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Published in: Cluster Computing 3/2019

28-07-2017

Identifying key influential parameters of high profile criminals through statistical correlation

Authors: Danish Wadood, Azhar Rauf, Shah Khusro, Shaukat Ali

Published in: Cluster Computing | Special Issue 3/2019

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Abstract

Identification of influential parameters using data mining and statistical tools is an important subject of criminology. These parameters are further used in criminal prediction and crime reduction which is a well-known problem in law enforcement agencies. In this research, we have analyzed the 5 years data of the Prison Department of the Government of KP. More than thirty parameters related to criminals were cleaned, preprocessed and analyzed. Correlation of these parameters with high act crimes was identified. There are five parameters which have strong correlation with high act crimes that includes total number of group members, recovery of assets, total number of hearings, crime frequency, and total number of non-blood visitors. On the other hand, there are three parameters that have negative correlation with high act crimes which includes education level of a criminal, total number of dependents, and prison duration. The overall objective of this research is to identify influential parameters related to high category crimes and support the law enforcement agencies of the KP province in reducing the crime ratio.

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Metadata
Title
Identifying key influential parameters of high profile criminals through statistical correlation
Authors
Danish Wadood
Azhar Rauf
Shah Khusro
Shaukat Ali
Publication date
28-07-2017
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 3/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-1059-1

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