  This paper focuses on finding spatial and temporal criminal hotspots. It
analyses two different real-world crimes datasets for Denver, CO and Los
Angeles, CA and provides a comparison between the two datasets through a
statistical analysis supported by several graphs. Then, it clarifies how we
conducted Apriori algorithm to produce interesting frequent patterns for
criminal hotspots. In addition, the paper shows how we used Decision Tree
classifier and Naive Bayesian classifier in order to predict potential crime
types. To further analyse crimes datasets, the paper introduces an analysis
study by combining our findings of Denver crimes dataset with its demographics
information in order to capture the factors that might affect the safety of
neighborhoods. The results of this solution could be used to raise awareness
regarding the dangerous locations and to help agencies to predict future crimes
in a specific location within a particular time.
