• Communications for Statistical Applications and Methods
• /
• v.10 no.2
• /
• pp.423-432
• /
• 2003

The data observed at different positions are called the estimate of interested variable at new observation point on the Kriging utilize the space estimate technique, in which case there is correlation spatially. In this paper we provide the estimate for Variogram and Kriging methods as a field of kriging theory and dealt with actually measured data. And at the same time we forecast the amount of ozone that was not measured at this point by Kriging method and compared Ordinary Kriging method with Inverse Distance Kriging method.

• Communications for Statistical Applications and Methods
• /
• v.6 no.3
• /
• pp.781-790
• /
• 1999

In this paper we consider estimation of cancer incidence rates for local areas. The raw estimates usually are based on small sample sizes and hence are usually unreliable. A hierarchical Bayes generalized linear model is used which connects the local areas thereby enabling one to 'borrow strength' Random effects with pairwise difference priors model the spatial structure in the data. The methods are applied to cancer incidence estimation for census tracts in a certain region of the state of New York.

• Journal of the Korean Geotechnical Society
• /
• v.27 no.7
• /
• pp.59-68
• /
• 2011

Subsurface Investigation results always reflect a level of soil uncertainty, which sometimes requires statistical corrections of the data for the appropriate engineering decision. This study suggests a closed-form framework to extract the outlying data points from the testing results using the statistical geo-spatial information analyses with outlier analysis and kring-based crossvalidation. The suggested analysis method is conducted to soil stratification using the borehole data in Yeouido.

• Journal of Biomedical Engineering Research
• /
• v.30 no.5
• /
• pp.373-380
• /
• 2009

In general, Independent component analysis (ICA) is a statistical blind source separation technique, used either in spatial or temporal domain. The spatial or temporal ICAs are designed to extract maximally independent sources in respective domains. The underlying sources for spatiotemporal data (sequence of images) can not always be guaranteed to be independent, therefore spatial ICA extracts the maximally independent spatial sources, deteriorating the temporal sources and vice versa. For such data types, spatiotemporal ICA tries to create a balance by simultaneous optimization in both the domains. However, the spatiotemporal ICA suffers the problem of source ambiguity. Recently, constrained ICA (c-ICA) has been proposed which incorporates a priori information to extract the desired source. In this study, we have extended the c-ICA for better analysis of spatiotemporal data. The proposed algorithm, i.e., constrained spatiotemporal ICA (constrained st-ICA), tries to find the desired independent sources in spatial and temporal domains with no source ambiguity. The performance of the proposed algorithm is tested against the conventional spatial and temporal ICAs using simulated data. Furthermore, its performance for the real spatiotemporal data, functional magnetic resonance images (fMRI), is compared with the SPM (conventional fMRI data analysis tool). The functional maps obtained with the proposed algorithm reveal more activity as compared to SPM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.33 no.4
• /
• pp.221-229
• /
• 2015

The urban crimes that threat individual's safety are parts of the serious social problems. However. the information of crime in Korea has only been provided by forms of hot spots around place of crime, or forms of crime statistics without positional information. Those could not provide enough information to users in identifying the vulnerable areas for substantive crimes. Therefore, this study suggested a methodology of extraction in criminal vulnerable areas by using the spatial information, the statistical information and the public sector information. The crime vulnerable areas were extracted through the grid-based spatial analysis and the overlapping analysis from each of the information. In fact, the extracted areas were able to provide detailed vulnerability information than the traditional hot spot-based crime information. Following the study, the extracted results in crime vulnerable areas have displayed highly coincide with Korea safety map, provided by national disaster management institute, which regards to be able to provide crime risk rating in terms of administrative business in future.

