A forecasting model of crime risk based on random forest

WANG Yu-chen; GUO Zhong-yang; WANG Yuan-yuan

doi:10.3969/j.issn.1000-5641.2017.04.008

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WANG Yu-chen, GUO Zhong-yang, WANG Yuan-yuan. A forecasting model of crime risk based on random forest[J]. Journal of East China Normal University (Natural Sciences), 2017, (4): 89-96. doi: 10.3969/j.issn.1000-5641.2017.04.008

Citation:

WANG Yu-chen, GUO Zhong-yang, WANG Yuan-yuan. A forecasting model of crime risk based on random forest[J]. Journal of East China Normal University (Natural Sciences), 2017, (4): 89-96. doi: 10.3969/j.issn.1000-5641.2017.04.008

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A forecasting model of crime risk based on random forest

doi: 10.3969/j.issn.1000-5641.2017.04.008

School of Geographic Sciences, East China Normal University, Shanghai 200241, China

Received Date: 2016-06-28
Publish Date: 2017-07-25

Abstract

Abstract

Crime prediction has always been an outstanding issue for public security department. Random forest is a combined classification method with high accuracy, high speed, and stable performance, which is suitable for solving the problem of predicting crime risk. In the meantime, this method can choose the index group for predicting crime risk more objectively. As proved by studies, the index group chosen by random forest method can significantly improve the accuracy of prediction, and the predictive model based of this method is more accurate and stable, so it can meet the demand of crime risk prediction.
- random forest,
- crime risk prediction,
- index group selection

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References(17)

References

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