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Original Article



Spatial Analysis and Geographic Variation of Fatal and Injury Crashes in Mazandaran Province from 2006 to 2010

Jamshid Yazdani-Charati, Hasan Siamian, Elham Ahmadi-Basiri.

Abstract
Background: Road safety and traffic accidents change in time and space. Although, time variations have always been considered the subject being focused by researchers, the effect of spatial correlation and spatial components on the risk of accident have been less investigated. Due to its specific geographical position, Mazandaran Province is one of the highest traffic provinces. This study aims to investigate the factors influencing suburban crashes of Mazandaran province by considering the spatial correlation. Methods: This study is aggregated (descriptive -analytical) and the study period was 2006 to 2010. Social and environmental factors effects on the risk of accidents have been studied considering the correlation structure of the regions and regardless of this structure with Poisson regression, negative binomial and Full Bayes hierarchical models. Geographical pattern of risk distribution for the observed values ​​of SMRs and the estimated values ​​after smoothing have been plotted and analyzed. Results: Comparing the measures of models goodness of fit indicates that hierarchical Bayes model fits the data better. Plotting the geographical pattern, the north central parts of the province have been identified as the high-risk areas. Human factors were identified as the important factors for the risk of accident. Conclusions: The purpose of this procedure is to separate the random effect of residuals correlation. Using this method, the measure of the model goodness of fit got reduced reflecting a better model than the prototype model. The significance of the structured spatial effect shows the existence of unknown explanatory variables with correlated structure whose identification and control can reduce the risk of accidents.

Key words: Full Bayes hierarchical model, spatial correlation, Negative binomial model, Crash risk.



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