Property Crimes - Literature Review

In the first article entitled, Exploring spatial patterns of property crime risks in Changchun, China, by Song & Liu, (2013) the authors evaluate the spatial distribution of property crime in the city Changchun. It also analyses the relationship between the crime patterns and the characteristics of the neighborhood in question. In this case, the writers obtain data from police records and apply standardized property crime rates to assess the relative risk of property crime across the city. They also use the geographical weighted regression model and the global ordinary least squares approach to evaluate the extent of property crime as a function of contextual neighborhood characteristics. In this regard, results from the study show that there is a significant local variation in the relationship between the social economic variables of various neighborhoods in Changchun, China and the prevalence of crime property (Song & Liu, 2013). In other words, low income areas are associated with a high levels of crime compared to high income neighborhoods.

The second article is entitled, The effects of hot spots policing on crime by Braga, Papachristos, & Hureau, (2014). It talks about the benefits of directing crime prevention efforts in specific crime areas or hotspots. It also examines whether focused police action on particular locations often result in a diffusion of crime control benefits or crime displacement. In this regard, the authors synthesize existing non-published and published empirical evidence from online sources to obtain relevant data for the study. The results from the research indicate that hotspot policing is an effective approach for preventing and minimizing crime (Braga et al., 2014). It also suggests that directing police efforts on high-activity crime places does not inevitably result in crime displacement and that crime control benefits are more likely to spread into the surrounding areas of the targeted locations (Braga et al., 2014).

The third article is entitled, The Effect of Income Inequality on Property Crime by Izadi & Piraee, (2012). It analyzes the relationship between property crime rates and income inequality. The authors utilize information from statistical year books that contain annual data for income inequality indices and property crime rates in Iran from 1984-2009. They also use the Gini coefficient and Atkinson index, with 1 and 2 degrees of inequality aversion (e=1, 2) to measure the inequality rates. Additionally, the writers employ the unrestricted error correlation model (UECM) to do bounds and assess any integration between income inequality and property crime. In this case, results from the study dispute the fact that property crime and income inequality are interrelated (Izadi & Piraee, 2012). That is to say, the authors found no relationship between the two variables during their research.

In the fourth article entitled, Effect of arrest and imprisonment on crime, by Wan, Moffatt, Jones, & Weatherburn, (2012), the writers discuss the impact of arrest, imprisonment, and the duration of incarceration on violent and property crime rates in New South Wales, Australia. In this case, they use a dynamic panel data model with fixed local government locations and time effects to conduct the research and the first-differenced generalized method of moments to measure the model apparatus. The findings of the study reveal that the criminal justice system in Australia plays a vital role in preventing and reducing crimes through arrests and detaining criminals in prisons (Wan et al., 2012). However, the researchers did not find any correlation between the duration of imprisonment and the rate of violent and property crimes.

The fifth article discusses and analyzes the inter-jurisdictional trends in theft and police-recorded robbery offences. It is entitled, The decline in robbery and theft: Inter-state comparisons by Weatherburn & Holmes, (2013). The authors obtain relevant data from the Australian Bureau of Statistics (ABS) and calculate the rates of theft and recorded robbery per head of population in each jurisdiction in Australia from 1994 to 2012. The criminal offenses are then classified into different categories including unarmed and armed robbery, burglary, and motor vehicle theft, just to mention. In this regard, results from the research indicate that the national decline in theft and robbery offences is attributed to the decline of heroin use in the country and improvements in the economy, among other contributing factors (Weatherburn & Holmes, 2013).

The last article is entitled, The dark figure of online property crime: is cyberspace hiding a crime wave? by Tcherni, Davies, Lopes, & Lizotte, (2016). It discusses the current trends of property crime and highlights the gap in crime reporting and accounting with regard the rising number of property crimes perpetrated online. It also evaluates the similarities and differences between the estimated costs of the traditional property crime and online property crime. Data is obtained from various nationally representative victim surveys. The results from the study show that the level of online property crime is higher than previously estimated. Moreover, the authors suggest that the financial losses experienced by victims of online property crime is much greater than the one caused by the traditional property crime.

A Comparative Analysis of Property Crimes

The researchers in the first article utilize the geographical weighted regression ( GWR) model and the global ordinary least squares (OLS) approach to assess the danger of property crime as a function of contextual neighborhood characteristics. GWS is an approach that is commonly used to measure spatially varying relationships (Wheeler, 2014). One advantage of using this approach is that it helps researchers identify the spatial relationship between different variables. On the other hand, it tends to produce unsmooth surfaces, particularly, when the mean parameters have substantial variations (Wheeler, 2014). The OLS is also an efficient method for analyzing the relationship between an independent and dependent variable. It is, however, restricted to linear relationships only between the variables inquestion (Wheeler, 2014). In this regard, I would opt for the conditional geographically weighted regression (CGWR) to avoid any varying bandwidth issues, and produce accurate surfaces, especially, when dealing with data that has variables with varying characteristics.

