Big data analytics and mining for effective visualization and trends forecasting of crime data.

Feng, Mingchen; Zheng, Jiangbin; Ren, Jinchang; Hussain, Amir; Li, Xiuxiu; Xi, Yue; Liu, Qiaoyuan

Abstract

Big data analytics (BDA) is a systematic approach for analyzing and identifying different patterns, relations, and trends within a large volume of data. In this paper, we apply BDA to criminal data where exploratory data analysis is conducted for visualization and trends prediction. Several the state-of-the-art data mining and deep learning techniques are used. Following statistical analysis and visualization, some interesting facts and patterns are discovered from criminal data in San Francisco, Chicago, and Philadelphia. The predictive results show that the Prophet model and Keras stateful LSTM perform better than neural network models, where the optimal size of the training data is found to be three years. These promising outcomes will benefit for police departments and law enforcement organizations to better understand crime issues and provide insights that will enable them to track activities, predict the likelihood of incidents, effectively deploy resources and optimize the decision making process.

Citation

FENG, M., ZHENG, J., REN, J., HUSSAIN, A., LI, X., XI, Y. and LIU, Q. 2019. Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE access [online], 7, pages 106111-106123. Available from: https://doi.org/10.1109/ACCESS.2019.2930410

• Journal Article Type: Article
• Acceptance Date: Jul 9, 2019
• Online Publication Date: Jul 22, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Jul 15, 2024
• Publicly Available Date: Jul 15, 2024
• Journal: IEEE access.
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Pages: 106111-106123
• DOI: https://doi.org/10.1109/ACCESS.2019.2930410
• Keywords: Big data analytics (BDA); Data mining; Data visualization; Neural network; Time series forecasting
• Public URL: https://rgu-repository.worktribe.com/output/2058942