Journal of Quantitative Criminology

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Online ISSN: 1573-7799
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of findings of the equality of coefficient comparisons and Heckman probit
Objectives Longitudinal data offer many advantages to criminological research yet suffer from attrition, namely in the form of sample selection bias. Attrition may undermine reaching valid inferences by introducing systematic differences between the retained and attrited samples. We explored (1) if attrition biases correlates of recidivism, (2) the magnitude of bias, and (3) how well methods of correction account for such bias.Methods Using data from the LoneStar Project, a representative longitudinal sample of reentering men in Texas, we examined correlates of recidivism using official measures of recidivism under four sample conditions: full sample, listwise deleted sample, multiply imputed sample, and two-stage corrected sample. We compare and contrast the results regressing rearrest on a range of covariates derived from a pre-release baseline interview across the four sample conditions.ResultsAttrition bias was present in 44% of variables and null hypothesis significance tests differed for the correlates of recidivism in the full and retained samples. The bias was substantial, altering effect sizes for recidivism by a factor as large as 1.6. Neither the Heckman correction nor multiple imputation adequately corrected for bias. Instead, results from listwise deletion most closely mirrored the results of the full sample with 89% concordance.Conclusions It is vital that researchers examine attrition-based selection bias and recognize the implications it has on their data when generating evidence of theoretical, policy, or practical significance. We outline best practices for examining the magnitude of attrition and analyzing longitudinal data affected by sample selection.
Treatment group coefficients across relative time in event study model, with 95% confidence intervals (CIs; fixed-effects Poisson model of 819 non-zero fatality agencies in core sample)
Trends in fatal encounters in core treatment and control groups, 2005/06 to 2018/19
Trends in fatal encounters in propensity score matched treatment and control groups, 2005/06 to 2018/19
Trends in fatal encounters in core treatment group and alternative comparison groups, 2005/06 to 2018/19
Objectives This study assesses the effects of body-worn cameras (BWCs) on rates of fatalities arising from police-citizen encounters. While existing experimental research has not examined this outcome because it is so rare, the staggered roll-out of BWCs across the nation’s law enforcement agencies provides an opportunity for quasi-experimental analysis. Methods Difference-in-difference (DID) analyses using Poisson models compare changes in U.S. law enforcement agencies’ fatality counts with changes in BWC acquisition. Using a federal law enforcement survey focused on body worn cameras (LEMAS-BWCS) and media-sourced data on fatal encounters from (FE), the research examines agencies acquiring BWCs between 2013/14 and 2015/16 and those that did not acquire them up to 2016 and had no plans to do so. It includes a fixed effects annual panel data analysis with data from 2005/06 to 2018/19 and two two-group analyses focusing on a pre-treatment period (2010/11 to 2012/13) and a post-treatment period (2016/17 to 2018/19). The latter includes a propensity score matched comparison. Results Two out of three DID analyses showed statistically significant negative effects of BWCs on citizen fatalities. The propensity score matched two-group analysis returned a non-significant negative effect. Conclusions The research finds some evidence for BWC effects on citizen fatalities. However, there are important validity threats to this conclusion. These include the possibility that BWC acquisition serves as a marker for other policy changes focused on BWC-acquiring agencies in the 2013/14 to 2015/16 period and beyond.
Objectives The Police Districting Problem concerns the definition of patrol districts that distribute police resources in a territory in such a way that high-risk areas receive more patrolling time than low-risk areas, according to a principle of territorial fairness. This results in patrolling configurations that are efficient and effective at controlling crime but that, at the same time, might exacerbate racial disparity in police stops and arrests. In this paper, an Equitable Police Districting Problem that combines crime-reduction effectiveness with racial fairness is proposed. The capability of this model in designing patrolling configurations that find a balance between territorial and racial fairness is assessed. Also, the trade-off between these two criteria is analyzed. Methods The Equitable Police Districting Problem is defined as a mixed-integer program. The objective function is formulated using Compromise Programming and Goal Programming. The model is validated on a real-world case study on the Central District of Madrid, Spain, and its solutions are compared to standard patrolling configurations currently used by the police. Results A trade-off between racial fairness and crime control is detected. However, the experiments show that including the proposed racial criterion in the optimization of patrol districts greatly improves racial fairness with limited detriment to the policing effectiveness. Also, the model produces solutions that dominate the patrolling configurations currently in use by the police. Conclusions The results show that the model successfully provides a quantitative evaluation of the trade-off between the criteria and is capable of defining patrolling configurations that are efficient in terms of both racial and territorial fairness.
Distance between offender’s home and snatching location (N = 1573)
Numbers of facilities in the study area (Chennai City)
Spearman correlation coefficients between facility frequencies in 201 wards
Objectives We aim to test the applicability of crime pattern theory in an Indian urban context by assessing the effects of offender residence, prior offending locations and presence of crime generators and crime attractors on where offenders commit offences. Methods The data comprise 1573 police-recorded snatching offenses committed by 1152 identified offenders across the 201 wards of Chennai City. We used discrete crime location choice models to establish the choice criteria that snatching offenders use when they decide where to offend. Data on the locations retail businesses, religious and transportation facilities were collected using Google location services. Results The results confirm that snatching offenders prefer to target locations closer to their residence and that they prefer to re-offend at or near their prior offending locations. The findings also demonstrate that some but not all crime attractors and generators influence the location choice of snatching offenders. Conclusions By replicating in an Indian context previously published crime location choice findings, our findings support the generality of crime pattern theory. We discuss limitations and make suggestions for future investigations.
A correction to this paper has been published:
Age, Period, and Cohort effects on seven index crimes. Note: Figure shows mean and 95% confidence intervals of relative annual effect
Annual age, period, and cohort effects on crime, 1980–2016. Note: Figure shows mean and 95% confidence interval of annual effects
Cumulative effect of age, cohort, and period effects (1980 = 100). Note: Figure shows mean and 95% confidence interval of cumulative effects
Objective Identify the effect of differences in criminal activity among birth cohorts on crime rates over time. Determine the extent to which cohort effects are responsible for nationwide crime reductions of the last thirty years. Methods Use a panel of state age-arrest data and frequently used economic, social, and criminal justice system covariates to estimate a proxy or characteristic function for current period effects. Combine these results with national age-arrest data to estimate nationwide age, current period, and birth cohort effects on crime rates for 1980–2016. Results Criminal activity steadily declined between the 1916 and 1945 birth cohorts. It increased among Baby Boomers and Generation X, then dropped rapidly among Millennials, born after 1985. The pattern was similar for all index crimes. Period effects were mostly responsible for the late 1980s crack boom and the 1990s crime drop, but age and cohort effects were primarily responsible for crime rate reductions after 2000. In general, birth cohort and current period effects are about equally important in determining crime rates. Conclusions Policies aimed at reducing delinquency among young children may be more effective in the long run than current policies aimed at incapacitation, deterrence, and opportunity reduction.
Objectives Few studies have examined the consequences of neighborhoods for job prospects for people on parole. Specifically, networks between neighborhoods in where people commute to work and their spatial distributions may provide insight into patterns of joblessness because they represent the economic structure between neighborhoods. We argue that the network of neighborhoods provides insight into the competition people on parole face in the labor market, their spatial mismatch from jobs, as well as their structural support. Methods We use data from people on parole released in Texas from 2006 to 2010 and create a network between all census tracts in Texas based on commuting ties from home to work. We estimate a series of multilevel models examining how network structures are related to joblessness. Results The findings indicate that the structural position of neighborhoods has consequences for people on parole’s joblessness. Higher outdegree, reflecting neighborhoods with more outgoing ties to other neighborhoods, was consistently associated with less joblessness, while higher indegree, reflecting neighborhoods with more incoming ties into the neighborhood, was associated with more joblessness, particularly for Black and Latino people on parole. There was also some evidence of differences depending on geographic scale. Conclusions Structural neighborhood-to-neighborhood networks are another component to understanding joblessness while people are on parole. The most consistent support was shown for the competition and structural support mechanisms, rather than spatial mismatch.
