• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.610-622
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• 2010

The proportion of overweight and obese individuals in the United States has been continuously increasing up to recently. Many studies related to obesity have concentrated on jurisdictional levels of aggregation, making it very difficult to dearly illustrate at risk regions. In other words, little research has been conducted in relation to spatial patterns considering spatial dependency and heterogeneity by spatial autocorrelation models over space. In response, this research analyzes spatial patterns between overweight/obesity and risk factors, such as high blood pressure and diabetes, over space. Specifically, the Moran''s I and Geary''s C will be conducted for global and local measures. What is more, the Ordinary Least Square (OLS) linear regression and Geographically Weighted Regression methods will be applied to identify spatial dependency and spatial heterogeneity. Data provided by the Behavioral Risk Factor Surveillance System (BRFSS) have Body-Mass Index (BMI) rates, containing 4 rates of under, healthy, overweight, and obesity. In addition, high blood pressure and diabetes rates in the United States will be used as independent variables. Lastly, we are confident that this research will be beneficial for a decision maker to make a prevention plan for obesity.

• 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.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.391-397
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• 2008

This paper analyzes the spatial patterns of population distribution in Seoul Metropolitan Area in terms of spatial association using spatial statistics and spatial exploratory technique. Our empirical analysis based on global index shows that, in Seoul Metropolitan Area, the population had been distributed with strong positive spatial association over the period of 1980-2005. It implies that the population of each region is affected by the population distribution of adjacent regions. In addition, the analysis using local index was conducted for detecting the local patterns of spatial association, and the result shows that the clusters of population had been moved in the direction of West(Incheon and Bucheon) and South(Anyang and Seongnam) of Seoul where a large scale of lands or towns were developed over the period. These results will be the preliminary data for establishing management and development plans of Seoul Metropolitan Area.