7. Crime data and criminal statistics: a critical reflection

Maguire, Mike; McVie, Susan

doi:10.1093/he/9780198719441.003.0008

Cited by 28 publications

(21 citation statements)

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“…In their explanation of the CHI, Sherman et al (2016b) appeared to treat harm as being synonymous with seriousness, although they did not explicitly discuss it. Since the focus of this article is to compare the two measures, rather than to critique the general concept of producing weighted counts of crimes (for which, see Maguire and McVie 2017), the words harm and severity will be used interchangeably in the discussion that follows.…”

Section: Methods For Estimating Crime Harmmentioning

confidence: 99%

Comparing Methods for Measuring Crime Harm/Severity

Ashby

2017

Policing: A Journal of Policy and Practice

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Ensuring police resources are focused where they are most needed requires understanding of the consequences of crime in relation to offenders, victims and places. Most crime analysis is based on counts of crimes, but not all crimes are equivalent to one another. Researchers have recently developed two methods-the Crime Harm Index and the Crime Severity Score-for weighting crime counts according to the severity of different crime types. This article compares these two methods by applying them to two common crimeanalysis scenarios: focusing resources on the most-important types of crime and in the areas most affected by crime. The two measures are found to produce substantially different results when other factors are held constant. The results of severity-weighted crime analysis (and the decision made based on them) could therefore be greatly influenced by the method chosen. The implications of this are discussed and future research avenues outlined.

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Section: Methods For Estimating Crime Harmmentioning

confidence: 99%

Comparing Methods for Measuring Crime Harm/Severity

Ashby

2017

Policing: A Journal of Policy and Practice

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“…While mostly focusing on just one or a small number of (similar) offenses, often analyzing only police crime data with all its epistemic restrictions and flaws (Maguire and McVie 2017), and typically translating only a few criminological theories into risk-assessment algorithms, a quite basic form of predictive policing is currently dominant. However, we should think about this as a snapshot in time, as the potential of crime prediction software is much greater than that of the software approaches currently being used.…”

Section: Essentials and Current Application Of Predictive Policingmentioning

confidence: 99%

Predictive Policing and the Platformization of Police Work

Egbert

2019

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Although the revolutionary potential of predictive policing has often been exaggerated, this novel policing strategy nonetheless implies something substantially new: the underlying methods of (crime) data analysis. Moreover, these police prediction tools matter not only because of their capacity to generate near-term crime predictions but also because they have the potential to generally enhance police-related data crunching, ultimately giving rise to the comprehensive datafication of police work, creating an ongoing drive for extensive data collection and, hence, surveillance. This paper argues that because of its enablement of crime data analysis in general, predictive policing software will be an important incubator for datafied police work, especially when executed via data mining platforms, because it has made police authorities aware that the massive amounts of crime data they possess are quite valuable and can now be easily analyzed. These data are perceived to be even more useful when combined with external data sets and when processed on the largest possible scale. Ultimately, significant transformative effects are to be expected for policing, especially in relation to data collection practices and surveillance imperatives.

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“…Obviously, some care also has to be taken in terms of interpreting these crime figures, as they are based upon official statistics relating to crimes that come to the attention of the police or other official bodies. Thus, they may not be representative of the actual total extent of predicate offences across each area (see Maguire and McVie 2017). In addition, it needs to be borne in mind that the highest numbers of OCGs, predicate offences and, ultimately, volumes of money to be laundered are concentrated in the larger metropolitan areas.…”

Section: Identification Operationalisation and Normalisation Of Riskmentioning

confidence: 99%

Identifying Money Laundering Risk in the United Kingdom: Observations from National Risk Assessments and a Proposed Alternative Methodology

Hopkins

Shelton

2018

Eur J Crim Policy Res

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Two National Risk Assessments (NRA) of money laundering (ML) have now been published in the United Kingdom (see HM Treasury 2015 and 2017). While both represent an attempt to identify the risks of ML, there are limitations in relation to the conceptual framework and the methodology used. This paper reviews the UK NRAs and considers whether revision of the methodology employed could help to both remedy these limitations and generate more robust findings. Drawing upon the findings of the UK strand of project Identifying and Assessing the Risk of Money Laundering in Europe (IARM), it outlines how a composite ML risk indicator was developed through analysis of threats and vulnerabilities across 43 police areas. The findings demonstrate that risks are highest in the City of London and the Metropolitan Police area, which is largely explained by the presence of organised crime groups, connections to risky jurisdictions and the cash intensity of businesses. Although the findings should be treated with caution, it is posited that this methodology could be used to help future NRAs develop a more robust framework to understand ML risk and ultimately develop more effective preventative strategies.

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7. Crime data and criminal statistics: a critical reflection

Cited by 28 publications

References 0 publications

Comparing Methods for Measuring Crime Harm/Severity

Comparing Methods for Measuring Crime Harm/Severity

Predictive Policing and the Platformization of Police Work

Identifying Money Laundering Risk in the United Kingdom: Observations from National Risk Assessments and a Proposed Alternative Methodology

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