The New Zealand Crime Harm Index: Quantifying Harm Using Sentencing Data

Curtis‐Ham, Sophie; Walton, Darren

doi:10.1093/police/pax050

Cited by 30 publications

(19 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The New Zealand Crime Harm Index is another example of measuring the harm of crime, in an effort to reallocate police resources more efficiently. Curtis-Ham & Walton (2018) developed this measure as an alternative to the traditional Crime Harm Index, to be more specific with crimes in New Zealand. Although it takes a similar approach, the New Zealand Crime Harm Index uses the actual time served by the offender, to maintain proportionality between short and long sentences due to early releases and parole, account for Home Detention allotment, as well as account for the proportion of the sentence served.…”

Section: Calculating Crime Harm Scoresmentioning

confidence: 99%

Redrawing Hot Spots of Crime in Dallas, Texas

Wheeler¹,

Reuter²

2020

CrimRxiv

View full text Add to dashboard Cite

In this work we evaluate the predictive capability of identifying long term, micro place hot spots in Dallas, Texas. We create hot spots using a clustering algorithm, using law enforcement cost of crime estimates as weights. Relative to the much larger current hot spot areas defined by the Dallas Police Department, our identified hot spots are much smaller (under 3 square miles), and capture crime cost at a higher density. We also show that the clustering algorithm captures a wide array of hot spot types; some one or two addresses, some street segments, and others an agglomeration of larger areas. This suggests identifying hot spots based on a specific unit of aggregation (e.g. addresses, street segments), may be less efficient than using a clustering technique in practice.

show abstract

Section: Calculating Crime Harm Scoresmentioning

confidence: 99%

Redrawing Hot Spots of Crime in Dallas, Texas

Wheeler¹,

Reuter²

2020

CrimRxiv

View full text Add to dashboard Cite

show abstract

“…6A measure of the relative harm from a crime or set of crimes that assist in the estimation of returns on investment from a given intervention. Refer to Curtis-Ham and Walton (2017) for an example of the application of a harm index.…”

mentioning

confidence: 99%

Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions

et al. 2018

View full text Add to dashboard Cite

Background The Manning Cost–Benefit Tool (MCBT) was developed to assist criminal justice policymakers, policing organisations and crime prevention practitioners to assess the benefits of different interventions for reducing crime and to select those strategies that represent the greatest economic return on investment. Discussion A challenge with the MCBT and other cost–benefit tools is that users need to input, manually, a considerable amount of point-in-time data, a process that is time consuming, relies on subjective expert opinion, and introduces the potential for data-input error. In this paper, we present and discuss a conceptual model for a ‘smart’ MCBT that utilises machine learning techniques. Summary We argue that the Smart MCBT outlined in this paper will overcome the shortcomings of existing cost–benefit tools. It does this by reintegrating individual cost–benefit analysis (CBA) projects using a database system that securely stores and de-identifies project data, and redeploys it using a range of machine learning and data science techniques. In addition, the question of what works is respecified by the Smart MCBT tool as a data science pipeline, which serves to enhance CBA and reconfigure the policy making process in the paradigm of open data and data analytics.

show abstract

“…(Sherman et al 2016a, Rinaldo 2015Curtis-Ham and Walton 2017). Therefore, it is important to acknowledge that some of the offences within this court data occurred in 2010 when minimum wage was different to what it was in 2017.…”

Section: Limitations and Data Cleaningmentioning

confidence: 94%

Developing a Crime Harm Index for Western Australia: the WACHI

House

Neyroud

2018

Camb J Evid Based Polic

View full text Add to dashboard Cite

Research Question Can a reliable measure of precise harm levels for the 100 most harmful and frequently occurring offences be developed in Western Australia (WA) based on analysis of actual court penalties for first-time offenders? Data Criminal and traffic court sentences in 2.2 million records over 6.5 years were analysed to extract the number of days of imprisonment actually imposed in sentencing decisions for approximately 52,000 first-time offenders (see House 2017). Methods Sentences for all first offenders in a sample of the 102 most common offence categories were analysed to compute for the median number of days of imprisonment to which each first offender was sentenced in each of the categories. Monetary penalties and conditional community sentences were converted to equivalent 'prison days' and added to the computation of the median of days of imprisonment per offence category. The number of reported offences in WA in the study period for each of the 102 categories was then multiplied by the median prison days sentenced per category. The sum of the products of median prison days times offence count was then tallied across all offence categories to form a weighted index of crime harm, which we define as the Western Australian Crime Harm Index (WACHI). Applying a minimum requirement of at least five separate court cases for each crime category, a total of 88 offence categories survived the reliability threshold for inclusion in the index. Findings The 88 offence categories in the WACHI contain both high-harm and highvolume offences, permitting 95% of all offences reported for over 5 years to be assessed for WACHI scores. The counts for these offences moved in different directions from the WACHI total in two of the four year-to-year comparisons. Changes in WACHI were shown to have been highly sensitive to increased reporting of historical sex crimes, isolated in one district each of both Metropolitan Perth and one Regional centre.Camb J Evid Based Polic (2018)

show abstract

The New Zealand Crime Harm Index: Quantifying Harm Using Sentencing Data

Cited by 30 publications

References 11 publications

Redrawing Hot Spots of Crime in Dallas, Texas

Redrawing Hot Spots of Crime in Dallas, Texas

Towards a ‘smart’ cost–benefit tool: using machine learning to predict the costs of criminal justice policy interventions

Developing a Crime Harm Index for Western Australia: the WACHI

Contact Info

Product

Resources

About