核心概念
The author proposes a methodological taxonomy to classify crime prediction algorithms, enhancing comparability and aiding in algorithm development.
摘要
This comprehensive survey paper delves into the analysis of crime prediction methodologies, focusing on statistical methods, machine learning algorithms, and deep learning techniques. The proposed methodological taxonomy categorizes algorithms into specific tiers for detailed evaluation. The integration of empirical and experimental evaluations provides insights into the strengths and weaknesses of various crime prediction techniques. By reviewing over 150 papers from esteemed publishers like IEEE and ACM, the study offers valuable insights for future research in this domain.
Key points include:
Classification of crime prediction algorithms based on methodology categories.
Importance of spatial and temporal data in predicting crimes accurately.
Utilization of CNNs, Residual Networks, BiLSTMs, BERT models for spatial-temporal classification.
Evaluation metrics such as scalability, interpretability, accuracy, efficiency are crucial for assessing model performance.
統計資料
Duan [24] proposed a Spatiotemporal Crime Network (STCN) using deep CNNs for automatic crime-referenced feature extraction.
Fu et [25] introduced a CNN-based approach for inferring crime rankings from street view images using preference learning framework.
Wei et [26] developed CrimeSTC framework utilizing CNN, GRU, fully connected layers for urban crime prediction.
引述
"The rationale behind the usage of the technique: ResNet-based spatiotemporal models excel in managing the complexity of crime data."
"Conditions for optimal performance: Customize ResNet architecture to data complexity and tasks."
"Limitations: Accurately capturing temporal dynamics is difficult due to unpredictable factors affecting crime patterns."