Crimes occur everywhere around us, affecting the quality of life and growth of the economy of a society. Although crimes occur everywhere, criminals target familiar areas for them whenever they get the opportunity. Thus, given a data mining approach, it is possible to process data such as the crime type, date, area and so on to find a pattern among these crimes. This could help us predict whether the area is safe or unsafe, for a given time, day and area. By this, we hope to raise the awareness among people about their surroundings and help them be aware and undertake safety measures at the dangerous time periods. This could also be useful to the police department of the area so they can send suitable police force and maintain tight security in crime prone areas and times. Our Data Mining assignment aims at performing Tactical Crime analysis on real-world crimes dataset of Los Angeles in California from 2010 to 2015. To extract frequent patterns of crime on the dataset, we used several techniques and algorithms to generate various graphs to help us analyze the data and be able to predict the crime rates in specific locations at a particular time. We have provided statistical analysis of different crime types with their demographic information.
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Our dataset initially consisted of several attributes such as date reported, date occurred, time occurred, area id, area name, reporting district, crime code, crime code description, victim age, victim sex, victim descent, weapon used code, weapon description, status code, status description, cross street, location.
But for the purpose of our assignment, we narrowed them down to 11 attributes. A detailed description of each attribute is given below.
