# Empirical and Experimental Insights into Data Mining Techniques for Crime Prediction: A Comprehensive Survey

arXiv: https://arxiv.org/abs/2403.00780 · HTML: https://mlsanity.com/paper/2403.00780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 172 references that are in the map.
- First posted on arXiv 17 Feb 2024.

## Builds on (most cited first, 15 of 15)

- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs](https://mlsanity.com/paper/1902.10191/) (2019)
- [Learning Spatio-Temporal Features with 3D Residual Networks for Action Recognition](https://mlsanity.com/paper/1708.07632/) (2017)
- [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://mlsanity.com/paper/1708.04617/) (2017)
- [Spatial-Temporal Sequential Hypergraph Network for Crime Prediction with Dynamic Multiplex Relation Learning](https://mlsanity.com/paper/2201.02435/) (2022)
- [HAGEN: Homophily-Aware Graph Convolutional Recurrent Network for Crime Forecasting](https://mlsanity.com/paper/2109.12846/) (2021)
- [CAN-BERT do it? Controller Area Network Intrusion Detection System based on BERT Language Model](https://mlsanity.com/paper/2210.09439/) (2022)
- [Crime prediction through urban metrics and statistical learning](https://mlsanity.com/paper/1712.03834/) (2017)
- [Public decision support for low population density areas: An imbalance-aware hyper-ensemble for spatio-temporal crime prediction](https://mlsanity.com/paper/1902.03237/) (2019)
- [Crime Prediction Using Spatio-Temporal Data](https://mlsanity.com/paper/2003.09322/) (2020)
- [AIST: An Interpretable Attention-based Deep Learning Model for Crime Prediction](https://mlsanity.com/paper/2012.08713/) (2020)
- [Forecasting Crime with Deep Learning](https://mlsanity.com/paper/1806.01486/) (2018)
- [Spatio-temporal prediction of crimes using network analytic approach](https://mlsanity.com/paper/1808.06241/) (2018)
- [A Spatio-Temporal Kernel Density Estimation Framework for Predictive Crime Hotspot Mapping and Evaluation](https://mlsanity.com/paper/2006.00272/) (2020)
- [Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks](https://mlsanity.com/paper/2005.02142/) (2020)

## Abstract (arXiv)

This survey paper presents a comprehensive analysis of crime prediction methodologies, exploring the various techniques and technologies utilized in this area. The paper covers the statistical methods, machine learning algorithms, and deep learning techniques employed to analyze crime data, while also examining their effectiveness and limitations. We propose a methodological taxonomy that classifies crime prediction algorithms into specific techniques. This taxonomy is structured into four tiers, including methodology category, methodology sub-category, methodology techniques, and methodology sub-techniques. Empirical and experimental evaluations are provided to rank the different techniques. The empirical evaluation assesses the crime prediction techniques based on four criteria, while the experimental evaluation ranks the algorithms that employ the same sub-technique, the different sub-techniques that employ the same technique, the different techniques that employ the same methodology sub-category, the different methodology sub-categories within the same category, and the different methodology categories. The combination of methodological taxonomy, empirical evaluations, and experimental comparisons allows for a nuanced and comprehensive understanding of crime prediction algorithms, aiding researchers in making informed decisions. Finally, the paper provides a glimpse into the future of crime prediction techniques, highlighting potential advancements and opportunities for further research in this field