Bayesian quantile regression has drawn more attention in widespread applications recently. Yu and Moyeed (2001) proposed an asymmetric Laplace distribution to provide likelihood based mechanism for Bayesian inference of quantile regression models. In this work, the primary objective is to evaluate the performance of Bayesian quantile regression compared with simple regression and quantile regression through simulation and with application to a crime dataset from 50 USA states for assessing the effect of potential risk factors on the violent crime rate. This paper also explores improper priors, and conducts sensitivity analysis on the parameter estimates. The data analysis reveals that the percent of population that are single parents always has a significant positive influence on violent crimes occurrence, and Bayesian quantile regression provides more comprehensive statistical description of this association.
Quantitative variables were expressed as mean values (M) and standard deviations (SD), while qualitative vari- ables were expressed as absolute frequencies (N) and rel- ative frequencies (%). For the comparison of proportions, Chi square and Fisher’s exact tests were used. Multiple logistic regression models with a stepwise method (p for entry 0.05, p for removal 0.10) were used in order to test whether PD types and PD “Clusters” were independently associated with violent crimes and homicides. PD types and PD “Clusters” were used as independent variables, whereas “violent/non-violent crimes” and “homicides– attempted homicides/non homicides” as dependent variables. Variables that regarded social, economic, demographic or clinical characteristics were not included in the analysis due to sample size. More statistical tests could have led to a lower study power. Adjusted odds ratios (OR) and the respective 95% confidence intervals (CI) were computed. Statistical significance was set at p ≤ 0.05. All reported p values are two-tailed and analyses were conducted using SPSS statistical software (version 19.0).
Exclusion criteria included: being arrested for non-violent crimes, seriously injured or unconscious suspects and children (these would not be able to give consent or respond appropriately to the questions). Written consent was obtained from all the participants, who agreed to be involved in the study, after a full explanation of the na- ture and purpose of the study. For those whose lawyers were around, permission was also sought from their lawyers who could sit in during the interview if the client so wish.
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rime has a history as long as humanity; is- sues related to offenders and prisoners, in particular, its relation to psychiatric issues and problems, are among the most chal- lenging and active research areas . Vio- lent crimes are the most severe crimes that most people fear, such as killing or murder, rape, robbery, and child abuse . Violent offenses are mainly “the intentional use of force, physical force, threats, the tendency to harm self or others, or to a group or society (that may be harmless or associated with injury), death, mental injury and growth disorder, or various deprivations” . Criminal offenses are different types of violent crimes. Murder or homicide, kidnapping, and taking hostage, rape, and armed robbery are among them. Seriousness and the intensity of violent crimes are different among various groups in the community. Extensive research has been conducted to identify individuals at risk for committing violent crimes . If personality traits are inflexible and lead to subjective or functional disorders, the diagnosis of personality disorder is raised. In the first group, i.e. schizoid, paranoid, and schizotypal personal- ity disorders often appear to be abnormal, whereas in the second group, i.e. histrionic, narcissistic, antisocial, and borderline personality disorders, suffering people are often emotional, impulsive, and unstable. In the third group, i.e. avoidant, dependent, obsessive-com- pulsive, passive-aggressive personality disorders, indi- viduals often appear anxious and fearful .
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While the existing 35 megacities account for 11 percentage of the total population in India, they account for 18.5 percentage of the total cognizable crimes (both IPC and SLL) in India (see Table-1). However, their share in VC is only 10.6 percentage of total national VC. Table 1 depicts two things. Firstly, the average violent crime rate of megacities is similar to the national average (19.4 and 19.2 respectively). This means that the generalization that the to- tal urban crime rate is higher than that in rural areas is not applicable for VC. But, it has been shown later in this paper that the distribution of crime rate among megacities is such that the average number conceals the actual picture. The gross figure may not reflect the large varia- tion in the distribution of VC at megacity and state level. Secondly, the combined average
The purpose of this analysis was to visualize San Francisco crime data from 2003 to 2015 over space and time. The city was divided by police districts and then analyzed with the main focus on violent crimes. Geospatial analysis like Hotspot and Tracking Analysis were performed along with statistical data exploration to gain insights. The tools used for the analysis were ArcGIS Pro, ArcMap, and Tableau. Insights such as trends in the crime rate over the years, which day of the week is criminally more active, which police districts are prone to violent crimes and also hotspots where the crime rate is high than usual were few of the insights achieved after the analysis. The conclusions obtained were consistent with information derived from the literature regarding why the regions with hotspots were found to have a considerable amount of criminal activity.
