This study examined the spatial autocorrelation of the 2016 foot and mouth disease (FMD) outbreaks in South Chungcheong to determine the association between the disease epidemics and pig farm vehicle movements. Two spatial autocorrelation testing methods were used: Moran’s I and Getis-Ord G statistics. The Moran’s I statistic for the FMD outbreak areas was -0.239, and its p-value was less than 0.007. The median Getis-Ord G statistic for the FMD outbreak areas was -0.323. The results indicated that the geographical distribution of the FMD outbreak areas was not spatially homogeneous. The spatial autocorrelation of the 2016 FMD epidemics was considered by applying a geographical weighted Poisson regression (GWPR) model in the analysis, in which pig farm vehicle movements were used as risk factors for the 2016 FMD epidemics. The number of FMD-infected farms per second-level administrative province (si or gun) was used as a dependent variable. The number of farm vehicle movements within the province (within variable), from one province to other provinces (outbound variable), or from other provinces to one province (inbound variable), were included as independent variables in the GWPR model. The results of the GWPR model were as follows. The estimated median coefficient of the log-transformed within variable, the log-transformed outbound variable, and log-transformed inbound variable were -0.000, 0.010, and -0.009, respectively. The optimal bandwidth for the GWPR model was 80.49, and the AIC score was 89.35. The results showed that the GWPR model would provide an understanding of the relationship between the 2016 FMD epidemics and pig farm vehicle movements

OSHA(Occupational Safety and Health Act) generally regulates employer's business principles in the workplace to maintain safety environment. This act has the fundamental purpose to protect employee's safety and health in the workplace by reducing industrial accidents. Authors tried to investigate the correlation between 'occupational injuries and illnesses' and level of regulation compliance using Survey on Current Status of Occupational Safety & Health data by the various statistical methods, such as generalized regression analysis, logistic regression analysis and poison regression analysis in order to compare the results of those methods. The results have shown that the significant affecting compliance factors were different among those statistical methods. This means that specific interpretation should be considered based on each statistical method. In the future, relevant statistical technique will be developed considering the distribution type of occupational injuries.