Social disorganization theory holds that neighborhoods with a greater population stability, higher socioeconomic status and more ethnic homogeneity experience less disorder because these neighborhoods have higher social cohesion and exercise more social control. Recent extensions of the theory argue that disorder in turn affects these structural characteristics and mechanisms. Using a dataset on 74 neighborhoods in the city of Utrecht in the Netherlands spanning ten years, we tested the extended theory, which to date only few studies have been able to do because of unavailability of neighborhoodlevel longitudinal data. We also improve on previous studies by distinguishing between potential for social control (feelings of responsibility) and actual social control behavior. Cross-sectional analyses replicate earlier findings, but the results of longitudinal cross-lagged models suggest that disorder has large consequences for subsequent levels of social control and population turnover, thus leading to more disorder. This is in contrast to previous research, which sees disorder more as a consequence than a cause. This study underlines the importance of longitudinal data, allowing for simultaneously testing the causes and consequences of disorder, as well as the importance of breaking down social control into the potential for social control and actual social control behavior.

No abstract

Objectives The crime and place literature lacks a standard methodology for measuring and reporting crime concentration. We suggest that crime concentration be reported with the Lorenz curve and summarized with the Gini coefficient, and we propose generalized versions of the Lorenz curve and the Gini coefficient to correct for bias when crime data are sparse (i.e., fewer crimes than places).Methods The proposed generalizations are based on the principle that the observed crime concentration should not be compared with perfect equality, but with maximal equality given the data. The generalizations asymptotically approach the original Lorenz curve and the original Gini coefficient as the number of crimes approaches the number of spatial units.Results Using geocoded crime data on two types of crime in the city of The Hague, we show the differences between the original Lorenz curve and Gini coefficient and the generalized versions. We demonstrate that the generalizations provide a better representation of crime concentration in situations of sparse crime data, and that they improve comparisons of crime concentration if they are sparse.Conclusions Researchers are advised to use the generalized versions of the Lorenz curve and the Gini coefficient when reporting and summarizing crime concentration at places. When places outnumber crimes, the generalized versions better represent the underlying processes of crime concentration than the original versions. The generalized Lorenz curve, the Gini coefficient and its variance are easy to compute.

No abstract

While businesses may attract potential offenders and thus be conducive to disorder, the number of employees could offset this by exercising social control on offenders. This study uses data from different sources to test this expectation across 278 Dutch neighborhoods in the four largest cities of the Netherlands, using multivariate multilevel analysis to disentangle individual perception differences of disorder and neighborhood effects. Attention is paid to traditional explanations of disorder (i.e., poverty, residential mobility, and ethnic heterogeneity). Results show a positive relationship between business presence and neighborhood disorder. We do not find consistent results of the number of employees (i.e., bigger businesses are not always better or worse). Our research demonstrates that the presence