Most studies (32-36) establish a greater likelihood of violent behavior among the population with a mental disorder, although it may be inferred that only a sub-group of those with mental disorders exhibit violent behavior (37). People with schizophrenia have been described as being four times more likely to be imprisoned than the general population, although they only represent 5% of violent crimes (27). In a study by Peterson et al. (38) of 143 delinquents with mental disorders, the frequency and consistency of the symptoms that might induce criminal behavior were examined. Of the 429 violent crimes considered, 4% were associated with psychosis and 10% with bipolar disorder, leading to the conclusion that few criminal acts were directly motivated by symptoms of illness. Factors such as substance use, premorbid personality, and social indolence are more relevant and underlie violent behavior in people with mental disorders (39, 40).
Table 2 shows the results of KSS-CHLL test with constant and linear trend. The estimators of the parameters of interest in equation (3), and , together with the corresponding t- statistics are reported. Note that the 10%, 5% and 1% simulated critical t values for 50 observations are -3.06, -3.38 and -4.05 2 respectively. Unit root is found in 39 states crime gaps, which provides evidence against average violent crime convergence between these sates with respect to the average violent crime in the United States. On the other hand, no unit root is found in the violent crime gaps of Arizona, Colorado, Hawaii, Idaho, Kansas, Louisiana, Minnesota, New Hampshire, Oklahoma, South Dakota, Washington DC, Wisconsin and Wyoming, meaning the rejection of violent crimes divergence. Therefore, we can further investigate whether these thirteen states are in the process of catching up or have attained long-run converging with respect to the average crime in the United States. The 10%, 5% and 1% simulated t-critical values of the left (right) tail are -2.63 (2.63), -3.07 (3.02) and -3.78 (3.76) respectively. It is observed that the trend term is significant in the case of Kansas, Oklahoma and Wisconsin, which provide evidence supporting catching up. The remainder ten states are said to have attained long-run converging with the average violent crime in the United States.
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Most juvenile violence occurred in the urban counties of the state, which together accounted for >72% and nearly 71% of the total violent crimes committed by juveniles and against juveniles, respectively. As with the incidence of violent crime, victim costs were higher in urban counties than in rural ones ($4.0 billion vs $1.4 billion), accounting for nearly 75% of total victim costs. In both urban and rural counties, the largest share of victim costs of juvenile violence was for crimes by adults against juveniles; the smallest share was for violent crimes by juveniles against adults. Several violent crimes—rape, assault, and robbery—were more likely to result in physical injury when committed in rural areas. The estimated total criminal justice costs for perpetra- tors of juvenile violence in Pennsylvania exceeded $46 million in 1993. Juvenile treatment program costs ac- counted for 55% of total perpetrator costs, and probation costs and detention costs ⬃ 20% each. Incarceration costs, although large per unit, accounted for only 6% of total costs.
Prisoners who have committed non-violent crimes, and have severe alcohol dependency problems that are related to their offending, would be better suited attending treatment programmes in the community as part of a non-custodial punishment rather than being given a short spell in prison during which they receive little, or no help with their alcohol problems.
Given the small number of cases used to fit this quad- ratic curve, we cautiously note that for all eight equa- tions, the y-intercepts (coefficient a) are positive and significant. If the number of visitors goes to zero, an aver- age CT will still have crime—predicted to be from 87 to 117 property crimes and from 28 to 36 violent crimes as baseline risk levels, likely generated by residential popu- lations. The same equations indicate that every thou- sand workers “bring” 43 property crimes and five violent crimes. At the other extreme, every thousand recreation visitors correspond to 156 property crimes and 29 violent crimes. Apparently, recreation visitors have the greatest relative impact on local crime. The work visitor equation for property crime has highest Multiple-R (0.878) of all eight equations. The recreation equation has the strong- est main effects for violent and property crimes, alike. The multiple R for education visitors is much smaller than the others, probably reflecting the data limitations already discussed. We drop the education variable from our summary analysis due to measurement limitations.
