• Communications for Statistical Applications and Methods
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• v.16 no.5
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• pp.851-858
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• 2009

In the last decades, there has been much interest in climate variability because its change has dramatic effects on humanity. Especially, the precipitation data are measured over space and their spatial association is so complicated. So we should take into account such a spatial dependency structure while analyzing the data. However, in linear models for analyzing the data, data sets show severely skewed distribution. In the paper, we consider the Box-Cox transformation to satisfy the normal distribution prior to the analysis, and employ a Bayesian hierarchical framework to investigate the spatial patterns. The data set we considered is monthly average precipitation of the third quarter of 2007 obtained from 347 automated monitoring stations in Contiguous South Korea.

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

• Spatial Information Research
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• v.20 no.1
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• pp.81-90
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• 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 Association of Geographic Information Studies
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• v.24 no.2
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• pp.91-101
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• 2021

This study attempted to conduct the study from a macro perspective more specifically through the temporal and spatial analysis of Minbuk villages according to a change in a Civilian Control Line, such as a social and spatial distribution and a change of the existing Minbuk villages. To this end, this study conducted the spatial analysis for the change in the Minbuk villages according to the adjustment of the Civilian Control Line in time series by using a map of the Armistice Agreement Vol. 2, Google Earth, a digital cadastral map, an administrative district map, and the like are used as spatial data, and summarizing and constructing, as attribute data, a statistical yearbook, Ministry of Defense and Cheorwon-Gun notification data, a Land Use Regulation Information System, and cadastral map attribute information. After the enactment of the Military Facility Protection Act, the analysis was performed on a 20-year basis based on the 1976 statistical yearbook of which the Civilian Control Line was drawn. As a result, the total area of the Civilian Control Zone in Cheorwon from 1975 to 2015 decreased by 105.8 km², and 9 of 14 Minbuk villages were released and only 6 villages existed. The unoccupied villages were analyzed as 14 villages, 10 fewer than the existing surveyed or statistical villages. The movement of the Civilian Control Line to the north may disappear the unique characteristics of the Minbuk villages but should be done carefully as it is closely related to the lives of the current residents, and policies should be established in terms of sustainable development and conservation of the villages. This study is significant in conducting the temporal and spatial analysis, which is the basis of the Minbuk regions and the Minbuk villages, and may be used as basic data necessary for subsequent analysis study.

• Journal of KIISE
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• v.45 no.2
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• pp.99-105
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• 2018

One of the spatial statistical analysis, hotspot analysis is one of easy method of see spatial patterns. It is based on the concept that "Adjacent ones are more relevant than those that are far away". However, in hotspot analysis is spatial adjacency must be considered, Therefore, distributed processing is not easy. In this paper, we proposed a distributed algorithm design for hotspot spatial analysis. Its performance was compared to standalone system and Hadoop, Spark based processing. As a result, it is compare to standalone system, Performance improvement rate of Hadoop at 625.89% and Spark at 870.14%. Furthermore, performance improvement rate is high at Spark processing than Hadoop at as more large data set.

• Journal of the Korean Data and Information Science Society
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• v.17 no.4
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• pp.1141-1150
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• 2006

In this study, we performed spatial cluster analysis which considered spatial information using earthquake data for Korean peninsula occurred on 1978 year to 2005 year. Also, we look into how to be clustered for regions using earthquake magnitude and frequency based on spatial scan statistic. And, on the basis of the results, we constructed earthquake map by earthquake outbreak risk and gave a possible explanation for the results of spatial cluster analysis.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.849-854
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• 2013

In this paper, a system technology-based algorithms and visualization is proposed to show a space data. Also the proposed system provides analysis function with combination of usual map and automatic document generation function to give a useful information for making an important decision based on spatial distributed data. In the proposed method, we employ the heat map analysis to present a suitable color distribution for 2 dimensional map data. The buffering analysis method is also used to define the spatial data access. By using the proposed system, spatial information in a variety of distribution will be easy to identify. Also, if we make a use of automatic document generation function in the proposed algorithm, a lot of time and cost savings are expected to make electronic document which representation of spatial information is required.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.91-95
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• 2004

Moments of skew t random vectors and their quadratic forms are derived. It is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on a skewness parameter.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.131-139
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• 2006

Statistical analyses for spatial data are important features for various types of fields. Spatial data are taken at specific locations or within specific regions and their relative positions are recorded. Lattice data are synoptic observation covering an entire spatial region, like cancer rates corresponding to each county in a state. The main purpose of this paper is to detect hotspots for the region with significantly high or low rates. Kulldorff(1997) detected hotspots based on circular spatial scan statistics. We propose a new method to find any shapes of hotspots by use of echelon analysis with spatial scan statistics.