Vrije Universiteit Amsterdam
New ways of working
Anastasia Sergeeva
Predictive policing in the Dutch police force
Line Nielsen 2680709
Suad Osman 2516952
Naomi Tilon 1722433
Carla Kabamba 1530623
Josefine Due Olsen 2680708
December 2019
1
Introduction
How safe would it be if the police knew everything in advance and could prevent all forms of
crime? Society would look very different. Making predictions has been there for more than a
hundred years. In ancient Greece, predictions were made based on the signs of the gods. These
days there is still a need for predictions. Making predictions has however changed into rational
science as part of intelligent working. Technology is increasingly playing a role in the police
force. The increasing amount of information that is available makes it possible to discover and
explain patterns and trends on which we can make predictions. In 2013, the Crime Anticipation
System (CAS) was introduced to the Dutch police force, and is currently being used in almost
168 Dutch police stations (Waardenburg, Sergeeva & Huysman, 2018b). CAS predicts where
and when crimes such as street robbery and burglary will take place. As a result, the police
works in a more targeted way, which can lead to less crime. Working in a more targeted way has
even become more crucial over the years, as the Dutch police force have been struggling with a
staff shortage for some time (NOS, 2019).
In this assignment, we wish to examine how CAS and predictive policing has changed
the work within the police force. We found that the introduction of CAS especially changed the
profession of the information officers, who are now titled as intelligence officers. This lead to the
following research question:
“How has predictive policing affected intelligence officers in the Dutch police force?”
Since this paper focuses merely on the occupation of an intelligence officer, Rudi Volti’s theory
from 2011 is used to analyze the collected empirical papers, which are introduced later in the
paper. The assignment is limited to answering the research question, knowing that the discussion
on predictive policing is far greater.
2
Background
Digital technology in crime prevention
In the traditional police work, officers would patrol on the streets looking for crimes that would
occur or that were occurring. The focus in the 60s was on random surveillance and fast response
times. Officers were mostly dependent on their gut feelings, observations, and information they
received from the public on a crime that would be committed. The work was reactive, in terms of
officers responding to a variety of situations as they develop.
In the 1980s there was a shift towards community led policing with the focus to be
problem solving and to achieve proximity. Later in the mid-90s, for the first time, crime was
viewed geographically through hotspot maps. With this new form of policing, they tried to better
target the police by visualizing the crime intensity in geographical areas (Rinks, 2015).
Nowadays, officers are even more dependent on effective information gathering. Law
enforcement agencies have adopted new technologies in order to control crime. Predictive
policing refers to the usage of analytical techniques to identify places and times with a high
crime risk (RAND Corporation, 2013). The more data the system has, the more accurate
predictions can be. In addition to the number of incidents from the past, the system also takes
into account the change in the number of incidents at that location over time and other factors
such as the presence of a police station. With the usage of predictive policing law enforcement
tries to identify the pattern of crimes to be able to take pre-emptive action. In addition, the
transfer towards predictive policing was followed by the emergence of the “intelligence officer”.
Intelligence officers were supposed to support police officers in the use of predictive policing
technology, by helping them to make sense of algorithmic outputs.
Technology continues to change the profession of law enforcement. The day-to-day
patrolling is effectively allocated to districts and neighbourhoods based on predictive policing,
where and when crime is most likely to occur. The goal of Criminal Anticipation System (CAS)
within the Dutch police force is to provide relevant and useful information to optimize patrolling
on the street. The software lays a grid over a city with squares of 125 by 125 meters and then
determines the risk of crime for each square. Where there is a greater chance of crime, the box
turns darker. Colleagues are advised, to pay more attention to certain areas than to others because
the calculated chance that incidents will occur there is simply higher (Doeleman & Willems,
2014).
3
Method & Theoretical framework
The Trait Approach
The research on which this report is based focuses on professions and occupational change due
to technology. Certain kinds of workers belong to a particular occupational category that allows
being termed by “professional”. What made profession distinguish from other occupational
categories is discussed by sociologists through time. A number of sociologists and other
researchers have created this so called “trait” or “attributes” approach, which is a kind of
checklist to determine if an occupation can be characterized as a profession. In relation to this
approach, there is a general agreement on the key traits of a profession between sociologists and
other researchers. At first, a profession requires specialized knowledge, which relies on a wellestablished theoretical foundation. Acquiring this knowledge takes time, effort, and formal
guidelines, which is why the second key trait of a profession is a university-based training that
makes it possible for individuals to become professional practitioners. Generally, this means an
education in a university setting.