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We offer a new methodological resolution to the dilemma of measuring small num- bers of high-frequency victims that does not generate excessive volatility. This is to use three-year moving averages and statistical modelling. Three-year moving averages effectively trebles the sample size. Statistical modelling of trends allows for the inclu- sion of many more data points than comparing the start and finish years. This is rel- evant to the estimation of official statistics on crime in general as well as on violence against women. Removing the arbitrary cap that excludes some violent crimes commit- ted against the most highly victimized increases the estimate of the amount of violent crime and increases the proportion of violent crime committed against women and by domestic perpetrators. The findings challenge the use of the cap in the presentation and publication of official statistics on violent crime, finding it incompatible with ONS standards of quality for official statistics. This challenge to remove the cap and to revise the methodology extends to surveys in the United States, Canada, Mexico and any other country that uses this practice.
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The Appeals Chamber’s decision in Ntganda may be used to improve ac- countability for sexual violence against men and boys. The ICC can hold perpetrators of sexually violent war crimes accountable for their actions, and can help shift the public perception of these male victims through public in- formation and judgments reflecting the severity and reality of sexually violent crimes. The lack of recognition given to sexually violent crimes against males has to do with several factors. Those factors include the tendency to conflate such violence with violence against women and girls. 273 There is also the
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The book published by the prominent female criminologist Freda Adler - Sister in Crime: The Rise of a New Female Criminal - in 1975, has helped to develop the masculinity theory. In fact, Adler‟s theory of masculinity was the new explanation of the masculinity complex in the arena of theories which derived from sociology. It has come from Sigmund Frued‟s theory of „Penis envy‟, according to which it is believed that „women revolt because of their subordinate positions to man in society.‟ Among three traditions of masculinity theory, the first was developed by the famous criminologist Cesare Lombroso, the father of the biological doctrine. Biologically, crime is mainly a male dominated phenomenon, where male characteristics are responsible for those crimes (Harrigton and Nye 2005: 3). For the internal physio-chemical characteristics, the females are more conservative and play a neutral role, consequently, committing less crime than males (Lombroso and Ferraro 1895). From some specific examples of female criminality, Lombroso argues that criminality is principally the product of inconsistent and altered thought of women, related to their indirect role in domestic affairs (Cf. Simpson, 2000: 4). The females who are involved with crime are dominated by male characteristics in their personality (Harrington and Nee 2005: 3-4).
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The data for this study were collected entirely from people living in Southern California. A study that collects data from a wider participant base may result in somewhat different findings from what was found in the current study. The sample could also use more members from certain social groups, such as Middle Eastern, Native American, working-class, upper-class, etc., to get a broader un- derstanding of any potential ethnic/racial and social class differences. The cur- rent study did not collect information on level of education, which also limits the ability to discern how a formal education may play into feelings and attitudes toward those with mental illnesses. Future studies could take a closer look at specific mental illnesses in regard to perceptions of dangerousness and violent behavior. It is clear from this study that people have much stronger feelings and negative views toward certain mental illnesses than they do toward others. Addi- tional studies could also place more of an emphasis on stigma and how people reject and avoid those with certain mental illnesses (e.g., schizophrenia, sub- stance use disorder, antisocial personality disorder, etc.). Future research could help to shed more light on this ongoing social problem.
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However, when trends over time are needed the additional concern of volatility must also be addressed. It is generally assumed that there is a trade-off between bias and volatility. The current ONS methodology trades increased bias for a reduction in volatility in order to prioritise the analysis of trends over time. However, Walby, Towers and Francis (2016) demonstrate that it is possible to reduce volatility without capping. They reduce volatility to the same order as that produced by capping (see table 1), whilst retaining the accuracy of estimates derived from all reported crimes by using three-year rolling averages. Smoothing methods, like rolling averages, have been in use for many years, for example they are used by ONS and Eurostat for calculating (and reducing volatility in) unemployment rates (see for example Bishop, 2004).
