DOI : https://doi.org/10.18517/ijaseit.8.4-2.6802

A Proposed Bi-layer Crime Prevention Framework Using Big Data Analytics

Shu Wei Tao (1), Ooi Chong Yang (2), Mohamed Sahil Mohamed Salim (3), Wahidah Husain (4)
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How to cite (IJASEIT) :
Tao, Shu Wei, et al. “A Proposed Bi-Layer Crime Prevention Framework Using Big Data Analytics”. International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 4-2, Oct. 2018, pp. 1453-9, doi:10.18517/ijaseit.8.4-2.6802.
Citation Format :
The future of science and technology sounds very promising. The need to adopt new technologies while navigating towards industry 4.0 has changed the perceptions of law enforcement agency to contend against criminal minds. It is sad but true that the conventional crime prevention system followed by government agencies is not effective for long-term implications. With advanced technologies that constantly generate and exchange data, big data analytics can be applied to predict and prevent crime from happening. However, dealing with the overwhelming amount of complex and heterogeneous crime-related data is never an easy task. Additionally, there are many data analytical techniques and each of them has its own strengths and weaknesses. In order to identify the most efficient techniques, recent literature is reviewed to spotlight the trend as well as to shed light on the research gaps and challenges in various areas. The areas include crime data collection and preprocessing, crime data analysis, crime prediction and crime prevention. These techniques are further analyzed by considering the advantages and disadvantages which then provides insight to propose a bi-layer crime prevention framework. The first layer intends to support the law enforcement agency’s daily operation while the second layer serves as a countermeasure for first layer. Both layers aim to reduce the crime rate by involving law enforcement agency through the utilization of various big data sources and techniques effectively. The proposed crime prevention framework will progressively collect data to deter criminal behavior for city’s environmental design. Ultimately, a safe and secure city is molded in the near future.

(2017) UrbiStat AdminStat in Demography [Online]. Available: https://ugeo.urbistat.com/AdminStat/en/fr/demografia/dati-sintesi/paris/75/3

(2017) “IoT: number of connect devices worldwide 2012-2025 | Statista”, Statista [Online]. Available:

https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/

G. Marc, Future crimes: Everything is connected, everyone is vulnerable and what we can do about it, 2015.

(2017) TechTarget Search Business Analytics [Online]. Available: http://searchbusinessanalytics.techtarget.com/definition/big-data-analytics

J. Jeon, and S. Jeong, “Designing a Crime-Prevention System by Converging Big Data and IoT,” in Journal of Internet Computing and Services (JICS), 2016, pp. 115-128.

D. W. Sohn, “D. (2016). Residential crimes and neighborhood built environment: Assessing the effectiveness of crime prevention through environmental design (CPTED),” 2016). Cities, 52, pp.86-93.

V. Luca, B. Elena, and T. Politecnico, “A spectral analysis of crimes in San Francisco,” 2016.

K. S. Er., A. Firoz, A. D. Hamza, and Sabigua, “Criminal Investigation Using Call Data Records (CDR) through Big Data Technology,” in International Conference on Nascent Technologies in the Engineering Field, 2017.

F. B. B. Nasution, N. E. N. Bazin, Daliyusmanto, and A. Zulfikar, “Big Data’s Tools for Internet Data Analytics: Modelling of System Dynamics,” International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 3, pp. 745-753, 2017. [Online]. Available:

http://dx.doi.org/10.18517/ijaseit.07.3.1088

J. Isuru, S. Chamath, L. Sampath, W. Tharindu, P. Indika and W. Adeesha, “Crime Analytics: Analysis of Crimes Through Newspaper Articles, 2015.

Y. Zhang, “Analysis of Crime Factors Correlation Based on Data Mining Technology,” in International Conference on Robots & Intelligent System, 2016.

R. Umar, I. Riadi, and G. M. Zamroni, “Mobile Forensic Tools Evaluation for Digital Crime Investigation,” International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no.3, pp. 949-955, 2018. [Online]. Available:

http://dx.doi.org/10.18157/ijaseit.8.3.3591

L. Teng, “Criminal Behavior Analysis Method based on Data Mining Technology,” in International Conference on Smart City and System Engineering, 2016.

A. Turki, S. Daming, W. David, and W. William, “Criminal Pattern Identification Based on Modified K-means Clustering,” in International Conference on Machine Learning and Cybernetics, Jeju, South Korea, 2016.

Soumya and A. S. Baghel, “A Predictive Model for Mapping Crime using Big Data Analytics,” vol. 04, no. 04, pp. 344-348, Apr. 2015.

P. Chen, H. Yuan, and X. Shu, “Forecasting Crime Using the ARIMA Model,” in Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Nov. 2008.

N. M. M. Noor, A. Retnowardh, M. L. Abd, and M. Y. M. Saman, “Crime Forecasting using ARIMA Model and Fuzzy Alpha-cut,” I in Journal of Applied Sciences, vol. 13, no. 1, pp. 167-172, Feb. 2013.

A. Ghazvini, M. Z. B. A. Nazri, S. N. H. S. Abdullah, M. N. Junoh and Z. Abidin bin Kasim, "Biography commercial serial crime analysis using enhanced dynamic neural network," 2015 7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Fukuoka, 2015, pp. 334-339.

J. Xu, “Predict Future Events with Point-process Modeling,” IBM Big Data & Analytics Hub, 18-Sep-2015. [Online]. Available: http://www.ibmbigdatahub.com/blog/predict-future-events-point-process-modeling

M. A. Awal, J. Rabbi, S. I. Hossain, and M. M. A. Hashem, “Using Linear Regression to Forecast Future Trends in Crime of Bangladesh,” in 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016.

A. Agarwal, D. Chougule, A. Agarwal, and D. Chimote, “Application for Analysis and Prediction of Crime Data using Data Mining,” in International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106., vol. 4, no. 5, pp. 9-12, May 2016.

(2017) “United Nations Office on Drugs and Crime [Online]. Available: https://www.unodc.org/unodc/en/justice-and-prison-reform/CrimePrevention.html

C. Xu and T. S. P. Yum, "Cross Entropy approach for patrol route planning in dynamic environments," in 2010 IEEE International Conference on Intelligence and Security Informatics, Vancouver, BC, 2010, pp. 114-119.

L. Li, Z. Jiang, N. Duan, W. Dong, K. Hu, and W. Sun "Police patrol service optimization based on the spatial pattern of hotspots," in Service Operations Logistics and Informatics (SOLI) 2011 IEEE International Conference, on, pp. 45-50 July 2011.

R. Danilo, M. Adriano, L. V. C. Andrí© and F. Vasco, “Towards Optimal Police Patrol Routes with Genetic Algorithms,” in S. Mehrotra et al. (Eds.): ISI 2006, 2006.

T. Bosse and C. Gerritsen, "Comparing Crime Prevention Strategies by Agent-Based Simulation," in 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Milan, Italy, 2009, pp. 491-496.

(2012) “Web Crawling: Data Scraping vs. Data Crawling [Online] Available:

https://www.promptcloud.com/data-scraping-vs-data-crawling/

CERT-MU, The WannaCry Ransomeware: White Paper, May 2017.

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