Third, the work of professional has to be of great value of the services to both society as
a whole and as an individual. As the fourth key feature of profession is code of ethics, which
consider the importance that the professionals do not use power for the wrong purposes, but
instead they put the needs of society and individuals ahead of their own interests. Fifth,
relationships among professionals are expected to be cooperative and, in some cases,
competitive, but their actions must be governed by commonly accepted ethical standards of
professionals. There are many more features discussed by researchers, but as the last common
key trait is autonomy and self-governance. The professionals’ ability to act with a high degree of
autonomy and governance, which relate to the authority of professionals and being accepted into
the ranks of a special profession (Volti, 2011).
The Professional Continuum
Later on, the early approach was criticized for only being appropriated for established
professions of medicine and law. While taken other occupations into consideration, the lines
between occupations and professions were blurred – some of them only fulfilled several traits
and others all of them but in attenuated form. For this reason, the terms of semi-professions and
para-professions were established and commonly used. Semi-professions are characterized by
only fulfilling some of the attributes or to a weak extent (Etzioni, 1987). Para-professions refer to
occupations that have some of the traits but mostly are assistants to an established profession.
The work of paraprofessionals involves routine activities, which are essential for the profession
to be effective. Important to point out is that the boundaries between profession, semi-profession
and para-profession are indistinct (Volti, 2011).
However, earlier researchers failed to give sufficient attention to the political processes of
professionalization in which some occupations were able to achieve professional status. Certain
occupations and their members gained the prestige, autonomy, and other advantages to attain
professional status. The political process leads to the power dimension of professions. The most
salient of a profession is the ability to control the most important aspects of the work they do.
This ability concerns to determine following; who is allowed to do the work, how they are
prepared to do the work, and who gets to evaluate their performance. Furthermore,
professionalization as a means of control also relate to the control of entry into a profession in
which the role of education and licensing are essential (Volti, 2011).
Due to technology and how it may change occupations and professions, it can basically
be analyzed through two views; can be a threat and can be leveraged. Technology challenges the
profession or power in control in ways of redistributing tasks to clients, performing tasks
automatically, or allowing semi-professions to do complex work. Besides, new technologies also
have reinforced the status and authority of professionals and made it possible to deepen the
specialization.
Research setting
A report on an ongoing ethnographic study at the Dutch police is collected to support our
research question (Waardenburg, Sergeeva & Huysman, 2018b). The study focuses on the
activities of the so-called “Criminal Anticipation System” (CAS), which is a system of
algorithms to identify patterns in existing data and creates a heat map that shows the most
vulnerable places to crime. Findings of the study indicate that the shift from traditional police
work to predictive policing has affected an emergence of a novel occupational role “Intelligence
officers”. Concerning occupational change due to technology, previous research access two
views; an occupation transforming the expertise and tensions or conflicts between occupations.
Thus, the study emphasizes what happened when introducing the new occupational role of
intelligence officers to support operational police officers in the shift from daily police practices
to data-driven work.
Another study of Waardenburg, Sergeeva, and Huysman (2018a) assesses that the
introduction of predictive policing requires police officers to alter their focus to more data-driven
work instead of the traditional way by looking at criminals’ social environment. To understand
and predict crime, complex analyses and algorithms are applied, and because of that a high
amount of certain knowledge is required. The study is relevant for our research to emphasize the
importance of support from intelligence officers in the altering to more data-driven work.
4
Analysis and results
To illustrate how predictive policing has affected the work of intelligence officers, it’s relevant
to look at how general police work has changed. As mentioned earlier, intelligence officers
gradually have more influence on the daily work of a police officer. From Volti’s checklist
approach to profession, traditional police work is defined as a profession (Volti, 2011). They
have specialized knowledge which they gain from a specific education program, the service that
they provide is of value to society and is not broken due to their code of ethics. They have
autonomy and authority over the public. The intelligence worker however, has no need to have
specialized knowledge within the police force, since the same algorithms are used in marketing
(Waardenburg, L., Sergeeva, A., & Huysman, M. 2018b). In fact, intelligence officers have no
requirement to be trained police officers. Furthermore, they have no authority over clients, since
they have no real interactions with the public. While distinct in terms of degree of
professionalism, due to technology, the two types of work blur more and more together, herby
challenging the traditional police work. Before CAS was introduced, there was a clear line
between the police officers and the information officers in terms of power. Now, because of the
need for analytical, meaningful work, these roles have been redefined by shifting a lot of power
and responsibility to these ‘semi-professionals’, the intelligence officers. Semi-professionals are
now able to do specialized work, without having the specialized background for it. This does not
only change the daily routine for occupational, it also challenges the status and authority that has
been prevalent within the police force up until now.
