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This paper focuses on finding spatial and temporal criminal hotspots. Itanalyses two different real-world crimes datasets for Denver, CO and LosAngeles, CA and provides a comparison between the two datasets through astatistical analysis supported by several graphs. Then, it clarifies how weconducted Apriori algorithm to produce interesting frequent patterns forcriminal hotspots. In addition, the paper shows how we used Decision Treeclassifier and Naive Bayesian classifier in order to predict potential crimetypes. To further analyse crimes datasets, the paper introduces an analysisstudy by combining our findings of Denver crimes dataset with its demographicsinformation in order to capture the factors that might affect the safety ofneighborhoods. The results of this solution could be used to raise awarenessregarding the dangerous locations and to help agencies to predict future crimesin a specific location within a particular time.

Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots