Economic Indicators Selection for Property Crime Rates using Grey Relational Analysis and Support Vector Regression

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.

Co-Occurrence of Online and Offline Victimization: A Latent Class Analysis in University Students

Most people are exposed to risks both in the online and offline world. Several studies have provided definitions and measures of cybervictimization based on different theoretical approaches and most of them have focused on specific forms of cybercrime, depicting a limited portrayal of victimization. The current study explored victimization configurations in a sample of 749 university undergraduates from Spain (61.6% women; M age = 26.9), utilizing latent class analyses to account for the nature and frequency of various types of online and offline victimization along their life span. Among them, 35.9% were victims of a cyberattack, 24.4% reported being victims of cyberfraud and 49% of property crime. The analysis uncovered two classes of cybervictims—consisting of economic cybervictimization (victims of economic cybercrimes only) and cyber-polyvictimization (victims of various types of cybercrimes)—and allowed us to compare them with a group of non-victims. Younger respondents (15 to 25 years old), conventional university students, women, people with lower incomes and LGBTQI+ individuals have a higher representation in the cyber-polyvictimization class. In addition, members of this class have suffered more offline victimization in all the areas analyzed. The present study has found co-occurrence between online and offline victimization, thus reinforcing the relevance of simultaneously studying both areas and the interaction between them. From this empirical ground, prevention strategies should not be focused merely on opportunity factors related to the online interactions and behavior of potential victims, without facing the deep human and social roots of victimization.

Vztahová vazba a copingové strategie u vězeňské populace

Objectives. The study is aimed at describing attachment and coping strategies in the prison population, and relations between these two variables were tested. Sample and setting. The sample consisted of 122 men serving middle-security sentences mainly for property crime. The attachment was determined by the Czech version of the Experiences in Close Relationships Scale, and coping strategies were measured by The Stress Coping Style Questionnaire SVF 78. Hypotheses. The prisoners’ attachment and coping strategies were expected to be different from those of the normal population. The attachment anxiety and avoidance were expected to be related to coping strategies. Statistical analyses. One sample t-test and Wilcoxon one sample test were used for analyzing the differences in scores between the prison and normal population while the Pearson correlation and linear regression were used to test relations between variables. Results. Inmates were significantly different from the normal population both in their attachment and coping strategies. They exhibited higher attachment anxiety and avoidance compared to the normative sample, fearful avoidant attachment prevailed. Prisoners demonstrated higher Play Down, Distraction from Situation, Substitutional Satisfaction, Flight Tendency, Self-accusation, and Active Avoidance, they exhibited lower Guilt Denial and Rumination. Relational avoidance correlated negatively with positive coping strategies, relational anxiety correlated positively with negative coping strategies. Limitations. The main limitation of this study is the use of a non-representative sample and the self-assessment form of the methods employed.

Re-Opening Dallas: A Short-Term Evaluation of COVID-19 Regulations and Crime

We investigated the relationship between COVID-19 stay-at-home regulations and property and violent crime indexes in Dallas, TX during the first 6 months of 2020. We tested for changes in property and violent crime trends using four key “intervention” dates: the stay-at-home order issued by Judge Clay Jenkins (March 24), the start of Governor Abbott’s phase one of re-opening (May 1), a second phase of more widespread re-openings (May 18), and a third phase of increased capacity limits for businesses (June 3). Our analyses point to two main findings: (1) the time between the initial stay-at-home policy and the phase one re-opening was associated with an increase in the trend of both violent and property crime (although at lower levels than pre-pandemic); and (2) the third phase of re-opening the City of Dallas was associated with higher daily counts of property and violent crime. Our findings suggest that policy makers need to consider policies not only related to police enforcement but also allocation of other social services, particularly when such a sudden policy (e.g., stay-at-home order) is implemented.

De doodstraf in het Scheldedepartement en de provincie Oost-Vlaanderen in de Franse en Hollandse tijd, 1796-1830

Summary The French revolutionary legislature imposed capital punishments for a number of serious crimes such as gang robbery, murder, poisoning, parental murder, infanticide, homicide and theft, arson and coin counterfeiting. These capital punishments reached their peak in the years 1798-1803, being the last two years of the Directoire and the first years under Napoleon. A total of 231 death sentences were handed down in the Scheldt Department and the Province of East Flanders, 70 in default and 161 contradictory, of which 129 were executed. Most death sentences were imposed for a property crime. Crimes against individuals then came only second.

