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Hierarchical Bayesian modeling for the spatial analysis of robberies in Toronto, Canada
Ravi Ancil Persad 대한공간정보학회 2020 Spatial Information Research Vol.28 No.2
This paper investigates the geographic variation of robbery risk in Toronto, Canada. A hierarchical Bayesian modeling approach is used for estimating the relative risk of robberies across Toronto’s 140 neighbourhood districts in 2017. The association between robbery risk and various socio-economic explanatory variables (i.e., business density, education and income levels) are analyzed using a Poisson-based spatial regression model. Markov Chain Monte Carlo model fitting is utilized for the estimation of relative risk and associated regression parameters. Results reveal that elevated levels of robbery risk are predominant in the eastern, north-western and southern neighbourhoods of Toronto whereas, lower risk areas are situated in the central neighbourhoods. Across all neighbourhoods, there was a geographical difference in robbery risk, ranging from 0.17 (95% CI 0.05–0.38) to 4.87 (95% CI 4.22–5.55). Education and income variables had a negative association with robberies at posterior probabilities of 96.9% and 85.5% respectively, whereas business density had a positive association with robberies at a posterior probability of 100%. Hence, neighbourhoods with higher amounts of businesses, lower education levels and lower household incomes tend to have a higher mean amount of robberies in Toronto and thus higher associated risks.
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