• Journal of Korea Water Resources Association
• /
• v.54 no.9
• /
• pp.693-704
• /
• 2021

The purpose of this study is to evaluate the relationship between heat wave and river water quality. The daily maximum air temperature (Tmax) of 91 meteorological stations of the Korea Meteorological Administration and 13 river water quality factors (DO, BOD, COD, TOC, TN, DTN, NH4-N, NO2-N, NO3-N, TP, DTP, PO4-P, Chl-a) of Ministry of Environment were analyzed. The correlation analysis was performed on Tmax and water quality factors, and the determination coefficients (R²) of DO, Chl-a, and TN with Tmax showed high values of 0.782, 0.609, and 0.691 respectively. To analyze the spatial impact between heat waves and water quality factors, the heat wave intensity (HWI) and heat wave duration (HWD) were calculated using the Tmax. The hotspot and spatial statistical analyses were applied for spatial impact evaluation. As a result of hotspot analysis, the heat wave index (HWD, HWI) showed high spatial pattern in the downstream of Nakdong River basin, and Chl-a and TN showed the same pattern. In case of spatial statistical analysis for water quality due to heat wave, the most obvious spatial variability was DO.

• The Korean Journal of Applied Statistics
• /
• v.18 no.2
• /
• pp.271-280
• /
• 2005

Kriging methods as traditional spatial data analysis methods and geographical weighted regression models as statistical analysis methods are compared. In this paper, we apply data from the Ministry of Environment to spatial analysis for practical study. We compare these methods to performance with monthly carbon monoxide observations taken at 116 measuring area of air pollution in 1999.

• Spatial Information Research
• /
• v.20 no.1
• /
• pp.81-90
• /
• 2012

When analyzing geographical phenomena, two properties need to be considered. One is the spatial dependence structure and the other is a variation or an uncertainty inhibited in a geographic space. Two problems are encountered due to the properties. Firstly, spatial dependence structure, which is conceptualized as spatial autocorrelation, generates heterogeneous geographic landscape in a spatial process. Secondly, generic statistics, although suitable for dealing with stochastic uncertainty, tacitly ignores location information im plicit in spatial data. GIS is a versatile tool for manipulating locational information, while spatial statistics are suitable for investigating spatial uncertainty. Therefore, integrating spatial statistics to GIS is considered as a plausible strategy for appropriately understanding geographic phenomena of interest. Geographic hot-spot analysis is a key tool for identifying abnormal locations in many domains (e.g., criminology, epidemiology, etc.) and is one of the most prominent applications by utilizing the integration strategy. The article aims at reviewing spatial statistical perspective for analyzing spatial processes in the framework of GIS by carrying out empirical analysis. Illustrated is the analysis procedure of using spatial scan statistic for detecting clusters in the framework of GIS. The empirical analysis targets for identifying spatial clusters of breast cancer incidents in Erie and Niagara counties, New York.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.32 no.4_1
• /
• pp.343-351
• /
• 2014

The correlation between regional Walkability Index and their physical socio-economic characteristics has evaluated by the spatial statistical analysis to understand the urban pedestrian environments, where has been emerging the significance, recently. Following to the study, the Walkability Indexes were calculated quantitatively from two administrative districts of Busan and measured Global Local spatial autocorrelation indices. Additionally, the Geographically Weighted Regression model was applied to define the correlation between Walkability Indexes and urban environmental variables. The spatial autocorrelation values and clusters on the Walkability Indexes were derived in statistically significant level. Furthermore, the Geographically Weighted Regression model has been derived more improved inference than the OLS regression model, so as the influence of local level pedestrian environment was identified. The results of this study suggest that the spatial statistical approach can be effective on quantitative assessing the pedestrian environment and navigating their associated factors.

• Communications for Statistical Applications and Methods
• /
• v.19 no.1
• /
• pp.193-211
• /
• 2012

This paper deals with robust nonparametric regression analysis when the regressors are functional random fields. More precisely, we consider $Z_i=(X_i,Y_i)$, $i{\in}\mathbb{N}^N$ be a $\mathcal{F}{\times}\mathbb{R}$-valued measurable strictly stationary spatial process, where $\mathcal{F}$ is a semi-metric space and we study the spatial interaction of $X_i$ and $Y_i$ via the robust estimation for the regression function. We propose a family of robust nonparametric estimators for regression function based on the kernel method. The main result of this work is the establishment of the asymptotic normality of these estimators, under some general mixing and small ball probability conditions.