Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities

Version 1 : Received: 5 February 2021 / Approved: 8 February 2021 / Online: 8 February 2021 (07:44:57 CET)

How to cite: Butt, U.M.; Letchmunan, S.; Hassan, F.H.; Ali, M.; Baqir, A.; Koh, T.W.; Sherazi, H.H.R. Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities. Preprints 2021, 2021020172 (doi: 10.20944/preprints202102.0172.v1). Butt, U.M.; Letchmunan, S.; Hassan, F.H.; Ali, M.; Baqir, A.; Koh, T.W.; Sherazi, H.H.R. Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities. Preprints 2021, 2021020172 (doi: 10.20944/preprints202102.0172.v1).

Abstract

Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years is posing challenges related to resource management, safety, and security. In order to ensure safe mobility and security in the smart city environment, this paper proposes a novel Artificial Intelligence (AI) based approach empowering the authorities to better visualize the threats and to help them identify the highly-reported crime zones yielding greater predictability of crime hot-spots in a smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crimes to be committed. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) exploited in each dense crime region to predict the number of crimes in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time period scenarios i.e. (a) year-wise, i.e., for each year and (b) for the whole period of ten years, using an 80:20 ratio where 80\% data was used for training and 20\% data was used for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47.

Subject Areas

Citizen Security; Smart Cities; Crime Prediction; Artificial Intelligence; Safe City

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