The Efficacy of Ideographic Models for Geographical Offender Profiling

Objectives

Current ‘geographical offender profiling’ methods that predict an offender’s base location from information about where he commits his crimes have been limited by being based on aggregate distributions across a number of offenders, restricting their responsiveness to variations between individuals as well as the possibility of axially distorted distributions. The efficacy of five ideographic models (derived only from individual crime series) was therefore tested.

Methods

A dataset of 63 burglary series from the UK was analysed using five different ideographic models to make predictions of the likely location of an offenders home/base: (1) a Gaussian-based density analysis (kernel density estimation); (2) a regression-based analysis; (3) an application of the ‘Circle Hypothesis’; (4) a mixed Gaussian method; and (5) a Minimum Spanning Tree (MST) analysis. These tests were carried out by incorporating the models into a new version of the widely utilised Dragnet geographical profiling system DragNetP. The efficacy of the models was determined using both distance and area measures.

Results

Results were compared between the different models and with previously reported findings employing nomothetic algorithms, Bayesian approaches and human judges. Overall the ideographic models performed better than alternate strategies and human judges. Each model was optimal for some crime series, no one model producing the best results for all series.

Conclusions

Although restricted to one limited sample the current study does show that these offenders vary considerably in the spatial distribution of offence location choice. This points to important differences between offenders in the morphology of their crime location choice. Mathematical models therefore need to take this into account. Such models, which do not draw on any aggregate distributions, will improve geographically based investigative decision support systems.     

The effect of imprisonment on recommitment: an analysis using exact,coarsened exact,and radius matching with the propensity score

Objectives

This study examines the effect of prison versus community sanctions on recommitment to prison and compares two levels of community supervision, community control (house arrest) and probation, evaluating whether the findings are contingent on the type of matching methods used in the analysis.

Methods

Logistic regression was conducted on unmatched and matched samples. Exact, coarsened exact, and radius-matching procedures were used to create a selection on observables design. Matching variables included current offense, demographics, criminal history, supervision violations, and a rich set of Florida Sentencing Guidelines information culled from an official scoring sheet. Florida judges use this instrument to sentence offenders within the framework of the state determinate sentencing system.

Results

The results show that with exact matching, there is no effect of imprisonment on recommitment, while the other procedures suggest a specific deterrent effect of imprisonment. All four analysis methods showed that offenders under community control are more likely to reoffend than those under normal probation. Analyses between the matched and unmatched prison observations demonstrate that the matched set of prisoners is composed of offenders who have less extensive criminal records and less serious conviction offenses than unmatched offenders regardless of the matching algorithm.

Conclusions

Contrary to a prior analysis of these data, which found a criminogenic effect of prison, a null effect was found using exact matching. Comparing the matching procedures, the more precise the match the less likely there was an effect of prison. However, community control was criminogenic regardless of the matching procedure.

     

I & We: The case for the Responsive Community

Individualism has become so pervasive in modern society that it now undercuts the legitimacy of any collectivity or social institution. An emerging position challenges this individualism as excessive, offering in its stead a synthesis that recognizes the moral standing of both individual and community. This synthesis, the I & We (or Responsive Community), traces its origins to two opposing historical positions. The Tory (or conservative) position views society as an organic whole, the sole source of authority, legitimacy, and value. Whigs (or liberals), in contrast, invest all value and legitimacy in the individual. The I & We view maintains that individual and society presuppose and necessitate one another, and that the tension between them is not only inevitable, but in part desirable; and, that attempts to redefine and alleviate the strain between individual and community provide the basis for social change. A renewed understanding of community's importance to the individual leads to a critique of psychological Whigism — overemphasis on the feelings and needs of the self — and also suggests a new approach to devising public policy.     

Measuring Long Term Individual Trajectories of Offending Using Multiple Methods

Criminal career researchers and developmental criminologists have identified describing individual trajectories of offending over time as a key research question. In response, recently various statistical methods have been developed and used to describe individual offending patterns over the life-course. Two approaches that are prominent in the current literature are standard growth curve modeling (GCM) and group-based trajectory models (GTM). The goal of this paper is to explore ways in which these different models with different sets of assumptions, do in fact lead to different outcomes on individual trajectories. Using a particularly rich dataset, the criminal career and life-course study, we estimate a unique trajectory for each individual in the sample using the GCM and GTM. We also estimate separate trajectories for each individual directly using the long time series. We then compare these three separate trajectories for each individual. We find that the average trajectories obtained from the different approaches match each other. However, for any given individual, these approaches tell very different stories. For example, each method identifies a rather different set of individuals as desistors. This comparison highlights the strengths and weaknesses of each approach, and more broadly, it reveals the uncertainty involved with measuring long term patterns of change in latent propensity to commit crimes.

