In recent years, big data technology has revolutionised many domains, including policing. There is a lack of research, however, exploring which applications are used by the police, and the potential benefits of big data analytics for policing. Instead, literature about big data and policing predominantly focuses on predictive policing and its associated risks. The present paper provides new insights into the police’s current use of big data and algorithmic applications. We provide an up-to-date overview of the various applications of big data by the National Police in the Netherlands. We distinguish three areas: uniformed police work, criminal investigation, and intelligence. We then discuss two positive effects of big data and algorithmic applications for the police organization: accelerated learning and the formation of a single police organization.

Predictive policing is the use of historical crime and other data in complex statistical models to predict where and when there is a high risk of new crime events. These predictions can then be used to direct police patrols proactively. Despite the increasing use and commercialisation of predictive policing worldwide, academic research into the methodological and operational dimensions of predictive policing is relatively limited. Since 2015 we have researched and tested several predictive crime models methodologically and operationally, based on police and other (big) data sources in several Belgian police districts. In this article, we summarise the results of six years of empirical research into predictive policing and look to the future of predictive policing research and practice.

The promises of big data have reached the domain of safety and security. After these techniques proved their added value in the private sector, they soon reached the attention of the public sector. In this article we present the results of our interviews with 27 executive professionals of the Dutch police and the department of public order and safety of local governments. What are their expectations of big data? Their experience with big data varied. Our respondents gave expression to a basic positive attitude towards intelligence led policing and the chances that big data brings to it. But anticipation of working with big data was not free from discussion and the respondents stressed the need for securing privacy and other constitutional rights of citizens before big data applications are implemented in the police function. With that, our respondents strongly aligned with the international, academic call for a constitutional reflection about the application of big data withing the police function.

The availability and use of big data sources is increasing exponentially. The variety of new and emerging data sources offers opportunities to complement, replace, improve or add to conventional data sources. Survey data are one kind of conventional data sources. In survey research, a framework to assess the accuracy of survey data already existed for quite some time. This framework is known as the Total Survey Error (TSE) framework. The philosophy behind this framework has only recently been universalized to (big) data in general in the form of the Total Error (TE) framework. This generic framework, which allows for assessing the accuracy of (big) data, is outlined in this article. Additionally, the TE framework is applied to big data sources that could be relevant for policing: police-registered crime data, Twitter data and mobile phone data.

This special issue reflects about big data applications in policing. Terpstra and Salet (2020) refer to this development as ‘one of the greatest changes within the police about the past decade’ (p. 25). Beside attention to this topic in Dutch literature (e.g. Janssen et al., 2020; Spithoven & Beerends, 2019), the relevance of the subject is also internationally recognized (e.g. Brayne, 2021; Ferguson, 2017; Ridgeway, 2018). In this special issue, we examine – with a view to the past, present and future – how big data could be used in policing in the Netherlands and Belgium. This special issue is not about empirical big data applications, for that we would like to refer to specific studies (e.g., Rummens et al., 2021); in this issue we especially want to outline the breadth of this research area.

Litter in the street is a great annoyance for many people, especially in the large cities. This includes overflowing garbage containers caused by people incorrectly placing garbage: next to these containers. In this study, carried out in the municipality of Rotterdam, we have developed a model that provides insight into which factors contribute to where this is done (or not). In summary, days when people are not working, the number of people that depend on the container(s), the bonding and social cohesion of that group, and the social-economic circumstances of the people in that group are the most important factors in determining at which container locations people incorrectly place their garbage. These insights provide starting points for an effective municipal approach to overflowing garbage containers and thereby reducing the residents’ feelings of nuisance and unsafety.