In addition to the professional continuum, a shift also occurred in terms of hierarchy,
which in turn challenged many of the traditional roles norms. Due to aforementioned
technological developments in predictive policing, of which, in this case, CAS is mostly focused
on, the occupational tasks of information officers changed from a position in the back-office
department to a more valued role in the office. In the introductory stages of predictive policing
methods these information officers were believed to be able to bridge the gap between ‘data
scientists and police officers’ (Waardenburg, L., Sergeeva, A., & Huysman, M. 2018b, p. 7). As
the shift from a reactionary approach to a more predictive one occurred, by being able to read,
interpret and integrate algorithmic outputs onto police work, the role of the information officer
became much more valued and important. In managing these databased system the information
officer stood more at the forefront in predictive policing and consequently a different order in
hierarchy occurred. Relating to the two views mentioned earlier, this technological development
could be seen as an advantage to the information officer as the technology deepens his
specialisation and with it, may have a higher and more prestigious role within the police force.
Consequently, the new role of the traditional police officer consisted of a set of tasks that
were described as ‘a messy and random list of activities’ (Waardenburg, L., Sergeeva, A., &
Huysman, M. 2018b, p. 11). This shows that, besides experiencing difficulty in integration of the
CAS system onto their work, the traditional police officer found himself somewhat driftlessly
manoeuvring through these innovations. Although, future police officers are somewhat more
prepared to a technologically enhanced future, through the use of ‘advanced audio-visual
technologies to diffuse professorial wisdom’ the question of how real-life experiments aided by
CAS are to be fully integrated in the police force still exists (Volti, 2011, p.11). In other words
the traditional police officer experienced a decrease in prestige and autonomy due to these
changes.
Lastly, these innovations caused data-driven decisions to be greatly based on the
collective findings of police officers and the individual judgement of a police officer to be
quickly found inferior. As a police officer generally becomes much more dependent on these
systems, the officer views it to be ‘superior and of higher purpose’ than its own tacit system of
knowledge as the effects and impacts of these systems are found to be much more precise and
efficient compared to former ways. In disregarding the set of traditional police tasks and the tacit
knowledge that is seen as an important element of police work, these innovative systems are
centralized even more so by the traditional police officer.
Emergence of intelligence police officers
The transfer towards predictive policing was followed by the emergence of the “intelligence
officer”. Initially, intelligence officers were supporting police officers in the use of predictive
policing technology, by helping them to interpret the algorithmic outputs. However, by investing
a lot of expertise into interpreting and translating algorithms and the outputs, intelligence officers
became increasingly influential and started to steer police action (Waardenburg, Sergeeva &
Huysman, 2018b). The predictions based on these analytic methods guide the decision-making of
law enforcement agencies, especially with allocating resources to areas that show an increased
crime risk. With the emergence of the intelligence police officer, the police force aims to move
policing from reactionary investigation to proactive investigation (National Crime Prevention
Council, 2006).
As mentioned, Volti (2011) argues that the introduction of algorithmic analytical tools,
can either be a threat or leveraged to a profession. It can be argued, that the introduction of CAS
is viewed as leveraged to both the operational and intelligence officers. Technology is not taking
over the actual occupation, thereby not being an actual threat to the professions. However, as
mentioned earlier, the introduction of CAS lead to a huge shift within the sphere of hierarchical
norms, thereby pressuring the operational officers to change their knowledge while allowing
semi-professions to have more authority over their decision-making (Volti, 2011).
5
Recommendations
Emphasis on predictive aspect of policing
Predictive policing through the CAS system mainly depends on knowledge and data sources to
which it is exposed. This implies that the use of these models are somewhat only functional on
the premise of past events. These systems, therefore, rarely rely on contextual changes in local
crime. As mentioned in Degelin’s article on the effectiveness of predictive policing: ‘burglars
looked for new areas and did not behave as expected, though they were clearly serial offenders’.
In this sense, predictive policing has an indirect effect on the behaviour of criminal offenders.
Although studies have proven forms of biases exist within the predictive policing systems, there
has not been enough emphasis on the prevention of these types of behaviours, and of other
external influences unrelated to new technological approaches. Given that the occurrence of
crime does not mainly depend on past events, a new way of technologically enhanced approach
on policing may be necessary. Advice regarding this aspect would be to include traditional tasks
of the police officers, but to develop existing digital technologies that may be able to aid and
reinforce traditional methods of policing.
