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.
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.
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 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.
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