S. N. H. S Abdullah, Farah Aqilah Bohani, Zakree Ahmad Nazri, Yasmin Jeffry, Mohammed Ariff Abdullah, Md Nawawi Junoh, Zainal Abidin Kasim


Serial crime recognition is a critical task. Usually, police officer investigates the serial crime behavior based on their heuristics, evidence or prior information from public. Sometimes, the police officer makes inadequate decision when handling the serial crime problems due to lack of preliminary study on relationship between serial crime and amenities. Therefore, this study explores k-means to identify pattern of surroundings area at serial comersial crime scene. In Malaysia, precisely Selangor, Wilayah Persekutuan Kuala Lumpur and Wilayah Persekutuan Putrjaya, a set data of serial crime including index and non-index, and its surroundings area at crime scene are being investigated. Experimental result shows that ‘hot spot’ amenities such as bank, commercial center, restorant, place of worship, resident and school are highly involved with three types of crime namely house breaking at night, day and robbery without firearm. Furthermore, radius distance with 0.2 km and 0.3 km between the crime scene location and its amenities at surroundings area captured from Safe City Monitoring System are also being evaluated and analyzed. Consequently, our finding helps the police to easily observe and prevent criminal behavior by assigning necessary human resource based on their ‘hot spot’ amenities.



Crime incident location, amenities, serial crime, k-min, Geographic Information System (GIS)

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M. J. S. J. B. A. 2014. Laporan Statistik Jenayah 2009-2013 Setiap Negeri di Malaysia PDRM Bukit Aman, Kuala Lumpur.

Yuen, B. 2004. Safety and Dwelling in Singapore. Cities. 21(1): 19-28.

PDRM, P.K.K. 10 & 26 Disember 2014. Faktor Geo Spatial Yang Menyumbang Kepada Jenayah Terindeks Bersiri. Kod:PK3/SBTSA/jarak jejari.(Temu bual).

Malaysia, P.D. 2011. Kategori Pengurusan. Laporan Projek Integrasi Pemantauan Jenayah Polis Diraja Malaysia Melalui Sistem Pemantauan Bandar Selamat (SPBS) Bagi Anugerah Inovasi Kementerian Dalam Negeri Tahun 2011. Available from : [07 December 2017].

Latif, F. M. 2017. Ke Arah Pengurangan Indeks Jenayah Jalanan di Pusat Bandar Kuala Lumpur (Towards Reducing The Street Crime Index of Kuala Lumpur City Centre). Geografia-Malaysian Journal of Society and Space. 11(4).

PDRM, P.K.K. 10 & 26 Disember 2014. Faktor Geo Spatial Yang Menyumbang Kepada Jenayah Terindeks Bersiri. Kod:PK4/PeSMG.(Temu bual).

Mohammed Ariff Abdullah, Siti Norul Huda Sheikh Abdullah, and M. J. Nordin. 2013. Smart City Security: Predicting The Next Location of Crime Uisng Geographical Information System with Machine Learning. Asia Geospatial Forum, 24-26 September 2013, Kuala Lumpur, Malaysia.

Ghazvini, A., et al. 2015. Biography Commercial Serial Crime Analysis Using Enhanced Dynamic Neural Network. Soft Computing and Pattern Recognition (SoCPaR), 2015 7th International Conference of. IEEE.

Asmai, S. A., et al. 2014. Predictive Crime Mapping Model Using Association Rule Mining For Crime Analysis. Age. 12: 21.

Tayal, D. K., et al. 2015. Crime Detection and Criminal Identification in India Using Data Mining Techniques. AI & SOCIETY. 30(1): 117-127.

Frank, R., et al. 2011. Finding Criminal Attractors Based on Offenders' Directionality of Crimes. Intelligence and Security Informatics Conference (EISIC), 2011 European. IEEE.

Aubaidan, B., M. Mohd, and M. Albared. 2014. Comparative Study of k-means and k-means++ Clustering Algorithms on Crime Domain. Journal of Computer Science. 10(7): 1197.

Nath, S. V. 2006. Crime Pattern Detection Using Data Mining. Web Intelligence and Intelligent Agent Technology Workshops, 2006. WI-IAT 2006 Workshops. 2006 IEEE/WIC/ACM International Conference on. IEEE.

Malathi, A., S. Babboo, and A. Anbarasi. 2011. An Intelligent Analysis of a City Crime Data Using Data Mining. International Conference Information Electronic Engineering.

Malathi, A. and S. S. Baboo. 2011. Evolving Data Mining Algorithms on the Prevailing Crime Trend–An Intelligent Crime Prediction Model. Int J Sci Eng Res. 2(6).

Ehlers, D. and G. Pimstone. 1998. Predicting Crime: A Statistical Glimpse of the Future. Nedbank Institute Security Studies (ISS) Crime Index. 2(2).

Visher, C. A. and D. Weisburd. 1997. Identifying What Works: Recent Trends in Crime Prevention Strategies. Crime, Law and Social Change. 28(3-4): 223-242.

Gorr, W. and R. Harries. 2003. Introduction to Crime Forecasting. International Journal of Forecasting. 19(4): 551-555.

Gorr, W., A. Olligschlaeger, and Y. Thompson. 2003. Short-term Forecasting of Crime. International Journal of Forecasting. 19(4): 579-594.

Chen, H., et al. 2004. Crime Data Mining: A General Framework and Some Examples. Computer. 37(4): 50-56.

Abdullah, S. N. H. S., et al. 2016. Crime Population Pattern Mining Using K-Means Clustering Case Study in Kuala Lumpur and Selangor. Proceeding 5th International Technical Conference 2016 (ITC2016).307-313.



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