Do mobile phone data provide a better denominator in crime rates and improve spatiotemporal predictions of crime?

This article assesses whether ambient population is a more suitable population-at-risk measure for crime types with mobile targets than residential population for the purpose of intelligence-led policing applications. Specifically, the potential use of ambient population as a crime rate denominator and predictor for predictive policing models is evaluated, using mobile phone data (with a total of 9,397,473 data points) as a proxy. The results show that ambient population correlates more strongly with crime than residential population. Crime rates based on ambient population designate different problem areas than crime rates based on residential population. The prediction performance of predictive policing models can be improved by using ambient population instead of residential population. These findings support that ambient population is a more suitable population-at-risk measure, as it better reflects the underlying dynamics in spatiotemporal crime trends. Its use has therefore much as-of-yet unused potential not only for criminal research and theory testing, but also for intelligence-led policy and practice

Predictive policing as a tool for crime prediction and prevention : a methodological and operational evaluation

Data-driven approaches are gaining in importance in our current digital society. In criminology, this has led to increased research interest in big data analysis and other innovative methods and applications for crime analysis such as predictive policing. Predictive policing is the use of historical crime and other data in advanced statistical models to predict where and when new crime events are likely to occur and use these predictions to proactively direct police patrols. Despite its increased use by law enforcement agencies around the world and the development of predictive policing applications by commercial software companies, academic research studying the methodological and operational dimensions of predictive policing is limited. This PhD research assesses some of the main methodological and operational considerations in the application of predictive policing and discusses the implications for policy, practice and future research

Comparison of near-repeat, machine learning and risk terrain modeling for making spatiotemporal predictions of crime

The main objective of this study is to test and compare the prediction performance of three of the most common predictive policing methods. A near-repeat model, a supervised machine learning model, and a risk terrain model are tested and compared against each other using retrospective analysis of home burglary crime data from a Belgian city. Hotspot analysis is included as a baseline. Predictions are made for three different months (January, May and September 2017) to account for seasonal differences. Variations in spatial context (city center vs. suburbs) and the number of predicted risk locations are also tested. Prediction performance is measured using accuracy, near-hit rate, precision and F1-score. The results show that there are some notable differences in prediction performance between the model types across the tested variations. In general, the ensemble model tends to be the most consistent high performer across all tested variations. Also notable is that hotspot analysis is not clearly outperformed by the other methods. The different methods have their own strengths and weaknesses and optimal prediction performance crucially depends on the specific location context. More comparative analyses of predictive policing methods in different contexts are needed to gain a more complete picture. Future research could also focus on how combining methods can help improve crime prediction performance

Social capital variables at the neighbourhood level as predictors in a predictive policing model

Predictive policing models aim to predict crime events based on available crime and socio-economic data at a micro-geographic level. The aim of this study is to investigate the potential of including data on social capital, disorder and fear of crime for improving prediction performance of the predictive policing model. To this end, data is used from the Social capital and Well-being In Neighbourhoods in Ghent (SWING) survey, the Social Capital in Neighbourhoods (SCAN) project and the quality of life monitor from the city of Ghent. These datasets are based on extensive surveys at the neighbourhood level in Ghent and contain variables related to among others social capital, collective efficacy, social and physical disorders, and fear of crime. The prediction performance of two models are compared against each other: one model with the social capital variables included and one base model without these variables, with only basic crime and socio-economic variables included. The results of this analysis and its implications for the prediction performance of the predictive policing model will be discussed

Using the synthetic minority oversampling technique (SMOTE) to overcome the data sparsity problem in predictive policing models

Predictive policing models aim to predict crime events at a micro geographic and temporal level. As a consequence, it is possible to be confronted with a sparsity problem, where the majority of observations have zero values (i.e. crime events are comparatively rare to non-events). This is especially a problem in contexts or crime types with a relatively low crime rate, but is not excluded in high crime rate contexts either, as the law of crime concentration estimates that 50% of all crime occurs in only 4% of micro places. The data sparsity problem influences the learning ability of the model and can lead to poor prediction performance. In machine learning algorithms, the synthetic minority oversampling technique (SMOTE) can be used to overcome this problem. The aim of this study is to test the potential of SMOTE to improve prediction performance of predictive policing models. To this end, the prediction performance of a model with SMOTE applied will be compared with a model without SMOTE. The results of this analysis and its implications for the prediction performance of the predictive policing model will be discussed