Crime rates and contextual characteristics: a case study in Connecticut, USA

There is a long-standing interest in the spatial relationship between contextual characteristics and crime rates in the U.S. since such a relationship allows police and stakeholders to design crime prevention programs to better target areas at risk for crime. The objective of this research is to examine the relationships between violent/property crime rates and contextual characteristics at the county-subdivision level in the State of Connecticut. The analysis shows that predictors such as population density, type of housing, education, poverty, and racial/ethnic diversity are significantly associated with violent and property crime rates. The results are discussed in the context of different crime hypotheses, which can explain spatial variations in crime rates. Most importantly, the association between crime rates and the explanatory variables in this study significantly varied over space, highlighting that different crime prevention policies/programs should be implemented in different county subdivisions in Connecticut.

Club convergence of crime rates in the United States of America

Purpose The cyclical behavior of US crime rates reflects the dynamics of crime in the country. This paper aims to investigate the US's club convergence of crime rates to provide insights into whether the crime rates increased or decreased over time. The paper also analyzes the factors influencing the probability of states converging to a particular convergence club of crime. Design/methodology/approach The analysis is based on balanced panel data from all 50 states and the district of Columbia on violent and property crime rates covering 1976–2019. This yields a cross-state panel of 2,244 observations with 55 time periods and 51 groups. In addition, the author used a club clustering procedure to investigate the convergence hypothesis in the study. Findings The empirical results support population convergence of violent crime rates. However, the evidence that supports population convergence of property crime rates in the study is not found. Further analysis using the club clustering procedure shows that property crime rates converge into three clubs. The existence of club convergence in property crime rates means that the variation in the property crime rates tends to narrow among the states within each of the clubs identified in the study. Analysis based on an ordered probit model identifies economic, geographic and human capital factors that significantly drive the state's convergence club membership. Practical implications The central policy insight from these results is that crime rates grow slowly over time, as evident by the convergence of violent crime and club convergence of property crime in the study. Moreover, the existence of club convergence of property crime is an indication that policies to mitigate property crime might need to target states within each club. This includes the efforts to use state rather than national crime-fighting policies. Social implications As crimes are committed at the local level, this study's primary limitation is the lack of community-level data on crime and other factors considered. Analysis based on community-level data might provide a better representation of crime dynamics. However, the author hopes to consider this as less aggregated data are available to use in future research. Originality/value The paper provides new insights into the convergence of crime rates using the club convergence procedure in the USA. This is considered an improvement to the methods used in the previous studies.

Offline crime bounces back to pre-COVID levels, cyber stays high: interrupted time-series analysis in Northern Ireland

Much research has shown that the first lockdowns imposed in response to the COVID-19 pandemic were associated with changes in routine activities and, therefore, changes in crime. While several types of violent and property crime decreased immediately after the first lockdown, online crime rates increased. Nevertheless, little research has explored the relationship between multiple lockdowns and crime in the mid-term. Furthermore, few studies have analysed potentially contrasting trends in offline and online crimes using the same dataset. To fill these gaps in research, the present article employs interrupted time-series analysis to examine the effects on offline and online crime of the three lockdown orders implemented in Northern Ireland. We analyse crime data recorded by the police between April 2015 and May 2021. Results show that many types of traditional offline crime decreased after the lockdowns but that they subsequently bounced back to pre-pandemic levels. In contrast, results appear to indicate that cyber-enabled fraud and cyber-dependent crime rose alongside lockdown-induced changes in online habits and remained higher than before COVID-19. It is likely that the pandemic accelerated the long-term upward trend in online crime. We also find that lockdowns with stay-at-home orders had a clearer impact on crime than those without. Our results contribute to understanding how responses to pandemics can influence crime trends in the mid-term as well as helping identify the potential long-term effects of the pandemic on crime, which can strengthen the evidence base for policy and practice.

The Effect of E-Verify Laws on Crime

E-Verify laws, which have been adopted by 23 states, require employers to verify whether new employees are eligible to legally work prior to employment. This study explores the impact of state E-Verify laws on crime. Using data from the 2004–2015 National Incident Based Reporting System, the authors find that the enactment of E-Verify is associated with a 7% reduction in property crime incidents involving Hispanic arrestees. This finding was strongest for universal E-Verify mandates that extend to private employers and its external validity bolstered by evidence from the Uniform Crime Reports. Supplemental analyses from the Current Population Survey suggest two mechanisms to explain this result: E-Verify-induced increases in the employment of low-skilled natives of Hispanic descent and out-migration of younger Hispanics. Findings show no evidence that arrests were displaced to nearby jurisdictions without E-Verify or that violent crime or arrests of African Americans were affected by E-Verify laws. The magnitudes of the estimates suggest that E-Verify laws averted $491 million in property crime costs to the United States.