• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1557-1561
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• 2014

Background: Breast cancer is the most frequently diagnosed cancer in women and estimating its relative risks and trends of incidence at the area-level is helpful for health policy makers. However, traditional methods of estimation which do not take spatial heterogeneity into account suffer from drawbacks and their results may be misleading, as the estimated maps of incidence vary dramatically in neighboring areas. Spatial methods have been proposed to overcome drawbacks of traditional methods by including spatial sources of variation in the model to produce smoother maps. Materials and Methods: In this study we analyzed the breast cancer data in Iran during 2004-2008. We used a method proposed to cover spatial and temporal effects simultaneously and their interactions to study trends of breast cancer incidence in Iran. Results: The results agree with previous studies but provide new information about two main issues regarding the trend of breast cancer in provinces of Iran. First, this model discovered provinces with high relative risks of breast cancer during the 5 years of the study. Second, new information was provided with respect to overall trend trends o. East-Azerbaijan, Golestan, North-Khorasan, and Khorasan-Razavi had the highest increases in rates of breast cancer incidence whilst Tehran, Isfahan, and Yazd had the highest incidence rates during 2004-2008. Conclusions: Using spatial methods can provide more accurate and detailed information about the incidence or prevalence of a disease. These models can specify provinces with different health priorities in terms of needs for therapy and drugs or demands for efficient education, screening, and preventive policy into action.

• Journal of the Korean Geotechnical Society
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• v.28 no.12
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• pp.65-76
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• 2012

In this study, probabilistic analysis of seepage through a two-layered soil foundation was performed. The hydraulic conductivity of soil shows significant spatial variations in different layers because of stratification; further, it varies on a smaller scale within each individual layer. Therefore, the deterministic seepage analysis method was extended to develop a probabilistic approach that accounts for the uncertainties and spatial variation of the hydraulic conductivity in a layered soil profile. Two-dimensional random fields were generated on the basis of the Karhunen-Lo$\grave{e}$ve expansion in a manner consistent with a specified marginal distribution function and an autocorrelation function for each layer. A Monte Carlo simulation was then used to determine the statistical response based on the random fields. A series of analyses were performed to verify the application potential of the proposed method and to study the effects of uncertainty due to the spatial heterogeneity on the seepage behavior of two-layered soil foundation beneath water retaining structure. The results showed that the probabilistic framework can be used to efficiently consider the various flow patterns caused by the spatial variability of the hydraulic conductivity in seepage assessment for a layered soil foundation.

• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.111-123
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• 2008

Geotechnical engineering problems are characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for a probabilistic analysis that considers the spatial variability of soil properties is presented to study the response of spatially random soil. The approach integrates a commercial finite difference method and random field theory into the framework of a probabilistic analysis. Two-dimensional non-Gaussian random fields are generated based on a Karhunen-$Lo{\grave{e}}ve$ expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation is then used to determine the statistical response based on the random fields. A series of analyses were performed to study the effects of uncertainty due to the spatial heterogeneity on the settlement and bearing capacity of a rough strip footing. The simulations provide insight into the application of uncertainty treatment to the geotechnical problem and show the importance of the spatial variability of soil properties with regard to the outcome of a probabilistic assessment.

• 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.

• Journal of Korean Tunnelling and Underground Space Association
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• v.15 no.1
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• pp.13-24
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• 2013

Various elastic wave-based site investigation methods have been used to characterize subsurface ground because the dynamic properties can be correlated with various geotechnical parameters. Although the inherent spatial variability of the geotechnical parameters affects the P-wave propagation characteristics, ground heterogeneity has not been considered as an influential factor. Thus, the effect of heterogeneous ground on the travel-time shift and wavefront characteristics of elastic waves through stochastic numerical analyses is investigated in this study. The effects of the relative correlation lengths and relative propagation distances on the travel-time shift of P-waves considering various intensities of ground heterogeneity were investigated. Heterogeneous ground fields of stiffness (e.g., the coefficient of variation = 10 ~ 40%) were repeatedly realized in numerical finite difference grids using the turning band method. Monte Carlo simulations were undertaken to simulate P-wave propagation in heterogeneous ground using a finite difference method-based numerical approach. The results show that the disturbance of the wavefront becomes more significant with stronger heterogeneity and induces travel-time delays. The relative correlation lengths and propagation distances are systematically related to the travel-time shift.

• Survey Research
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• v.10 no.3
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• pp.1-19
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• 2009

This study investigates the utilities of spatial analysis in the context of survey research using Geographical Information System(GIS) and Geographically Weighted Regression (GWR) which take account of spatial heterogeneity. Many social phenomena involve spatial dimension, and with the development of GIS, GPS receiver, and online location-based services, spatial information can be collected and utilized more easily, and thus application of spatial analysis in the survey research is getting easier. The traditional OLS regression models which assume independence of observations and homoscedasticity of errors cannot handle spatial dependence problem. GWR is a spatial analysis technique which utilizes spatial information as well as attribute information, and estimated using geographically weighted function under the assumption that spatially close cases are more related than distant cases. Residential survey data from a Primary Autonomous District are used to estimate a model of public service satisfaction. The findings show that GWR handles the problem of spatial auto-correlation and increases goodness-of-fit of model. Visualization of spatial variance of effects of the independent variables using GIS allows us to investigate effects and relationships of those variables more closely and extensively. Furthermore, GIS and GWR analyses provide us a more effective way of identifying locations where the effect of variable is exceptionally low or high, and thus finding policy implications for social development.