The second article utilized both randomized and quasi-experimental designs to examine empirical evidence obtained from online sources to determine the impact of hot spot policing on crime. Quasi-experimental designs entails selecting individual groups for a particular study upon which the variables in question are tested (Ott & Longnecker, 2015). One advantage of using this type of method in analyzing property crimes is that it is not technical and saves time. On the other hand, quasi-experimental designs are subject to human error because they lack proper controls and may involve the manipulation of variables in some instances. In contrast, randomized experimental designs tend to be more accurate compared to quasi-experimental designs since the variables are randomly selected (Ott & Longnecker, 2015). In this regard, I would have used both approaches to evaluate the impact of hot spot policing on crime.

The Gini coefficient and Atkinson index were utilized in the third article to determine the effect of income inequality on property crime. The Gini coefficient is usually used to measure the level of inequality within a given region or country (Ott & Longnecker, 2015). Similarly, the Atkinson index is commonly used to measure income inequality. The Gini coefficient differs from the Atkinson index in that it is purely statistical. On the other hand, the Atkinson is based on the loss that is often incurred from unequal income distribution and an explicit formulation of social welfare (Ott & Longnecker, 2015). In this regard, one advantage of using the Atkinson index over the Gini coefficient is that it allows the researcher to attach a given weight to inequality at different points. On the other hand, it is not possible for persons to differentiate between two varying inequalities using the Gini coefficient. As a result, using both measures is essential in evaluating the impact of income inequality on property crime.

The researchers used the generalized method of moments (GMM) to estimate the model apparatus and establish the impact of imprisonment, arrest, and duration of incarceration on violent and property crime rates. The GMM is a generic strategy that is commonly used to assess parameters in statistical models (Ott & Longnecker, 2015). The advantage of using this approach in analyzing property crime data is that it is the only estimation strategy that one can use when experiencing indigeneity problems. However, the method is complex and consumes a lot of time when analyzing data. In this case, I would have used the principal component-based IV reduction (PCIVR) to make up for the short comings of the GMM strategy. In other words, the PCIVR is a method that is used to estimate statistical data and minimize information loss.

In the fifth article the rates for recorded theft and robbery are calculated based on the number of theft per head in the given population. The criminal offences are then categorized into different groups including armed and unarmed robbery, motor vehicle theft, and burglary, among other crimes. All this is done to identify the reasons behind the decline in robbery and theft in Australia (Ott & Longnecker, 2015). Categorizing the criminal activities into various groups simplifies the data analysis process. On the other hand, it is exhaustive in natures and consumes a lot a lot of time. In contrast, the researchers in the last article obtain data from various nationally representative victim surveys to examine the spread and effects of online property crime. In this regard, the surveys are useful in acquiring such information. As a result, I would include surveys and interviews in both studies because they are not technical and are efficient in conducting extensive research (Ott & Longnecker, 2015). However, the surveys can be tiresome in some instances.

References

Braga, A. A., Papachristos, A. V., & Hureau, D. M. (2014). The effects of hot spots policing on crime: An updated systematic review and meta-analysis. Justice quarterly, 31(4), 633-663.

Izadi, N., & Piraee, K. (2012). The Effect of Income Inequality on Property Crime: Evidence from Iran. Journal of Economics and Behavioral Studies, 4(5), 245-251.

Ott, R. L., & Longnecker, M. T. (2015). An introduction to statistical methods and data analysis. Nelson Education.

Song, W., & Liu, D. (2013). Exploring spatial patterns of property crime risks in Changchun, China. International Journal of Applied Geospatial Research (IJAGR), 4(3), 80-100.

Tcherni, M., Davies, A., Lopes, G., & Lizotte, A. (2016). The dark figure of online property crime: is cyberspace hiding a crime wave?. Justice Quarterly, 33(5), 890-911.

Wan, W. Y., Moffatt, S., Jones, C., & Weatherburn, D. (2012). Effect of arrest and imprisonment on crime, The. BOCSAR NSW Crime and Justice Bulletins, 20.

Weatherburn, D., & Holmes, J. (2013). The decline in robbery and theft: Inter-state comparisons. Bureau Brief, 89.

Wheeler, D. C. (2014). Geographically weighted regression. In Handbook of regional science (pp. 1435-1459). Springer Berlin Heidelberg.