This study examines to what extent new and emerging data sources or big data have been empirically used to measure key theoretical concepts within environmental criminology. By means of a scoping review, aimed at studies published between 2005 and 2018, insight is provided into the characteristics of studies that used big data sources within environmental criminology. The type and extent of big data sources used, as well as the strengths and weaknesses of these data sources, are synthesized. After the selection procedure, 84 studies were included for further analysis. Although the number of studies increased each year, there has been a remarkable increase in the number of studies since 2014. The findings suggest that most studies used administrative data or user-generated content as one type of research data. However, innovative data sources (automated and volunteered data) have gained in importance in recent years. Also, most studies are of a descriptive or predictive nature, predominantly conducted by computational (social) scientists. Since these approaches pay little to no attention to mechanisms that bring about social outcomes, an alternative philosophical framework is proposed. We put forward a scientific realist approach as a solution to integrate data-driven and theory-driven research. This approach responds to recent calls to move towards an ‘analytical criminology’. The results are discussed within this framework, and translated into avenues for future research.

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.

This study examines the extent to which crime concentrations occur at micro places, in order to test Weisburd’s law of crime concentration at places, in two large Belgian cities. Police-registered crime data for the period 2004–2012 were used. Analyses were conducted at the grid level (using 200 meters by 200 meters grid cells), as a proxy for behavior settings. This study assesses Weisburd’s theoretical proposition and by (partly) replicating prior empirical research, we conclude that the findings are in line with those from prior studies regarding crime concentration at micro places. Finally, opportunities and avenues for future research on crime places are discussed.

The principal aim of this multilevel study was to assess the impact of collective efficacy and disorder, as neighborhood characteristics, and individual social capital on an individual’s avoidance behavior, independent of the neighborhood composition. The theoretical backdrop to the present study integrates insights from social capital theory, collective efficacy theory, and broken windows theory. The multilevel model is based on an individual-level questionnaire of inhabitants (N = 2,730) and a neighborhood-level questionnaire of key informants in neighborhoods in Ghent, Belgium (N = 142). The results suggest small but significant neighborhood effects on an individual’s avoidance behavior. Individuals with lower levels of individual social capital and who live in neighborhoods with higher levels of disorder report more avoidance behavior.

Employing a multilevel perspective on the health effects of social capital, this study analyzes how individual and neighborhood differences in self-rated health in Ghent (Belgium), relate to individual and collective social mechanisms, when taking demographic and socioeconomic characteristics of individuals into account. This study estimates the health effects of social trust, informal social control and disorder at the neighborhood level and social support and network size at the individual level, using indicators indebted to both the normative and resource-based approaches to social capital. Instead of the mere aggregation of individual indicators of social capital, this study uses the key informant technique as a methodologically superior measurement of neighborhood social capital, which combined with a multilevel analysis strategy, allows to disentangle the health effects of individual and neighborhood social capital. The analysis highlights the health benefits of individual social capital, i.e., individual social support and network size. The study indicates that controlling for individual demographic and socioeconomic characteristics reduces the effect of the neighborhood-level counterparts and the neighborhood characteristics social trust and neighborhood disorder have significant, but small health effects. In its effects on self-rated health, social capital operates on the individual level, rather than the neighborhood level.

This article presents a further development of the existing crime scripting framework to enhance insight in the financial aspects of profit-driven crime: financial crime scripting. By drawing on the foundations of crime script analysis, financial crime scripting allows to generate, organise and systematise knowledge about the financial aspects of the crime commission processes of a variety of crime types, and accounts for linking the dots with financial crimes, such as bribery, bankruptcy fraud and money laundering. Viewing these financial crimes as supporting or succeeding offences in light of profit-driven crimes, and at the same time providing guidance to analyse these offences as profit-driven crimes in itself, opens the door for detailed analyses without losing sight of the bigger picture, i.e., the interconnectedness with other crimes. This analytical method helps crime researchers to take into account the financial aspects of crime-commission processes in crime script analyses and could help law enforcement agencies and other crime prevention partners to go beyond a proceeds-of-crime approach and put a follow-the-money approach in practice. Financial crime scripting takes full account of the financial aspects of profit-driven crime and puts relevant concepts in broader perspective, enhancing understanding with conceptual clarity. In addition to outlining the framework, the relevance for policy and practice is unravelled and avenues for future research are discussed.