Demography and Crime: A Spatial analysis of geographical patterns and risk factors of Crimes in Nigeria

This paper explores the spatial distribution of crime incidences in Nigeria and evaluates the association between the geographical variations and the socio-demographic determinants of crimes. The analyses are based on 2017 reported crime Statistics obtained from the Nigeria's National Bureau of Statistics. This paper analysed the spatial patterns of four types of crimes (armed robbery, theft, rape and kidnapping) in relation to their geographical distributions across states in Nigeria. In contrast to the traditional regression analysis, a Poisson mixed model was formulated to incorporate the spatial dependence effects (clustering) and the specific state-level heterogeneity effects of crimes. The study modelled six explanatory variables (unemployment rate, population density, education index, Gross National Income (GNI), percentage males population (PMP), age 18-35 years and policing structure) as the determinants of crimes in Nigeria. A full Bayesian approach via Markov Chain Monte Carlo simulation was used to estimate the model parameters. The results show that the unemployment rate was positively associated with rape, kidnapping and armed robbery, but negatively associated with theft. The results further reveal that GNI and PMP show positive correlation with all the crimes. In addition to the risk factors of the crimes, the proportion variation attributed to clustering effect of the total variation was explained by 29.27 % in armed robbery incidents, 31.30% for theft (stealing), 27.07% for kidnapping and 41.40% in rape cases occurrence. Our approach also produces spatial predictive maps that identified areas of high crime concentration, which can assist the relevant agencies in crimes prevention, effective policing and areas needed urgent attention. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This study investigates the spatial distribution of crime incidences in Nigeria and assesses the relationship between geographical variations and socio-demographic determinants of crimes. The results show that unemployment rate is positively associated with rape, kidnapping, and armed robbery, but negatively associated with theft. GNI and PMP show positive correlations with all crimes. Additionally, clustering effect plays a significant role in explaining the variation of different crime incidents.