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

PURPOSES: The purpose of this study is to develop a traffic accident prediction model using statistical data, to analyze child traffic accidents in school zones. Furthermore, we analyze the factors affecting child traffic accidents, as obtained from the results of the developed model. METHODS : From the literature review, we obtained data for child traffic accidents and various variables relating to road geometry and traffic safety facilities in school zones. We used these variables and data to develop a child traffic accident analysis model. The model was then developed into three types using the Limdep 9.0 statistical tool. RESULTS: As a result of the overdispersion test, the Poisson regression model was applied to all types of models with an overdispersion coefficient of close to zero. The results of the model development are as follows. The main model (all scope of analysis) was for a kindergarten, considering a local roadway, the accessibility of the roadway, the number of unsignalized intersections, and the school zones in commercial area as variables that increase traffic accidents. Sub-model typeⅠ(only the roadway connected to the main entrance) was for a kindergarten, considering a local roadway, skid resistant pavement, no-parking signs, the number of unsignalized intersections, and the number of commercial facilities as variables that increase traffic accidents. The main model and sub-model type Ⅰ showed a reduction in accidents when using forward-type traffic signals. Sub-model typeⅡ(only the roadway not connected to the main entrance) shows that the local roadway is the variable that most increases the probability of traffic accidents. However, when the roadway and walkway are separated, the probability of traffic accidents decreases significantly, by up to 90%. CONCLUSIONS: The results of this study demonstrate the need to restructure the method used to improve school zones. Moreover, the effect of various traffic safety facilities was quantitatively analyzed.