A framework of intelligent decision support system for Indian police

Author:

Gupta Manish,Chandra B.,Gupta M.P.

Abstract

Purpose – The purpose of this paper is to introduce architecture of an Intelligent Decision Support System to fulfill the emerging responsibilities of law enforcement agencies. Design/methodology/approach – The proposed Intelligent Police System (IPS) is designed to meet the emerging requirements and provide information at all levels of decision making by introducing a multi-level structure of user interface and crime analysis model. The proposed framework of IPS is based on data mining and performance measurement techniques to extract useful information like crime hot spots, predict crime trends and rank police administration units on the basis of crime prevention measures. Findings – IPS has been implemented on actual Indian crime data provided by National Crime Records Bureau (NCRB), which illustrates effectiveness and usefulness of the proposed system. IPS can play a vital role in improving outcome in the crime investigation, criminal detection and other major areas of functioning of police organization by analyzing the crime data and sharing of the information. Research limitations/implications – The research in intelligent police information system can be enhanced with some important additional features which include web-base management system, geographical information system, mobile adhoc network technology, etc. Practical implications – IPS can easily be applied to any police system in the world and can equally be useful for any law enforcement agencies for carrying out homeland security effectively. Originality/value – The research reported in this manuscript is outcome of the research project funded by NCRB. This paper is the first attempt to build framework of IPS for Indian police who deal with large volume and high rate of crimes that are unmatched to any police force of the world.

Publisher

Emerald

Subject

Information Systems,Management of Technology and Innovation,General Decision Sciences

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