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Aflevering 4, 2021 Alle samenvattingen uitklappen

Big-datatoepassingen bij de politie: een verkenning van een actueel en veelomvattend thema

Trefwoorden big data, politie
Auteurs Thom Snaphaan, Wim Hardyns en Remco Spithoven

    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.

Thom Snaphaan
Thom Snaphaan is doctoraatsonderzoeker aan het Institute for International Research on Criminal Policy, Vakgroep Criminologie, Strafrecht en Sociaal Recht, Universiteit Gent.

Wim Hardyns

Remco Spithoven

Access_open Predictive policing: een balans na zes jaar ­empirisch evaluatieonderzoek in België

Trefwoorden predictive policing, big data, police, crime statistics, Belgium
Auteurs Wim Hardyns en Anneleen Rummens

    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.

Wim Hardyns
Wim Hardyns is professor in de Criminologische Wetenschappen aan het Institute for International Research on Criminal Policy (IRCP), Vakgroep Criminologie, Strafrecht en Sociaal Recht, Universiteit Gent, en gastprofessor in de Veiligheidswetenschappen aan de Universiteit Antwerpen.

Anneleen Rummens
Anneleen Rummens werkte onder promotorschap van Wim Hardyns als onderzoekster aan het Institute for International Research on Criminal Policy (IRCP), Vakgroep Criminologie, Strafrecht en Sociaal Recht, Universiteit Gent.

Big data, kleine rechtsstaat?

Over de roep van uitvoerende professionals om rechtstatelijke bezinning bij big-datatoepassingen in de Nederlandse politiefunctie

Trefwoorden big data, politiefunctie, rechtsstaat, technologische innovatie
Auteurs Remco Spithoven en Elsa Foppen

    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.

Remco Spithoven
Dr. Remco Spithoven is lector Maatschappelijke Veiligheid bij Hogeschool Saxion en tevens hoofdredacteur van Tijdschrift voor Veiligheid.

Elsa Foppen
Elsa Foppen is onderzoeker bij het lectoraat Maatschappelijke Veiligheid en docent bij de opleiding Integrale Veiligheidskunde/Security Management bij Hogeschool Saxion.

Big data in het veiligheidsdomein: onderzoek naar big-datatoepassingen bij de Nederlandse politie en de positieve effecten hiervan voor de politieorganisatie

Trefwoorden artificial intelligence, big data, police, surveillance, ethics by design
Auteurs Marc Schuilenburg en Melvin Soudijn

    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.

Marc Schuilenburg
Marc Schuilenburg is bijzonder hoogleraar Digital Surveillance aan de Erasmus Universiteit Rotterdam en universitair docent aan Vrije Universiteit Amsterdam.

Melvin Soudijn
Melvin Soudijn is senior onderzoeker bij de afdeling Analyse & Onderzoek van de Landelijke Eenheid Nationale Politie en research fellow bij het Nederlands Studiecentrum Criminaliteit en Rechtshandhaving.

Handvatten voor een kwaliteitsbeoordeling van big data: de introductie van het Total Error raamwerk

Trefwoorden big data, criminology, data quality, total error framework, accuracy
Auteurs Thom Snaphaan en Wim Hardyns

    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.

Thom Snaphaan

Wim Hardyns