Immunity and Susceptibility in Illicit Drug Initiation in Israel

We model the initiation process into cannabis and hard drugs using long-term survivor analysis. This approach hypothesizes two sub-populations: a population that is immune to drugs, who will never use them no matter how long they live, and a population which is susceptible to drugs for whom it is a matter of time until they begin to use drugs. We use data for Israel to test competing hypotheses concerning the timing of drug use initiation and the determination of susceptibility. Cigarettes do not significantly affect immunity to drugs, but they tend to speed up the rate of initiation for those who smoke them. This implies that anti-smoking policy may only delay drug use initiation without affecting immunity. Finally, drug initiation in Israel is not explained by variables that are traditionally associated with criminality.     

Elemental analysis of white cotton fiber evidence using solution ICP-MS and laser ablation ICP-MS (LA-ICP-MS)

The purpose of this research is to investigate a method for the forensic elemental analysis of cotton fibers for the purpose of increasing the discrimination between otherwise similar cotton evidence using microwave digestion inductively coupled plasma mass spectrometry (ICP-MS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). A quadrupole ICP-MS and UV laser ablation (266 nm) instruments were used for the analysis. A cotton standard reference material (IAEA V-9) was used to validate the developed methods producing good accuracy with typically <10% bias and good precision (typically <5% RSD) for the element list: ²⁵Mg, ²⁷Al, ⁵⁵Mn, ⁵⁷Fe, ⁸⁸Sr and ¹³⁷Ba. It was found that the LA-ICP-MS method resulted in improved precision over the solution ICP-MS method. Twenty-four raw cotton samples and five white cotton T-shirts were analyzed with the developed methods. It was also found that all the raw cotton samples from different sources were distinguishable from each other, as were all the cotton T-shirts resulting in zero type I errors and zero type II errors for the pairwise comparisons. The laser ablation method was slightly faster than the solution-based analysis, requiring 3 h for the laser analysis of 10 samples vs. 3.5 h for the solution analysis, including sample preparation time. One additional advantage of the LA-ICP-MS method was the extremely low sample consumption (1.75 μg were consumed vs. 250 mg for the solution-based method) and the fact that the LA samples are amenable for reanalysis.     

Growth curves of deviant behavior in early adolescence: A multilevel analysis

Multilevel growth curve models provide a means of analyzing individual differences in the growth of deviance, allow a number of theories to be integrated in a single model, and can help to unify research on deviant/delinquent/criminal careers at different stages of the life cycle. Building on the distinction between population heterogeneity and state dependence as alternative explanations of persistent individual differences in deviance (Heckman, 1981; Nagin and Paternoster, 1991), we show that models with two levels can be used to represent and analyze a variety of criminological theories. The first level (level 1) uses repeated measurements on individuals to estimate individual-level growth curves. The second level treats the level 1 growth curve parameters (e.g., slope, intercept) as outcome variables and uses time-invariant factors to explain variation in these parameters across individuals. We illustrate this approach by estimating a model of growth in deviance drawn from Gottfredson and Hirschi's deviant propensity theory. An innovative feature is the assumption that adolescents' expected growth curves of deviance follow a classical Pearl-Verhulst logistic growth model (Pearl, 1930). The results suggest that five risk factors—parental psychiatric problems, lack of parental support, living arrangements with zero or one parent in residence, low family income, and male gender—have strongly positive effects on deviant propensity. For example, adolescents with no supportive parents, and no other risk factors, have expected asymptotic levels of deviance (peak levels attained at about age 18) that are about twice as high as those of adolescents with no risk factors. Yet more than two-thirds of the individual-level variability in growth curves is unexplained by the five risk factors. This unobserved heterogeneity would remain hidden in analyses using conventional structural equations models and the same explanatory variables.     

Quasi-experimental designs for community-level public health violence reduction interventions: a case study in the challenges of selecting the counterfactual

Objectives

We highlight the importance of documenting the step-by-step processes used for the selection of comparison areas when evaluating a community-level intervention that targets a large-scale community.

