• Journal of the Korean Geographical Society
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• v.41 no.3 s.114
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• pp.319-330
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• 2006

The objective of this research is to achieve an objectivity of site decision after extracting site decision factors on a large-scale discount store(LSDS) and utilize any hidden information using the association rules mining through huge database. To catch this objective, we collect a census, economic, and environmental dataset related with locating of LSDS. And then, we construct a spatial data on the research area. These data is used for the extraction of a spatial association rules. To verify whether the extracted rules are suitability or not, we use the sales of some LSDS. As the result of test, the more sales, the more factors of the extracted rules relate with the sales it coincides. Consequently, the spatial association rules mining is efficient method which support the ideal site decision of LSDS.

• Spatial Information Research
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• v.20 no.6
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• pp.69-76
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• 2012

In this study, we derived the characteristics of the geo-spatial information industry by using input-output analysis. For this analysis, we classified the geo-spatial information industry and reorganized the input-output table. And we derived the production inducement coefficient, index of the power of dispersion and index of the sensitivity of dispersion in the geo-spatial information industry. We confirmed that geo-spatial information industry has a small production inducement coefficient and a great forward linkage effect. Based on these facts, we suggested the strategy direction as follows: 1) building the industrial eco-system, 2) managing both advance and applicability enhancement, 3) Establishing from a long-term point of view.

• Journal of the Korean Geographical Society
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• v.39 no.3
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• pp.457-472
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• 2004

The purpose of this study is to analyze spatial patterns of traffic accidents and to investigate spatial relations among neighboring spatial objects by applying GIS and spatial data mining methods. This study investigated traffic accident data in Kangnam-gu, Seoul, as a case study. As a result, four clusters were emerged based on individual attributes of traffic accidents. Each cluster showed distinctive properties. In spatial associations between individual attributes of traffic accidents and neighboring spatial objects, there were many rules according to concept hierarchy and definition of spatial relations. Although all rules were not be interesting and significant, they could be a clue to investigate more.

• Journal of KIISE:Databases
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• v.31 no.2
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• pp.163-173
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• 2004

Recently, location-based services are becoming very Popular in mobile environments. In this paper, we propose a new association based prefetch algorithm (called by STAP) that efficiently supports information service based on the large quantity of spatial database in mobile environments. We apply the spatial-temporal relations that are meaningful for location-based queries in mobile environments. Moreover, STAP considers user's mobility and the weight of spatial data. The relation of services is a new aspect not considered in previous cache politics. So STAP is the first prefetch algorithm considering the spatial-temporal relations and thus the cache policy begins to gain a new dimension. We evaluate the performance of STAP and prove the efficiency of STAP.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.29 no.1
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• pp.29-37
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• 2011

The purpose of this research is to measure Quality of Life indices using Factor Analysis and Principle Component Analysis and to analyze the spatial patterns of Quality of life distribution in the Seoul Metropolitan Area in terms of spatial association using spatial statistics and spatial exploratory technique. In order to check the degree of clustering, this study used spatial autocorrelation indices, global Moran's I index. In addition, local scale analysis was conducted using Moran Scatterplot and Local Moran's I to identify the spatial association pattern and the high Quality of life. The analysis based on global statics showed that, in the Seoul Metropolitan Area, QoL Indices had been distributed with positive spatial association. According to the local spatial statistics, the general tendency of clustering H-H clusters which were mainly concentrated on the Seoul, L-H clusters were concentrated on the Kyunggi-Do and L-L Clusters showed the regional extent of lagging behind. However, in case of H-H, L-H Clusters they had been spread out in the Newtown as population increase.

• Patent21
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• s.72
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• pp.20-27
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• 2007

• Proceedings of the Korean Information Science Society Conference
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• 1998.10b
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• pp.161-163
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• 1998

지리정보시스템과 같이 방대한 양의 공간데이터를 다루는 응용시스템에서 공간데이터베이스로부터 규칙적인 특성이나, 혹은 관심 있는 지식을 추출해내는 공간데이터마이닝의 역할은 매우 중요하다. 이를 위해 지금까지 이루어진 방법들에는 여러 가지가 있지만 그 중에서 대표적인 방법이 클러스터링으로 이는 단지 기하학적인 거리에 기반을 둔 공간적인 집중성과 분포도를 찾는 데에만 한정되어 있다. 그러나, 공간데이터마이닝을 위해서는 공간클러스터가 형성된 원인을 분석하는 것 또한 필요하다. 따라서 본 연구에서는 공간 클러스터링에서 얻어진 결과를 다른 공간적인 객체와의 연관성을 분석하여 공간적 집중성과 분포도를 유발하는 원인을 찾는 방법을 다룬다. 우선 몇 가지의 거리를 정의하는 것에 의해 클러스터와 공간객체사이의 연관성을 분석하는 방법을 제시하고, 생성된 공간 클러스터가 다수의 공간객체에 영향을 받을 경우, 그 공간 클러스터를 각각 단위클러스터로 분리하는 방법을 제시한다.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.27-34
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• 2006

Recently, the study about the technology which effectively manage spatial information is actively conducted. For the effective knowledge inquiry, various extended data mining methods are applied in spatial data mining. However, former spatial association rule system appears the problem that does not reflect various non-spatial property along the inquiries because it searches the rule from the calculation among predicates. To resolve the problem, present study suggests the system that extends the inquiries using in spatial database, searches the association rule among non-spatial object property after setting the data based on space information. Especially, the model which is applicable to geographical information system is embodied. Embodied system with this method enables to search more useful spatial association rule in real life since it shows high migration property with extended spatial database and considers spatial property and various non-spatial property.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.564-579
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• 2008

It is often challenging to evaluate local spatial association among onedimensional vectors generally representing paired-location events where two points are physically or functionally connected. This is largely because of complex process of such geographic phenomena itself and partially representational complexity. This paper addresses an alternative way to identify spatially autocorrelated paired-location events (or vectors) at a local scale. In doing so, we propose a statistical algorithm combining univariate point pattern analysis for evaluating local clustering of origin-points and similarity measure of corresponding vectors. For practical use of the suggested method, we present an empirical application using transactions data in a local housing market, particularly recorded from 2004 to 2006 in Franklin County, Ohio in the United States. As a result, several locally characterized similar transactions are identified among a set of vectors showing various local moves associated with communities defined.

• Proceedings of the Korean Information Science Society Conference
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• 2002.04b
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• pp.469-471
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• 2002

높은 연관성을 갖는 문서들을 서로 집단화시키는 문서 클러스터링은 문서와 문서간의 연관성을 확인할 수 있는 문서의 주제어 추출이 중요한 문제이며 일반적인 정보검색 시스템에서 사용하는 출현빈도에 의한 주제어 추출은 성능 향상에 한계가 있다. 또한, 문서 클러스터링은 문서를 집단화시키기 위해 문서간 연관성을 확인하기 위해 유사도 계산에 따른 시간과 공간을 많이 소비하는 문제를 가지고 있다. 본 논문에서는 주제어 추출 기법을 적용하여 주제어 연관성에 의해 문서들을 집단화시키는 새로운 방법의 문서 클러스터링 알고리즘을 제안한다.