Objectives There is an increasing understanding that mental health may be a collateral consequence of joining a gang. The objective of the present study is to assess the effect of gang joining on a set of diverse mental health outcomes that include depression, anxiety, hostility, and paranoid ideation. Methods To reduce bias in our comparisons, we balance gang-joiner and gang-abstainer groups by applying the entropy balancing algorithm to longitudinal data from the Pathways to Desistance study. Results The results indicate that joining a gang is implicated in poor outcomes for all four measures of mental health considered in our analysis. The observed associations persist both at the first and second wave after joining a gang. Conclusions To understand more comprehensively both the short- and long-term consequences of gang joining, scholars of crime and justice must expand their focus to include mental health—not solely as a predictor of group offending but also as its consequence. Future studies should also consider mental health in the context of gang desistance.
Objectives Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro−geographic units, measuring this is difficult.Methods This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes.ResultsThe presence of more persons in the environment is associated with higher levels of crime. The auto−oriented measures—vehicles and pavement—were positively associated with crime rates. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted−U relationship with two crime types.Conclusions The results demonstrate the efficacy of this approach for measuring the built environment.
Bivariate cholesky decomposition model for intimate partner victimization and depressive symptoms. Bivariate model where depressive symptoms are regressed on latent additive genetic and environmental variance components of intimate partner victimization. Path coefficients a11, c11, e11, a22, c22, e22 are additive genetic (a), shared environmental (c), and nonshared environmental (e) effects that are unique to each phenotype. Path coefficients a12, c12, and e12 are additive genetic, shared environmental, and nonshared environmental effects that are common between phenotypes. Path diagram is shown for one twin pair
Prevalence and frequency of intimate partner victimization by sex
Cross-lagged monozygotic twin-difference model for intimate partner victimization and depressive symptoms. N = 207 MZ twin pairs. CFI = .94, TLI = .97, RMSEA = .02. Standardized path estimates presented. 95% confidence intervals in brackets. *p < .05
Objectives While a wealth of research reports a robust association between intimate partner victimization and depression, the relationship has not been tested using twin-based research designs to control for unmeasured genetic and shared environmental confounding.Methods Twin data from the National Longitudinal Study of Adolescent to Adult Health are analyzed to test the causal hypothesis that intimate partner victimization increases depressive symptoms across the life course. A series of twin-based research methodologies are used to examine whether twin differences in intimate partner victimization during late adolescence are associated with differences in depressive symptoms in young adulthood.ResultsMales and females did not significantly differ in their prevalence or frequency of reported intimate partner victimization during late adolescence. Genetic and nonshared environmental effects were found to account for the covariance between intimate partner victimization and depressive symptoms. After controlling for common genetic effects, within-twin pair differences in intimate partner victimization were positively associated with within-twin pair differences in depressive symptomatology.Conclusions The results offer further support for the mental health consequences associated with intimate partner victimization and help strengthen causal inference arguments for the relationship between intimate partner victimization and depressive symptoms later in life.
Plea discount distribution
Predicted discount by evidence, race, and sex
Objectives It is well established that defendants who plead guilty receive reduced sentences compared to the likely outcome if convicted at trial. Prominent theories of plea bargaining posit that the plea discount is determined by the strength of the evidence against the defendant. Research on this claim has produced mixed findings, however, and others have suggested that discounts may be influenced by extra-legal characteristics such as race, age, and sex. To date, there have been few attempts to directly compare the effects of these factors on plea discount estimates. Methods This study uses a penalized ridge regression to predict counterfactual trial sentences for a sample of defendants who pled guilty. Plea discounts are estimated using each defendant’s predicted trial sentence and observed plea sentence. Discount estimates are then regressed on variables related to case evidence and the demographic characteristics of the defendant. Results Results suggest that increases in the amount of evidence associated with a case lead to decreases in the size of the plea discount. Both main and interaction effects are observed for race/ethnicity and sex, with Hispanic and male defendants receiving significantly smaller discounts than White or female defendants. Calculation of standardized effect sizes further indicates that demographic characteristics exert larger effects on plea discount estimates than evidentiary variables. Conclusions Plea discounts appear to be influenced by both evidence and extra-legal factors. Legal participants may indeed consider the strength of the evidence when determining acceptable plea discounts, but this alone appears to be an insufficient explanation.
Objectives This study examines the role of law enforcement procedures for environmental offenses. We test whether reaching the statute of limitations is associated with the recidivism of offenses against the flora in Brazil. Methods We analyze the universe of infractions issued by Brazil’s Federal Environmental Agency from 2000 to 2010 using survival analysis and reweighting methods. Results Findings indicate that reaching the statute of limitations in administrative procedures increases the risk of recidivism for individuals by 188% and firms by 34%. Conclusion Ineffective sentencing practices stimulate repeated offenses against the environment and have significant consequences for environmental degradation in Brazil, a country that is central for actions to mitigate global environmental change.
a Number of sentences by sanction regime, 2008–2017, b recidivism rate trends by sanction regime, 2008–2015
DID non-custodial reform effect on recidivism per year
Sensitivity analysis for effect on 2013
Objectives This paper assesses the impact of the new regime of non-custodial sanctions implemented in Chile in 2013. It aims to contribute to the evidence regarding structural changes in systems involving non-custodial sanctions. Methods To identify the causal effect of the new regime of non-custodial sanctions on recidivism, we perform three complementary estimations. First, a before and after regression model of recidivism was estimated. Second, in order to compare cohorts with non-custodial and custodial sanctions, we build a difference-in-difference estimation to control for time-invariant confounding factors. Additionally, we use controls to address potential differences across groups not related to the change in treatment status. Third, we estimate the yearly effect through difference-in-differences for multiple periods. Results The results suggest a small statistically significant increase of 1.54 percentage points in the recidivism rate, attributable to the reform of non-custodial sanctions during the first year of its implementation. After that, the reform impact on recidivism begins to stabilise at zero. Conclusions The results are consistent with a neutral scenario whereby the recidivism rate eventually remains stable after the reform was implemented despite the increase in the number of people sentenced. Considering that the reform helped to decongest prisons, a neutral scenario on recidivism seems favourable for the Chilean penal system.
Cumulative criminal justice contact, NLSY97. Note: Weighted percentages
Self-reported criminal activity distributions by criminal justice contact, NLSY97. Note: This figure displays the distribution of age-adjusted percentile scores for self-reported criminal activity across the 1997–2003 survey years by criminal justice contact (by 2017). Criminal activity percentile scores are based on respondents’ responses to questions asking whether the respondent had carried a gun, destroyed property, stolen goods worth less than $50, stolen goods worth more than $50, committed any property crimes, assaulted anyone, sold marijuana, sold hard drugs, used marijuana, and/or used hard drugs since their last interview
Objectives I examine housing instability among individuals with a felony conviction but no incarceration history relative to formerly incarcerated individuals as a means of separating the effect of felon status from that of incarceration per se—a distinction often neglected in prior research. I consider mechanisms and whether this relationship varies based on gender, race/ethnicity, time since conviction, and type of offense. Methods I use National Longitudinal Survey of Youth 1997 data and restricted comparison group, individual fixed effects, and sibling fixed effects models to examine residential mobility and temporary housing residence during early adulthood. Results I find robust evidence that never-incarcerated individuals with felony convictions experience elevated risk of housing instability and residential mobility, even after adjusting for important mediators like financial resources and relationships. The evidence that incarceration has an additional, independent effect on housing instability is weaker, however, suggesting that the association between incarceration and housing instability found in prior studies may largely be driven by conviction status. Conclusions These findings reveal that conviction, independent of incarceration, introduces instability into the lives of the 12 million Americans who have been convicted of a felony but never imprisoned. Thus, research that attempts to identify an incarceration effect by comparing outcomes to convicted individuals who receive non-custodial sentences may obscure the important independent effect of conviction. Moreover, these findings highlight that the socioeconomic effects of criminal justice contact are broader than incarceration-focused research suggests. Consequently, reform efforts promoting the use of community corrections over incarceration may do less to reduce the harm of criminal justice contact than expected.
