• Journal of Korean Society for Geospatial Information Science
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• v.24 no.2
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• pp.63-70
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• 2016

A housing market in an urban area is not just a single market but a combination of regionally different submarkets. This study begins with a critical mind that previous researches did not consider the spatial autocorrelation of each area where the housings are located. The clustering analysis of housing submarket which considers spatial autocorrelation is performed as it follows. First, 4 housing market attribute variables are reducted to 1 variable by principle component analysis. Then, after calculating $Gi^*max$ by AMOEBA, 7 housing submarkets which have similar characteristics based on $Gi^*max$ are classified. The characteristics of each submarket are investigated, then political implication is deduced as the following. Different level of housing policy should be made to each cluster because each cluster has different level of spatial autocorrelation.

• Journal of Korean Society of Transportation
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• v.7 no.1
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• pp.57-70
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• 1989

The purpose of study is to analyze the spatial population distribution and accessibility of network in urban areas. This study examines the forty-six political subdivision cities in Korea at the end of 1983, except the four metrpolitans (Seoul, Pusan, Daeku and Incheon). Evaluation indexes are classified the spatial pupulation distribution and accessibility of network. To analyze the cities, 10 indexes and the statistical techniques such as descriptive analysis, correlation analysis, factor analysis and cluster analysis were used. According to the results of cluster analysis, 15 cities (Ulsasn, Suwon, Bucheon, Chungju and etc.) are classified dispersed cities and another 15 cities (Kwangju, Daejun, Sungnam, Mokpo and etc.) are classified concentrated cities.

• 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.

• Journal of Korea Water Resources Association
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• v.57 no.2
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• pp.87-97
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• 2024

This study analyzed the water shortage hotspot areas in South Korea using spatial clustering analysis for water shortage estimates in 2030 of the Master Plans for National Water Management. To identify the water shortage cluster areas, we used water shortage data from the past maximum drought (about 50-year return period) and performed spatial clustering analysis using Local Moran's I and Getis-Ord Gi*. The areas subject to spatial clusters of water shortage were selected using the cluster map, and the spatial characteristics of water shortage areas were verified based on the p-value and the Moran scatter plot. The results indicated that one cluster (lower Imjin River (#1023) and neighbor) in the Han River basin and two clusters (Daejeongcheon (#2403) and neighbor, Gahwacheon (#2501) and neighbor) in the Nakdong River basin were found to be the hotspot for water shortage, whereas one cluster (lower Namhan River (#1007) and neighbor) in the Han River Basin and one cluster (Byeongseongcheon (#2006) and neighbor) in the Nakdong River basin were found to be the HL area, which means the specific area have high water shortage and neighbor have low water shortage. When analyzing spatial clustering by standard watershed unit, the entire spatial clustering area satisfied 100% of the statistical criteria leading to statistically significant results. The overall results indicated that spatial clustering analysis performed using standard watersheds can resolve the variable spatial unit problem to some extent, which results in the relatively increased accuracy of spatial analysis.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.67.4-68
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• 2021

In studying the dynamical evolution of galaxy clusters, one intriguing approach is to compare the spatial distributions of various components, such as the dark matter, the member galaxies, the gas, and the intracluster light (ICL; the diffuse light from stars, which are not bound any individual cluster galaxy). If we find a visible component whose spatial distribution coincides with the dark matter distribution, then we could draw a dark matter map without requiring laborious weak lensing analysis. Furthermore, if the component traces the dark matter distribution better for more relaxed galaxy cluster, we could use the similarity as a dynamical stage estimator of the galaxy cluster. We present a novel new methodology to quantify the similarity of two or more 2-dimensional spatial distributions. We apply the method to a sample of galaxy clusters at different dynamical stages simulated within N-cluster Run, which is an N-body simulation using the galaxy replacement technique. Among the various components (stellar particles, galaxies, ICL), the velocity defined ICL+ brightest cluster galaxy (BCG) component traces the dark matter best. Between the sample galaxy clusters, the relaxed clusters show stronger similarity of the spatial distribution between the dark matter and ICL+BCG than the dynamically young clusters.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.68-84
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• 2020

This study analyzed the relationship between spatial characteristics and heat waves in the distribution area of the elderly population in Changwon, Gyeongsangnam-do. For analysis, the Statistics Census data, the Ministry of Environment land cover, Landsat 8 surface temperature, and the Meteorological Agency's heat wave days data were used. The spatial characteristics of the distribution of the elderly population was classified into 5 types through K-mean cluster analysis considering the land use types. The characteristics of the elderly population by spatial type were higher in the urbanized type(cluster-3), but the proportion of the elderly population was higher in the agricultural and forest area types(cluster-1, cluster-2). In the characteristics of the surface temperature and the heat wave days, the surface temperature was the highest in the urban area, but heat wave days were the highest in the rural area. As a result of analyzing the heat wave characteristics according to the spatial type of the distribution area of elderly population, cluster-2 with the largest area in agricultural areas was highest at 15.95 days, and cluster-3 with a large area in urbanized types was the lowest at 9.41 days and 9.18 days. In other words, the elderly population living in rural areas is more exposed to heat waves than the elderly population living in urban areas, and the damage is expected to increase. The results of this study could be used as basic data to prepare various policy measures for effective management and prevention of vulnerable areas in summer.

