Predicting crime through data: analysis of the data assemblage of the Dutch Crime Anticipation System

Abstract

Data-driven decision-making plays an increasingly important role in politics, and society as a whole. This trend can also be seen within policing practices. The main goal of this thesis is to analyze how the Dutch National police tries to predict hot spots, places where more police attention is needed, through predictive policing. This thesis is situated in the academic discourse of Critical Data Studies, which will let us critically understand the challenges which face with contemporary data practices. From this perspective, the data assemblage is the key concept in this thesis. For this analysis a framework is constructed through which the usage of the Crime Anticipation System (CAS) of the Dutch police is analyzed. The aim of this framework is to operationalize the data assemblage. The framework which is proposed in this thesis shows that to fully grasp the workings of data practices, we should consider more characteristics than merely technical aspects. Social and institutional context, and usage also shape a data practice.