Trends in the robbery rate per 1000 population (Moving averages) for crime analysts’ precincts (Critical areas) and Software Precincts (Per Week in 2014 and 2015). Note: The vertical line in week 22 of 2015 marks the beginning of the experiment period. Robbery rates are expressed in moving averages per 1000 population. Source: Prepared by the authors
Map of Montevideo. Treatment assignments.
Source Fraiman (2016)
Objectives The paper studies the impact of predictive policing on crime in a developing country. It also assesses the impact of different police trainings. Method We analyze a randomized controlled trial conducted in Montevideo, Uruguay to assess the implementation of a predictive policing software developed in the United States. Half of the precincts were randomly assigned to the software and half to the local crime analysts (status quo). The second experiment allocated randomly a specially trained police force to targeted patrol areas per shift and day. Results No statistically significant differences were found in crime outcomes between the precincts assigned to the foreign predictive software and those assigned to local crime analysts. On the second experiment, given determined targeted places, the specially trained task force showed more compliance with the assigned patrol sites (20% more patrol time) and a greater potential for reducing crime (reduction of 30% in robberies only during high crime shifts in comparison to the control group (no special training). There is also evidence of a diffusion of benefits to adjacent areas. Conclusions The implementation of an international predictive policing software did not outperform local crime analysts in terms of crime reduction. Local crime analysts are more cost-effective. Given determined targeted places, a modest increase in police dosage of a specially trained police force could reduce crime in high-crime times. In developing countries new policing technologies and training require a deep understanding of the context to channel limited resources in the most efficient way.
Objectives In light of empirical findings suggesting no substantive main effects of an incarcerated person’s (IP’s) race or ethnicity on the odds of placement in restrictive housing (RH) for rule violations, we investigated whether these effects are dependent on offense severity and context, including characteristics of facilities that could theoretically increase stakeholder reliance on biased stereotypes and also prison staff members’ perceptions of danger and order in a facility. Methods Multilevel analyses of race and ethnicity effects on RH decisions, both at the time of the incident (pre-trial) and after the rule infraction hearing, were conducted for all persons admitted to Ohio’s prisons between 2007 and 2016 and found guilty of prison rule violations (N1 = 81,673; N2 = 33). Results We found no significant main effects of an IP’s race or ethnicity on the odds of RH placement for rule infractions, either at the time of the incident or as punishment after a hearing, once the types of violations were controlled. Upon further investigation, we found that African American and Latinx IPs were more likely to receive RH for certain insubordination-related violations, which may invoke greater punitive discretion. Race effects were also stronger in prisons with tighter security, where officers generally relied less on IPs’ acknowledgements of their formal authority for rule enforcement, and in facilities for men. Conclusions Variance in the magnitude of racial and ethnic disparities in the use of RH for rule violations makes sense across prison settings and, as opposed to general race and ethnicity effects, should guide our understanding of the sources of these disparities with the goal of reducing their impacts.
Objectives Brokers are said to be the oiling chain of illicit networks, facilitating the efficient flow of illicit products to destination. Yet, most of the available brokerage measures focus on local or individual networks, missing the brokers who connect others across communities, such as market levels. This study introduces a robust measure that uncovers, scores, and positions these community brokers. Methods We used network data aggregated from numerous investigations related to 1,800 criminal entrepreneurs operating in Western Canada. After uncovering the communities using the Leiden algorithm, we developed a community brokerage score that assesses individual potential reach and control at the meso level, and that accounts for individual position changes due to different community structures. We examined how the score relates to brokerage and structural hole measures as well as seriousness of involvement in criminality. Results We found that the illicit network studied has a strong and stable community structure, and community brokers form about 9% of the population. The score developed is statistically robust and is not strongly related to network and structural hole measures, which confirms the need for a novel measure that captures this strategic position in illicit and other networks. Conclusions Community brokers are especially important in illicit networks where large-scale covert coordination among criminal entrepreneurs is risky. The measure we propose is not overlapping with currently existing brokerage measures and has the potential to contribute to our understanding of how products and information flow beyond local networks, in criminology and other fields.
Paths between impulsivity and delinquency within peer networks
Objectives Drawing on criminological research about peer delinquency and self-control, we employ a network perspective to identify the potential paths linking impulsivity, peers, and delinquency. We systematically integrate relevant processes into a set of dynamic network models that evaluate these interconnected pathways. Methods Our analyses use data from more than 14,000 students in Pennsylvania and Iowa collected from the evaluation of the PROSPER partnership model. We estimate longitudinal social network models to disentangle the paths through which impulsivity and delinquency are linked in adolescent friendship networks. Results We find evidence of both peer influence and homophilic selection for both impulsivity and delinquency. Further, results indicate that peer impulsivity is linked to individual delinquent behavior through peer influence on delinquency, but not on impulsivity. Finally, the results suggest that impulsivity moderates both influence and selection processes, as adolescents with higher levels of impulsivity are more likely to select delinquent peers but less likely to change their behavior due to peers. Conclusions In sum, this study offers a more holistic framework and stronger theoretical tests than similar studies of the past. Our results illustrate the need to consider the simultaneous network processes related to peers, impulsivity, and delinquency. Further, our findings reveal that a large dataset with ample statistical power is a valuable advantage for detecting the selection processes that shape friendship networks.
Direct and indirect exposure. This figure shows how exposure is computed from a single ego’s point of view. Since ego observed two peers using their firearms, ego’s Direct exposure at December 2011 is two. At the same time, since none of ego’s peers in November 2011 had a history of firearm use, ego’s Indirect exposure at December 2011 is zero. With exposure accumulating in time, by May 2013—the last event observed in the data set—ego’s Direct and Indirect exposure amounts to eight and ten, respectively
Sequence of actions in an event. Several factors could influence officers’ actions: Who takes action first, what suspects do, and how their peers react
Observed data. Of the many parts that may give influence individuals’ actions, we only observe a handful. Furthermore, we do not know the order in which things occurred, how the subjects reacted, and whether other external factors played out
Objectives We reconstruct the networks of officers co-involved in force incidents to test whether interactions with weapon-prone peers impact firearm use. Methods We draw from a statewide dataset of force incidents across law enforcement agencies in New Jersey, and employ conditional likelihood models to estimate whether exposure to peers with histories of firearm use is associated with an officer’s own likelihood of firearm use net of other contextual confounders. Results We find preliminary evidence that officer firearm behaviors, including drawing, pointing, and discharging a firearm, is influenced by an officer’s peers. Greater exposure to colleagues with histories of firearm use is associated with a lower risk of using a firearm. We also find that officer features, including experience and race/ethnicity, are associated with the risk of firearm use. Conclusions Our study suggests officers’ peers structure the risk of firearm use. Our data allow us to look at time order and rule out situational confounders pertaining to firearm use; however, do not allow us to infer causality. We discuss the study’s implications for understanding firearm behaviors and the role of network science in moving policing research forward.
A correction to this paper has been published:
Fourteen pathways through juvenile justice system
Objectives To test the cumulative disadvantage hypothesis—that system-level racial and ethnic disparities accumulate from intake to final disposition—by investigating relative and absolute disparities across different pathways through the juvenile justice system. Methods Using a sample of 95,670 juvenile court referrals across 140 counties in four states, the present study employed multinomial logistic regression to examine racial and ethnic disparities across 14 possible combinations of juvenile justice outcomes (i.e., pathways), ranked from least to most punitive. We then estimated predicted probabilities and marginal effects of race and ethnicity for each pathway. Results We found limited support for the cumulative disadvantage hypothesis. Racial and ethnic disparities were greatest for the most punitive pathways, but the findings do not point to extensive evidence of cumulative disadvantage. Specifically, neither relative nor absolute disparities accumulated from least to most punitive pathways, and some of the least punitive pathways were actually more likely for minority defendants. Conclusions The results underscore the need for more careful measurement and analysis of disadvantage and disparities in the criminal and juvenile justice systems. In particular, more attention should be paid to early outcomes such as detention, where large differences between racial and ethnic groups were observed, as well as to relative and absolute differences in processing outcomes.
