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

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

Research output: Contribution to journalArticle

12 Citations (Scopus)
108 Downloads (Pure)

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.

Original languageEnglish
Article number8768367
Pages (from-to)106111-106123
Number of pages13
JournalIEEE Access
Volume7
Early online date22 Jul 2019
DOIs
Publication statusE-pub ahead of print - 22 Jul 2019

Keywords

  • big data analytics (BDA)
  • data mining
  • data visualization
  • neural network
  • time series forecasting

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