this context?

34 See Article 126ff of the Code of Criminal Procedure


One of the differences with predictive policing in relation to present forms of policing is that work is done more with risks and risk models and less on the basis of facts. Learning to deal with false positives, or a false alarm, will be no easy task. While the fire brigade is confronted with six out of ten cases of false alarm (Centraal bureau voor de statistiek, 2012) and the 112 alarm number is also acquainted with this phenome-non35, it is bound to occur more frequently in policing practice too. We shall have to act based on a suspicion that does not always turn out to be correct. The question of how to remedy this and at the same time remain a reliable partner is one of the challenges we will have to face.

Advancing technology will develop further and as more experience is ob-tained with predictive applications for different domains, predictions will become better and more accurate. This is only the start. The observation armamentarium of the police will also expand with the arrival of new technology. Some sensor networks with national coverage in the form of the ANPR, or LiveView which was mentioned earlier, are already being deployed. The diversity and the intricacies will continue to proliferate. More knowledge in a transferable form will also become collectively avai-lable, as will techniques for obtaining knowledge from large quantities of different data. Decision-supporting systems will propose customised interventions capable of realising maximum deployment of capacity. The available capacity will also be equipped in advance for carrying out tasks optimally with an armamentarium, a location and time. When these are put to good use by the police, safety will increase in the Netherlands and it is safe to assume that during the next few years predictive policing will really take off.

At the same time, it is a fact that where the police can make use of new possibilities and techniques, the criminal outside world can too. Whether it is about the internet, observation cameras or new weapon technology, criminals will also be making use of them. If predictive technology is used against the police, this could possibly result in a new race. This is perhaps a worrying thought, but at the same time, it could also be a chance to jump right in.

35 http://www.rijksoverheid.nl/onderwerpen/alarmnummer-112/misbruik-van-112


These examples show that predictive policing is not a holy grail for a safer society. Predictive policing is a new instrument and a new form of working that can support the police task. New dilemmas will arise that will have to be examined within the context of that moment. Frame-works for this should be developed separately, frameworks for ensuring meticulousness and legitimacy of use, whereby citizens are protected and crime is reduced. In the short term predictive policing will definitely give the police an advantage in respect of interaction with the outside world, even if only because knowledge will become more explicit and collective. Nevertheless, if a car drives through a red traffic light, there will always be the chance that a pedestrian who was crossing the road gets knocked down. Only the technology in the car itself could possibly do something to alter this. People will continue to take risks and the police will never be able to prevent them all.

Although there is much to be done, I dare to predict that the potential is enormous and the possibilities will be infinite. During the past decade, predictive policing has made its cautious entrance; the time has come to do the real work on it!

Epilogue

Sometimes you find the time has come and you want to share your thoughts with others about certain developments and trends. Sometimes you want to set pen to paper and share your thoughts and give them a wider dissemination. This book is the result of such an impulse. An impulse that became a challenge. The combination of writing, having a fantastic family with three children, as well as a regular job as chief of a police department during a reorganisation was not really ideal.

Fortunately it was winter and the topic of this book was such that I could not allow myself to wait any longer. I really do believe that predictive policing will become the new mantra of the police during the next few years. During the process of writing this book, on 4 March 2015 predic-tive policing as an innovative domain was officially added to the business intelligence road map of the National Police. The game is on!

It is no longer merely information, but increasingly it is also knowledge, that will become a strategic asset of the police. This, in combination with increasingly intelligent technology, is what will make the difference.

I would like to say a very big thank you to everyone who participated in discussions and cooperated in realising this book. I want to thank Christene Beddow for the English translation and, in random order: Tim Postema, Jaap Wiersma, Dick Willems, Tobias de Wit, Marleen Ribbens, Reinier Ruissen, Remco van der Hoorn, Mariëlle den Hengst, Jeroen van der Kammen, Hans J.G. de Lange, John Tamerus, Hugo Elings, Mieke Schuijers, Ruud Staijen, William Swaters, Karst Grit and Jan ter Mors. Above all, I want to thank my beloved girlfriend, Yvonne Moolenaar, for the many evenings she had to miss me because I felt compelled to sit behind the computer working on the book until late in the night. I can-not thank you enough for your help and efforts.

Here’s looking forward to a sensible and controlled introduction of the wonderful resource that goes by the name of predictive policing!

Rutger RienksMarch, 2015

Index

analytics, 57artificial intelligence, 50, 56bias, 79biased sampling, 79big data, 45business intelligence, 47Classification, 61Clustering, 63codification, 52cross-validation, 50data, 40Data, 40data preparation, 47data science, 57dependent variable, 49independent variable, 49indicators, 49information retrieval, 71Information-pull, 43information-push, 43Intelligence-Led Policing, 9knowledge management, 51

knowledge rules, 26machine learning, 56model induction, 50model-building, 50multi-agent systems, 57prediction, 48predictors, 49pre-processing, 47Proportionate distribution, 59Regression, 60Reinforcement-learning, 51Scripting, 34signals, 30stereotyping, 79storytelling, 43systematic approach, 35tacit knowledge, 41tagging engine, 47target variable, 49temporal characteristics, 60truth-finding, 25what works, 37

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9 789402 13640115045

What would it be like if you knew in advance what was

going to happen? For ages people have been trying to

make predictions. For instance, on where fertile soil can

be found or what the weather will be like tomorrow.

How safe would life be if the police knew everything in

advance and could prevent all forms of crime? Society

would be completely different. This book is about a

complex form of predicting: predictive policing, or

the police doing their work based on predictions. The

power of the police force can be increased by focusing

on predictive policing. This is a chance that we, the

police, must not miss out on.

It will be a challenge to implement this promising

research with the right guarantees. Its introduction

demands not only new people and new technologies;

there are also ethical and organisational challenges

that deserve our attention. Is it possible to predict

crime? Can crime be recognised in its early stages?

What role does technology play in all this? Which

police interventions are effective? It is to this type of

questions that this book tries to provide answers. This

is an initial attempt to explain the topic of predictive

policing in a Dutch context and to emphasise just how

important it is. It shows which advantages are to be

gained from predicting criminal behaviour, but also

which disadvantages are involved. Is the crystal ball

of the police reliable (enough)? Initial exploratory

thoughts on an emerging phenomenon.

hans koenderink - the national police of the netherlands - predictive policing

Government & Nonprofit

netherlands police

police service

police performance

police data

years predictive

capacity of police officers

predictive analyses

netherlands safer