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

TL;DR

This paper analyzes spatial and temporal crime hotspots using datasets from Denver and Los Angeles, applying data mining and classification techniques to predict crime types and understand influencing factors.

Contribution

It introduces a combined approach using Apriori, decision trees, and demographic analysis to improve crime hotspot detection and prediction.

Findings

Identification of criminal hotspots in Denver and LA

Effective prediction of crime types using classifiers

Insights into demographic factors influencing crime

Abstract

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.