Submit your paper : editorIJETjournal@gmail.com Paper Title : Crime Data Analysis and Predicting Crime Hotspots using Machine Learning Algorithms ISSN : 2395-1303 Year of Publication : 2022 10.5281/zenodo.7296054 MLA Style: -Dr. P. Shanmuga Priya, S. Aishwarya, P. Sai Hamshika, V. Ashritha Crime Data Analysis and Predicting Crime Hotspots using Machine Learning Algorithms , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org APA Style: -Dr. P. Shanmuga Priya, S. Aishwarya, P. Sai Hamshika, V. Ashritha Crime Data Analysis and Predicting Crime Hotspots using Machine Learning Algorithms , Volume 8 - Issue 5 September - October 2022 International Journal of Engineering and Techniques (IJET) ,ISSN:2395-1303 , www.ijetjournal.org Abstract 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. Reference [1] U. Thongsatapornwatana, ``A survey of data mining techniques for analyzing crime patterns,'' in Proc. 2nd Asian Conf. Defence Technol. (ACDT), Jan. 2016, pp. 123128.J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68-73. [2] A. Almehmadi, Z. Joudaki, and R. Jalali, ``Language usage on Twitter predicts crime rates,'' in Proc. 10th Int. Conf. Secur. Inf. Netw. (SIN), 2017, pp. 307310.K. Elissa, “Title of paper if known,” unpublished. [3] S. Chainey, L. Tompson, and S. Uhlig, ``The utility of hotspot mapping for predicting spatial patterns of crime,'' Secur. J., vol. 21, nos. 12, pp. 428, Feb. 2008. [4] A. Babakura, M. N. Sulaiman, and M. A. Yusuf, ``Improved method of classication algorithms for crime prediction,'' in Proc. Int. Symp. Biometrics Secur. Technol. (ISBAST), 2015, pp. 250255. [5] L. Lin, J. Jiakai, S. Guangwen, L. Weiwei, Y. Hongjie1, and L. Wenjuan, ``Hotspot prediction of public property crime based on spatial differentiation of crime and built environment,'' J. Geo-Inf. Sci., vol. 21, no. 11, pp. 16551668, 2019. Keywords — Machine Learning, LSTM, KNN, SVM |