• Communications for Statistical Applications and Methods
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• v.28 no.4
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• pp.315-327
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• 2021

The Spatial Autoregressive (SAR) models have drawn considerable attention in recent econometrics literature because of their capability to model the spatial spill overs in a feasible way. While considering the Bayesian analysis of these models, one may face the problem of lack of robustness with respect to underlying prior assumptions. The generalized Bayes estimators provide a viable alternative to incorporate prior belief and are more robust with respect to underlying prior assumptions. The present paper considers the SAR model with a set of linear restrictions binding the regression coefficients and derives restricted generalized Bayes estimator for the coefficients vector. The minimaxity of the restricted generalized Bayes estimator has been established. Using a simulation study, it has been demonstrated that the estimator dominates the restricted least squares as well as restricted Stein rule estimators.

• Spatial Information Research
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• v.9 no.2
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• pp.239-261
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• 2001

A large suite of official statistical data sets has been compiled for geographical units under the national directives, and it is the quantitative regional analysis procedures that could add values to them. This paper reports our attempts at prototyping a statistical GIS which is capable of serving over the Web a variety of regional analysis routines as well as value-added statistics and maps. A pilot database of some major statistical data was ingested for the city of Seoul. The baseline subset of regional analysis methods of practical usage was selected and accommodated into the business logic of the target system, which ranges from descriptive statistics, regional structure/inequality measures, spatial ANOVA, spatial (auto) correlation to regression and residual analysis. The leading-edge information technologies including the application server were adopted in the system design and implementation so that the database, analysis modules and analytic mapping components may cooperate seamlessly behind the Web front-end. The prototyped system supports tables, maps, and files of downloadable format for input and output of the analyses. One of the most salient features of out proposed system is that both the database and analysis modules are extensible via the bi-directional interface for end users; The system provides users with operators and parsers for algebraic formulae such that the stored statistical variables may be transformed and combined into the newly-derived set of variables. This functionality eventually leads to on-the-fly fabrication of user-defined regional analysis algorithms. The stored dataset may also be temporarily augmented by user-uploaded dataset; The extension of this form, in essence, results in a virtual database which awaits for users commands as usual. An initial evaluation of the proposed system confirms that the issues involving the usage and dissemination of information can be addressed with success.

• Economic and Environmental Geology
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• v.28 no.3
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• pp.271-277
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• 1995

The geological information analysis based on spatial statistical techniques have been studied in relation to mineral exploration. The applicability of outlier detection using moving-window statistics and directional cross-variography analysis have been verified by using geochemical data sets surveyed in the Taebaek area for mineral exploration. The directional variogram analysis has been basically known as a geostatistical method for spatial continuity measures. In this study, the application of this proposed method was extended to measure spatial correlation or similarity problems between two geochemical elements. For the appraisal of the usefulness of this scheme, five kinds of variogram functions were computed for original data and revised data, obtained by removing outliers detected by moving-window statistics and the results were compared. It is concluded that these advanced spatial statistical methods at the interpretation stage of spatial similarity provide us with valuable quantitative results as decision-supporting information for regional mineral exploration task.

• Communications for Statistical Applications and Methods
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• v.18 no.4
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• pp.549-557
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• 2011

In this paper, we apply cluster analysis to "Okayama parking data" that is one of the spatial point patterns data that includes locations and the fare structure of car parking space in Okayama central area. This study classifies the characteristics of small areas through Okayama parking data as well as visualizes the results of the cluster analysis. We develop web applications that connect the results of a cluster analysis and overlay objects including points of balloons and rectangles of small areas over a map of Okayama central area.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.6
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• pp.650-662
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• 2011

The statistical tools such as empirical orthogonal function (EOF), and singular value decomposition (SVD) have been applied to analyze the characteristic of air pollutant over southeast Asia as well as to evaluate Zimeke's tropospheric column ozone (ZTO) determined by tropospheric residual method. In this study, we found that the EOF and SVD analyses are useful methods to extract the most significant temporal and spatial pattern from enormous amounts of satellite data. The EOF analyses with OMI $NO_2$ and OMI HCHO over southeast Asia revealed that the spatial pattern showed high correlation with fire count (r=0.8) and the EOF analysis of CO (r=0.7). This suggests that biomass burning influences a major seasonal variability on $NO_2$ and HCHO over this region. The EOF analysis of ZTO has indicated that the location of maximum ZTO was considerably shifted westward from the location of maximum of fire count and maximum month of ZTO occurred a month later than maximum month (March) of $NO_2$, HCHO and CO. For further analyses, we have performed the SVD analyses between ZTO and ozone precursor to examine their correlation and to check temporal and spatial consistency between two variables. The spatial pattern of ZTO showed latitudinal gradient that could result from latitudinal gradient of stratospheric ozone and temporal maximum of ZTO in March appears to be associated with stratospheric ozone variability that shows maximum in March. These results suggest that there are some sources of error in the tropospheric residual method associated with cloud height error, low efficiency of tropospheric ozone, and low accuracy in lower stratospheric ozone.

• Ocean and Polar Research
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• v.44 no.2
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• pp.179-190
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• 2022

Data missing in optical satellite images caused by natural variations have been a crucial barrier in observing the status of marine surfaces. Although there have been many attempts to fill the gaps of non-observation, there is little research to analyze the ratio of missing grids to overall sea grids and their seasonal patterns. This report introduces the method of quantifying the distribution of missing points and then shows how the missing points have spatial correlation and seasonal trends. Both temporal and spatial integration methods are compared to assess the effectiveness of reducing missing data. The temporal integration shows more outstanding performance than the spatial integration. Moran's I and K-function with statistical hypothesis testing show that missing grids are clustered and there is a non-random distribution from daily integration. The result of the seasonality test for Moran's I through a periodogram shows dependency on full-year, half-year, and quarter-year periods respectively. These analysis results can be used to deduce appropriate integration periods with permissible estimation errors.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.457-461
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• 2007

In many applications, received signal strength indicator is used for location tracking and sensor nodes localization. For location finding, the distances between sensor nodes can be estimated by converting received signal's power into distance using path loss prediction model. Many researches have done the analysis of power-distance relationship for radio channel characterization. In indoor environment, the general conclusion is the non-linear variation of RSSI values as distance varied linearly. This has been one of the difficulties for indoor localization. This paper presents works on indoor RSSI characterization based on statistical methods to find the overall trend of RSSI variation at different places and times within the same room From experiments, it has been shown that the variation of RSSI values can be determined by both spatial and temporal factors. This two factors are directly indicated by the two main parameters of path loss prediction model. The results show that all sensor nodes which are located at different places share the same characterization value for the temporal parameter whereas different values for the spatial parameters. Using this relationship, the characterization for location estimation can be more efficient and accurate.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.561-575
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• 2023

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Magnetoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static/temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network differences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.229-244
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• 2007

Among the small area estimation methods, it has been known that hierarchical Bayesian(HB) approach is the most reasonable and effective method. However any model based approaches need good explanatory variables and finding them is the key role in the model based approach. As the lacking of explanatory variables, adopting the spatial terms in the model was introduced. Here in this paper, we evaluate the model based methods with/without spatial terms using the diagnostic methods which were introduced by Brown et al. (2001). And Economic Active Population Survey(2005) is used for data analysis.