Although risk profiling can by no means be considered as a ‘new’ crime prevention tool, its application has gained popularity over the years. Both in the screening of asylum seekers, in border control at airports (e.g. behavioural profiling or the use of risk profiles in the European Passenger Information Unit) and in the designation of hotspots at the local level (e.g. the Crime Anticipation System), attempts are now being made to identify persons with criminal intentions more often on the basis of ordinal risk assessment tools. This sudden increase in popularity is not remarkable, since random police patrols or undirected security checks have long been shown to have negligible impact on crime reduction. Risk profiles, on the other hand, have a certain potential for shaping more effectively the interventions by law enforcement authorities at border crossing points. Nevertheless, the use of such profiles also raises some pertinent questions. It is precisely in the light of this ambivalence that the present theoretical contribution will consider the pitfalls and success factors of risk profiling during border checks, in order to determine when one can speak of ‘risk profiling’ and when the concept of ‘risky profiling’ must be used.

In the reduction of undermining crime in the Netherlands, the role of local government has become more important over the past decade. One of the means used for this is Article 13b of the Opium Act (the Damocles Act). That has evolved from a way of closing down local dealer properties to a means of disrupting the criminal drug world. In this article we analyse the developments that the application of the article has gone through. We make an inventory of the actual use of the article in a region known as one of the hotbeds of subversive crime in the Netherlands, the region of West and Central Brabant. Finally, we will discuss the consequences that application of the article has for the position of the mayor. Does this lead to a mayorship in which this officer becomes more of a ‘sheriff’ and less of a mayor? We show that this is not the case.

In this article we show how the concept of ‘institutional work’ can be used to study citizen participation in the field of security arrangements. Institutional work starts from the perspective of actors trying to influence security institutions, and not from the perspective of government (as concepts like the ‘participation society’ and ‘ladder’ do). As such, this dynamic concept can be a useful addition to the conceptual arsenal of security experts. By applying the concept to the case of the Voluntary Fire Brigade in nineteenth-century Utrecht (1879-1890) we show how institutional work can work in practice. Finally, we reflect on the contribution that institutional work can make to our understanding of citizen participation in the field of safety.

The media are a main source of information on crime for citizens. Prior research shows that media and fear of crime are not independent of each other. Since fake news is spread through (social) media, the question arises what the relationship is between (perceived) fake news and fear of crime. To date, no large-scale representative research has been conducted on this topic. This study is based on a representative population survey (n = 1566) from 2019. This exploratory study shows a small but significant relation between the perceived prevalence of fake news and fear of crime.

Crisis teams from waterauthorities want to have insight into the current risks for the area. The ‘Continuous insight’-method is used by them to monitor the risks of drought and high water and to draw up what-if scenarios for training and exercise. Various cases have now shown that Expert Judgement can be used to gain a clearer picture of the effectiveness of measures and to take a more balanced decision. The Expert Judgement method has been tested and applied by a group of international experts as part of the EU project DRIVER +. During an extreme situation, the crisis team may encounter dilemmas. The engaged group of experts follows a fixed procedure to provide advice to reduce flood risk evacuate to prevent loss of life.
Each expert makes his own assessment of the effect and impact of the measure envisaged. Each expert provides an explanation of the individual assessment. Then, under the guidance of a process supervisor, the experts discuss individual assessments and exchange arguments. This can lead to new insights. Each expert makes his own assessment again. The result is a probability distribution of estimates and an expected value. This expert assessment of the measure is taken to the crisis team, so that the chance of success and the assessment of the consequences of the proposed measure can be adjusted. The crisis team can take the decision immediately afterwards.
By following this procedure risk information is made visible. By linking expert knowledge with observations, a better substantiated expectation can be drawn up. According to the participants in the international meetings, the Expert Judgement method in combination with ‘Continuous insight’ helps to make better informed and faster choices. The method fits within the existing crisis management structure. The next step is to implement this method in the day-to-day affairs of crisis management.

In this paper, we present a mathematical model for the prediction of chimney fires using data collected by the Twente safety region over the years 2004 up to 2015. The explanatory variables used are the number of inhabitants per areal unit of 500 by 500 metres, the daily mean temperature and an indicator variable for October. Spatially and temporally correlated noise is added to be able to capture latent factors such as human behaviour. The model is validated using the data over the years 2016 and 2017. The model is implemented as a dashboard on a public website and yields as output a hazard map for chimney fires in Twente based on the weather forecast for the coming six days. This way, the dashboard can be used for prevention and planning purposes.

