• norsk
    • English
  • norsk 
    • norsk
    • English
  • Logg inn
Vis innførsel 
  •   Hjem
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • Vis innførsel
  •   Hjem
  • Norges Handelshøyskole
  • Thesis
  • Master Thesis
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Likelihood of Arrests for Violent Crime Incidents in America: An exploratory study using logistic regression and random forest methods

Shukla, Mayank
Master thesis
Thumbnail
Åpne
masterthesis.PDF (834.2Kb)
Permanent lenke
https://hdl.handle.net/11250/2982412
Utgivelsesdato
2021
Metadata
Vis full innførsel
Samlinger
  • Master Thesis [3762]
Sammendrag
The use of policing algorithms to predict for arrest is rising in America. However, research

indicates that these algorithms may be biased against certain populations. These false

perceptions of who commits these crimes, and who is impacted by them is also skewed

by the media. Hence, it is important to understand which demographic and situational

characteristics of a violent crime incident impact the likelihood of arrest. In this thesis, I

will predict for arrest in incidents of violent crime as reported in the National Incident Based Reporting System 2014. The outcome of arrest was predicted using two types

of classification methods, logistic regression and random forest. The models that were

built for the aggregate of all violent crime, as well as the subsets of offense types had

a good predictive power with an accuracy of greater than 50%. Additionally, adjusted

models were built to address class imbalance and leveraged cross-validation methods.

Using odds ratios from the logistic regression results, and the variable importance plots

from the random forest - likelihood of arrest was ascertained. The results indicate that

generally the likelihood of arrest increases under certain conditions. These conditions

are: in incidents where the race of the offender is white, in incidents where the race of

the victim is white, in incidents where the offender is a female (for aggravated assault

instances), and in incidents where if the victim of a violent crime is a female. Generally,

the likelihood of arrest decreases as the age of the offender increases, and the likelihood

of arrest increases as the age of the victim increases. The likelihood of arrest decreases

for incidents where the offender is armed with a deadly weapon, and where the offender

and victim are strangers. Additionally, the likelihood of arrest increases for all violent

crimes if the incident takes place at night time compared to day time, and in incidents

where the offender is using substances. The results show that media perceptions, and

predictive policing algorithms are skewed. These typically represent black individuals as

more dangerous more likely to be incarcerated than white offenders. However, the results

from this thesis show the converse relationship. Additionally, this thesis also shows that

variables such as time of day, substance use, and the age of the victim and offender can

be leveraged to make more powerful predictions on the likelihood of arrest.

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit
 

 

Bla i

Hele arkivetDelarkiv og samlingerUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifterDenne samlingenUtgivelsesdatoForfattereTitlerEmneordDokumenttyperTidsskrifter

Min side

Logg inn

Statistikk

Besøksstatistikk

Kontakt oss | Gi tilbakemelding

Personvernerklæring
DSpace software copyright © 2002-2019  DuraSpace

Levert av  Unit