Time series plot, average count of non-criminal caretaking calls, pocket-dialed calls, and criminal activity calls per section per week, January 2014–December 2017
Spatial distribution of section-level negative Community–Police Relationship Index Score
Objectives Examine whether a death-in-police-custody incident affected community reliance on the police, as measured through citizen calls requesting police assistance for non-criminal caretaking matters. Methods This study used Baltimore Police Department (BPD) incident-level call data (2014–2017) concerning non-criminal caretaking matters (N = 234,781). Counts of non-criminal caretaking calls were aggregated by week for each of 279 unique sections derived from census-tract and police district boundaries. This study devised a Negative Community–Police Relationship Index Score that operationalized the expected risk of a negative community–police relationship for each of the sections. In April 2015, a Baltimore resident, Freddie Gray, died while in BPD custody. A Poisson regression model assessed whether this high-profile death-in-police-custody incident adversely affected the volume of non-criminal caretaking calls to the police and whether that effect was strongest in sections at a high risk of a negative community–police relationship. A falsification test used pocket-dialed emergency calls to verify that any observed trends were not the result of overall telephone usage. Results There was no statistical evidence that the death-in-police-custody incident produced any changes in community reliance on the police for non-criminal caretaking matters, even in high-risk sections. A supplemental analysis using calls for criminal matters yielded similar results. As the falsification test demonstrated, the observed trends were not the result of overall telephone usage. Conclusions Despite a divisive death-in-police-custody incident, citizens were still willing to enlist police assistance. More broadly, the caretaking role of the police may be an important mechanism to strengthen community–police relations, particularly in marginalized neighborhoods vulnerable to strained community–police relations.
Average female to male ratio, unadjusted
Objectives To provide quantitative attention to the correlates of the gender gap in illegal pay. Guided by the literatures on the gendered nature of offending, illegal earnings, and the gender gap in legal pay, we ask: what factors are associated with the gender gap in illegal pay? Methods We use the Delaware Decision Making Study, a sample of incarcerated offenders, to unpack the gender gap in illegal pay with the Blinder-Oaxaca decomposition technique. Results The gender gap in illegal pay is partly accounted for by criminal analogs—criminal capital and psychosocial attributes—to correlates for the gender gap in legal pay and differences in reward structures. Race also emerges as an important factor. Conclusions The disadvantage women face in the legal workforce extends to illegal markets, and our understanding about the gender gap in legal pay can be translated to criminal contexts.
Objectives Mass shootings seemingly lie outside the grasp of explanation and prediction, because they are statistical outliers—in terms of their frequency and severity—within the broader context of crime and violence. Innovative scholarship has developed procedures to estimate the future likelihood of rare catastrophic events such as earthquakes that exceed 7.0 on the Richter scale or terrorist attacks that are similar in magnitude to 9/11.Methods Because the frequency and severity of mass public shootings follow a distribution resembling these previously studied rare catastrophic event classes, we utilized similar procedures to forecast the future severity of these incidents within the United States.ResultsUsing a dataset containing 156 mass public shootings that took place in the U.S. between 1976 and 2018, we forecast the future probability of attacks reaching each of a variety of severity levels in terms of the number of gunfire victims killed and wounded across three different choices of tail model, three different scenarios for future incident rates, and other parameters. Using a set of mid-range parameters, we find that the probability of an event as deadly as the 2017 massacre in Las Vegas occurring before 2040 is 35% (90% uncertainty interval [8, 72]) and we characterize how this projection varies substantially with choice of modeling parameters.Conclusions Our results suggest an uncertain, but concerning, future risk of large-scale mass public shootings, while also illustrating how such forecasts depend on assumptions made about the tail location and other details of the severity distribution model.
Objectives Examines the neighborhood-level relationship between gang graffiti and gang violence in a large city in the western region of the US during a peak period of local gang feuds in 2014–15.Methods Bayesian Poisson log-linear mixed regression models with a spatio-temporal autoregressive process are estimated using a combination of data for N = 42,276 space–time units.ResultsConsistent with the view of graffiti an important means of street-level communication between gangs and an integral part of group processes associated with violence escalation and contagion, results reveal a roughly 40 to 60% increase in the expected rate of gang homicides, gang assaults, and gang firearm offenses (but not gang robberies) for each unit increase in neighborhood density of gang graffiti. Somewhat unexpectedly, the relationship with both gang homicide and gang assault was stronger for non-threatening gang graffiti than gang graffiti involving explicit threats or disrespect.Conclusions Findings suggest gang graffiti provides clear clues about local “staging grounds,” where gang status is on the line and violence is expected and easily provoked. Thus, while gangs increasingly are dissing rivals and airing beefs through music (e.g., “diss tracks”) and in cyberspace, many still occupy and defend turf and write graffiti that communicates threats to other gangs and feeds into group processes associated with violence escalation and contagion.
Objectives To examine how the practice of daily proactivity affects and responds to changes in crime at micro geographic and temporal scales. Methods Police calls for service and automated vehicle location data from a large suburban jurisdiction were used to create comprehensive measures of police proactivity. Panel data and the generalized method of moments framework were applied to tease out the endogenous relationships between crime and police proactivity and understand the unique impact of proactive patrol and crime upon one another. Results Daily police proactivity in this locality was highly stable at micro places, although police did intensify their activities very briefly in response to recent changes in crime. In turn, increases in proactive patrol generated immediate increases in crime reporting, followed by fleeting residual deterrent effects that were weaker and less robust. The patterns remained relatively consistent when varying the units of analysis or focusing on hot spots with different profiles of proactivity, but the deterrent effects appeared more sensitive to model specification. Of all measures of proactivity, patrols of medium length and non-traffic enforcement activities were associated with stronger evidence of crime reduction effects. Conclusions Short-term adjustments in hot spot patrols appear to produce both reporting effects and temporary residual deterrent effects as measured through calls for service and police vehicle location data. Police could potentially enhance and prolong their deterrence by adopting more deliberate strategies with their daily proactive behaviors, including making their proactive activities more targeted and sustained.
Area-based prevention studies often produce results that can be represented in a 2-by-2 table of counts. For example, a table may show the crime counts during a 12-month period prior to the intervention compared to a 12-month period during the intervention for a treatment and control area or areas. Studies of this type have used either Cohen’s d or the odds ratio as an effect size index. The former is unsuitable and the latter is a misnomer when used on data of this type. Based on the quasi-Poisson regression model, an incident rate ratio and relative incident rate ratio effect size and associated overdispersion parameter are developed and advocated as the preferred effect size for count-based outcomes in impact evaluations and meta-analyses of such studies.
An illustration of the variation in the concentration of crime across neighborhoods. a The distribution of composite Gini coefficients for all census tracts, with two neighborhoods with the same amount of crime but different levels of concentration highlighted. Below is a direct comparison of the distribution of violent events across the streets of the two neighborhoods b, d and corresponding maps c, e. Note that scales for numbers of crimes (x-axis and coloration) were kept consistent between the two tracts to enable easier comparison
Scatter plots and fit lines depicting the relationship between the concentration of crime in a census tract and a the level of diversity, calculated as a standardized combination of ethnic heterogeneity and income inequality, and b the proportion of commercial streets, each controlling for other variables included in the final model
Objectives Much recent work has focused on how crime concentrates on particular streets within communities. This is the first study to examine how such concentrations vary across the neighborhoods of a city. The analysis evaluates the extent to which neighborhoods have characteristic levels of crime concentration and then tests two hypotheses for explaining these variations: the compositional hypothesis, which posits that neighborhoods whose streets vary in land usage or demographics have corresponding disparities in levels of crime; and the social control hypothesis, which posits that neighborhoods with higher levels of collective efficacy limit crime to fewer streets. Methods We used 911 dispatches from Boston, MA, to map violent crimes across the streets of the city. For each census tract we calculated the concentration of crime across the streets therein using the generalized Gini coefficient and cross-time stability in the locations of hotspots. Results Neighborhoods did have characteristic levels of concentration that were best explained by the compositional hypothesis in the form of demographic and land use diversity. In addition, ethnic heterogeneity predicted higher concentrations of crime over and above what would be expected given the characteristics of the individual streets, suggesting it exacerbated disparities in crime. Conclusions The extent to which crime concentrates represents an underexamined aspect of how crime manifests in each community. It is driven in part by the diversity of places in the neighborhood, but also can be influenced by neighborhood-level processes. Future work should continue to probe the sources and consequences of these variations.
