These data were collected from the New Zealand Ministry of Justice and comprises selected years from 2001 to 2022.
data("crim.nz")A data frame with 257600 observations on the following 7 variables.
prisona logical vector;
FALSE if and only if the sentence was either
"Imprisonment",
"Imprisonment sentences" or
"Life Imprisonment".
So the other sentences were not imprisonment.
agegpan ordered factor with
levels 17-19 < 20-24 <
25-29 < 30-39 <
40+.
The age of the offender.
offencea factor with
levels
abduction,
injury,
endanger,
fraud,
homicide,
drugs,
miscoff,
antigovt,
weapons,
property,
order,
robbery,
sexoff,
theft,
burglary.
The main offence.
In more detail,
the levels correspond to
"Abduction, harassment and other offences against the person",
"Acts intended to cause injury",
"Dangerous or negligent acts endangering persons",
"Fraud, deception and related offences",
"Homicide and related offences",
"Illicit drug offences",
"Miscellaneous offences",
"Offences against justice procedures, gov. security and gov. operations",
"Prohibited and regulated weapons and explosives offences",
"Property damage and environmental pollution",
"Public order offences",
"Robbery, extortion and related offences",
"Sexual assault and related offences",
"Theft and related offences",
"Unlawful entry with intent/burglary, break and enter",
respectively.
gendera factor with levels
F and M.
ethnicitya factor with levels
Asian,
European,
Maori,
Other,
Polynesian.
yeara numeric vector. The calendar year when the crime occurred.
sentencea factor with levels
ComDet,
ComSent,
ComWork,
HomeDet,
Prison,
PrisonSent,
IntSupv,
Life,
Money,
Other,
PrevDet,
Supv.
In more detail,
the levels correspond to
"Community Detention",
"Community sentences",
"Community Work",
"Home Detention",
"Imprisonment",
"Imprisonment sentences",
"Intensive Supervision",
"Life Imprisonment",
"Monetary ",
"Other",
"Preventive Detention",
"Supervision" respectively.
The data were collected in late 2023 and is described in detail in Garcia et al. (2023). Almost all the information here comes from that document. The original data comprised each year from 2001 to 2022 inclusive, however only the years 2001, 2010, 2019 and 2022 are included here for brevity.
Variables with values
"Unknown/Organisation"
were treated as missing and deleted.
The rownames were
stripped off to make the data frame
much smaller.
A matching New Zealand Police data set on proceedings against offenders was also collected but not included in VGAMdata because of its size.
Offences are categorised using the Australian and New Zealand Standard Offence Classification (ANZSOC). There are sixteen top level `divisions' which are further subdivided into `subdivisions' and `groups'. One of the sixteen main divisions (Traffic Offences) are excluded. Only the most serious offence by the offender is recorded here.
Each row contains the count of sentences received by offenders with particular demographic information in the given period, along with the ANZSOC offence code. However, since ANZSOC is a hierarchical classification, a unique offender is represented in three separate rows: one for the ANZSOC Group of the offence, one for the Subdivision of the offence, and one for the Division of the offence.
Fairness analysis and machine learning model implementation using New Zealand prosecution and conviction data. Garcia, F. and Omidyar, P. and Rodrigues, L. and Xie, J. (2023). Postgraduate Report, Computer Science Department, University of Auckland.
Only ANZSOC Divisions 01--16 were collected. More details can be found at https://www.police.govt.nz/about-us/publications-statistics/data-and-statistics/policedatanz/proceedings-offender-demographics and https://www.abs.gov.au/statistics/classifications/australian-and-new-zealand-standard-offence-classification-anzsoc/latest-release
data(crim.nz)
summary(crim.nz)
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