Methods

We demonstrate the proposed method using a propensity score matching framework for an impact analysis of the Cure Violence Public Health Model in Philadelphia. To select comparison communities, propensity score models are run using different levels of aggregation to define the intervention site. We discuss the trade-offs made.

Results

We find wide variation in documentation and explanation in the extant literature of the methods used to select comparison communities. The size of the unit of analysis at which a community is measured complicates the decision processes, and in turn, can affect the validity of the counterfactual.

Conclusions

It is important to carefully consider the unit of analysis for measurement of comparison communities. Assessing the geographic clustering of matched communities to mirror that of the treated community holds conceptual appeal and represents a strategy to consider when evaluating community-level interventions taking place at a large scale. Regardless of the final decisions made in the selection of the counterfactual, the field could benefit from more systematic diagnostic tools that document and guide the steps and decisions along the way, and ask: “could there have been another way of doing each step, and what difference would this have made?” Overall, across community-level evaluations that utilize quasi-experimental designs, documentation of the counterfactual selection process will provide a more fine-grained understanding of causal inference.

     

Pitfalls in the Use of Time Series Methods to Study Deterrence and Capital Punishment

Objectives

Evaluate the use of various time series methods to measure the deterrence effect of capital punishment.

Methods

The analysis of the time series approach to deterrence is conducted at two levels. First, the mathematical foundations of time series methods are described and the link between the time series properties of aggregate homicide and execution series and individual decision making is developed. Second, individual studies are examined for logical consistency.

Results

The analysis concludes that time series methods used to study the deterrence effects of capital punishment suffer from fundamental limitations and fail to provide credible evidence. The common limitation of these studies is their lack of attention to identification problems. Suggestions are made as to directions for future work that may be able to mitigate the weaknesses of the current literature.

Conclusions

Time series studies of capital punishment suffer from sufficiently serious identification problems that existing empirical findings are compatible with either the presence or the absence of a deterrent effect.     

Heterogeneity in the Frequency Distribution of Crime Victimization

Objectives

Tests the idea that the frequency distribution typically observed in crosssectional crime victimization data sampled from surveys of general populations is a heterogeneously distributed result of the mixing of two latent processes associated, respectively, with each of the tails of the distribution.

Methods

Datasets are assembled from a number of samples taken from the British Crime Survey and the Scottish Crime Victimization Survey. Latent class analysis is used to explore the probable, latent distributions of individual property crime and personal crime victimization matrices that express the frequency and type of victimization that are self-reported by respondents over the survey recall period.

Results

The analysis obtains broadly similar solutions for both types of victimization across the respective datasets. It is demonstrated that a hypothesized mixing process will produce a heterogeneous set of local sub-distributions: a large sub-population that is predominantly not victimized, a very small ‘chronic’ sub-population that is frequently and consistently victimized across crime-type, and an ‘intermediate’ sub-population (whose granularity varies with sample size) to whom the bulk of victimization occurs. Additionally, attention is paid to the position of very high frequency victimization within these sub-populations.

Conclusions

The analysis supports the idea that crime victimization may be a function of two propensities: for immunity, and exposure. It demonstrates that zero-inflation is also a defining feature of the distribution that needs to be set alongside the significance that has been attached to the thickness of its right tail. The results suggest a new baseline model for investigating population distributions of crime victimization.     

Behind closed doors: the effect of pretrial publicity on jury deliberations

Abstract

Effective treatment of aggressive behaviour and accurate release decision making are necessary components of adequate clinical practice in forensic psychiatric units. Unfortunately, methods to identify treatment targets and ameliorate aggressive behaviour have developed at a slower pace than risk assessment technologies. Recent progress on the identification of offence paralleling or functionally equivalent behaviour offers a framework for individually tailored treatment and idiographic release decision making, although empirical scrutiny of this approach is inadequate. This paper describes an examination of the relationship between aggressive behaviour prior to admission with aggression during inpatient psychiatric treatment, and reconviction for violent offending following discharge. Results showed a relationship between pre- and post-admission aggression but no relationship between aggression during inpatient psychiatric treatment with either pre-admission aggressive behaviour or violent recidivism. These findings indicate the importance of state psychological variables, specifically those states affected by symptoms of psychiatric illness, as well as environmental activators and inhibitors of violence that operate within the hospital. These require inclusion in an adequate functional analysis of aggressive behaviour for forensic psychiatric patients.     

