Sai Krishna Vithal Lolla, Jiawen Liu, Oklahoma State University
Metropolitan cities are often at the top of crime charts. Therefore, police departments in big cities want to understand, predict and if possible prevent or to mitigate potential damage from crimes. This project is aimed at analyzing crime in Chicago City. The data set has been obtained from City of Chicago’s website. It has crime records across Chicago over the past decade. Final data set used is a subset with even distribution across the time period. Data about offenses includes fields depicting the nature of crime, time of crime, block-level location information, legal treatment of crime and resulting punishment. This data set is then transformed for further analysis. JMP® Pro 10’s sophisticated graph building techniques are used in this project. The objective is to analyze the data and find possible patterns between crime rate and crime location. In addition to that, offense rate variation based on time is also explored. With such understanding, authorities can proactively take measures to prevent some of the potential crimes.