• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1169-1182
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• 2016

Cancer is a typical cause of death in Korea that becomes a major issue in health care. According to Cause of Death Statistics (2014) by National Statistical Office, SMRs (standardized mortality rates) in Busan were counted as the highest among all cities. In this paper, we used data of Busan Regional Cancer Center to estimate the extent of the cancer incidence rate and cancer mortality rate. The data are considered in small areas of administrative units such as Gu/Dong from years 2003 to 2009. All cancer including four major cancers (stomach cancer, colorectal cancer, lung cancer, liver cancer) have been analyzed. We carried out model selection and parameter estimation using spatial multi-level model incorporating a spatial correlation. For the spatial effects, CAR (conditional autoregressive model) has been assumed.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.239-239
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• 2019

• Communications for Statistical Applications and Methods
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• v.10 no.3
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• pp.835-844
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• 2003

A difficulty in spatial data analysis is to choose a suitable theoretical variogram. Generally mean squares error(MSE) is used as a criterion of selection. However researchers encounter the case that the values of MSE are almost the same whereas the estimates of parameters are different. In this case, the selection criterion based on MSE should take into account the parameter estimates. In this paper we study on the method of selecting a variogram using spatial correlation.

• Fire Science and Engineering
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• v.21 no.2 s.66
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• pp.14-23
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• 2007

In this thesis, we analysed urban fires and developed the predictive mapping technique by using GIS and spatial statistics. It presented the correlation between the fire data of last 5 years ($2001{\sim}2005$) and the factor of civilization environment in Daegu city. We produced a model of fire hazard predictive map by analyzing uncertainty of fire with the quadrat analysis and the poisson distribution.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.57-67
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• 2006

In sampling survey, nonresponse tend to occur inevitably. If we use information from respondents only, the estimates will be baised. To overcome this, various non-response imputation methods have been studied. If there are few auxiliary variables for replacing missing imputation or spatial autocorrelation exists between respondents and nonrespondents, spatial autocorrelation can be used for missing imputation. In this paper, we apply several nonresponse imputation methods including spatial imputation for the analysis of farm household economy data of the Gangwon-Do in 2002 as an example. We show that spatial imputation is more efficient than other methods through the numerical simulations.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.5
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• pp.487-496
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• 2020

This study introduces how GISs (Geographic Information Systems) are used to assess spatial disparities in urban green spaces in the Chicago. Green spaces provide us with a variety of benefits, namely environmental, economic, and physical benefits. This study seeks to explore socioeconomic relationships between green spaces and their surrounding communities and to evaluate spatial disparities from a variety of perspectives, such as health-related, socioeconomic, and physical environment factors. To achieve this goal, this study used spatial statistics, such as optimized hotspot analysis, network analysis, and space-time cluster analysis, which enable conclusions to be drawn from the geographic data. In particular, 12 variables within the three factors are used to assess spatial disparities in the benefits of the use of green spaces. Finally, the variables are standardized to rank the community areas and identify where the most vulnerable community areas or parks are. To evaluate the benefits given to the community areas, this study used the z- and composite scores, which are compared in the three different combinations. After identifying the most vulnerable community area, crime data is used to spatially understand when and where crimes occur near the parks selected. This work contributes to the work of urban planners who need to spatially evaluate community areas in considering the benefits of the uses of green spaces.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.421-430
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• 2006

In sample survey sample designs are performed by geographically-based domain such as countries, states and metropolitan areas. However mostly statistics of interests are smaller domain than sample designed domain. Then sample sizes are typically small or even zero within the domain of interest. Shin and Lee(2003) mentioned Spatial Autoregressive(SAR) model in small area estimation model-based method and show the effectiveness by MSE. In this study, Bayesian Auto-Poisson Model is applied in model-based small area estimation method and compare the results with SAR model using MSE ME and bias check diagnosis using regression line. In this paper Survey of Disability, Aging and Cares(SDAC) data are used for simulation studies.

• Journal of Korean Society on Water Environment
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• v.25 no.5
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• pp.735-741
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• 2009

In order to support the basic information for planning and performing the environment management such as Total Maximum Daily Loads (TMDLs), it is highly recommended to understand the spatial distribution of water quality and runoff data in the unit watersheds. Therefore, in the present study, we applied Self-Organizing Map (SOM) to detect the characteristics of spatial distribution of Biological Oxygen Demand (BOD) concentration and runoff data which have been measured in the Yeongsan, Seomjin, and Tamjin River basins. For the purpose, the input dataset for SOM was constructed with the mean, standard deviation, skewness, and kurtosis values of the respective data measured from the stations of 22-subbasins in the rivers. The results showed that the $4{\times}4$ array structure of SOM was selected by the trial and error method and the best performance was revealed when it classified the stations into three clusters according to the basic statistics. The cluster-1 and 2 were classified primarily by the skewness and kurtosis of runoff data and the cluster-3 including the basic statistics of YB_B, YB_C, and YB_D stations was clearly decomposed by the mean value of BOD concentration showing the worst condition of water quality among the three clusters. Consequently, the methodology based on the SOM proposed in the present study can be considered that it is highly applicable to detect the spatial distribution of BOD concentration and runoff data and it can be used effectively for the further utilization using different water quality items as a data analysis tool.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.345-356
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• 2010

In this paper, we introduce the linear regression model taking the parametric spatial association structure into account and employ it to five-year averaged wind speed data measured at 460 meteorological monitoring stations in South Korea. From the prediction map obtained by the model with spatial association parameters, we can see that inland area has smaller wind speed than coastal regions. When comparing the spatial linear regression model with classical one by using one-leave-out cross-validation, the former outperforms the latter in terms of similarity between the observations and the corresponding predictions and coverage rate of 95% prediction intervals.

• Communications for Statistical Applications and Methods
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• v.16 no.1
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• pp.31-40
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• 2009

Spatial time series data can be viewed either as a set of time series collected simultaneously at a number of spatial locations. In this paper, We estimate the parameters of spatial time autoregressive moving average (SIARMA) process by method of Gibbs sampling. Finally, We apply this method to a set of U.S. Mumps data over a 12 states region.