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At the end of the first paragraph under the subheading “Statistical Challenges to Measuring Crime Concentration”, the citation “(for an exception, see Levin et al. 2015)” should be changed to “(for an exception, see Levin et al. 2016).” The reference is listed with this erratum. In the second paragraph in the “Methods” section, the end of the first sentence should be changed from “… we adopted a small Monte Carlo simulation” to “… we adopted a Monte Carlo simulation procedure similar to Levin et al. (2016).”.

I propose a general theory for examining the spatial distribution of crime by specifically addressing and estimating the spatial distribution of the residences of offenders, targets, guardians, and their respective expected movement patterns across space and time. The model combines information on the locations of persons, typical spatial movement patterns, and situational characteristics of locations to create estimates of crime potential at various locations at various points in time and makes four key contributions. First, the equations make the ideas involved in the theory explicit, and they highlight points at which our current state of empirical evidence is lacking. Second, by creating measures of spatial “potentials” of offenders, targets, and guardians, this theory provides a precise grounding for operationalizing spatial effects in studies of place and crime. Third, the equations provide an explicit consideration of offenders and where they might travel and, therefore, incorporates offenders into crime-and-place research. Fourth, these equations suggest ways that researchers could use simulations to predict stable patterns, as well as changes, in the levels of crime at both micro and macro scales. Finally, I provide an empirical demonstration of the added explanatory power provided by the theory to a study of place and crime.

Although numerous theories suggest that voluntary organizations contribute to lower crime rates in neighborhoods, the evidence for this proposition is weak. Consequently, we propose a dynamic perspective for understanding the relationship between voluntary organizations and neighborhood crime that involves longitudinal analyses and the measurement of the age of organizations. By using longitudinal data on a sample of census blocks (N = 87,641) located across 10 cities, we test the relationship between age-graded measures of different types of voluntary organizations and neighborhood crime rates. We use fixed-effects negative binomial regression models that focus on change within neighborhoods of the relationship between voluntary organizations and neighborhood crime. Our results show that although each type of voluntary organization is found to exhibit crime-reducing behavior in neighborhoods, we find that many of them are consistent with what we refer to as the "delayed impact scenario"-there is a pronounced delay between the placement of a voluntary organization and a neighborhood subsequently experiencing a reduction in crime. With protective effects of organizations typically not demonstrated until several years after being in the neighborhood, these patterns suggest a need for long-term investment strategies when examining organizations.

© 2015 by the American Society of Criminology. Whereas one line of recent neighborhood research has placed an emphasis on zooming into smaller units of analysis such as street blocks, another line of research has suggested that even the meso-area of neighborhoods is too narrow and that the area surrounding the neighborhood is also important. Thus, there is a need to examine the scale at which the social ecology impacts crime. We use data from seven cities from around the year 2000 to test our research questions using multilevel negative binomial regression models (N = 73,010 blocks and 8,231 block groups). Our results suggest that although many neighborhood factors seem to operate on the microscale of blocks, others seem to have a much broader impact. In addition, we find that racially and ethnically homogenous blocks within heterogeneous block groups have the most crime. Our findings also show the strongest results for a multitude of land-use measures and that these measures sharpen some of the associations from social characteristics. Thus, we find that accounting for multiple scales simultaneously is important in ecological studies of crime.

Social disorganization theory holds that neighborhoods with greater residential 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 data set on 74 neighborhoods in the city of Utrecht in the Netherlands spanning 10 years, we tested the extended theory, which to date only a few studies have been able to do because of the unavailability of neighborhood-level longitudinal data. We also improve on previous studies by distinguishing between the potential for social control (feelings of responsibility) and the 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 residential instability, thus leading to more disorder. This is in contrast to most previous studies, which assume disorder to be more 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 two dimensions of the potential for social control and the actual social control behavior. © 2011 American Society of Criminology.