Left: number of OCG and non-OCG members active in the OC milieu at the time of each of the events in the data, for Δt=2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Delta t = 2$$\end{document} years. Right: number of OCG-instigated violent events by year
Victimization network for violence against a person aggregated over the period 2000 to 2016. The red nodes represent OCG members, the black nodes represent non-members
OCG-instigated violent event characteristics related to the history of the involved victims (e.g., for 35 events the victim had been a victim of violence prior to the violence event of interest)
Odds ratios with 95% confidence intervals for victim characteristics effects in Model 3, estimated on the OCG-instigated violent events (gray dots indicate non-significant effects)
Objective This study examines the mechanisms underpinning the emergence of violence among individuals in the organized crime milieu. Methods Relying on criminal event data recorded by a UK Police Force, we apply a longitudinal network approach to study violent interactions among offenders. The data span the period from 2000 to 2016 and include 6,234 offenders and 23,513 organized crime-related events. Instead of aggregating these data over time, we use a relational event-based approach to take into consideration the order of events. We employ an actor-oriented framework to model offenders’ victim choices in 156 violent events in the OC milieu. Results We find that the choice of offenders to target a particular victim is strongly affected by their mutual history. A violent act is often preceded by a previous act of violence, both in the form of repeated violence and reciprocated violence. We show that violence is strongly associated with prior co-offending turning sour. We uncover a strong effect for previous harassment as a retaliation cum escalation mechanism. Finally, we find evidence of conflicts within organized crime groups and of violence being directed to offenders with the same ethnic background. Conclusions Relational effects on victimization are consistently stronger than the effects of individual characteristics. Therefore, from a policy perspective, we believe that relational red flags (or risk factors) should play a more central role. A focus on harassment could be valuable in the development of an early intervention strategy.
Transitions from one profile of crime in disorder to another in the following year that were greater than expected by chance (quantified with odds ratios), based on 2-year Markov chains. Note: Graphical representation of the results of the 2-year Markov chains reported in full in Table 3. Lines are proportional to odds ratios with a ceiling at OR = 10 because of outliers. Parcel-years with no major issues were excluded as their transitions to or from all other profiles were not more likely than expected by chance
Pathways of aggravation to violent hubs and their mirror-image de-escalations, with likelihoods relevant to expectations by chance (quantified with odds ratios), based on 3-year Markov chains. Pathways are differentiated by their origination in concentrations of private neglect (dark gray) and gun-related events (light gray). Note: Graphical representation of results from the three-year Markov chains reported in full in Appendix C. Lines are proportional to odds ratios
Objectives Scholars and practitioners have paid increasing attention to problematic properties, but little is known about how they emerge and evolve. We examine four phenomena suggested by life-course theory that reflect stability and change in crime and disorder at properties: onset of issues; persistence of issues; aggravation to more serious types of issues; and desistance of issues. We sought to identify the frequency and dynamics of each. Methods We analyze how residential parcels (similar to properties) in Boston, MA shifted between profiles of crime and disorder from 2011 to 2018. 911 dispatches and 311 requests provided six measures of physical disorder, social disorder, and violence for all parcels. K-means clustering placed each parcel into one of six profiles of crime and disorder for each year. Markov chains quantified how properties moved between profiles year-to-year. Results Onset was relatively infrequent and more often manifested as disorder than violence. Pathways of aggravation led from less serious profiles to a mixture of violence and disorder. Desistance was more likely to occur as de-escalations along these pathways then complete cessation of issues. In neighborhoods with above-average crime, persistence was more prevalent whereas desistance less often culminated in cessation, even relative to local expectations. Conclusions The results offer insights for further research and practice attentive to trends of crime and disorder at problematic properties. It especially speaks to the understanding of stability and change; the role of different types of disorder; and the toolkit needed for problem properties interventions.
Monthly trends in night-time and day-time crimes in the Thames Valley region
Number of street segments with lighting interventions over the study period
Objectives This paper estimates the effect of changes in street lighting at night on levels of crime at street-level. Analyses investigate spatial and temporal displacement of crime into adjacent streets. Methods Offense data (burglaries, robberies, theft of and theft from vehicles, and violent crime) were obtained from Thames Valley Police, UK. Street lighting data (switching lights off at midnight, dimming, and white light) were obtained from local authorities. Monthly counts of crime at street-level were analyzed using a conditional fixed-effects Poisson regression model, adjusting for seasonal and temporal variation. Two sets of models analyzed: (1) changes in night-time crimes adjusting for changes in day-time crimes and (2) changes in crimes at all times of the day. Results Switching lights off at midnight was strongly associated with a reduction in night-time theft from vehicles relative to daytime (rate ratio RR 0.56; 0.41–0.78). Adjusted for changes in daytime, night-time theft from vehicles increased (RR 1.55; 1.14–2.11) in adjacent roads where street lighting remained unchanged. Conclusion Theft from vehicle offenses reduced in streets where street lighting was switched off at midnight but may have been displaced to better-lit adjacent streets. Relative to daytime, night-time theft from vehicle offenses reduced in streets with dimming while theft from vehicles at all times of the day increased, thus suggesting temporal displacement. These findings suggest that the absence of street lighting may prevent theft from vehicles, but there is a danger of offenses being temporally or spatially displaced.
Objectives To provide a detailed understanding of how the prevalence and frequency of offending vary with age in the Cambridge Study in Delinquent Development (CSDD) and to quantify the influence of early childhood risk factors such as high troublesomeness on this variation. Methods We develop a statistical model for the prevalence and frequency of offending based on the hurdle model and curves called splines that allow smooth variation with age. We use the Bayesian framework to quantify estimation uncertainty. We also test a model that assumes that frequency is constant across all ages. Results For 346 males from the CSDD for whom the number of offenses at all ages from 10 to 61 are recorded, we found peaks in the prevalence of offending around ages 16 to 18. Whilst there were strong differences in prevalence between males of high troublesomeness and those of lower troublesomeness up to age 45, the level of troublesomeness had a weaker effect on the frequency of offenses, and this lasted only up to age 20. The risk factors of low nonverbal IQ, poor parental supervision and low family income affect how prevalence varies with age in a similar way, but their influence on the variation of frequency with age is considerably weaker. We also provide examples of quantifying the uncertainty associated with estimates of interesting quantities such as variations in offending prevalence across levels of troublesomeness. Conclusions Our methodology provides a quantified understanding of the effects of risk factors on age-crime curves. Our visualizations allow these to be easily presented and interpreted.
Objectives We study interpretable recidivism prediction using machine learning (ML) models and analyze performance in terms of prediction ability, sparsity, and fairness. Unlike previous works, this study trains interpretable models that output probabilities rather than binary predictions, and uses quantitative fairness definitions to assess the models. This study also examines whether models can generalize across geographic locations. Methods We generated black-box and interpretable ML models on two different criminal recidivism datasets from Florida and Kentucky. We compared predictive performance and fairness of these models against two methods that are currently used in the justice system to predict pretrial recidivism: the Arnold PSA and COMPAS. We evaluated predictive performance of all models on predicting six different types of crime over two time spans. Results Several interpretable ML models can predict recidivism as well as black-box ML models and are more accurate than COMPAS or the Arnold PSA. These models are potentially useful in practice. Similar to the Arnold PSA, some of these interpretable models can be written down as a simple table. Others can be displayed using a set of visualizations. Our geographic analysis indicates that ML models should be trained separately for separate locations and updated over time. We also present a fairness analysis for the interpretable models. Conclusions Interpretable ML models can perform just as well as non-interpretable methods and currently-used risk assessment scales, in terms of both prediction accuracy and fairness. ML models might be more accurate when trained separately for distinct locations and kept up-to-date.
