CAN HOT SPOTS POLICING REDUCE CRIME IN URBAN AREAS? AN AGENT‐BASED SIMULATION*

Over the past two decades, there has been a growing consensus among researchers that hot spots policing is an effective strategy to prevent crime. Although strong evidence exists that hot spots policing will reduce crime at hot spots without immediate spatial displacement, we know little about its possible jurisdictional or large‐area impacts. We cannot isolate such effects in previous experiments because they (appropriately) compare treatment and control hot spots within large urban communities, thus, confounding estimates of area‐wide impacts. An agent‐based model is used to estimate area‐wide impacts of hot spots policing on street robbery. We test two implementations of hot spots policing (representing different levels of resource allocation) in a simulated borough of a city, and we compare them with two control conditions, one model with constant random patrol and another with no police officers. Our models estimate the short‐ and long‐term impacts on large‐area robbery levels of these different schemes of policing resources. These experiments reveal statistically significant effects for hot spots policing beyond both a random patrol model and a landscape without police. These simulations suggest that wider application of hot spots policing can have significant impacts on overall levels of crime in urban areas.     

School Vandalism in Mexico

Mexico, like other countries in Latin America, is currently facing significant problems with regard to juvenile antisocial behavior and crime. The current study explores some of the factors associated with school vandalism in public high schools. Multilevel modeling with survey data from 22,345 students from 249 schools, acquired over two years, was used to determine how much variation in the rates of school vandalism exists between and within schools, and to detect individual and contextual characteristics that explain significant proportions of the variance. Results demonstrated significant differences across schools in the rates of student vandalism, most of which were associated with gang involvement, drug use, bullying, no attachment to school rules, and unequal treatment of students by teachers. These results are discussed in relation to the literature on school vandalism, and implications for the prevention of school vandalism are discussed.     

Preparation of Tool Mark Standards with Jewelry Modeling Waxes

Abstract:  This paper presents how jewelry modeling waxes are used in the preparation of tool mark standards from exemplar tools. We have previously found that jewelry modeling waxes are ideal for preparing test tool marks from exemplar tools. In this study, simple methods and techniques are offered for the replication of accurate, highly detailed tool mark standards with jewelry modeling waxes. The techniques described here demonstrate the conditioning and proper use of jewelry modeling wax in the production of tool mark standards. The application of each test tool's working surface to a piece of the appropriate wax in a manner consistent with the tool's design is clearly illustrated. The resulting tool mark standards are exact, highly detailed, 1:1, negative impressions of the exemplar tool's working surface. These wax models have a long shelf life and are suitable for use in microscopic examination comparison of questioned and known tool marks.     

Contrasting simulated and empirical experiments in crime prevention

This paper argues that simulated experiments of crime prevention interventions are an important class of research methods that compare favorably with empirical experiments. It draws on Popper’s demarcation between science and non-science (Conjectures and refutations: the growth of scientific knowledge. Routledge, London, 1992) and Epstein’s principle of generative explanation (Generative social science: studies in agent-based computational modeling. Princeton University Press, Princeton, NJ, 2006) to show how simulated experiments can falsify theory. The paper compares simulated and empirical experiments and shows that simulations have strengths that empirical methods lack, but they also have important relative weaknesses. We identify three threats to internal validity and two forms of external validity peculiar to simulated experiments. The paper also looks at the problem of validating simulations with crime data and suggests that simulations need to mimic the error production processes involved in the creation of empirical data. It concludes by listing ways simulations can be used to improve empirical experiments and discussing the differing operating assumption of empirical and simulation experimentalists.

Analyzing Criminal Trajectory Profiles: Bridging Multilevel and Group-based Approaches Using Growth Mixture Modeling

Over the last 25 years, a life-course perspective on criminal behavior has assumed increasing prominence in the literature. This theoretical development has been accompanied by changes in the statistical models used to analyze criminological data. There are two main statistical modeling techniques currently used to model longitudinal data. These are growth curve models and latent class growth models, also known as group-based trajectory models. Using the well known Cambridge data and the Philadelphia cohort study, this article compares the two “classical” models—conventional growth curve model and group-based trajectory models. In addition, two growth mixture models are introduced that bridge the gap between conventional growth models and group-based trajectory models. For the Cambridge data, the different mixture models yield quite consistent inferences regarding the nature of the underlying trajectories of convictions. For the Philadelphia cohort study, the statistical indicators give stronger guidance on relative model fit. The main goals of this article are to contribute to the discussion about different modeling techniques for analyzing data from a life-course perspective and to provide a concrete step-by-step illustration of such an analysis and model checking.