• The Korean Journal of Ecology
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• v.27 no.2
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• pp.99-106
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• 2004

We developed the estimation model for the vegetation developmental processes on the severely burned slope areas after forest fire in the east coastal region, Korea. And we calculated the vegetation indices as a useful parameter for the development of land management technique in the burned area and suggested the changes of the vegetation indices after forest fire. In order to estimate the woody standing biomass in the burned area, allometric equations of the 17 woody species regenerated by sprouter were investigated. According to the our results, twenty year after forest fire need for the development to the normal forest formed by 4 stratum structure, tree, sub-tree, shrub and herb layer. The height of top vegetation layer, basal area and standing biomass of woody species show a tendency to increase linearly, and the ground vegetation coverage and litter layer show a tendency to increase logarithmically after forest fire. Among vegetation indices, Ive and Ivcd show a tendency to increase logarithmically, and Hcl and Hcdl show a tendency to increase linearly after forest fire. The spatial variation of the most vegetation factors was observed in the developmental stages less than the first 5 years which were estimated secondary disaster by soil erosion after forest fire. Among vegetation indices, Ivc and Ivcd were the good indices for the representation of the spatial heterogeneity in the earlier developmental stages, and Hcl and Hcdl were the useful indices for the long-term estimation of the vegetation development after forest fire.

• Journal of the Korean Geographical Society
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• v.48 no.6
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• pp.897-913
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• 2013

The main purpose of the paper is to examine the spatial characteristics of school choice through residential mobility by conducting a correlation analysis on the relationships between the middle schools' entrance rates to special high schools and the elementary schools' net transfer rates. Analyses are done at both the individual school level and the school catchment area level. Prior to the calculation, the two variables involved in the correlation analysis are transformed via a standardization equation, and the standardized scores are mapped and explored. Both the global and local correlation analyses are done for the standardized variables. Main findings are twofold. First, the global correlation analysis reports that there exists a statistically significant correlation between the two variables at both the analytical levels. Second, albeit the prominent positive correlation at the global level, the local analysis reveals the existence of a considerable level of spatial heterogeneity in terms of bivariate association. While several school catchment areas with very high local correlation coefficients (the HH association type) are popped up, other areas with various types of bivariate association including ones even opposite to the global trend are also observed.

• The Korean Journal of Ecology
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• v.23 no.3
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• pp.241-253
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• 2000

The degree to which microenvironmental factors are linked to spatial patterns of vegetational factors within ecosystems has important consequences for our understanding of how ecosytems are structured and for conservation of rare species in ecosystems. We studied this relationships between the spatial patterns of microenvironmental factors and vegetational factors in temperate hardwood forest in Mt. Jumbong Biological Reserve Area, Korea. To do this, environmental and vegetational factors from 196 micropoints in a 0.49 ha plot were investigated. Most of all environmental factors and vegetational factors showed the variations among micropoints. Microtopographic factors, litter depth, soil moisture content and relative light intensity at this site were spatially dependent at a scale of 14∼62 m. Coverage of tree and shrub layer and species diversity of herb layer in autumn were spatially dependent at a scale of < 15 m. Species richness and species diversity of herb layer in spring and species richness of herb layer in autumn were spatially dependent at a scale of 28∼48 m. Multiple regression analysis showed that spatial patterns of species richness and species diversity of herb layer in spring and autumn were affected by litter depth, slope, subtree layer, shrub, Sasa borealis etc. The best predictor for the spatial patterns of species richness and species diversity of herb layer at this site was the spatial pattern of litter depth. Species richness and species diversity of herb layer showed strongly negative correlation with litter depth. We estimate that the spatial pattern of litter depth at this site were affected by direction of wind, microtopography and spatial pattern of shrub layer.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.51-64
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• 2001

Schema integration is one of the most difficult issues in the heterogeneous and distributed geographic database systems (GDSs). As the use of spatial information in various application areas becomes increasingly popular, the integration of geographic information has become a crucial task for decision makers. Most existing schema integration techniques described in the database literature, however, do not address the problems of managing heterogeneities among complex objects that contain visual data and/or spatial and temporal information. The difficulties arise not only from the semantic conflicts, but also from the different representations of spatial models. Consequently, it is much more complex to achieve interoperability in the area of geographic databases. This research attempts to provide a solution to such problems. The research reported in this paper describes a schema integration methodology and a prototype toolkit developed to assist in schema integration activities for GDSs.