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Rasoul Kiani, et.al (2015) stated that organizations and institutions were affected by frequently occurred offenses someway. Therefore, it was imperative to analyze factors, causes and connection of different crimes and to find out suitable means for controlling and avoiding further offenses. The classification of clustered crimes on the basis of occurrence frequency during different years was the major aim of this study. In terms of scrutiny, research and detection of patterns for the incidence of several offenses, data mining techniques had been utilized . In this study, the weights to the features were assigned by which quality of the model was improved and low value was removed. For the optimization of Outlier Detection operator parameters, the RapidMiner tool was utilized by Genetic Algorithm (GA).
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The second aspect of the crime decline in Amsterdam that will be discussed is the length that crime has a downwards trend during the period 2003-2012. This will be done by looking at the trend line of all six crimes separately and taking into account different periods within this 10 year period under study. The period will be divided in five year periods from 2003-2007 and 2008-2012. Figure 4 presents the trend in crime rates for six crimes in Amsterdam. All crime rates are presented as the rate per 10,000 population. The rates for property crimes are the highest. The rates of violent crimes, on the other hand, are fewer in number. These rates are in accordance with the theoretical expectation, because property crimes are many times more reported than violent crimes (Reiner, 2007). Several points are in order when taking into account figure 4. Firstly, not a single crime shows a consecutive decline during this period. All the crime rates rise at one point in time. Secondly, with the exception of burglary, pick pocketing, robbery, and theft of motor vehicles a (more or less) clear downwards trend can be detected for two of the six crimes. The rate of theft of motor vehicles is relatively steady from 2006-2012 but declined mostly during the period 2003-2005. The rate of burglary declined mostly in the period 2003-2005, but was steady during the period 2005-2012. The rate of pick pocketing declined during the period 2003-2011, but increased rapidly in 2012. The rate of robbery is relatively stable from 2003-2008, but declined rapidly from 2009-2012. Thirdly, the most steady drop in crime seem to be street robbery and theft out/from motor vehicles (small increase during the period 2004- 2006) during this period under study. In chapter seven and eight it will be analysed why some crimes show a steady decline but others increase, decrease or remain stable in certain periods. Crime declined in Amsterdam, that is clear, but which factors are associated with this decline remains unclear in this chapter. The consistency of the crime decline will be discussed in more depth by the numbers in table 1 on page 33 of this report.
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tional Crime Victimization Survey (NCVS), which is conducted by the Bureau of the Census on behalf of the U.S. Department of Justice. The NCVS gathers information from citizens on crimes including whether and how they are reported. It also col- lects a great deal of information about the characteristics of victimizations (to the degree that the victim can report them), including victim and perpetrator demographics; the nature of the incident location; and a description of the inci- dent, including, but not limited to, type of crime, use of weapons, and injuries to the victim. The active sample contains about 55,000 households and approxi- mately 100,000 individual respondents. Response rates for both eligible house- holds and individuals are more than 90 percent. When data from 1995 and 1996 are combined, information on approxi- mately 32,000 victimizations of all types is available.
Teenage pregnancy seems to be another source of poverty in the county. Whereas the teenage mother and her baby become dependents of the teenage mother’s parents, the per capital income of the family will decrease drastically. The poverty arising from the increased membership of this type of family is capable of instigating the teenage mother or the offspring, or both, to engage in crimes. Unless the teenage mother is highly motivated, getting the basic education and raising children that would understand the societal conventions that could diminish crimes may be an up-hill battle. In this situation, having education and having children could be close substitutes. An intergenerational vicious circle of poverty and crimes could take place where the preference for fertility dominates in the substitution. Crime is likely to be in a family that has an absence of a father who is needed for controlling the code of conduct of the kids. Comanor & Phillips (2002) find the absence of fathers in families to be the most important factor describing child delinquency. Young single families are most likely to have both emotional and economic deprivations, and with these deprivations, teenage mothers could generate some costs to the state. Beside the societal cost of crimes, the state could incur costs for the welfare of the babies of teenage mothers, and also for providing training and skills to the teenage mothers.
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