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Analysis of Total Crime Count Data Based on Spatial Association Structure
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 Title & Authors
Analysis of Total Crime Count Data Based on Spatial Association Structure
Choi, Jung-Soon; Park, Man-Sik; Won, Yu-Bok; Kim, Hag-Yeol; Heo, Tae-Young;
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Reliability of the estimation is usually damaged in the situation where a linear regression model without spatial dependencies is employed to the spatial data analysis. In this study, we considered the conditional autoregressive model in order to construct spatial association structures and estimate the parameters via the Bayesian approaches. Finally, we compared the performances of the models with spatial effects and the ones without spatial effects. We analyzed the yearly total crime count data measured from each of 25 districts in Seoul, South Korea in 2007.
Crime counts;spatial association;conditional autoregressive model;generalized Poisson distribution;negative binomial distribution;
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