As opposed to primary crime prevention, which focuses on the identification of physical and social conditions/factors that facilitate crime perpetration, secondary Crime prevention focuses on early identification of offenders and intervenes in such a way that the crime is never committed. In other words, secondary crime prevention is based on trying to identify possible offenders, places, or issues that have a greater likelihood of crime. The importance of secondary crime prevention in controlling the crime rate cannot be overstated. Researchers have found a correlation between secondary crime prevention and reduced crime rate (Donkin, 2014). This paper will examine secondary crime prevention theories of VIVA (i.e., value, inertia, visibility, and accessibility) put forward by Cohen and Felson (1979) and the Concealable, Removable, Available, Valuable, Enjoyable, and Disposable (CRAVED) theory developed by Clarke and Webb (1999) in the identification of hot spots for crime as well as how the two theories aid in the identification and prevention of theft of hot products.
Secondary Crime Prediction
In nearly every period of human civilization, crime has been a menace lamented over by the public, in governance, and by the media. However, with the advents in science and technology, crime statistics, data mining, big data analytics, criminal behavior theories have gained popularity as prime methods of Predicting and preventing crime. Therefore, by definition, crime prediction refers to the use of quantitative, qualitative, and mixed techniques or practices to identify location and dates where specific crimes may occur and concomitantly sending response units to these locales to prevent such an eventuality. In contemporary times, crime prediction technology has been invaluable in achieving these ends (Grover et al., 2006). For instance, Knowledge Discovery in Databases (KDD) through data mining and big data analytics is applicable in almost all crime types and has been instrumental in predicting criminality and crime within certain geographical areas.
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Clinical and Actuarial Prediction Models of Crime Prediction
In clinical prediction, the decision-maker processes information in his/her head and comes up with a decision that he/she feels is best suited for the situation. In layman terms, it is called a 'gut feeling.' In the actuarial technique, the criminologist uses statistical or empirically founded reasoning based on relationships between the data at hand and the conditions or circumstances of interest to come up with a predicate conclusion. Both are effective methods of crime prediction, but most researchers argue that actuarial prediction is far more effective than clinical prediction owing to the fact that Human judgment is inherently biased and susceptible to bias. However, in real-life practice, actuarial techniques are rarely applied in practice due to the messiness of the real world; that is, there are circumstances that cannot be quantified or expressed as an algorithm, which invalidate the actuarial technique (Lab, 2014). Nevertheless, with limited information and knowledge, and also considering that the human brain is not a computing platform, decisions made out of clinical judgments may not be as viable as those made from the actuarial methodology.
Risk Factors for Crime Categorized
Risk factors are the situations that may raise an individual's likelihood of getting engaged in criminal behaviors. Risk factors are categorized into four main categories. These are individual, family, school, peer group, and community.
Individual factors : these entail antisocial behavior and alienation, involvement in general delinquency involvement, drug use, illegal gun ownership or possession, alcoholism, early onset of aggression or violence, underlying mental health problems, early engagement in sexual activity, and exposure to violence.
Family ; these include; Family history of problem behavior, parent criminality and conviction, victimization and maltreatment by the parent, family poverty, Broken homes, Sibling anti-social behavior, low parent education, and literacy as well as erratic discipline practices.
School: these include; poor academic achievement, Truancy, and frequent absenteeism, Negative attitude, commitment, and attachment towards school, students, and staff, frequent suspensions, school dropouts, negative labeling by teachers, and hostile school climate.
Peers: these include; Gang involvement, association with delinquents, drug users, and aggressive peers, as well as Peer rejection.
VIVA and CRAVE and Their Correlation to Hot Products and Hot Spots
Theft is among the leading forms of crime in the US and globally. Criminologist applies various crime prediction models to identify at-risk areas for crime (Hot Spots) and the most at-risk items for Theft (Hot products). Two models of crime prediction towards this are the VIVA and the more recent and efficacious CRAVED models.
The routines activity theory presented by Cohen and Felson (1979) argued that crime increases due to routine activities by society. According to the authors, routine activities are mainly precipitated by the availability of targets away from their home setting/guardianship (hotspots) viewed as attractive/suitable (Hot products). They suggested that the hop product suits at least four qualities, Value (perceived value by the offender), Inertia (have size and weight that makes the illegal crime possible), Visibility (physically visible to offenders), and Access (accessible to the potential offender, hence the VIVA proposition. VIVA had some limitations, including the fact that it was too broad, and it did not encompass motivation and contemplation by offenders, which is a key feature in crime perpetration. Hence to overcome this, Clarke and Webb (1999) developed the CRAVED model. Concealing and disposing of the hot products forms part of contemplation; this is not captured in VIVA. Felson and Boba (2010) expanded the use of CRAVED beyond theft to explaining sexual crimes.
Some hot products that fit the VIVA model include diamonds, luxury household goods, Motor vehicles, marine vessels, and money, which are valuable, inert, visible, but access is limited, and contemplation is needed to get insights as to how to access them (Beauregard & Martineau, 2015). CRAVED model can be applied to the aforementioned products and objects items and also to human beings (women and children) who may be prime victims of sexual assault, kidnapping, and homicide).
References
Beauregard, E., & Martineau, M. (2015). An application of CRAVED to the choice of a victim in sexual homicide: A routine activity approach. Crime Science , 4 (1), 24.
Clarke, R. V. G., & Webb, B. (1999). Hot products: Understanding, anticipating, and reducing demand for stolen goods (Vol. 112). London: Home Office, Policing and Reducing Crime Unit, Research, Development, and Statistics Directorate.
Cohen, L. E., & Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American sociological review , 588-608.
Donkin, S. (2014). Crime Prevention: Back to Basics. In Preventing Terrorism and Controlling Risk (pp. 69-78). Springer, New York, NY.
Felson, M., & Boba, R. L. (Eds.). (2010). Crime and everyday life . Sage.
Grover, V., Adderley, R., & Bramer, M. (2006, December). Review of current crime prediction techniques. In International Conference on Innovative Techniques and Applications of Artificial Intelligence (pp. 233-237). Springer, London.
Lab. S. P., (2014). Crime prevention: Approaches, practices, and evaluations . Anderson.