Charge reduction: An analysis of prosecutorial discretion in burglary and robbery cases

Although a substantial number of researchers have studied charge reductions taking place within the context of guilty plea negotiations, few have focused on estimating the determinants of charge reductions taking place at the initial screening decision. The prosecutor's decision to reduce original felony charges to a misdemeanor has serious social, legal, and economic consequences for the suspect. This paper presents a model of the variables affecting the likelihood of such a reduction in burglary and robbery offenses. Drawing from Littrell's principled charging perspective and earlier research on labeling, the analysis involves estimating logistic regression equations specifying both main and interaction effects of the suspect's gender and race and variables related to suspect character, case seriousness, and legal seriousness. Partial support is found for Littrell's perspective.     

Investigating the Functional Form of the Self-control–Delinquency Relationship in a Sample of Serious Young Offenders

Objective

This work further examines the functional form of the self-control–delinquency relationship as an extension of recent work by Mears et al. (J Quant Criminol, 2013). Given the importance of the authors’ conclusions regarding the nonlinear relationship between these two variables and the recognition that there are some potential limitations in the sample and assumptions required for the analytic methods used, we apply both similar and alternative techniques with a data set comprised of serious youthful offenders to determine whether key findings can be replicated.

Methods

Data from the Pathways to Desistance study, which comprise extensive individual and social history interviews with 1,354 offenders over multiple waves spread out over 84 months, is utilized in this analysis. These data are well-suited to investigating the questions of interest as the target population comprises youth with offending histories that are more extensive than those likely to be found in general surveys of adolescents. The analyses consider the self-control–delinquency relationship in an alternative sample with the previously used Generalized Propensity Score (GPS) procedure, which requires strong assumptions, as well as nonparametric regression which requires far weaker assumptions to consider the functional form of the self-control–delinquency relationship.

Results

The results generally show that the identified functional form of the self-control–delinquency relationship seems to be at least partly dependent on aspects of the modeling of dose–response associated with GPS procedures. When nonparametric general additive models are used with the same data, the relationship between self-control and delinquency seems to be approximately linear.

Conclusions

Identifying functional form relationships has importance for many criminological theories, but it is a task that requires that the balance of model assumptions to exploratory data analysis falls toward the latter. Nonparametric approaches to such questions may be a necessary first step in learning about the nature of mechanisms presumed to be at work in important explanations for crime and criminality.     

On The Application of Fuzzy Clustering for Crime Hot Spot Detection

   One of the fundamental challenges in crime mapping and analysis is pattern recognition. Efforts and methods to detect crime hot-spots, or geographic areas of elevated criminal activity, are wide ranging. For aggregate data, such as total crime events in a census tract(s), measures of spatial autocorrelation have proven useful. For disaggregate data (i.e. individual crime events), kernel density smoothing and non-hierarchical cluster analysis (e.g. k-means), are widely used. Non-hierarchical techniques are particularly effective in delineating geographic space into areas of higher or lower crime concentrations, because each observation is assigned to one and only one cluster. The resulting set of partitions provides clear-cut spatial boundaries that can be used for hot-spot evaluation and interpretation. However, the strength of non-hierarchical methods can also be viewed as a weakness. Although the hard-clustering of observations into a set of discrete clusters is helpful, there are many cases where ambiguity exists in the data. In such cases, a more generalized approach for hot-spot detection would be helpful. The purpose of this paper is to explore the use of a generalized partitioning method known as fuzzy clustering for hot-spot detection. Functional and visual comparisons of fuzzy clustering and two hard-clustering approaches (medoid and k-means), across a range of cluster values are analyzed. The empirical results suggest that a fuzzy clustering approach is better equipped to handle intermediate cases and spatial outliers.

The “care package,” prison domestic violence programs and recidivism: a quasi-experimental study

Objectives

Although there are many evaluations of domestic violence rehabilitation programs, it is still unclear “what works” in this field, especially when it comes to programs within prison walls. Today, most studies indicate that domestic violence programs based on cognitive behavioral treatment, or psycho-educational models show small positive results. Yet, there is still insufficient empirical literature providing adequate evidence for the impact of integrative treatment, where different methods and approaches toward domestic violence prisoners are employed within the same rehabilitation-program framework while incarcerated. Our study examined the effects of an integrative domestic violence program with a therapeutic “package” implemented in Israel with the goal of reducing recidivism rates among prisoners in general, and especially with regard to violent offenses.

