International Journal of Data Science and Analysis
Volume 5, Issue 6, December 2019, Pages: 111-116
Received: Oct. 8, 2019;
Accepted: Oct. 29, 2019;
Published: Nov. 4, 2019
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George Ngogoyo Chege, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (Jkuat), Nairobi, Kenya
Samuel Musili Mwalili, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (Jkuat), Nairobi, Kenya
Anthony Wanjoya, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology (Jkuat), Nairobi, Kenya
Nairobi is a county in Kenya that is more prone to crime occurrence. This has made many researchers, for the past years, to study about crime occurrence in its suburbs and which factors promote crime. The theories around crime are always coupled with an attempt to predict their occurrence, for better crime analysis, and management, in case they happen, the associated covariates and their changes are analyzed. At the sub-county level, the crime occurrence is highly studied and understood. In this study, using Bayesian theory, this study builds spatial-temporal Bayesian model approach to crime to analyze its spatial-temporal patterns and determine any developing trends using data regarding robberies that occurred in Nairobi County in Kenya from January 1, 2011 to December 31, 2018. Of the diverse socio-economic variables associated with crime rate, including unemployment rate, poverty, weak law enforcement, Alcohol and drug abuse, and illiteracy, this study finds that robbery crime rate is significantly correlated with the poverty index and the unemployment rate. This finding provides a statistical reference for County safety protection. For further work, we recommend that further study can be done to determine factors associated with the dynamics and the distribution of crime in Nairobi County while accounting for measurement error that might be present in the covariates.
George Ngogoyo Chege,
Samuel Musili Mwalili,
Bayesian Spatial-temporal Modelling and Mapping for Crime Data in Nairobi County, International Journal of Data Science and Analysis.
Vol. 5, No. 6,
2019, pp. 111-116.
P. Brantingham and P. Brantingham, “Crime pattern theory,” in Environmental criminology and crime analysis, Willan, 2013, pp. 100–116.
S. D. Johnson et al., “Space--time patterns of risk: A cross national assessment of residential burglary victimization,” J. Quant. Criminol., vol. 23, no. 3, pp. 201–219, 2007.
L. E. Cohen and M. Felson, “Social Change and Crime Rate Trends: A Routine Activity Approach (1979),” in Classics in Environmental Criminology, CRC Press, 2016, pp. 203–232.
J. Law and R. Haining, “A Bayesian approach to modeling binary data: The case of high-intensity crime areas,” Geogr. Anal., vol. 36, no. 3, pp. 197–216, 2004.
D. Liu, W. Song, and C. Xiu, “Spatial patterns of violent crimes and neighborhood characteristics in Changchun, China,” Aust. N. Z. J. Criminol., vol. 49, no. 1, pp. 53–72, 2016.
W. J. Zheng, X. Y. Li, and K. Chen, “Bayesian statistics in spatial epidemiology,” Zhejiang da xue xue bao. Yi xue ban= J. Zhejiang Univ. Med. Sci., vol. 37, no. 6, pp. 642–647, 2008.
J. Law and M. Quick, “Exploring links between juvenile offenders and social disorganization at a large map scale: a Bayesian spatial modeling approach,” J. Geogr. Syst., vol. 15, no. 1, pp. 89–113, 2013.
L. W. Mburu and M. Helbich, “Crime risk estimation with a commuter-harmonized ambient population,” Ann. Am. Assoc. Geogr., vol. 106, no. 4, pp. 804–818, 2016.
M. Blangiardo and M. Cameletti, Spatial and spatio-temporal Bayesian models with R-INLA. John Wiley & Sons, 2015.
S. J. Rey, E. A. Mack, and J. Koschinsky, “Exploratory space--time analysis of burglary patterns,” J. Quant. Criminol., vol. 28, no. 3, pp. 509–531, 2012.
J. Besag, “Spatial interaction and the statistical analysis of lattice systems,” J. R. Stat. Soc. Ser. B, pp. 192–236, 1974.
J. Besag, J. York, and A. Mollié, “Bayesian image restoration, with two applications in spatial statistics,” Ann. Inst. Stat. Math., vol. 43, no. 1, pp. 1–20, 1991.
D. J. Spiegelhalter, N. G. Best, B. P. Carlin, and A. Van Der Linde, “Bayesian measures of model complexity and fit,” J. R. Stat. Soc. Ser. B (Statistical Methodol., vol. 64, no. 4, pp. 583–639, 2002.
T. Hu, X. Ye, L. Duan, and X. Zhu, “Integrating near repeat and social network approaches to analyze crime patterns,” in 2017 25th International Conference on Geoinformatics, 2017, pp. 1–4.
L. W. Sherman and J. E. Eck, “Policing for crime prevention,” Evidence-based crime Prev., vol. 295, 2002.
I. Ehrlich, “On the relation between education and crime,” in Education, income, and human behavior, NBER, 1975, pp. 313–338.
T. Almanie, R. Mirza, and E. Lor, “Crime prediction based on crime types and using spatial and temporal criminal hotspots,” arXiv Prepr. arXiv1508.02050, 2015.
S. D. Johnson, K. Bowers, and A. Hirschfield, “New insights into the spatial and temporal distribution of repeat victimization,” Br. J. Criminol., vol. 37, no. 2, pp. 224–241, 1997.
T. H. Grubesic and E. A. Mack, “Spatio-temporal interaction of urban crime,” J. Quant. Criminol., vol. 24, no. 3, pp. 285–306, 2008.
D. Simpson, H. Rue, A. Riebler, T. G. Martins, S. H. Sørbye, and others, “Penalising model component complexity: A principled, practical approach to constructing priors,” Stat. Sci., vol. 32, no. 1, pp. 1–28, 2017.