• 제목, 요약, 키워드: spatial heterogeneity

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The Production and Spatial Heterogeneity of Litterfall in the Mixed Broadleaved-Korean Pine Forest of Xiaoxing'an Mountains, China

  • Jin, Guangze;Zhao, Fengxia;Liu, Liang;Kim, Ji Hong
    • Journal of Korean Society of Forest Science
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    • v.97 no.2
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    • pp.165-170
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    • 2008
  • Litterfall has been recognized an important part of the forest ecosystem production, playing a major pathway in energy flow and nutrient cycling through the ecosystem. This study was carried out to examine the quantity and components, temporal variation, and spatial heterogeneity of the litterfall in the mixed broadleaved-Korean pine forest. The data were collected from the 9ha permanent experimental plot, of which on the center area, i.e. $150m{\times}150m$, the total number of 319 circular litterfall traps with the size of $0.5m^2$ were established to collect falling litterfall. The results showed that the annual amount of litterfall was totalized 3,033.7 kg/ha, occupying broad-leaves of 39.3%, conifer-leaves of 29.5%, others of 18.5%, branches of 10.4%, and seeds of 2.3%. The peak point of the litterfall production was made at the end of September, proportionating 32.2% of total amount. The analysis of semivariogram revealed the existence of high spatial heterogeneity, calculated the scale of spatial heterogeneity ranged from 11.6 m to 29.1 m. The result of proportion (C/[Co+C]) showed that spatial heterogeneity of autocorrelation in total spatial heterogeneity were from 97.0% to 100%. The relatively heavy branches and others had significant differences in litterfall production between the areas of canopy gap and closed canopy in the 95% probability level, but the other components did not show statistical differences.

Spatial Dependency and Heterogeneity of Adult Diseases : In the Cases of Obesity, Diabetes and High Blood Pressure in the U.S.A. (성인병의 공간적 의존성과 이질성 : 미국의 비만, 당뇨, 고혈압을 사례로)

  • Yang, Byung-Yun;Hwang, Chul-Sue
    • 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.

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Spatial Pattern Analysis of CO2 Emission in Seoul Metropolitan City Based on a Geographically Weighted Regression (공간가중회귀 모형을 이용한 서울시 에너지 소비에 따른 이산화탄소 배출 분석)

  • Kim, Dong Ha;Kang, Ki Yeon;Sohn, So Young
    • Journal of Korean Institute of Industrial Engineers
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    • v.42 no.2
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    • pp.96-111
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    • 2016
  • Effort to reduce energy consumptions or CO2 emissions is global trend. To follow this trend, spatial studies related to characteristics affecting energy consumptions or CO2 emissions have been conducted, but only with the focus on spatial dependence, not on spatial heterogeneity. The aim of this study is to investigate spatial heterogeneity patterns of CO2 emission based on socio-economic factors, land-use characteristics and traffic infrastructure of Seoul city. Geographically Weighted Regression (GWR) analysis was performed with 423 administrative district data in Seoul. The results suggest that population and employment densities, road density and railway length in most districts are found to have positive impact on the CO2 emissions. Residential and green area densities also have the highest positive impact on CO2 emissions in most districts of Gangnam-gu. The resulting model can be used to identify the spatial patterns of CO2 emissions at district level in Seoul. Eventually it can contribute to local energy policy and planning of metropolitan area.

The Effects of Neighborhood Segmentation on the Adequacy of a Spatial Regression Model (인근지역 범위 설정이 공간회귀모형 적합에 미치는 영향)

  • Lee, Chang Ro;Park, Key Ho
    • Journal of the Korean Geographical Society
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    • v.48 no.6
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    • pp.978-993
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    • 2013
  • It can be advantage as well as disadvantage to use the spatial weight matrix in a spatial regression model; it would benefit from explicitly quantifying spatial relationships between geographical units, but necessarily involve subjective judgment while specifying the matrix. We took Incheon City as a study area and investigated how the fitness of a spatial regression model changed by constructing various spatial weight matrices. In addition, we explored neighborhood segmentation in the study area and analyzed any influence of it on the model adequacy of two basic spatial regression models, i.e., spatial lagged and spatial error models. The results showed that it can help to improve the adequacy of models to specify the spatial weight matrix strictly, that is, interpreting the neighborhood as small as possible when estimating land price. It was also found that the spatial error model would be preferred in the area with serious spatial heterogeneity. In such area, we found that its spatial heterogeneity can be alleviated by delineating sub-neighborhoods, and as a result, the spatial lagged model would be preferred over the spatial error model.

