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

arXiv: https://arxiv.org/abs/1508.02050 · HTML: https://mlsanity.com/paper/1508.02050/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2015.

## Built on by (most cited first, 6 of 6)

- [Spatiotemporal Data Mining: A Survey on Challenges and Open Problems](https://mlsanity.com/paper/2103.17128/) (2021)
- [Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks](https://mlsanity.com/paper/2408.04193/) (2024)
- [Spatial-Temporal Meta-path Guided Explainable Crime Prediction](https://mlsanity.com/paper/2205.01901/) (2022)
- [A Survey on Societal Event Forecasting with Deep Learning](https://mlsanity.com/paper/2112.06345/) (2021)
- [Crime Prediction using Machine Learning with a Novel Crime Dataset](https://mlsanity.com/paper/2211.01551/) (2022)
- [Classifying Crime Types using Judgment Documents from Social Media](https://mlsanity.com/paper/2306.17020/) (2023)

## Abstract (arXiv)

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.