• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1225-1239
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• 2016

We investigate pediatric tumor incidence data collected by the Florida Association for Pediatric Tumor program using various models commonly used in disease mapping analysis. Particularly, we consider Poisson normal models with various conditional autoregressive structure for spatial dependence, a zero-in ated component to capture excess zero counts and a spatio-temporal model to capture spatial and temporal dependence, together. We found that intrinsic conditional autoregressive model provides the smallest Deviance Information Criterion (DIC) among the models when only spatial dependence is considered. On the other hand, adding an autoregressive structure over time decreases DIC over the model without time dependence component. We adopt weighted ranks squared error loss to identify high risk regions which provides similar results with other researchers who have worked on the same data set (e.g. Zhang et al., 2014; Wang and Rodriguez, 2014). Our results, thus, provide additional statistical support on those identied high risk regions discovered by the other researchers.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1155-1168
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• 2016

This paper considers a probability density estimation problem of climate values. In particular, we focus on estimating probability densities of summer extreme temperature over South Korea. It is known that the probability density of climate values at one location is similar to those at near by locations and one doesn't follow well known parametric distributions. To accommodate these properties, we use a mixture of conditional autoregressive species sampling model, which is a nonparametric Bayesian model with a spatial dependency. We apply the model to a dataset consisting of summer maximum temperature and minimum temperature over South Korea. The dataset is obtained from University of East Anglia.

• Journal of the Korean Society for Railway
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• v.12 no.5
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• pp.656-662
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• 2009

In general, the analysis of travelers' mode choice behavior is accomplished by developing the utility functions which reflect individual's preference of mode choice according to their demographic and travel characteristics. In this paper, we propose a methodology that takes the spatial effects of individuals' departure locations into account in the mode choice model. The statistical models considered here are spatial logistic regression model and conditional autoregressive model taking a spatial association parameter into account. We employed the Bayesian approach in order to obtain more reliable parameter estimates. The proposed methodology allows us to estimate mode shares by departure places even though the survey does not cover all areas.

• 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.

• Journal of the Korean Geographical Society
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• v.39 no.2
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• pp.195-211
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• 2004

The innovation of information and communication technology has brought the emergence of the digital economy in which the growing importance of the Internet for the production and consumption of information has caused a rapid increase of commercial domains. Domains are basic form of Internet address for the delivery of information, but the location of registered commercial domains is not free from a spatial context. Utilizing a database of commercial domain registrations, spatial statistical methods and GIS, the spatial dimensions of the commercial domains are explored for the city of Seoul. Through this research, it was found that the commercial domains were unevenly distributed, namely 44% of commercial domains are located at 3 Gus in Seoul. The locations of commercial domains by themselves represented a strong spatial autocorrelation among adjacent places. In order to identify factors affecting spatial variation in the development of the commercial domains among Dongs, a conditional spatial autoregressive model which effectively eliminates a spatial autocorrelation was used. As a result of this research, it is clearly shown that the selective location of firms having commercial domains and their role in economic activities are influencing the spatial structure of urban with dynamic mix of spatial characteristic.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.1
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• pp.361-367
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• 2016

We have reported a high prevalence of breast cancer in light-polluted areas in Korea. However, it is necessary to analyze the spatial effects of light polluted areas on breast cancer because light pollution levels are correlated with region proximity to central urbanized areas in studied cities. In this study, we applied a spatial regression method (an intrinsic conditional autoregressive [iCAR] model) to analyze the relationship between the incidence of breast cancer and artificial light at night (ALAN) levels in 25 regions including central city, urbanized, and rural areas. By Poisson regression analysis, there was a significant correlation between ALAN, alcohol consumption rates, and the incidence of breast cancer. We also found significant spatial effects between ALAN and the incidence of breast cancer, with an increase in the deviance information criterion (DIC) from 374.3 to 348.6 and an increase in $R^2$ from 0.574 to 0.667. Therefore, spatial analysis (an iCAR model) is more appropriate for assessing ALAN effects on breast cancer. To our knowledge, this study is the first to show spatial effects of light pollution on breast cancer, despite the limitations of an ecological study. We suggest that a decrease in ALAN could reduce breast cancer more than expected because of spatial effects.

• The Korean Journal of Applied Statistics
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• v.23 no.2
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• pp.335-344
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• 2010

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.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.325-334
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• 2015

Elderly suicide rates tend to be high in Korea. Suicide by the elderly is no longer a personal problem; consequently, further research on risk and regional factors is necessary. Disease mapping in epidemiology estimates spatial patterns for disease risk over a geographical region. In this study, we use a simultaneous conditional autoregressive model for spatial correlations between neighboring areas to estimate standard mortality ratios and mapping. The method is illustrated with cause of death data from 2006 and 2010 to analyze regional patterns of elderly suicide in Korea. By considering spatial correlations, the Bayesian spatial models, mean educational attainment and percentage of the elderly who live alone was the significant regional characteristic for elderly suicide. Gibbs sampling and grid method are used for computation.

• Journal of the Korean Data and Information Science Society
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• v.27 no.5
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• pp.1203-1214
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• 2016

Obesity is a risk factor for various diseases as well as itself a disease and associated with socioeconomic factors. The obesity proportion has been increasing in Korea over about 15 years so that investigation of the socioeconomic factors related with obesity is important in terms of preventation of obesity. In particular, the association between obesity and socioeconomic status varies with gender and has spatial dependency. In the paper, we estimate the effects of socioeconomic factors on obesity proportion by gender, considering the spatial correlation. Here, a conditional autoregressive model under the Bayesian framework is used in order to take into account the spatial dependency. For the real applicaiton, we use the obestiy proportion dataset at 25 districts of Seoul in 2010. We compare the proposed spatial model with a non-spatial model in terms of the goodness-of-fit and prediction measures so the spatial model performs well.

• Journal of Preventive Medicine and Public Health
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• v.44 no.6
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• pp.242-248
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• 2011

Objectives: Busan is reported to have the highest mortality rate among 16 provinces in Korea, as well as considerable health inequality across its districts. This study sought to examine overall and cause-specific mortality and deprivation at the town level in Busan, thereby identifying towns and causes of deaths to be targeted for improving overall health and alleviating health inequality. Methods: Standardized mortality ratios (SMRs) for all-cause and four specific leading causes of death were calculated at the town level in Busan for the years 2005 through 2008. To construct a deprivation index, principal components and factor analysis were adopted, using 10% sample data from the 2005 census. Geographic information system (GIS) mapping techniques were applied to compare spatial distributions between the deprivation index and SMRs. We fitted the Gaussian conditional autoregressive model (CAR) to estimate the relative risks of mortality by deprivation level, controlling for both the heterogeneity effect and spatial autocorrelation. Results: The SMRs of towns in Busan averaged 100.3, ranging from 70.7 to 139.8. In old inner cities and towns reclaimed for replaced households, the deprivation index and SMRs were relatively high. CAR modeling showed that gaps in SMRs for heart disease, cerebrovascular disease, and physical injury were particularly high. Conclusions: Our findings indicate that more deprived towns are likely to have higher mortality, in particular from cardiovascular disease and physical injury. To improve overall health status and address health inequality, such deprived towns should be targeted.