Prisoners often encounter multiple barriers when returning to society, resulting in higher risks of recidivism. To overcome these barriers, prison-based and community-based professionals assist with preparation for release. Prison-based professionals, such as the case manager and mentor, screen and monitor the problems regarding work and income, housing, healthcare, financial debts and valid identification. Community-based professionals, such as municipal officials, parole officers, healthcare professionals and volunteers, can provide additional and specialized help. First, this research discusses the current policy of the Dutch Custodial Institutions Agency (DJI) and the role of different types of professionals. Second, it presents a nationwide picture of the extent to which prisoners report contact with prison-based and community-based professionals, and to what degree prisoners appreciate this contact. The results are specified for various types of regimes and time served and are based on 4308 prisoner surveys of the Dutch Prison Visitation Study (DPVS), part of the Life in Custody Study (LIC-study). It turns out that most prisoners seem to be in close contact with prison-based professionals and that prisoners positively value this contact. However, contact with community-based professionals is limited and prisoners are somewhat dissatisfied about their contact with parole officers and municipal officials. Furthermore, the amount of contact differs across various types of regimes and time served. In particular, individuals who recently entered prison report less contact. To conclude, policy implications will be discussed.

This article provides a case description of the development of an integrated safety monitor for the governance on policy goals. The governance on these safety goals took place in cooperation between eight municipalities and Police, Justice, Fireworks, Health Care providers for addiction or domestic violence and partners in incident and crisis management. Two problems were adressed. The first problem is that the evidence on process and results of monitoring from data to interpretation and policy choices in cooperation with different safety partners, is scarce. The second problem is that monitoring of a wide range of indicators on integrated safety is also scarce. In our case, the gathering of data and the interpretation of these data together with all the partners formed a cyclical process. This corresponds with the theory of governance developed by Ansell and Gash in a meta-analysis in 2008. The starting conditions were made clear. The design of the monitoring process was translated into themes of working sessions with a group of policy advisors, giving them a structure for the governance on the policy goals. This group developed a layered set of indicators. These set of indicators was broken down in the four policy goals as well as in different steps in the safety chain. Indicators from different registrations of the safety partners were combined with indicators about the perception of safety measured by a representative sample of the residents. The layered set of indicators was supportive in the discussion about a well balanced monitoring on prevention, signals, interventions and effect. The data were interpreted together with the safety partners. This was helpful in creating a joint perspective on safety problems and in the discussion about meaningful policy actions. The challenge now is to persevere in the cyclical process from data to interpretation and shared understanding about policy goals.

Policy coherence in the area of safety and justice – A longitudinal analysis of service delivery per euro spent in the S&J sector during the period 1980-2016

In the safety and justice policy area, there is a strong intertwining between performances of the various sectors, such as police, judiciary and prison system. The question is whether policy takes these interdependencies into account sufficiently. Are the resources used – from a broader wealth perspective – optimally allocated amongst the various safety and justice provisions? The authors answer this question on the basis of an integrated time series analysis of the productivity development of the Dutch safety and justice system in the period 1980-2016. The analysis shows that productivity of safety and justice services has hardly changed since 1980. At best, in 2016, citizens will receive as much value per euro of taxpayers’ money as in 1980, but probably slightly less. It is striking, however, that the S&J system as a whole operates more efficient than the sum of its parts (the individual sectors). There have been substantial changes in the allocation of resources over time. Obviously money from the police was transferred to the judiciary and municipalities.

In this study we examined the developmental trajectories of hackers, based on their self-reported web defacements. During a web defacement, the content of a website is altered without permission. In total, 50,330 defacements of websites with a Dutch extension (.nl websites) were analyzed, which were executed by 3,640 defacers between January 2010 and march 2017. Using trajectory analysis, six different groups of defacers could be distinguished: two chronic groups of attackers and four groups who only defaced websites during a limited time period. These groups also differed in their motivations and modus operandi. The high frequent chronic attackers consist of less than 2% of the hackers, but were responsible for more than half of all defacements. It would therefore be most efficient when future interventions focus on this small group of chronic offenders. For future studies it would be an interesting topic to examine the content of the web defacements.