Abstract
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.
Highlights
With the quickening urbanization in Kenya, urban crime has progressively turned out to be one of the real difficulties confronting Kenyan urban communities which have turned out to be increasingly various and separated along social and conservative classes
The fast modernization and urbanization has heightened the danger of criminal exploitation City government, approach producers, and policing organizations all perceive the significance of better understanding the elements of urban crime so as to control the crime
The far reaching utilization of geographic Information Systems (GIS) with the advancement of R-INLA method and programming for example, Win BUGS, Bayesian methodologies are being connected to the examination of numerous social and wellbeing issues, In particular, the utilization of Bayesian measurements for breaking down crime data was considered by these studies [3, 4]
Summary
With the quickening urbanization in Kenya, urban crime has progressively turned out to be one of the real difficulties confronting Kenyan urban communities which have turned out to be increasingly various and separated along social and conservative classes. Choices to perpetrate violations are Designed, and the way toward carrying out Crimes are designed [1] In this manner, breaking down Crime designs and foreseeing Crime patterns is essential to decreasing the rate of crime. The theorem follows the ideas developed by Bayes and Laplace regarding inverse probability: the probability of an event B, given that an event A occurs. It all occurs following the process: is computed before the event A is observed; the is computed and used to access the and | is accessed. The will be dictated both by the prior information, as well as by the results of the experiment itself [9]
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