YongJei Lee

This study examines whether crime locations are similar or different for juveniles and adults by comparing the distribution of offenses across land uses and their changes over time. Using 6 years of crime data from Colorado Springs, we calculated rank products for each of 35 land uses, stratified by age group and offense type, and summarized rank stability with interquartile ranges. Findings show both similarities and disparities in the rank order of crime locations for juveniles versus adults, with overlaps reflecting shared perceptions of opportunity and differences reflecting age-specific routines. Wider interquartile ranges indicate juvenile crime locations fluctuate more across years, whereas adult patterns exhibit greater spatial consistency and specialization. Prevention strategies should reflect this age-contingent spatial ecology of offending.

This study examines how place management practices influence crime distribution at pot shops. We assess whether these practices explain the varying crime levels observed across different pot shops. Using data from Colorado Springs, Colorado, we apply a Poisson distribution to compare expected and actual crime distributions among pot shop owners. Multilevel Poisson regression models are then employed to quantify the extent to which crime variation can be attributed to ownership. Our results indicate that approximately 25% of the variance in crime is attributed to management practices. Importantly, even after controlling for environmental factors surrounding pot shops, place management remains significant. This research highlights the critical role of owners in implementing effective crime prevention strategies at pot shops.

This study explores the effectiveness of machine learning algorithms in predicting recidivism, focusing on the impact of race and geographic location variables. Leveraging a dataset from the prisons in Georgia, we assess six algorithms’ forecasting performance, both with and without these key variables. Our findings indicate that geographic location generally enhances predictive accuracy more consistently than race across models. This research highlights the importance of methodological diversity and the complex, model-dependent impact of demographic factors on recidivism prediction. It underscores the potential of machine learning in criminology and criminal justice to provide nuanced insights into criminal behavior, challenging traditional assumptions, and informing evidence-based policy development.

Sexual harassment and gang rape in Egypt have garnered attention from both traditional and digital media. This study employed a volunteer HarassMap to analyse sexual harassment crimes (SHCs) across Egypt from a spatial perspective. The specific aims were to apply the Hierarchical Density‐Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm to locate clusters of reported SHCs, and to assess their spatial dependence on land use types. To accomplish this task, ring buffers of 100, 200, 300, 400, and 500 metres were established around each crime scene to determine which land use was mostly associated with the incidence of these SHCs. Local bivariate relationships were used to explore the associations between SHC and each land‐use category. Results from the HDBSCAN algorithm revealed four crime clusters within the study domain, mainly located in Greater Cairo, Alexandria, and Behaira. Notably, commercial establishments and transit stations showed a significantly positive correlation with SHC. The study shows how land uses shape SHC and showed that it is possible to identify environmental risk factors for harassment. These risk factors can help policymakers, urban planners, and community stakeholders prevent and reduce sexual harassment and gender inequality, and promote just and inclusive societies.

Correctional authorities require accurate, unbiased, and interpretable tools to predict individuals’ chances of recidivating if released into the community. However, existing prediction models have serious limitations meeting these requirements. We overcome these limitations by applying an established medical diagnostic approach: a relaxed naïve Bayes classifier. Using logistic regression in the form of a naïve Bayes classifier, we estimate the weights of observed features of offenders on recidivism. We apply these weights in a relaxed naïve Bayes classifier to predict the probability of recidivism. Results show that acquired features are stronger predictors of recidivism than innate features. Relaxed naïve Bayes classifier produces far less racial disparity than most alternatives. Critically, it is easier for users to interpret than its alternatives.

This study examines school-based crime prevention tactics using a place management framework in conjunction with deterrence and labeling theoretical perspectives. We examine the effects of four different aspects of place management on student offending and explore whether and how the effects are distinctive for female versus male students. We analyze survey data from students and principals across three waves of the Rural Substance abuse and Violence Project using multilevel-binomial models. Findings reveal that several aspects of place management interacted with gender, thus differentially impacting male versus female offending. Overall, this study extends past work in providing evidence of the gendered impact of some crime prevention practices—conditional effects that are consistent with insights from both deterrence and labeling theoretical perspectives.

This study examines deviant identity in relation to youth offending by combining items tapping both self-appraisal and reflected appraisal. In particular, using survey data from 3,446 Korean youth across five waves of the Korea Youth Panel Survey (KYPS), findings from group-based trajectory modeling (GBTM) present four distinct offending groups—a high-rate chronic group, stable non-offending group, adolescence-limited group, and declining group. Then, findings from the multinomial logit model reveal that deviant identity is a robust predictor of offending for subgroups of adolescents involved in offending at any level in comparison to stable non-offenders. Accordingly, this study supports the idea that deviant identity should be considered as a prominent predictor of a variety of types of youth offending.

Interpretations of two bodies of crime-place research conflict. Land use and crime studies claim particular facilities increase crime. Risky facilities studies show most places of a single type have little or no crime, but a few of that type have a great deal of crime. How can a facility be generally criminogenic and mostly safe? To resolve this conflict, we make use of the fact that a single owner can own multiple facilities and each owner may have consistent management practices in their facilities. We first replicate findings of earlier land use studies with crime and land parcel data from Cincinnati. Second, we cluster land parcels by property owners and re-estimate the land use and crime relationship. The links between land use and crime in the replication decline or disappear after clustering. Findings suggest owners’ place management influences crime at their places, thus resolving the conflict between the two bodies of research.

This study examines the effect that individuals’ perceptions of police have on their adoption of crime prevention measures. Unlike past research that conceptualized police perceptions as inversely associated with crime prevention, we introduce a framework that distinguishes between the traditional policing and community policing/procedural justice models. We analyze multilevel data from Canada’s General Social Survey for 13 crime prevention measures (e.g. locking doors, installing burglar alarms), and estimate Item Response Theory models to account for differing levels of difficulty in the implementation of these measures. Results show that the effect of police perceptions on the adoption of crime prevention measures varies by policing model. Residents who have favorable perceptions of the police as to the performance of traditional policing duties are less inclined to take measures against crime. In contrast, those with favorable perceptions of the police as engaging in community policing/procedural justice are more inclined to take such measures.

Real-time crime hot spot forecasting presents challenges to policing. There is a high volume of hot spot misclassifications and a lack of theoretical support for forecasting algorithms, especially in disciplines outside the fields of criminology and criminal justice. Transparency is particularly important as most hot spot forecasting models do not provide their underlying mechanisms. To address these challenges, we operationalize two different theories in our algorithm to forecast crime hot spots over Portland and Cincinnati. First, we use a population heterogeneity framework to find places that are consistent hot spots. Second, we use a state dependence model of the number of crimes in the time periods prior to the predicted month. This algorithm is implemented in Excel, making it extremely simple to apply and completely transparent. Our forecasting models show high accuracy and high efficiency in hot spot forecasting in both Portland and Cincinnati context. We suggest previously developed hot spot forecasting models need to be reconsidered.