Vivek Jaglan el at. International Journal of Recent Research Aspects ISSN: 2349-7688, Vol. 3, Issue 4, December 2016, pp. 71-76 © 2017 IJRRA All Rights Reserved page-71- Review of modeling technologies used for Predicting Crime Vivek Jaglan CSE Deptt, Amity University, Gurugram, Haryana Abstract—Crime against women these days has become problem of every nation around the globe many countries are trying to curb this problem. Preventive are taken to reduce the increasing number of cases of crime against women. A huge amount of data set is generated every year on the basis of reporting of crime. This data can prove very useful in analysing and predicting crime and help us prevent the crime to some extent. Crime analysis is an area of vital importance in police department. Study of crime data can help us analyses crime pattern, inter-related clues& important hidden relations between the crimes. That is why data mining can be great aid to analyses, visualize and predict crime using crime data set. Classification and correlation of data set makes it easy to understand similarities & dissimilarities amongst the data objects. We group data objects using clustering technique. Dataset is classified on the basis of some predefined condition. Here grouping is done according to various types of crimes against women taking place in different states and cities of India. Crime mapping will help the administration to plan strategies for prevention of crime, further using data mining technique data can be predicted and visualized in various form in order to provide better understanding of crime patterns. Keywords—Modeling Technologies Used For Predicting Crime Against Women I. INTRODUCTION The concern about national security has increased significantly since the terrorist attacks on November 26, 2008 at Mumbai. Intelligence agencies such as the CBI and NCRB (National Crime Record Bureau) are actively collecting and analyzing information to investigate terrorists’ activities [12]. Local law enforcement agencies like SCRB(State Crime Record Bureau) and DCRB(District Crime Record Bureau)/CCRB (City Crime Record Bureau) have also become more alert to criminal activities in their own jurisdictions. One challenge to law enforcement and intelligence agencies is the difficulty of analyzing large volumes of data involved in criminal and terrorist activities. Data mining holds the promise of making it easy, convenient, and practical to explore very large databases for organizations and users. In this paper, we review data mining techniques applied in the context of law enforcement and intelligence analysis. The notion of crime forecasting dates back to 1931, when sociologist Clifford R. Shaw of the University of Chicago and criminologist Henry D. McKay of Chicago's Institute for Juvenile Research wrote a book exploring the persistence of juvenile crime in specific neighborhoods. Scientists have experimented with using statistical and geospatial analyses to determine crime risk levels ever since. In the 1990s, the National Institute of Justice (NIJ) and others embraced geographic information system tools for mapping crime data, and researchers began using everything from basic regression analysis to cutting-edge mathematical models to forecast when and where the next outbreak might occur. But until recently, the limits of computing power and storage prevented them from using large data sets. In 2006, researchers at the University of California, Los Angeles (UCLA), and UC Irvine teamed up with the Los Angeles Police Department (LAPD). By then, police departments were catching up in data collection, making crime forecasting "a real possibility rather than just a theoretical novelty," says UCLA anthropologist Jeffrey Brantingham. LAPD was using hot spot maps of past crimes to determine where to send patrols—a strategy the department called "cops on the dot." Brantingham's team believed they could make the maps predictive rather than merely descriptive. Figure 1: Process Making “predictions” is only half of prediction-led policing; the other half is carrying out interventions, acting on the predictions that lead to reduced crime (or at least solve crimes). What we have found in this study is that predictive policing is best thought of as part of a comprehensive business process. That process is summarized in Figure S.1. We also identified some emerging practices for running this business process successfully through a series of discussions with leading predictive policing practitioners. At the core of the process shown in Figure S.1 is a four-step cycle (top of figure). The first two steps are collecting and analyzing crime, incident, and offender data to produce predictions. Data from disparate