Met datascience op zoek naar indicatoren van georganiseerde criminaliteit en ondermijning

Abstract

Increasing digitization and datafication lead to an increasingly important role of data in our society and to changes in the way institutions work and decisions are made. Although it can lead to changes in the type of crime (e.g. cybercrime), datafication also facilitates shifts from visible and registered crime to crime that has not (yet) been measured and registered, like manifestations of organized crime. Analyzing so-called big data can help to recognize new forms of crime, predict risk factors, and decrease the dark number of these forms of crime.In this study, we illustrate which indicators determine the stage of an industrial area regarding the occurrence of organized crime. Our supervised machine learning analysis shows that a number of indicators actually have predictive value for the degree of organized crime. These indicators could be used in the future to distinguish which industrial areas run an increased risk of organized and subversive crime.