• Korean Journal of Acupuncture
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• v.29 no.3
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• pp.431-440
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• 2012

Objectives : Recently, network science is very popular topic in various scientific fields and many studies have reported that it gives meaningful results on studying characteristics of a complex system. In this study, based on network theory, we made acupoints network using data of combined acupoints which appeared at "Beijiqianjinyaofang". We focused to find out the distinctive roles of remote and local combinations on the network. Furthermore, we aimed to identify the possibility of numerical and quantitative application to acupuncture researches. Methods : Based on examples of combined acupoints in "Beijiqianjinyaofang", the network consisted of 291 nodes and 2,431 links. The spatial distances between combined acupoints were calculated by the human dummy model. We removed the links step by step for the three cases - remote, local, and random cases, and observed the characteristic changes by calculating path lengths, similarity indices, and clustering coefficients. Also cluster analysis was carried out. Results : The network had a small number of remote links, and a large number of local links. These two links had the distinct characteristics. Whereas the local links formed a cluster of nearby nodes, remote links played a role to increase the correlation between the clusters. Conclusions : These results suggest that acupoints network increases the connectivity between the distal part and the trunk of human body, and enables various combinations of the acupoints. This finding conclusively showed that mechanism of combined acupoints could be interpreted meaningfully by applying network theory in acupuncture researches.

• Journal of Korean Society of Rural Planning
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• v.26 no.4
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• pp.107-116
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• 2020

The purpose of this study is to analyze the characteristics of the 11 southeastern states in the United States by using regional characteristics variables and to classify the regions. First, 19 variables from four categories of population, society, industry-economy and urban service were selected and factor analysis were conducted, and the result showed five major factors of population, economic condition, job and commuting. Based on the following factor scores, a cluster analysis was conducted, and eight types of big city, medium-sized city, bed town, small town, urban hinterland, retirement town, and rural village were derived. These types of spatial distribution characteristics showed big cities were by different types of regions and they formed metropolitan areas. Each types of classified regions were located along the road network with hierarchy. The study focused on cases in the southeastern regions of the United States and can be used as a comparison with Korean cases. If the same research method is applied to Korea in the future, or if the time series of changes is tracked by analyzing different time points, it will greatly help identify the characteristics of urban and rural mixed areas.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.77.3-77.3
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• 2016

In our previous study, we showed that the peculiar globular cluster M22 contains two distinct stellar populations with different physical properties, having different chemical compositions, spatial distributions and kinematics. We proposed that M22 is most likely formed via a merger of two GCs with heterogeneous metallicities in a dwarf galaxy environment and accreted later to our Galaxy. In their recent study, Mucciarelli et al. claimed that M22 is a normal mono-metallic globular cluster without any perceptible metallicity spread among the two groups of stars, which challenges our results and those of others. We devise new strategies for the local thermodynamic equilibrium abundance analysis of red giant branch stars in globuar clusters and show there exists a spread in the iron abundance distribution in M22.

• Journal of The Korean Astronomical Society
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• v.52 no.5
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• pp.145-158
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• 2019

We conducted a survey of open clusters within 1 kpc from the Sun using the astrometric and photometric data of the Gaia Data Release 2. We found 655 cluster candidates by visual inspection of the stellar distributions in proper motion space and spatial distributions in l - b space. All of the 655 cluster candidates have a well defined main-sequence except for two candidates if we consider that the main sequence of very young clusters is somewhat broad due to differential extinction. Cross-matching of our 653 open clusters with known open clusters in various catalogs resulted in 207 new open clusters. We present the physical properties of the newly discovered open clusters. The majority of the newly discovered open clusters are of young to intermediate age and have less than ~50 member stars.

• Journal of Korean Society of Environmental Engineers
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• v.33 no.7
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• pp.501-510
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• 2011

In this study, water pollution sources of 14 major tributaries of Han river and characteristics of water quality for each target streams were evaluated based on water quality data in 2007.1-2009.12 (14 data sets) using a statistical package, SPSS-17.0. Cluster analysis over time and space for each stream resulted in 4 groups for the spatial variations in which type and density of pollution sources in the basins showed the greatest impact on grouping. Moreover, cluster analysis for the time variation in which rainfall, temperature and eutrophication were shown to contribute to the clustering, produced 2 groups, from summer to fall (July-Oct.) and from winter to early summer (Nov.-June). Four factors were found as responsible for the data structure explaining 71-90% of the total variance of the data set depending on the streams and they were organic matter, nutrients, bacterial contamination. Factor analysis showed main factors (water pollutants) changed according to the season with different pattern for each stream. This study demonstrated that water quality of each stream could produce useful outcomes when factor and pollution source of basin were evaluated together.

