The scope of crime prevention has grown considerably in the last few years. What was previously the sole concern of the police and the private security industry has spread into areas from real estate developer, car manufacturers, residents’ groups, building public facilities like society offices and shopping centers. All these call for continuously using improved new ways to prevent crime. Therefore, how to discover the variables which have salience in affecting crime rate becomes crucial. In this thesis, multivariate statistical analysis is utilized on the crime data of L.A. city to explore the above topic. Specifically, we use Principal Component Analysis to discover the distinct influential variables in the identification of a community with high or low crime rate and try to construct a baseline to classify a community as safe or unsafe via Discriminant Analysis. The above analytical techniques not only can make police departments have a sense of dangerous communities to pay more enforcement on them, it assist governments in finding out what variables they need to change to improve the city to become a better place to live as well.