Specialized educational framework
Considering the fact that the lines between intelligence officers and police officers are blurred,
but the intelligence officers do not need university based-training, a specialized educational
framework is a logical next step for this relatively new function. It would legitimize the position
of intelligence officers more as equals of police officers and this equalization can take away
existing tensions between both groups, thus bridging the gap.
Code of ethics
Intelligence officers are finding their way into police work in an organic but unregulated way,
which can cause the quality of their work and ways of working to differ from place to place.
They also deal with sensitive data and need to be held accountable just like police officers, to
help ensure an agreed upon level of quality in a city or even country.
Autonomy and authority over data
With the obligation of being held accountable, autonomy and authority over the data is a more
viable option for intelligence officers. While being held to a higher standard, supervised, and
gaining influence because of their knowledge regarding data, a fitting increase in responsibility
seems to be a natural next step.
Independent supervision
Police officers are supervised, this is common knowledge, and this supervision ensures a level of
quality and adherence to the law. Intelligence officers deal with sensitive data and influence how
law enforcement carries out their duties, so independent supervision will add necessary levels of
safety to the data. In addition, it levels the playing field for police officers who can only do their
job with supervision.
Further training
The police officers are still getting used to the increased influence of intelligence officers, there
is knowledge gap between what the intelligence officers do, and what the police officers know
about it. This brings us to further training. Further training can bridge the gap between traditional
policing and the current databased work methods. This prevents the two groups from alienating
each other and can address any tensions based on lack of knowledge or understanding about each
other’s fields of work.
Reorganisation
In order to safeguard the tacit knowledge of police officers, while allowing the space to improve
technology in this field, a reorganization can offer solutions. Though, police officers are
adjusting to and need to learn about databased work, their tacit knowledge is not obsolete and
can have a place in an innovating police force.
6
References
Cope, N. (2004). Intelligence Led Policing or Policing Led Intelligence?: Integrating Volume
Crime Analysis into Policing. British Journal of Criminology, 44(2), 188–203. doi:
10.1093/bjc/44.2.188
Doeleman, R., Willems, D. (2014). Predictive Policing – wens of werkelijkheid? Het Tijdschrift
voor de Politie.
NOS (2019). Politiebonden doen dringende oproep: 'Maak keuze bij inzet agenten'. Retrieved
November 27, 2019, from https://nos.nl/artikel/2311389-politiebonden-doen-dringende-oproepmaak-keuze-bij-inzet-agenten.html
RAND corporation. (2013). Predictive Policing: Forecasting Crime for Law Enforcement.
Rinks, R. (2015). Predictive Policing: Kansen voor een veiligere toekomst. Lectoraat
Intelligence.
Volti, R. (2011). Professions and professionalization. Chapter in: “An introduction to the
sociology of work and occupations” Sage Publications.
Waardenburg, L., Sergeeva, A., & Huysman, M. (2018a). Digitizing crime: How the use of
predictive policing influences police work practices. Paper presented at 34th European Group for
Organizational Studies (EGOS) Colloquium, Tallinn, Estonia.
Waardenburg, L., Sergeeva, A., & Huysman, M. (2018b). Hotspots and Blind Spots: A Case of
Predictive Policing in Practice. Springer New York LLC.
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Appendix
We chose predictive policing as research topic to gain more knowledge on how technology has
changed the police work over time. What are the differences between the traditional and new
ways of preventing crime within the police force?
Google scholar was used to find relevant articles. Our search terms were for example:
“predictive policing”, “forecasting crime”, “police information technology”, “police work
transforming”, “impact of technology on policing”, “intelligence led policing”.
The focus in our paper is the Dutch police force as the Netherlands uses ‘predictive policing’ on
a large scale. Furthermore, for the past few years Dutch police force is under staff shortage. The
papers of Doeleman and Willems(2014), and Rinks(2015) provide insights on the
implementation of predictive policing in the Dutch police force.
Crime Anticipation System (CAS) is being introduced further across the Netherlands and more
than 160 teams are going to work with the system. The papers of Waardenburg, Sergeeva &
Huysman (2018a, 2018b) discuss predictive policing in practice, in particular CAS within the
Dutch police force.
We found that the introduction of CAS especially changed the profession of the intelligence
officers. This led to the research question: how has predictive policing affected intelligence
officers in the Dutch police force? Our paper focuses on the digital transformation of this new
profession and what traits the intelligence officer as a profession has, based on the checklist of
Volti (2011). Furthermore, the introduction of predictive policing changed the police work into
more data-driven work instead of the traditional way of relying on the public to provide
information.