Objective To demonstrate how visualization can aid in understanding crime rate data and provide new insights and hypotheses for some central criminological questions about homicide offending over time. Methods The research uses arrest data that is based on a mixture of single year age data and other age groupings to produce single years of age estimates of homicide offending for those 15–64 from 1964 to 2019. This data is then presented in surface plots, contour plots, and with graphs based on simple statistics to address four areas of research: the increase in homicide rates from the mid-1960s to the mid-1970s, the drop in homicide offending from the early 1990s to 2000, the epidemic of youth homicide, and the invariance of the homicide age-curve. Results The epidemic of youth homicide (ages 15–24) lasts well into the period when homicide rates dropped in the 1990s. For most of the population (excluding those 15–24) the homicide drop is initiated around 1980 rather than the early 1990s. However, the homicide increase of mid-1960s to mid-1970s included increases in the homicide rate for both those 15–24 and those not in the 15–24 age group. Conclusions Researchers can learn much about important areas in criminology by examining the relationship between age and homicide offending using simple visualizations based on raw data and pursue what they learn using line graphs based on elementary statistics and simple statistical methods. Of course, complicated statistical methods are called for in many situations.
The multiplex network structure for the organized crime model
General Structure of the Organized Crime Model
Dependent variables. Average values per round of simulation and per policy scenario
Objectives: We test the effects of four policy scenarios on recruitment into organized crime. The policy scenarios target (i) organized crime leaders and (ii) facilitators for imprisonment, (iii) provide educational and welfare support to children and their mothers while separating them from organized-crime fathers, and (iv) increase educational and social support to at-risk schoolchildren. Methods: We developed a novel agent-based model drawing on theories of peer effects (differential association, social learning), social embeddedness of organized crime, and the general theory of crime. Agents are simultaneously embedded in multiple social networks (household, kinship, school, work, friends, and co-offending) and possess heterogeneous individual attributes. Relational and individual attributes determine the probability of offending. Co-offending with organized crime members determines recruitment into the criminal group. All the main parameters are calibrated on data from Palermo or Sicily (Italy). We test the effect of the four policy scenarios against a baseline no-intervention scenario on the number of newly recruited and total organized crime members using Generalized Estimating Equations models. Results: The simulations generate realistic outcomes, with relatively stable organized crime membership and crime rates. All simulated policy interventions reduce the total number of members, whereas all but primary socialization reduce newly recruited members. The intensity of the effects, however, varies across dependent variables and models. Conclusions: Agent-based models effectively enable to develop theoretically driven and empirically calibrated simulations of organized crime. The simulations can fill the gaps in evaluation research in the field of organized crime and allow us to test different policies in different environmental contexts.
Objectives I draw on general strain theory, a framework often used to understand adolescent behavior, and augment it with aspects of the stress process perspective to examine the time-varying consequences of paternal incarceration for adolescent behavior. Methods I use six waves of data from the Fragile Families and Child Wellbeing Study, a cohort of children born around the turn of the twenty-first century, and inverse probability of treatment weighting models to estimate the time-varying relationship between paternal incarceration and adolescent behavior problems and the mechanisms underlying this relationship. Results Results document three main findings. First, adolescents exposed to paternal incarceration at any point in the life course have more behavior problems than their counterparts not exposed to paternal incarceration. Second, exposure to paternal incarceration during early childhood, but not during middle childhood or early adolescence, is positively associated with behavior problems. Third, this relationship is partially explained by family adversities stemming from paternal incarceration. Conclusions This research builds on our criminological understanding of how strains, such as paternal incarceration, can facilitate inequalities in adolescent behavior by considering dynamic selection into paternal incarceration, the time-varying repercussions of paternal incarceration, and the mechanisms linking paternal incarceration to adolescent behavior. Early life course paternal incarceration facilitates chains of adversity that accumulate throughout early childhood, middle childhood, and adolescence.
Comparison of incident count in supplementary homicide reports versus Fatal Encounters: gunshots only
Comparison of incident count in supplementary homicide reports versus Fatal Encounters: all intentional use of force homicides
Comparison of incident count in supplementary homicide reports versus Fatal Encounters: all police-related-deaths
NY State police instructions for “justifiable homicides” as officer/victim
IntroductionThe most widely used data set for studying police homicides—the Supplementary Homicide Reports (SHR) kept by the Federal Bureau of Investigation—is collected from a voluntary sample.Materials and Methods Using a journalist-curated database of police-related deaths, we find the SHR police homicide data to be substantially incomplete. This is due to both non-reporting and substantial under-reporting by agencies. Further, our inquiry discloses a pattern of error in identifying “victims” and “offenders” in the data, and finds that investigating agencies are often incorrectly listed as the responsible agency, which seriously jeopardizes police department-level analyses. Finally, there is evidence of sample bias such that the SHR data system is not representative of all police departments, nor is it representative of large police departments.Conclusions We conclude that the SHR data is of dubious value for assessing correlates of police homicides in the United States, as all analyses using it will reflect these widespread biases and significant undercounts. Analysis of SHR data for these purposes should cease.
Monthly frequency of offending
Average illegal earnings for property and market crimes
Objectives We examine how responsive offenders are to illegal monetary incentives. We draw from rational choice theory, prospect theory, and models of labor supply to develop expectations regarding the relationship between criminal efficiency, which is the average earnings per offense, and frequency of offending.Methods We use OLS, fixed effects, and first-difference estimators to analyze data from 152 incarcerated male inmates from Quebec, Canada to study within individual monthly changes in criminal efficiency and offending frequency.ResultsThere is an inverse relationship between criminal efficiency and frequency of offending, net of individual fixed effects, for market crimes, but not property crimes. We also find that the supply of crime is inelastic, meaning it is not highly sensitive to illegal wage changes.Conclusions In the months that offenders have an average bigger pay-off per crime, they offended less frequently. We conjecture that this negative relationship could be explained by two mechanisms: an income effect and/or through reference dependence. However, we are not able to disentangle between the two mechanisms. Moreover, we note that criminal efficiency is likely endogenous and should be treated as such in future scholarship.
Light towers in the field. Credit: Ruddy Roye;
Relationship between lighting and outdoor nighttime index crimes. Note: Plots illustrate the relationship between the natural logarithm of the number of nighttime outdoor index crimes for the March through August 2016 study period and the natural logarithm of each housing development’s randomly assigned number of additional lights per square block (treatment dosage). Panel A considers crimes that occurred on the development’s campus, Panel B considers crimes that occurred within a 550 foot catchment area of the development and Panel C considers crimes that occurred either on the development’s campus or in the catchment area. Each hollow circle represents one of the N = 40 treatment sites with the size of the circle corresponding to the official population of the development. The dashed line represents a linear regression line through the data. A few sites did not experience any crimes over the study period. An approximation to the log value for these data points is obtained using a parametric correction suggested by Chalfin and McCrary (2018). These data points are denoted by a plus sign enclosed within the hollow circle
Robustness of Estimated Treatment Effects to Alternative Sets of Control Variables. Note: Histograms report estimated treatment effects from a series of Poisson regressions of outdoor nighttime index crimes for the March through August 2016 study period on the natural logarithm of each housing development’s randomly assigned number of additional lights per square block (treatment dosage). Each model controls for population plus an additional random set of covariates (between 1 and 8). Covariates are drawn from a pool of aggregate crime counts and development demographics. For the full list of potential covariates, see Online Appendix Table 1, excluding the individual annual crime counts. In order to test the robustness of model estimates to our choice of control variables, we randomly sampled from among candidate control variables, drawing 5,000 samples
Objectives This paper offers novel experimental evidence that violent crimes can be successfully reduced by changing the situational environment that potential victims and offenders face. We focus on a ubiquitous but understudied feature of the urban landscape—street lighting—and report the first experimental evidence on the effect of street lighting on crime. Methods Through a unique public partnership in New York City, temporary street lights were randomly allocated to 40 of the city’s public housing developments. Results We find evidence that communities that were assigned more lighting experienced sizable reductions in nighttime outdoor index crimes. We also observe a large decline in arrests indicating that deterrence is the most likely mechanism through which the intervention reduced crime. Conclusion Results suggests that street lighting, when deployed tactically, may be a means through which policymakers can control crime without widening the net of the criminal justice system.