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Data Exchange between Cadastre and Physical Planning by Database Coupling

  • Kim, Kam-Rae;Choi, Won-Jun
    • Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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    • v.25 no.1
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    • pp.69-75
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    • 2007
  • The information in physical planning field shows the socio-economic potentials of land resources while cadastral data does the physical and legal realities of the land. The two domains commonly deal with land information but have different views. Cadastre has to evolved to the multi-purpose ones which provide value-added information and support a wide spectrum of decision makers by mixing their own information with other spatial/non-spatial databases. In this context, the demands of data exchange between the two domains is growing up but this cannot be done without resolving the heterogeneity between the two information applications. Both of either discipline sees the reality within its own scope, which means each has a unique way to abstract real world phenomena to the database. The heterogeneity problem emerges when an GIS is autonomously and independently established. It causes considerable communication difficulties since heterogeneity of representations forms unique data semantics for each database. The semantic heterogeneity obviously creates an obstacle to data exchange but, at the same time, it can be a key to solve the problems too. Therefore, the study focuses on facilitating data sharing between the fields of cadastre and physical planning by resolving the semantic heterogeneity. The core job is developing a conversion mechanism of cadastral data into the information for the physical planning by DB coupling techniques.

Estimation of Groundwater Recharge with Spatial-Temporal Variability (시공간적 변동성을 고려한 지하수 함양량의 산정방안)

  • Kim, Nam Won;Chung, Il Moon;Won, Yoo Seung
    • Proceedings of the Korea Water Resources Association Conference
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    • pp.691-695
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    • 2004
  • In recent years, mary studies for efact estimation of groudwater recharge has been performed. They can be categorized into three groups : analytical method by means of groundwater recession curve, water budget analysis based on watershed, and the method using groundwater model. Since groundwater recharge rate shows the spatial-temporal variability due to hydrogeological heterogeneity, existing studies have various limits to deal with these characteristics. The method of estimating daily recharge rate with spatial-temporal variation based on rainfall-runoff model is suggested in this study for this purpose. This method is expected to enhance existing indirect method by means of reflecting climatic conditions, land use and hydrogeological heterogeneity.

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SPATIAL AND TEMPORAL INFLUENCES ON SOIL MOISTURE ESTIMATION

  • Kim, Gwang-seob
    • Water Engineering Research
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    • v.3 no.1
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    • pp.31-44
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    • 2002
  • The effect of diurnal cycle, intermittent visit of observation satellite, sensor installation, partial coverage of remote sensing, heterogeneity of soil properties and precipitation to the soil moisture estimation error were analyzed to present the global sampling strategy of soil moisture. Three models, the theoretical soil moisture model, WGR model proposed Waymire of at. (1984) to generate rainfall, and Turning Band Method to generate two dimensional soil porosity, active soil depth and loss coefficient field were used to construct sufficient two-dimensional soil moisture data based on different scenarios. The sampling error is dominated by sampling interval and design scheme. The effect of heterogeneity of soil properties and rainfall to sampling error is smaller than that of temporal gap and spatial gap. Selecting a small sampling interval can dramatically reduce the sampling error generated by other factors such as heterogeneity of rainfall, soil properties, topography, and climatic conditions. If the annual mean of coverage portion is about 90%, the effect of partial coverage to sampling error can be disregarded. The water retention capacity of fields is very important in the sampling error. The smaller the water retention capacity of the field (small soil porosity and thin active soil depth), the greater the sampling error. These results indicate that the sampling error is very sensitive to water retention capacity. Block random installation gets more accurate data than random installation of soil moisture gages. The Walnut Gulch soil moisture data show that the diurnal variation of soil moisture causes sampling error between 1 and 4 % in daily estimation.

