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…
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