Interaction effect: Presence of a train station and concentration of violence in the home territory. Notes: The figure demonstrates the outcome for the fifth interpolated dataset for income. We note the confidence intervals suggest that while the results are statistically significant, the effect is minor and should be interpreted with caution
Objectives Living in close proximity to recent, violent crime may undermine sense of safety in the home territory by increasing perceived crime risk. Yet it is also possible that practicing active guardianship by responding to local problems will moderate this association by reducing perceived vulnerability to crime. In this study we examine the association between residents’ proximity to recent violence, perceived safety and the moderating effect of active guardianship. Methods Controlling for individual characteristics and features of the individual’s home territory, we estimate mixed effects regression models to investigate the effect of proximity to violence and active guardianship on feelings of safety. We also examine the moderating effect of active guardianship. Results The findings indicate that individuals living in closer proximity to recent violence feel less safe and those who report taking action when they observe local problems feel safer than those who do not engage in guardianship action. Despite the direct association between active guardianship and feeling safer, active guardianship did not moderate the association between proximity to violence and feelings of safety. Conclusions While this study cannot ascertain temporal ordering, the findings suggest people who feel safer are more likely to engage in active guardianship, rather than active guardianship leading to reduced vulnerability. On a promising note, the direct association between active guardianship and feeling safer suggests that empowering residents via grass-roots crime prevention strategies has the potential to benefit communities by both addressing crime and improving perceived safety.
Objectives Although previous studies have theorized the importance of physical and social boundaries (edges) in understanding crime in place, the relationship between edges and the level of crime has been less studied empirically. The current study examines the effects of physical and social boundaries on crime in street segments. Methods To empirically measure boundaries, we introduce an approach of looking at the differences of land use (physical boundary), socioeconomic status, or racial composition (social boundaries) on both sides of a street segment. We estimated a series of negative binomial regression models in which measures of the physical and social boundaries are included while controlling for the effects of structural characteristic and the conventional physical boundary measures of highways, parks, and rivers. Results We observed that there are positive relationships between all three of these boundary measures and violent and property crimes. The results indicated that physical and social boundaries are important to consider in understanding the spatial patterns of crime. Moreover, the current study confirmed the moderating effects between social and physical boundaries. Conclusions Our results indicate that although much empirical research focuses solely on physical boundaries, our measures of social and physical boundaries have important consequences for the spatial location of crime, and therefore are worthy of further research.
Objectives Longitudinal studies from the criminology of place suggest crime hot spots are repeatedly found in the same locations within cities over extended periods of time. Program evaluations of hot spots policing interventions often use much shorter temporal windows to define hot spots. This study examines if stability of patterns is still found when using short and intermediate periods of time to measure crime hot spots. Methods We examined 765,235 total crime incidents reported to the Cincinnati Police Department from 1997 to 2016. These incidents were geocoded to 13,189 street segments. We created measures of crime hot spots based on varying temporal periods using three different strategies: pooled observations, group-based trajectory modeling, and k-means clustering. These measures were compared using techniques associated with survival analyses to determine the influence of temporal specification on the retrospective identification of crime hot spots. Results Our findings suggest regardless of the temporal specification, most street segments identified as crime hot spots remained crime hot spots across the observed follow-up periods. There was still much variability in patterns based upon temporal specifications and the use of additional years of incident report data did not uniformly provide an improved understanding of which street segments remained crime hot spots. Conclusions Program evaluations of hot spots policing strategies do not need to use extended periods of time to observe stability in crime hot spots. The criminology of place should provide more attention to the topic of temporal specification and continue exploring the utility of crime hot spots.
Predicted Assets, Debt, and Net Worth at Ages 20, 25, and 30 among Respondents who Were and Were Not Arrested as Juveniles
Objectives We tested the impact of juvenile arrest on asset accumulation, debt accumulation, and net worth from ages 20–30. We also examined whether indicators of family formation, school and work attainment, and subsequent justice system contacts explained any effects. Methods We used longitudinal data on 7916 respondents from the National Longitudinal Survey of Youth 1997 Cohort. Our treatment variable was a dichotomous indicator of whether respondents were arrested as juveniles. Our focal outcomes were combined measures of the values of 10 types of assets, 6 types of debt, and net worth (assets minus debt) at ages 20, 25, and 30. We used propensity score methods to create matched groups of respondents who were and were not arrested as juveniles, and we compared these groups on the outcomes using multilevel growth curve analyses. Results Arrested juveniles went on to have lower assets, debts, and net worth during young adulthood compared to non-arrested juveniles. These differences were most pronounced at age 30. The differences were largely explained by educational attainment, weeks worked, and income. Conclusions The fact that juvenile arrest predicted early adult economic attainment net of 43 matching covariates provides strong evidence that these effects are not merely artifacts of selection. The additional finding that education, employment, and income explain much of the juvenile arrest effect highlights several potential areas of intervention for protecting young arrestees’ later net worth.
Objectives Prior meta analyses of hot spots policing show that the approach reduces crime, but report relatively small mean effect sizes based on Cohen’s d. The natural logarithm of the relative incidence rate ratio (log RIRR) has been suggested as a more suitable effect size metric for place-based studies that report crime outcomes as count data. We calculate the log RIRR for hot spots policing studies to assess whether it changes interpretation of hot spots policing’s impact on crime. Methods Cohen’s d and log RIRR effect size metrics were calculated for 53 studies representing 60 tests of hot spots policing programs. Meta-analytic techniques were used to compare the estimated impacts of hot spots policing on crime and investigate the influence of moderating variables using the two differing effect size metrics. Results The Cohen’s d meta-analysis revealed a “small” statistically significant mean effect size favoring hot spots policing in reducing crime outcomes at treatment places relative to control places (d = .12) of approximately 8.1%. In contrast, the log RIRR meta-analysis suggests that hot spots policing generated a more substantive 16% (d = .24) statistically significant crime reduction. The two metrics also produced differing rank orders in magnitudes of effect for the same studies. Conclusion Cohen’s d provides misleading results when used to calculate mean effect size in place based studies both in terms of the relative ranking of the magnitude of study outcomes, and in the interpretation of average impacts of interventions. Our analyses suggest a much more meaningful impact of hot spots policing on crime than previous reviews.
SAT’s principles of “moral correspondence” and of the “conditional relevance of controls” (figure adapted from Wikström, 2010, 233, Fig. 12.4., see also Kammigan 2017, 51, Fig. 5)
Expected interactions between self-control and its moral conditions in SAT
Expected effects of self-control when the role of (a measure of) morality is interpreted differently: (1) Morality enhances effects of self-control, (2) morality dampens effects of self-control, (3) morality enhances and dampens effects of self-control (nonlinear relationship)
Self-reported offending predicted by self-control at medium and high morality (logged predictions, based on the models in Tables 3 and 4)
Self-reported offending predicted by self-control at medium and high morality (predictions based on the models in Tables 3 and 4)
Objectives. Pointing to issues that pose a threat to the falsifiability of Situational Action Theory’s (SAT) assumptions on the interaction between self-control and morality on crime: Despite a theoretical ambiguity, SAT most likely predicts that morality enhances the effects of self-control. But morality’s correlation with setting rules opens up the possibility to interpret the common finding of dampening interactions also in line with SAT. Further, it is even possible to generate opposing interactions, depending on whether the conditional effects of self-control are modeled in absolute or relative terms. Suggestions to overcome these problems will be applied to an empirical test of the interaction. Methods. Analyses are based on survey data from n = 66,395 students in 28 countries that participated in the ISRD-3 study. Multilevel negative binomial regressions of each of four measures of self-reported offending on self-control, morality, and their interaction are fitted, alternatively with and without adjusting for respondents’ low risk of exposure to criminogenic settings. As proposed, the types of conditional effects of self-control answer different research questions and are thus chosen accordingly. Results. In line with SAT, morality enhances relative effects of self-control, even after adjusting for low exposure risk. Morality further dampens absolute effects of selfcontrol, but after adjusting, this interaction becomes considerably smaller, indicating that it was indeed partly caused by morality’s correlation with setting moral rules. Conclusions. SAT is most likely not inherently unfalsifiable, and certainly not empirically untestable if researchers model their analyses carefully.