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Estimation of the Number of Sampling Points Required for the Determination of Soil CO2 Efflux in Two Types of Plantation in a Temperate Region

  • Lee, Na-Yeon(Mi-Sun);Koizumi, Hiroshi
    • Journal of Ecology and Environment
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    • v.32 no.2
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    • pp.67-73
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    • 2009
  • Soil $CO_2$ efflux can vary markedly in magnitude over both time and space, and understanding this variation is crucial for the correct measurement of $CO_2$ efflux in ecological studies. Although considerable research has quantified temporal variability in this flux, comparatively little effort has focused on its spatial variability. To account for spatial heterogeneity, we must be able to determine the number of sampling points required to adequately estimate soil $CO_2$ efflux in a target ecosystem. In this paper, we report the results of a study of the number of sampling points required for estimating soil $CO_2$ efflux using a closed-dynamic chamber in young and old Japanese cedar plantations in central Japan. The spatial heterogeneity in soil $CO_2$ efflux was significantly higher in the mature plantation than in the young stand. In the young plantation, 95% of samples of 9 randomly-chosen flux measurements from a population of 16 measurements made using 72-$cm^2$ chambers produced flux estimates within 20% of the full-population mean. In the mature plantation, 20 sampling points are required to achieve means within $\pm$ 20% of the full-population mean (15 measurements) for 95% of the sample dates. Variation in soil temperature and moisture could not explain the observed spatial variation in soil $CO_2$ efflux, even though both parameters are a good predictor of temporal variation in $CO_2$ efflux. Our results and those of previous studies suggest that, on average, approximately 46 sampling points are required to estimate the mean and variance of soil $CO_2$ flux in temperate and boreal forests to a precision of $\pm$ 10% at the 95% confidence level, and 12 points are required to achieve a precision of $\pm$ 20%.

Spatial Data Analysis for the U.S. Regional Income Convergence,1969-1999: A Critical Appraisal of $\beta$-convergence (미국 소득분포의 지역적 수렴에 대한 공간자료 분석(1969∼1999년) - 베타-수렴에 대한 비판적 검토 -)

  • Sang-Il Lee
    • Journal of the Korean Geographical Society
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    • v.39 no.2
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    • pp.212-228
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    • 2004
  • This paper is concerned with an important aspect of regional income convergence, ${\beta}$-convergence, which refers to the negative relationship between initial income levels and income growth rates of regions over a period of time. The common research framework on ${\beta}$-convergence which is based on OLS regression models has two drawbacks. First, it ignores spatially autocorrelated residuals. Second, it does not provide any way of exploring spatial heterogeneity across regions in terms of ${\beta}$-convergence. Given that empirical studies on ${\beta}$-convergence need to be edified by spatial data analysis, this paper aims to: (1) provide a critical review of empirical studies on ${\beta}$-convergence from a spatial perspective; (2) investigate spatio-temporal income dynamics across the U.S. labor market areas for the last 30 years (1969-1999) by fitting spatial regression models and applying bivariate ESDA techniques. The major findings are as follows. First, the hypothesis of ${\beta}$-convergence was only partially evidenced, and the trend substantively varied across sub-periods. Second, a SAR model indicated that ${\beta}$-coefficient for the entire period was not significant at the 99% confidence level, which may lead to a conclusion that there is no statistical evidence of regional income convergence in the US over the last three decades. Third, the results from bivariate ESDA techniques and a GWR model report that there was a substantive level of spatial heterogeneity in the catch-up process, and suggested possible spatial regimes. It was also observed that the sub-periods showed a substantial level of spatio-temporal heterogeneity in ${\beta}$-convergence: the catch-up scenario in a spatial sense was least pronounced during the 1980s.