Sains Malaysiana 44(9)(2015): 1363–1370

Statistical Analysis of Vehicle Theft Crime in Peninsular Malaysia using Negative Binomial Regression Model

(Analisis Statistik Jenayah Kecurian Kenderaan di Semenanjung Malaysia menerusi Model Regresi Binomial Negatif)

MALINA ZULKIFLI¹, AHMAD MAHIR RAZALI²*, NURULKAMAL MASSERAN² & NORISZURA ISMAIL²

¹School of Quantitative Sciences, College of Arts and Science, Universiti Utara Malaysia

06010 Sintok, Kedah Darul Aman, Malaysia

²School of Mathematical Sciences, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor Darul Ehsan, Malaysia

Diserahkan: 17 Jun 2014/Diterima: 20 Mei 2015

ABSTRACT

The aim of this paper was to identify the determinants that influence vehicle theft by applying a negative binomial regression model. The identification of these determinants is very important to policy-makers, car-makers and car owners, as they can be used to establish practical steps for preventing or at least limiting vehicle thefts. In addition, this paper also proposed a crime mapping application that allows us to identify the most risky areas for vehicle theft. The results from this study can be utilized by local authorities as well as management of internal resource planning of insurance companies in planning effective strategies to reduce vehicle theft. Indirectly, this paper has built ingenuity by combining information obtained from the database of Jabatan Perangkaan Malaysia and insurance companies to pioneer the development of location map of vehicle theft in Malaysia.

Keywords: Crime; mapping; negative binomial; spatial analysis; vehicle theft

ABSTRAK

Tujuan penulisan kertas ini adalah untuk mengenal pasti penentu yang mempengaruhi kecurian kenderaan dengan menggunakan model regresi binomial negatif. Pengenalpastian penentu ini penting kepada pembuat dasar, pembuat kereta dan pemilik kereta kerana maklumat ini boleh digunakan untuk mewujudkan langkah-langkah praktikal dalam mencegah atau sekurang-kurangnya menghadkan kejadian kecurian kenderaan. Di samping itu, kertas ini juga mencadangkan suatu aplikasi pemetaan jenayah yang membolehkan kita mengenal pasti kawasan yang paling berisiko untuk berlakunya kecurian kenderaan. Hasil daripada kajian ini boleh digunakan oleh pihak berkuasa tempatan dan juga pihak pengurusan perancangan sumber dalaman syarikat insurans untuk merancang strategi yang berkesan bagi mengurangkan kecurian kenderaan. Secara tidak langsung, kertas kerja ini telah membina satu jalan pintar dengan menggabungkan maklumat yang diperoleh daripada pangkalan data Jabatan Perangkaan Malaysia dan syarikat-syarikat insurans untuk merintis kepada pembinaan peta lokasi kecurian kenderaan di Malaysia.

Kata kunci: Analisis reruang; binomial negatif; jenayah; kecurian kenderaan; pemetaan

RUJUKAN

Aitkin, M., Anderson, D., Francis, B. & Hinde, J. 1990. Statistical Modelling in GLIM. New York: Oxford University Press.

Beime, P. 1993. Inventing Criminology. Albany, NY: State University of New York Press.

Brockmann, M.J. & Wright, T.S. 1992. Statistical motor rating: making effective use of your data. Journal of the Institute of Actuaries 119(3): 457-543.

Cameron, A.C. & Trivedi, P.K. 1986. Econometric models based on count data: Comparisons and applications of some estimators and tests. Journal of Applied Econometrics 1: 29-53.

Consul, P.C. 1989. Generalized Poisson Distribution: Properties and Application. New York: Marcel Dekker.

Consul, P.C. & Famoye, F. 1992. Generalized Poisson regression model. Communications in Statistics (Theory & Methodology) 2(1): 89-109.

Demombynes, G. & Ozler, B. 2005. Crime and local inequality in South Africa. Journal of Development Economics 76: 265-292.

Di Tella, R. & Schargrodsky, E. 2004. Do police reduce crime? Estimates using the allocation of police forces after a terrorist attack. The American Economic Review 94(1): 115-133.

Hengl, T. 2007. A Practical Guide to Geostatistical Mapping of Environmental Variables. Italy: European Communities.

Insurance Services Malaysia Bhd. 2007. Insurance industry statistics on stolen vehicles. http://www.piam.org.my/news/ piamnews/p014.htm. Accessed 22 June 2011.

Ismail, N. & Jemain, A.A. 2007. Handling overdispersion with negative binomial and generalized Poisson regression models. Casualty Actuarial Society Forum Winter. pp. 103-158.

Kelly, M. 2000. Inequality and crime. The Review of Economics and Statistics 82(4): 530-539.

Kenwitz, J.W. 1987. Cartography in France: 1660-1848. Chicago, IL: University of Chicago Press.

Kleck, G. & Chiricos, T, 2002. Unemployment and property crime: A target-specific assessment of opportunity and motivation as mediating factors. Criminology 40(3): 649-680.

Lawless, J.F. 1987. Negative binomial and mixed Poisson regression. Canadian Journal of Statistics 15(3): 209-225.

Masseran, N., Razali, A.M. & Ibrahim, K. 2012a. An analysis of wind power density derived from several wind speed density functions: The regional assessment on wind power in Malaysia. Renewable and Sustainable Energy Reviews 16(8): 6476-6487.

Masseran, N., Razali, A.M., Ibrahim, K., Zin, W.Z.W. & Zaharim, A. 2012b. On spatial analysis of wind energy potential in Malaysia. WSEAS Transactions on Mathematics 11(6): 467-477.

McCullagh, P. & Nelder, J.A. 1989. Generalized Linear Models. 2nd ed. London: Chapman and Hall.

Osgood, W. 2000. Poisson-based regression analysis of aggregate crime rates. Journal of Quantitative Criminology 16: 21-43.

Renshaw, A.E. 1994. Modelling the claims process in the presence of covariates. ASTIN Bulletin 24(2): 265-285.

Schabenberger, O. & Gotway, C.A. 2005. Statistical Methods for Spatial Data Analysis. Boca Raton: Chapman & Hall/ CRC Press.

Sidhu, A.S. 2005. The rise of crime in Malaysia: An academic and statistical analysis. Journal of the Kuala Lumpur Royal Malaysia Police College 4: 1-28.

Wang, W. & Famoye, F. 1997. Modeling household fertility decisions with generalized Poisson regression. Journal of Population Economics 10: 273-283.

Weisburd, D. & McEwen, T. 1997. Introduction: Crime Mapping and Crime Prevention. Monsey, NY: Criminal Justice Press.

Zamani, H. & Ismail, N. 2012. Functional form for the generalized Poisson regression model. Communications in Statistics - Theory and Methods 41(20): 3666-3675.

Zamani, H. & Ismail, N. 2014. Functional form for the zero-inflated generalized Poisson regression model. Communications in Statistics - Theory and Methods 43(3): 515-529.

Zulkifli, M., Ismail, N. & Razali, A.M. 2013. Analysis of vehicle theft: A case study in Malaysia using functional forms of negative binomial regression models. Applied Mathematics and Information Sciences 7(2L): 389-395.

*Pengarang untuk surat-menyurat; email: mahir@ukm.edu.my