Measuring The Validity Of The Figures For Doncaster Town Centre

A system called “CompStat” was used to track crime reports and other data in an effort to track areas with high crime rates and gang hot spots. Karoliszyn reports that “after patrolling these hot spots for a year, murders had dropped by 60 percent. By 2003, murders were the lowest they had been since 1964” (338). With these statistics, Karoliszyn proves the effectiveness of the system when actually used in the workforce. There is a flaw in his claim. The author only proved the system’s effectiveness in one city, and in one year. As with a great majority of statistics, these numbers could vary either towards or against the system’s value if done in different cities and in different amounts of time. With more data spread across the spectrum, a bigger picture could be seen regarding the reliability of a precognitive policing system.

In order to further assess this argument one must look at the social context and social control (9) in a particular city that influence Blalock’s curve. Social context is the physical and social environment in which people live and interact, including; city size, racial differences, social interactions, economy, and political impression. The social context of a certain area can affect the fear of crime and minority group threat. Depending on a regions history and traditional interactions among majority and minority groups, can influence police expenditure and level of attention to the crime problem. Another facet of social context

Understanding Crime Measurement The first Azure Eureka moment occurred when I realized the importance of measuring crime accurately. Previously, I had been influenced by media portrayals, which often sensationalized crime and contributed to a perception of it being more widespread than it actually is. However, through the course materials, I learned that measuring crime serves multiple critical purposes. This

Richard Rosenfeld takes note to the rising crime statistics and relationship between the police and public, but also notes “It may also have to do with local factors specific to a particular city,”(Schuppe).

The results of this experiment was the reduction of 53 violent crimes comprises a reduction of 90 crimes in the targeted area, which was offset by a 37 offense increase occurring in the displacement areas immediately surrounding target areas (Ratcliffe,

In order to test the truth of our hypothesis, we compiled a cross-sectional data set using public resources provided by the United States Census Bureau, and converted them into Stata format (as we have been doing in class). After rigorously checking the data set we had gathered for issues that would impact the significance of a regression analysis such as multicollinearity, we concluded that the data set showed no serious issues and proceeded to test different transformations of the dependent and independent variable, before settling on the most significant and logical options. We then carried out the process of economic analysis, checking to see if our results for their logical consistency as well as economic validity, fine-tuning our regression in order to garner the most rigorous interpretation of the data. After extensive work our analysis reached not only highly significant, but also highly satisfying results, yielding a confirmation of both our hypothesis of the relationship between the frequency of crime and household income, but also our auxiliary expectations for the coefficients of the other independent variables.

Looking back to 1960 we can notice that violent crime rates always vary. In 1960 murder was at 824, but the 1961 it dropped to 788, then goes back up to 983 in 1966. There is no real pattern to how it varies. Many might think that with increased population we will have increased crimes, but that isn’t the case. In 1975 the population was an approximate 12,237,000 and violent crime was at 1,639. In 1976 the population grew to 12,487,000 but the violent crime dropped to 1,519. (4)

These particular areas tend to be inner-city, with a high population of ethnic minorities, poverty and crime (Lynn and Elliot, 2000) . Thus not only does the sample and the data it acquires become less representative of the population as a whole, it in turn over represents particular groups of people, and the types of crimes committed; which in turn gives across bias ideologies of criminal groups, criminal areas and the level of crime. As exemplified by Tony Blair who stated gun crime was a problem within the black community (Wintour and Dodd, 2007), these crime statics can give across an ideological bias of the relationship between a particular crime and a particular group. Arguably, higher levels of police are distributed in these areas which in turn creates a cyclical effect whereby ideological biases fuels higher levels of policing, which in turn increases levels of offending in statistics, as described by Sharp and Budd (2005), which consequently fuels the ideological biases as demonstrated by Tony Blair.

Throughout crime in Australia, a noticeable increase in crime occurred between the 1970’s to the 1990’s but has declined to a stable rate of crime which is similar trend in America. However, crime itself is often complex to define due to the variety of crime. Therefore, it is difficult to accurately measure crime and if crime cannot be measured efficiently and it proposes concerns of whether crime in Australia is stable or not. Although Australia’s system of collecting crime data is striving to be as accurate as possible, the media will often manipulate the data which misleads the Australian public of crime stability. Inclusively, through gaining an understanding of defining crime, accurately measuring data and comparing Australia’s crime data

In chapter 4 the chapter considers a variety of possible explanations for the significant drop in crime and crime rates that occurred in the 1990s. Based on articles that appeared in the country’s largest newspapers, the authors compile a list of the leading, commonly offered explanations. The next step is to systematically examine each explanation and consider whether available data support the explanation. What the authors, in fact, demonstrate is that in all but three cases–increased reliance on prisons, increased number of police, and changes in illegal drug markets–correlation was erroneously interpreted as causation and in some cases, the correlation wasn’t even that strong.

For the context of this paper one must assume that Philips was correct in that, there was in fact a rapid rise in crime rates.

The chapter goes on to overview the crime crisis happening the United States in the 1980s through the 1990s. After exploring possible causes of the problem and how bad everything has gotten, the authors go on to explore possible solutions to the crime epidemic. The solutions are quite varied, well thought out, and are supported with statistical facts. For example in talking about the effect of gun buyback programs and their effect on homicide: “Given the number of handguns in the United States and the number of homicides each year, the likelihood that a particular gun was used to kill someone that year in 1 in 10,000. The typical gun buyback program

Data from the Crime Survey for England and Wales (CSEW) from 2013/2014 have been used. The CSEW is a face-to-face survey asking households about their perception and experience of crime in the 12 months preceding the interview. The information at my disposal are part of a special unrestricted access teaching dataset produced by the UK Data Archive. Out of a sample of 35,371 households chosen in England and Wales for the actual survey, I had access to a 25% sample of 8,843 households. Representative of households were chosen through the Postcode Address File which excludes aggregative accommodation such as residential halls and prisons.

During this essay, I will be discussing recorded crime statistics and victimisation surveys as they are our primary techniques of measuring levels and trends of crime. After briefly explaining what is meant by these terms, I will seek to evaluate their strengths and weaknesses in order to question the extent to which they are reliable resources that provide us with accurate information.

Regarding the final products of my analysis, I am pleased with the maps and results that I have created. The results were as I expected, with large hotspots of violent and sexual crime located in the city center of Liverpool as well as the football grounds. I also expected a substantial portion of the map to show little or no clustering of crimes, which most likely represent isolated incidents in which location bears no factor. I did not expect to see such a large variety of rate changes between all the wards, but this illustrates just how dynamic crime rates and crime distribution can be. Moreover, this analysis does have real-world implications, as from this analysis police forces can identify areas where more resources should be allocated, and where specialist units should patrol to curb sharp rises in certain types of crime. Ideally, this analysis should be performed as frequently as possible as to keep pace with the dynamic changes in crime frequency and distribution that this research project has highlighted. If this project was to be conducted in the future, there are several alterations that would be made to enhance the quality and accuracy of the results. First, accurate population and deprivation index data should be added for 2016 to provide accurate violent and sexual crime