A fresh look at a policy sciences methodology: collaborative modeling for more effective policy

Collaborative modeling offers a novel methodology that integrates core ideals in the policy sciences. The principles behind collaborative modeling enable policy researchers and decision makers to address interdisciplinarity, complex systems, and public input in the policy process. This approach ideally utilizes system dynamics to enable a multidisciplinary group to explore the relationships in a complex system. We propose that there is a spectrum of possibilities for applying collaborative modeling in the policy arena, ranging from the purely academic through full collaboration among subject matter experts, the general public, and decision makers. Likewise, there is a spectrum of options for invoking collaboration within the policy process. Results from our experiences suggest that participants in a collaborative modeling project develop a deeper level of understanding about the complexity in the policy issue being addressed; increase their agreement about root problems; and gain an appreciation for the uncertainty inherent in data and methods in studying complex systems. We conclude that these attributes of collaborative modeling make it an attractive option for improving the decision-making process as well as on-the-ground decisions.     

Using group-based trajectory modeling in conjunction with propensity scores to improve balance

This paper builds upon two prior papers by Haviland and Nagin (Psychometrika 70:1–22, 2005) and Haviland, Nagin, and Rosenbaum (Working paper, 2006) that attempt to bring the key attributes of an experiment to the analysis of non-experimental longitudinal data. Using a case study of the facilitation effect of gang membership on violence, it systematically examines the contribution of group-based trajectory modeling to the achievement of covariate balance in observational data. In this case study, inclusion of the posterior probabilities of group membership (PPGM), from a model on the pre-treatment measures of the outcome variable, created closer balance on these key covariates than did analyses that did not include them. Still closer balance was obtained on these key covariates by stratifying the analysis by trajectory group. This stratification was achieved by fitting separate propensity score models and matching gang joiners to gang abstainers within trajectory group. In addition, we demonstrated that further balance could be obtained on additional covariates by including PPGM from a model on pre-treatment longitudinal data of these covariates. While this case study is only one empirical example, we believe that it provides useful empirical evidence on the value of performing within trajectory group causal inference in observational longitudinal data and on the use of the PPGM in achieving balance in propensity score-based causal inference.

Analyzing Approaches to the Backlog of Untested Sexual Assault Kits in the U.S.A.

Motivated by the debate over how to deal with the huge backlog of untested sexual assault kits in the U.S.A., we construct and analyze a mathematical model that predicts the expected number of hits (i.e., a new DNA profile matches a DNA sample in the criminal database) as a function of both the proportion of the backlog that is tested and whether the victim–offender relationship is used to prioritize the kits that are tested. Refining the results in Ref. (Criminol Public Policy, 2016, 15, 555), we use data from Detroit, where government funding was used to process ≈15% of their backlog, to predict that prioritizing stranger kits over nonstranger kits leads to only a small improvement in performance (a 0.034 increase in the normalized area under the curve of the hits vs. proportion of backlog tested curve). Two rough but conservative cost‐benefit analyses—one for testing the entire backlog and a marginal one for testing kits from nonstranger assaults—suggest that testing all sexual assault kits in the backlog is quite cost‐effective: for example, spending ≈$1641 to test a kit averts sexual assaults costing ≈$133,484 on average.     

RACE,ETHNICITY, THREAT AND THE LABELING OF CONVICTED FELONS*

Florida law allows judges to withhold adjudication of guilt for persons who have either pled guilty or been found guilty of a felony. This provision may apply only to persons who will be sentenced to probation, and it allows such individuals to retain all civil rights and to truthfully assert they had not been convicted of a felony. This paper examines the effects of race and Hispanic ethnicity on the withholding of adjudication for 91,477 males sentenced to probation in Florida between 1999 and 2002. Hierarchical Generalized Linear Modeling is used to assess the direct effects of defendant attributes as well as the cross‐level interactions between race, ethnicity and community level indicators of threat, such as percentage black and Hispanic and concentrated disadvantage. Our results show that Hispanics and blacks are significantly less likely to have adjudication withheld when other individual and community level factors are controlled. This effect is especially pronounced for blacks and for drug offenders. Cross‐level interactions show that concentrated disadvantage has a substantial effect on the adjudication withheld outcome for both black and Hispanic defendants. The implications of these results for the conceptualization of racial/ethnic threat at the individual, situational and social levels are discussed.     

SENTENCING IN CONTEXT: A MULTILEVEL ANALYSIS

Criminal sentencing is, along with arresting and prosecuting, among the most important of formal social control decisions. In this study we use hierarchical modeling to test hypotheses about contextual level influences and cross level interaction effects on local court decisions. Most of the explanatory "action," our analysis shows, is at the individual case level in criminal sentencing. We also find evidence that local contextual features–such as court organizational culture, court caseload pressure, and racial and ethnic composition–affect sentencing outcomes, either directly or in interaction with individual factors. We conclude by discussing theoretical implications of our findings, and how our study points out some dilemmas among civil rights, local autonomy and organizational realities of criminal courts.