• 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.

• Structural Engineering and Mechanics
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• v.37 no.2
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• pp.197-213
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• 2011

Concrete is a heterogeneous material exhibiting quasi-brittle behaviour. While homogenization of concrete is commonly accepted in general engineering applications, a detailed description of the material heterogeneity using a mesoscale model becomes desirable and even necessary for problems where drastic spatial and time variation of the stress and strain is involved, for example in the analysis of local damages under impact, shock or blast load. A mesoscale model can also assist in an investigation into the underlying mechanisms affecting the bulk material behaviour under various stress conditions. Extending from existing mesoscale model studies, where use is often made of specialized codes with limited capability in the material description and numerical solutions, this paper presents a mesoscale computational model developed under a general-purpose finite element environment. The aim is to facilitate the utilization of sophisticated material descriptions (e.g., pressure and rate dependency) and advanced numerical solvers to suit a broad range of applications, including high impulsive dynamic analysis. The whole procedure encompasses a module for the generation of concrete mesoscale structure; a process for the generation of the FE mesh, considering two alternative schemes for the interface transition zone (ITZ); and the nonlinear analysis of the mesoscale FE model with an explicit time integration approach. The development of the model and various associated computational considerations are discussed in this paper (Part 1). Further numerical studies using the mesoscale model for both quasi-static and dynamic loadings will be presented in the companion paper (Part 2).

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.1-18
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• 2019

This research measured and visualized the spatial dependency and the spatial heterogeneity of the small business in Cheonan-si, Asan-si with $100m{\times}100m$ grids based on global and local spatial autocorrelation. First, we confirmed positive spatial autocorrelation of small business in the research area using Moran's I Index, which is ESDA(Exploratory Spatial Data Analysis). And then, through Getis-Ord $GI{\ast}$, one kind of LISA(Local Indicators of Spatial Association), local patterns of spatial autocorrelation were visualized. These verified that Spatial Regression Model is valid for the location factor analysis on small business commercial buildings. Next, GWR(Geographically Weighted Regression) was used to analyze the spatial relations between the distribution of small business, hourly mobile traffic-based floating population, land use attributes index, residence, commercial building, road networks, and the node of traffic networks. Final six variables were applied and the accessibility to bus stops, afternoon time floating population, and evening time floating population were excluded due to multicollinearity. By this, we demonstrated that GWR is statistically improved compared to OLS. We visualized the spatial influence of the individual variables using the regression coefficients and local coefficients of determinant of the six variables. This research applied the measured population information in a practical way. Reflecting the dynamic information of the urban people using the commercial area. It is different from other studies that performed commercial analysis. Finally, this research has a differentiated advantage over the existing commercial area analysis in that it employed hourly changing commercial service population data and it applied spatial statistical models to micro spatial units. This research proposed new framework for the commercial analysis area analysis.

• Journal of the Korean Geographical Society
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• v.40 no.2 s.107
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• pp.221-241
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• 2005

Land Use and Land Cover Changes (LUCC) occur over a wide range of space and time scales, and involve complex natural, socio-economic, and institutional processes. Therefore, modelling and predicting LUCC demands an understanding of how various measured properties behave when considered at different scales. Understanding spatial and temporal variability of driving forces and constraints on LUCC is central to understanding the scaling issues. This paper aims to 1) assess the heterogeneity of land cover change processes over the landscape in northern Ghana, where intensification of agricultural activities has been the dominant land cover change process during the past 15 years, 2) characterise dominant land cover change mechanisms for various spatial scales, and 3) identify the optimal spatial scale for LUCC modelling in a savanna landscape. A multivariate statistical method was first applied to identify land cover change intensity (LCCI), using four time-sequenced NDVI images derived from LANDSAT scenes. Three proxy land use change predictors: distance from roads, distance from surface water bodies, and a terrain characterisation index, were regressed against the LCCI using a multi-scale hierarchical adaptive model to identify scale dependency and spatial heterogeneity of LUCC processes. High spatial associations between the LCCI and land use change predictors were mostly limited to moving windows smaller than 10$\times$10km. With increasing window size, LUCC processes within the window tend to be too diverse to establish clear trends, because changes in one part of the window are compensated elsewhere. This results in a reduced correlation between LCCI and land use change predictors at a coarser spatial extent. The spatial coverage of 5-l0km is incidentally equivalent to a village or community area in the study region. In order to reduce spatial variability of land use change processes for regional or national level LUCC modelling, we suggest that the village level is the optimal spatial investigation unit in this savanna landscape.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.2
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• pp.149-160
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• 2007

KoFlux Gwangneung Supersite comprises complex topography and diverse vegetation types (and structures), which necessitate complementary multi-disciplinary measurements to understand energy and matter exchange. Here, we report the results of this ongoing research with special focuses on carbon/water budgets in Gwangneung forest, implications of inter-dependency between water and carbon cycles, and the importance of hydrology in carbon cycling under monsoon climate. Comprehensive biometric and chamber measurements indicated the mean annual net ecosystem productivity (NEP) of this forest to be ${\sim}2.6\;t\;C\;ha^{-1}y^{-1}$. In conjunction with the tower flux measurement, the preliminary carbon budget suggests the Gwangneung forest to be an important sink for atmospheric $CO_2$. The catchment scale water budget indicated that $30\sim40%$ of annual precipitation was apportioned to evapotranspiration (ET). The growing season average of the water use efficiency (WUE), determined from leaf carbon isotope ratios of representative tree species, was about $12{\mu}mol\;CO_2/mmol\;H_2O$ with noticeable seasonal variations. Such information on ET and WUE can be used to constrain the catchment scale carbon uptake. Inter-annual variations in tree ring growth and soil respiration rates correlated with the magnitude and the pattern of precipitation during the growing season, which requires further investigation of the effect of a monsoon climate on the catchment carbon cycle. Additionally, we examine whether structural and functional units exist in this catchment by characterizing the spatial heterogeneity of the study site, which will provide the linkage between different spatial and temporal scale measurements.