Call For Paper

Volume 7 Issue 4

April 2021

Submit Paper Here
Download Paper Format
Copyright Form
News for Authors:

We have started accepting articles by online means directly through website. Its our humble request to all the researchers to go and check the new method of article submission on below link: Submit Manuscript

Follow us on Social Media:

Dear Researchers, to get in touch with the recent developments in the technology and research and to gain free knowledge like , share and follow us on various social media. Facebook




Giridharan s



Crime prediction is of great significance to the formulation of policing strategies and the implementation of crime prevention and control. Machine learning is the current mainstream prediction method. However, few studies have systematically compared different machine learning methods for crime prediction. This paper takes the historical data of public property crime from 2015 to 2018 from a section of a large coastal city in the southeast of China as research data to assess the predictive power between several machine learning algorithms. Results based on the historical crime data alone suggest that the LSTM model outperformed KNN, random forest, support vector machine, naive Bayes, and convolutional neural networks. In addition, the built environment data of points of interests (POIs) and urban road network density are input into LSTM model as covariates. It is found that the model with built environment covariates has better prediction effect compared with the original model that is based on historical crime data alone. Therefore, future crime prediction should take advantage of both historical crime data and covariates associated with criminological theories. Not all machine learning algorithms are equally effective in crime prediction.

Other Details

Published in: Volume : 7, Issue : 4
Publication Date: 4/1/2021

Article Preview

Download Article