Crime Prediction Based On Crime Types And Using Spatial And Temporal Criminal Hotspots
This work addresses crime prediction for law enforcement and public safety agencies, but it is incremental as it applies existing methods to new datasets.
The paper analyzes spatial and temporal crime hotspots in Denver and Los Angeles datasets, using Apriori algorithm for pattern mining and classifiers like Decision Tree and Naive Bayesian to predict crime types, with results aimed at raising awareness and aiding crime prediction.
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.