• Journal of Korean Society for Geospatial Information Science
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• v.22 no.4
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• pp.21-30
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• 2014

Lately, new influentials have secured a large number of volunteers on social networks due to vitalization of various social media. There has been considerable research on these influential people in social networks but the research has limitations on location information of Location Based Social Network Service(LBSNS). Therefore, the purpose of this study is to propose a spatial detection methodology and application plan for influentials who make comments about diverse social and cultural issues in LBSNS using spatial statistical analysis methods. Twitter was used to collect analysis object data and 168,040 Twitter messages were collected in Seoul over a month-long period. In addition, 'politics,' 'economy,' and 'IT' were set as categories and hot issue keywords as given categories. Therefore, it was possible to come up with an exposure index for searching influentials in respect to hot issue keywords, and exposure index by administrative units of Seoul was calculated through a spatial joint operation. Moreover, an influential index that considers the spatial dependence of the exposure index was drawn to extract information on the influential areas at the top 5% of the influential index and analyze the spatial distribution characteristics and spatial correlation. The experimental results demonstrated that spatial correlation coefficient was relatively high at more than 0.3 in same categories, and correlation coefficient between politics category and economy category was also more than 0.3. On the other hand, correlation coefficient between politics category and IT category was very low at 0.18, and between economy category and IT category was also very weak at 0.15. This study has a significance for materialization of influentials from spatial information perspective, and can be usefully utilized in the field of gCRM in the future.

• 한국지구물리탐사학회:학술대회논문집
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• 2008.10a
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• pp.63-67
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• 2008

Geostatistical inverse approach using geophysical data was applied to indirectly make the RMR classification at points apart from boreholes. The geostatistical appoach was usually used to find optimized estimation which supports two or more different physical properties at unsampled points. However, in this study, an approach to solve inverse problem was proposed. The primary variable, RMR values obtained at known boreholes, is geostatistically simulated with many realization at pre-defined grid point according to the variogram model. The simulated values are sequentially compared with the physical property resulted from geophysical survey at an arbitrary grid point, and the most similar one is chosen. This process means that the spatial distribution of primary variable, RMR, is conformed well to the original pattern of the borehole observation, and ensure to fit the geophysical survey result to reflect the correlation between different physical properties.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.95-107
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• 2014

In general, topographical phenomena are represented graphically by data in the spatial domain, while attributes of the non-spatial domain are expressed by alpha-numeric texts. GIS functions for analysis of attributes in the non-spatial domain remain quite simple, such as search methods and simple statistical analysis. Recently, graph modeling and network analysis of social phenomena are commonly used for understanding various social events and phenomena. In this study, we applied the network analysis functions to the non-spatial domain data of GIS to enhance the overall spatial analysis. For this purpose, a novel design was presented to integrate the spatial database and the graph database, and this design was then implemented into a WebGIS system for better decision makings. The developed WebGIS with underlying synchronized databases, was tested in a simulated application about the selection of water supply households during an epidemic of the foot-and-mouse disease. The results of this test indicate that the developed WebGIS can contribute to improved decisions by taking into account the social proximity factors as well as geospatial factors.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.49 no.4
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• pp.85-94
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• 2012

Lane marking detection is essential to both ITS and DAS systems. The objective of this paper is to provide more robust technique for lane marking detection than traditional techniques by using scale-space technique. Variable thresholding that is based on the local statistics may be very effective for detecting such objects as lane markings that have prominent intensities. However, such techniques that only rely on local statistics have limitations containing irrelevant areas as well. We reduce the candidate areas by combining the variable thresholding result with cost-efficient horizontal 1D LoG filtered scale space. Through experiments using practical images, we could achieve significant improvement over the techniques based not only on the variable thresholding but also on the Hough transform that is another very popular technique for this purpose.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.424-425
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• 2010

본 논문에서는 영상의 종류에 따른 움직임 정보를 통계 값을 계산 하여 분산 및 평균값을 계산하여 적용하는 움직임 적응형 디인터레이싱 기법을 제안한다. 제안하는 알고리즘은 움직임 검출, 영상의 따른 분산 및 평균값 계산, 영상의 유형에 따른 상한, 하한 값 결정을 하여 시간적, 공간적 디인터레이싱을 선택적으로 하여 보간 하게 된다. 또한 Modified DOI 방식을 제안하여 복잡도가 높은 영상과 수직성분에서 개선이 된 M-ELA 를 사용하고, 수평 성분에서는 우수한 성능을 가진 DOI 방식을 개선한 Modified DOI 방식을 사용하여 DOI 계열의 복잡한 연산을 개선한 공간적 디인터레이싱 방법을 적용하였다. 다양한 영상에 대한 실험을 통하여 제안한 방식이 기존의 디인터레이싱 방법에 비해 좋은 성능을 보임을 확인하였다.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.3
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• pp.67-81
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• 2006

The present study suggests an analytical method to overcome the spatial problems that traditional hedonic methods have. The concept of overlapping neighborhoods is introduced in order to solve the problems of global parameter estimate methods that treat the whole city by the gross. Moreover, a 3rd party program for the tight coupling of GIS and statistics is developed in order to explore hedonic methods efficiently. By using these, this study analyses the spatial variation of location variables that affect the real estate price. The results show that the influences of urban centers do not reach to the whole city, but only to the catchment areas of them. And the coefficients of location variables are different depending on the space. The tight coupling of GIS and statistics offers a powerful tool in analysing the real estate price efficiently.

• Geophysics and Geophysical Exploration
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• v.12 no.4
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• pp.309-315
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• 2009

Integrated interpretation of multi-geophysical data has been continuously used in terms that it has provided more confident information than the result from single-geophysical data. Especially, geostatistical integration has its own superiority that it is possible to deal with spatial characteristics as well as physical properties of survey data and the process of integration is clear. This paper further extends the previous work of geostatistical inversion for integrated interpretation. In this paper, we propose a new way of dealing with the case that the multi-geophysical data do not share the measurement range. According to the geostatistical kriging, the closer between the measurement points, the smaller kriging variance we get, and vice versa. We used this spatial properties as a weighting value to the process of geostatistical inversion for the geophysical data integration. An objective way to integrate different kinds of geophysical data measured at different ranges is provided with this algorithm.

• The Korean Journal of Applied Statistics
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• v.19 no.1
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• pp.121-134
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• 2006

This paper presents the resampling method for time series data in the frequency domain obtained by using discrete cosine transforms(DCT) The advantage of the proposed method is to generate bootstrap samples in time domain comparing with existing bootstrapping method. When time series are stationary, statistical properties of DCT coefficients are investigated and provide the verification of the proposed procedure.

• Korean Journal of Remote Sensing
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• v.20 no.6
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• pp.383-396
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• 2004

The application of spatial statistics to obtain the spatial uncertainty distributions in classification of remote sensing images is investigated in this paper. Two quantitative methods are presented for describing two kinds of uncertainty; one related to class assignment and the other related to the connection of reference samples. Three quantitative indices are addressed for the first category of uncertainty. Geostatistical simulation is applied both to integrate the exhaustive classification results with the sparse reference samples and to obtain the spatial uncertainty or accuracy distributions connected to those reference samples. To illustrate the proposed methods and to discuss the operational issues, the experiment was done on a multi-sensor remote sensing data set for supervised land-cover classification. As an experimental result, the two quantitative methods presented in this paper could provide additional information for interpreting and evaluating the classification results and more experiments should be carried out for verifying the presented methods.

• 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.