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

Tools

Feng, Mingchen and Zheng, Jiangbin and Ren, Jinchang and Hussain, Amir and Li, Xiuxiu and Xi, Yue and Liu, Qiaoyuan (2019) Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access, 7. pp. 106111-106123. 8768367. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2019.2930410)

[thumbnail of Feng-etal-IEEE-Access-2019-Big-data-analytics-and-mining-for-effective-visualization-and-trends-forecasting]
Preview
 Text. Filename: Feng_etal_IEEE_Access_2019_Big_data_analytics_and_mining_for_effective_visualization_and_trends_forecasting.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (10MB)| Preview

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.

ORCID iDs

Feng, Mingchen, Zheng, Jiangbin, Ren, Jinchang ORCID logoORCID: https://orcid.org/0000-0001-6116-3194, Hussain, Amir, Li, Xiuxiu, Xi, Yue and Liu, Qiaoyuan;
CORE (COnnecting REpositories)