Covariate-specific risk of police misconduct (5% decay factor) for the Dallas Police Department (2013–2014). Parameter estimates β^\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\beta }$$\end{document} (log intensity ratios; bullets) in descending order and 95% confidence intervals from a repeated-events survival model of days until a Dallas police officer engages in sanctioned police misconduct alongside the estimated variance of the gamma-distributed frailty parameters capturing officer-specific excess risk (inset, above). Note, when reading p values, e\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$e$$\end{document} symbolizes base-10 scientific notation such that p=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p = $$\end{document} 8.97e−05 =\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ =$$\end{document} 8.97 ×10-5=\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times 10^{ - 5} =$$\end{document} 0.0000897
Risk of police misconduct for the Dallas Police Department (2013–2014) associated with Calls with Deviant/Non-Deviant Colleagues using decay factors from 5 to 50%, identical control variables, and identical risk intervals (see Fig. 1)
Officer-specific excess risk of police misconduct for the Dallas Police Department (2013–2014) based on the repeated-events survival model depicted in Fig. 1
Objectives Understanding if police malfeasance might be “contagious” is vital to identifying efficacious paths to police reform. Accordingly, we investigate whether an officer’s propensity to engage in misconduct is associated with her direct, routine interaction with colleagues who have themselves engaged in misbehavior in the past. Methods Recognizing the importance of analyzing the actual social networks spanning a police force, we use data on collaborative responses to 1,165,136 “911” calls for service by 3475 Dallas Police Department (DPD) officers across 2013 and 2014 to construct daily networks of front-line interaction. And we relate these cooperative networks to reported and formally sanctioned misconduct on the part of the DPD officers during the same time period using repeated-events survival models. Results Estimates indicate that the risk of a DPD officer engaging in misconduct is not associated with the disciplined misbehavior of her ad hoc, on-the-scene partners. Rather, a greater risk of misconduct is associated with past misbehavior, officer-specific proneness, the neighborhood context of patrol, and, in some cases, officer race, while departmental tenure is a mitigating factor. Conclusions Our observational findings—based on data from one large police department in the United States—ultimately suggest that actor-based and ecological explanations of police deviance should not be summarily dismissed in favor of accounts emphasizing negative socialization, where our study design also raises the possibility that results are partly driven by unobserved trait-based variation in the situations that officers find themselves in. All in all, interventions focused on individual officers, including the termination of deviant police, may be fruitful for curtailing police misconduct—where early interventions focused on new offenders may be key to avoiding the escalation of deviance.
Research area and theft spatial pattern for the full week
The spatial pattern of different ambient population for the full week
Objectives The residential population of an area is an incomplete measure of the number of people that are momentarily present in the area, and of limited value as an indicator of exposure to the risk of crime. By accounting for the mobility of the population, measures of ambient population better reflect the momentary presence of people. They have therefore become an alternative indicator of exposure to the risk of crime. This study considers the heterogeneity of the ambient population by distinguishing residents, employees and visitors as different categories, and explores their differential impact on thefts, both on weekdays and weekends.Methods We analyze one-year of police recorded thefts across 2104 1 km2 grid cells in a central area in Beijing, China. Controlling for the effects of attractiveness, accessibility, and guardianship, we estimate a series of negative binominal models to investigate the differential effects of the three groups (residents, employees and visitors) in the ambient population on crime frequencies, both on weekdays and during weekends and holidays.ResultsOverall, larger ambient populations imply larger theft frequencies. The effect of visitors is stronger than the effects of residents and employees. The effects of residents and employees vary over the course of the week. On weekdays, the presence of residents is more important, while the reverse holds true during weekends and holidays.DiscussionThe effects of ambient population on thefts vary by its composition in terms of social roles. The larger role of visitors is presumably because in addition to being potential victims, residents and employees may also exercise informal social control. In addition, they spend more time indoors than where risk of theft is lower, while visitors might spend more time outdoors and may also bring about greater anonymity and weaken informal social control.
Objectives Research consistently shows that crime concentrates on a few repeatedly victimized places and targets. In this paper we examine whether the same is true for extortion against businesses. We then test whether the factors that explain the likelihood of becoming a victim of extortion also explain the number of incidents suffered by victimized businesses. The alternative is that extortion concentration is a function of event dependence. Methods Drawing on Mexico’s commercial victimization survey, we determine whether repeat victimization occurs by chance by comparing the observed distribution to that expected under a Poisson process. Next, we utilize a multilevel negative binomial-logit hurdle model to examine whether area- and business-level predictors of victimization are also associated with the number of repeat extortions suffered by businesses. Results Findings suggest that extortion is highly concentrated, and that the predictors of repeated extortion differ from those that predict the likelihood of becoming a victim of extortion. While area-level variables showed a modest association with the likelihood of extortion victimization, they were not significant predictors of repeat incidents. Similarly, most business-level variables significantly associated with victimization risk showed insignificant (and sometimes contrary) associations with victimization concentration. Overall, unexplained differences in extortion concentration at the business-level were unaffected by predictors of extortion prevalence. Conclusions The inconsistent associations of predictors across the hurdle components suggest that extortion prevalence and concentration are fueled by two distinct processes, an interpretation congruent with theoretical expectations regarding extortion that considers that repeats are likely fueled by a process of event dependence.
a Estimated Black-White Disparities in Police UOF by Department and Coding Scheme, Population Benchmark, Relative Risk (Ratio) Measure, b Estimated Black-White Disparities in Police UOF by Department and Coding Scheme, Population Benchmark, Relative Risk (Ratio) Measure, Axes Distinct (Color figure online)
Estimated Black-White Disparities in Police UOF by Department and Coding Scheme, Arrest Benchmark, Relative Risk (Ratio) Measure (Color figure online)
Estimated Nonwhite-White Disparities in Police UOF by Department and Coding Scheme, Population Benchmark, Relative Risk (Ratio) Measure (Color figure online)
Estimated Nonwhite-White Disparities in Police UOF by Department and Coding Scheme, Arrest Benchmark, Relative Risk (Ratio) Measure (Color figure online)
Objectives To understand the impact of measurement and analytic choices on assessments of police use of force (UOF) and racial disparities therein.Methods We collected and standardized UOF data (N = 9982 incidents) from a diverse set of 11 police departments, and measured departments’ aggregate force severity in five ways. We assessed the sensitivity of racial disparities in UOF severity to a series of analytic choices, using a 5 × 2 × 2 × 2 design comparing force severity to population and arrest benchmarks, using two definitions of minority group (Black/Nonwhite), and two modes of comparison (ratios/differences).ResultsSignificant racial disparities were observed under most analytic choices in most departments. However, lethal force was rare, and estimates of lethal force disparities were statistically uncertain, as were departments’ relative ranks as equitable or disparate. Ratios of minority to White force severity were less sensitive to measurement differences within measures including nonlethal force. The choice of a population or arrest benchmark had implications for which departments emerged as highly disparate, while focal minority group and mode of comparison had less systematic effects.Conclusions Given increased scrutiny of police activity by advocates and policymakers, it is important to understand how measurement and other analytic choices affect our understanding of equity in police practices. Our findings demonstrate that analytical decisions interact in complex ways and that standardization is essential when comparing multiple departments. We recommend comprehensive data collection that includes nonlethal as well as lethal force, and make recommendations for measuring and contextualizing racial disparities in UOF and other police activity.
Top-cited authors
David L. Weisburd
  • Hebrew University of Jerusalem
Shane D Johnson
  • University College London
Jeff Rojek
  • University of South Carolina
Scott E. Wolfe
  • Michigan State University
Robert J. Kaminski
  • University of South Carolina