• International Journal of Costume and Fashion
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• v.12 no.1
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• pp.29-48
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• 2012

The purpose of this study was to examine the relationship between consumer spatial behavior and consumer characteristics based on trade area selection criteria 469 female consumers who lived in the two new towns near Seoul, Bundang and Ilsan, participated in the study by completing questionnaires. Data were analyzed by using cluster analysis, ANOVA, Duncan's multiple range test, chi-square analysis, etc. The findings of the empirical research were as follows: 1. Five groups were identified by cluster analysis based on trade area selection criteria of clothing price-oriented group, time convenience-oriented group, shopping convenience-oriented group, variety/entertainment-oriented group, and passive shopping group. 2. Each group differed in spatial behavior such as clothing shopping area, the visiting frequency, and spatial movement type. 3. Each group showed differences in fashion involvement and demographic characteristics(age, marital status, education, occupation and social status).

• Journal of Environmental Impact Assessment
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• v.20 no.3
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• pp.299-311
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• 2011

The objective of this paper is to present a methodology for the classification of spatial patterns considering the parameters of urban form which play a significant role in the formation of the urban climate. The urban morphological parameters, i.e. building coverage, impervious pavement, vegetation, water, farmland and landuse types were used to classify the spatial patterns by a K-means cluster analysis. And the presented methodology was applied on Changwon city, South Korea. According to the results of cluster analysis, the total spatial patterns were classified as 24 patterns. First of all, The spatial patterns(A-1, A-2, A-3, B-1, B-2, B-3, C-1, C-2, C-3, D-1, D-2, D-3, E-1, E-2, E-3, F-1, F-2, F-3, G-1, G-2, G-3), which distributed in the rural area and the suburban area, can have the positive impacts of cold air generation and wind corridor on an urban climate environment, were distributed in the rural area. On the other hand, the spatial patterns of the downtown area including A-4, B-4, C-4 and D-4 are expected to have the negative impacts on urban climate owing to the of artificial heat emission or the wind flow obstruction. Finally, it will require the future research to analysis the climatic properties according to the same spatial patterns by the field survey.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2737-2753
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• 2013

In this study, we propose a novel salient object detection strategy based on regional contrast and relative spatial compactness. Our algorithm consists of four basic steps. First, we learn color names offline using the probabilistic latent semantic analysis (PLSA) model to find the mapping between basic color names and pixel values. The color names can be used for image segmentation and region description. Second, image pixels are assigned to special color names according to their values, forming different color clusters. The saliency measure for every cluster is evaluated by its spatial compactness relative to other clusters rather than by the intra variance of the cluster alone. Third, every cluster is divided into local regions that are described with color name descriptors. The regional contrast is evaluated by computing the color distance between different regions in the entire image. Last, the final saliency map is constructed by incorporating the color cluster's spatial compactness measure and the corresponding regional contrast. Experiments show that our algorithm outperforms several existing salient object detection methods with higher precision and better recall rates when evaluated using public datasets.

• Journal of Korean Society of Rural Planning
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• v.29 no.3
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• pp.79-89
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• 2023

As the demand for environmental-friendly agricultural products continues to rise due to increased concerns regarding food safety and ecosystem conservation, it is becoming important to identify regions and spatial locations where environmental-friendly should be intensively established for production integration. This study aims to analyze the spatial distribution of environmental-friendly certified farms in Jeollanam-do, South Korea. Spatial statistical analysis based on Local Moran's I and Getis-Ord Gi^* were used to identify spatial cluster characteristics and landscape indices were utilized to analyze spatial patterns of environmental-friendly certified farms. The results indicated that Haenam-gun, Gangjin-gun, Muan-gun, and Jindo-gun were identified as hotspots, while Muan-gun, Goheung-gun, and Jindo-gun exhibited high connectivity. This suggests that environmental-friendly certified farms in Muan-gun and Jindo-gun were clustered and closely connected to one another. Based on the results of the spatial distribution of environmental-friendly certified farms, areas belonging to the hotspot and with high connectivity should be managed as clustered districts to secure the foundation and system of environmental-friendly certified farms. Areas that belong to cold spots and have low connectivity should be preceded by measures to promote conversion to environmental-friendly agriculture. In addition, it is necessary to make it possible to create a large-scale cluster district through a long-term spatial planning strategy to expand the environmental-friendly certified farms. The findings of this study can provide quantitative data on policies and discussions for developing a model for rural spatial planning.