• Journal of the Korea Convergence Society
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• v.10 no.12
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• pp.315-325
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• 2019

The purpose of this study is to design and construct a data mart that anyone can easily analyze subway OD movement patterns. Subway OD data of the year 2017 was downloaded from the Seoul Open Data Plaza and used as the source data. A multidimensional model was designed, and Gaussian mixed cluster analysis and visualization analysis using Tableau were performed. Interestingly, movement between suburban and Seoul accounts for 23% of the total traffic. The passengers of Suwon Station move to the suburbs much more than Seoul, while Pangyo Station mostly moves to Seoul. As a result of Gaussian mixed cluster, eight clusters of OD segments were found, and the characteristics of each cluster were characterized by segment distance and passenger size.

• Journal of Korean Society of Rural Planning
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• v.19 no.1
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• pp.81-90
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• 2013

The aim of the research was to analyze a correlation between rural villages and a space of amenity resources in order to provide objective basic data for rural renewal planning in the future. 15 villages were selected to analyze amenity resources. A Space Syntax Method(SSM) was used to analyze a spacial structure of each village and location characteristics of amenity resources. Finally, Statistical Package for the Social Sciences(SPSS) was used for a cluster analysis. The results of spacial analysis showed that the MeanDepth of rural villages was 4.482 and Global Integration Value(GInteg) was 0.956. Relatively, a depth was lower and GInteg was higher, compared to other villages. Rural villages were easily recognized and accessible by outsiders, compared to mountain and fishing villages. In the case of rural villages, the MeanDepth of amenity resources was low and GInteg was high in the results of cluster analysis using a nonhierachical method. Results indicated that an access was easy and amenity resources were closely located each other. However, the deviation of each village was great in mountain villages. This research suggests that an effective maintenance of road network for improving accessibility would be given priority in an undeveloped farming and fishing villages' renewal. Especially, using a spacial analysis in village renewal planning process can improve accessibility and maximize an utilization of public facilities and amenity resources.

• Journal of the Korean association of regional geographers
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• v.15 no.4
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• pp.510-523
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• 2009

Geodemographics(GD) can be defined as an analytical approach of socio-economic and behavioral data about people to investigate geographical patterns. GD is based on the assumptions that demographical and behavioral characteristics of people who live in the same neighborhood are similar and then the neighborhoods can be categorized with spatial classifications with the geographical classifications. Thus, this paper, in order to identify the applicability of the geographical classification of the GD, explores the concepts of the geodemographics into Seoul city areas with Korea census data sets that contain key characteristics of demographic profiles in the area. Then, this paper attempt to explain each area classification profile by using clustering techniques with Ward's and k-means statistical methods. For this as as as, this paper employs 2005 Census dataset released by Korea National Statistics Office and the neighborhood unit is based on Dong level, the smallest administrative boundary unit in Korea. After selecting and standardizing variables, several areas are categorized by the cluster techniques into 13, this paps as distinctive cluster profiles. These cluster profiles are used to cthite a short description and expand on the cluster names. Finally, the results of the classification propose a reasonable judgement for target area types which benefits for the people who make a spatial decision for their spatial problem-solving.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.63-79
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• 2008

With the recent development of advanced GIS and complex spatial analysis technologies, the more sophisticated technologies are being required to support the advanced knowledge for solving geographical or spatial problems in various decision support systems. In addition, necessity for research on scientific crime investigation and forensic science is increasing particularly at law enforcement agencies and investigation institutions for efficient investigation and the prevention of crimes. There are active researches on geographic profiling to predict the base location such as criminals' residence by analyzing the spatial patterns of serial crimes. However, as previous researches on geographic profiling use simply statistical methods for spatial pattern analysis and do not apply a variety of spatial and temporal analysis technologies on serial crimes, they have the low prediction accuracy. Therefore, this paper identifies the typology the spatio-temporal patterns of serial crimes according to spatial distribution of crime sites and temporal distribution on occurrence of crimes and proposes STA-BLP(Spatio-Temporal Analysis based Base Location Prediction) algorithm which predicts the base location of serial crimes more accurately based on the patterns. STA-BLP improves the prediction accuracy by considering of the anisotropic pattern of serial crimes committed by criminals who prefer specific directions on a crime trip and the learning effect of criminals through repeated movement along the same route. In addition, it can predict base location more accurately in the serial crimes from multiple bases with the local prediction for some crime sites included in a cluster and the global prediction for all crime sites. Through a variety of experiments, we proved the superiority of the STA-BLP by comparing it with previous algorithms in terms of prediction accuracy.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.4
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• pp.139-144
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• 2011

This study proposed a classification method of LIDAR data by using simultaneously the color information (R, G, B) and reflection intensity information (I) obtained from terrestrial LIDAR and by analyzing the association between these data through the use of statistical classification methods. To this end, first, the factors that maximize variance were calculated using the variables, R, G, B, and I, whereby the factor matrix between the principal factor and each variable was calculated. However, although the factor matrix shows basic data by reducing them, it is difficult to know clearly which variables become highly associated by which factors; therefore, Varimax method from orthogonal rotation was used to obtain the factor matrix and then the factor scores were calculated. And, by using a non-hierarchical clustering method, K-mean method, a cluster analysis was performed on the factor scores obtained via K-mean method as factor analysis, and afterwards the classification accuracy of the terrestrial LiDAR data was evaluated.