Methods

Using propensity score matching methods, we compared treated offenders to a matched sample drawn from all convicted prisoners who were released from prison between 2004 and 2012.

Results

The findings indicate that the percentages of reincarceration and rearrests of inmates, who participated in integrative domestic violence program, were significantly lower during a period of up to 4 years after release.

Conclusions

Our conclusion is that the integrative effect of different treatments along with a supportive prison climate increased the success of inmates who participated in the domestic violence program.

     

Treatment planning with the Psychopathy Checklist-Revised (PCL-R)

The Psychopathy Checklist-Revised (PCL-R) is an essential component of any assessment protocol within forensic and correctional settings. Both nomothetic and idiographic interpretations aid the clinician in screening and treatment planning. Whereas the PCL-R can be effective in identifying those who are at highest risk of disrupting treatment efforts and jeopardizing the safety of those around them, through item analysis, it also provides clues to the nonpsychopath's unique treatment needs. Specific recommendations are offered regarding the implementation of the PCL-R for screening and treatment planning, and illustrative case examples are provided to enliven essential points.     

Explaining Regulatory Non-compliance: A Survey Study of Rule Transgression for Two Dutch Instrumental Laws, Applying the Randomized Response Method

Within a rational choice framework, secondary data analysis of a survey study on compliance with two Dutch regulatory laws is carried out. Selection of explanatory variables to be considered is guided by a heuristic device, called the Table-of-Eleven. Using adapted logistic regression analysis, we show that self-reported compliance, measured by means of a randomized response procedure, can be explained in terms of benefits of non-compliance, social norms and deterrence, while knowledge and general norm-conformity have no role to play. The impact of various contributing factors turns out to be rather different in size for the two laws.     

Missing person alerts: does repeated exposure decrease their effectiveness?

Objectives

Prospective person memory refers to situations where one is asked to be on the lookout for a missing or wanted individual. Some researchers have hypothesized that because people see missing person alerts for multiple people over a period of time, that people habituate to missing persons alerts much like they do to car alarms. The purpose of this research was to test that hypothesis.

Methods

Some participants saw three different mock missing person videos, depicting three different target individuals, with one video being shown on each of 3 days (Monday, Wednesday, Friday). Other participants engaged in unrelated tasks on the first 2 days and saw a single mock missing person video on the third day. All participants were told that if they saw a person from a mock missing person video and contacted the experimenters they would win a cash prize. On the final day of the study, the target individual was located in the hallway a short distance from the experiment room in a location that participants had to pass on their way out of the building.

Results

Correct sightings of the target individual were significantly lower in the multiple video condition than in the single video condition.

Conclusions

The results suggest that overuse of missing person alert systems can decrease their effectiveness in a manner consistent with a “car alarm” effect.

     

Dissecting the relationship between mental illness and return to incarceration

Purpose

We examine all releases to parole supervision in a single state over a period of four years to consider how a diagnosis of mental illness is associated with return to incarceration.

Methods

We use survival methods and Cox regression to understand patterns of and influences on return to prison. Our measure of mental illness is based on in-prison clinical diagnoses. Data include a rich set of administrative variables with demographic, criminal history and institutional controls.

Results

Our findings suggest that (1) there is a statistically significant relationship between having a DSM diagnosis and reincarceration, (2) substance-related disorders account for most of that relationship, and (3) there are some important variations among types of disorders examined.

Conclusions

Research that examines mental illness and recidivism without controlling for substance use disorders/problems is likely to be uninformative and misleading. Findings provide qualified support for the notion that programming addressing criminogenic risks and needs may be as important, or more so, than therapeutic programming focusing on mental illness when recidivism reduction is the goal.     

A Comparison of Methods for Analyzing Criminological Panel Data

We demonstrate that fixed- and random-effects models for pooled cross-sectional and time series data, and latent growth curve models for panel data are special cases of a more general model. We compare the estimates obtained from each type of model for a data set consisting of homicide rates and a vector of explanatory variables for 400 US counties over a 15-year period. Most, but not all, estimates are similar in the two models. We identify circumstances under which one approach may be advantageous to the other.