Abstract
Crime analysis and prediction is a systematic approach for analyzing and identifying different patterns, relations and trends in crime. In this paper we conduct exploratory data analysis to analyze criminal data in San Francisco, Chicago and Philadelphia. We first explored time series of the data, and forecast crime trends in the following years. Then predicted crime category given time and location, to overcome the problem of imbalance, we merged multiple classes into larger classes and did feature selection to improve accuracy. We have applied several state-of-the-art data mining techniques that are specifically used for crime prediction. The experimental results show that the Tree classification models performed better on our classification task over k-NN and Naive Bayesian approaches. Holt-Winters with multiplicative seasonality gives best results when predicting crime trends. The promising outcomes will be beneficial for police department and law enforcement to speed up the process of solving crimes and provide insights that enable them track criminal activities, predict the likelihood of incidents, effectively deploy resources and make faster decisions.
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Acknowledgements
This work has been supported by HJSW and Research & Development plan of Shaanxi Province (Program No. 2017ZDXM-GY-094, 2015KTZDGY04-01).
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Feng, M., Zheng, J., Han, Y., Ren, J., Liu, Q. (2018). Big Data Analytics and Mining for Crime Data Analysis, Visualization and Prediction. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_59
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DOI: https://doi.org/10.1007/978-3-030-00563-4_59
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