• Proceedings of the KGS Conference
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• 2003.05a
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• pp.207-210
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• 2003

지리학적 정보는 지구의 표면이나 가까이에 나타나는 현상과 사상에 대한 정보로서 정의된다(Goodchild et al., 1999). 지리학에서, 이러한 지리학적 정보는 특정한 현상을 연구하기 위한 공간자료로 이용되는데, 이는 공간적 패턴을 통해 유형화된다. 이러한 공간자료는 현지답사를 통해 수집ㆍ분석되며, 관찰자의 주관적 판단, 기술적인 오류로 인해, 오차의 필연적 발생 가능성을 안고 있다(Maffini, 1989; Bolstad, 1990; Dunn, 1990; Keeler, 1991). (중략)

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1516-1522
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• 2007

We had proposed the FPA(Fixed Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. The algorithm worked not bad in many area, but it lead to some overhead for memory usage and lengthy computation in the multi classes area. We propose an Repetitive FPA algorithm which repetitively partitioning pattern space in the multi classes area. Our proposed methods have been successfully shown to exhibit comparable performance to k-NN with a lot less number of patterns and better result than EACH system which implements the NGE theory.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.25-45
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• 2020

The primary purpose of this study is to compare and analyze the performance of local spatial autocorrelation techniques in identifying spatial distribution patterns of green spaces. To achieve the objective, this researcher uses satellite image analysis and spatial autocorrelation techniques. The result of the study shows that the LISA cluster map with the spatial outlier cluster is superior to other analytical methods in identifying the spatial distribution pattern of urban green space. This study can contribute to the related fields in that it uses several different research methods than the existing ones. Despite this differentiation and usefulness, this study has limitations in using low-resolution satellite imagery and NDVI among vegetation indices in identifying spatial distribution patterns of green areas. These limitations may be overcome in future studies by using UAV images or by simultaneously using several vegetation indices.

• Journal of Cadastre & Land InformatiX
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• v.50 no.2
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• pp.203-219
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• 2020

The primary purpose of this study is to identify spatial distribution patterns of co-authoring activities in Korean cadastral science. The analysis showed that a small number of researchers played an essential role in the Korean cadastral co-authorship network. In particular, some authors not only had a significant influence on other nodes in the network but also served as intermediaries between researchers. Moreover, the distance between researchers influenced co-authorship decisions to a limited extent. This study differs considerably from previous studies in that it used spatial analysis techniques to identify spatial distribution patterns of co-authoring activities. However, this research is limited in that it applied only 2019 data to determine the spatial distribution pattern of co-authoring activities. We can overcome this limitation if we analyze the spatial distribution patterns of co-authoring activities using multi-year data in future studies.

• 한국HCI학회:학술대회논문집
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• 2006.02b
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• pp.744-752
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• 2006

사이버 공간이 현실 생활의 일부분으로 통합되어가고 사이버 공간에서의 대인간 의사소통이 보편적인 현상으로 나타남에 따라 사이버 공간에서의 의사소통과 경험 그리고 그것의 영향력에 대한 논의는 그 동안 많이 있어왔다. 그러나 기존의 연구들은 사용자의 행동 특성에 따라 사이버 공간에서의 활동과 영향력, 그리고 그 의미가 달라진다는 점을 간과하였다. 따라서 본 연구에서는 동일한 커뮤니케이션 공간인 싸이월드도 그것에 부여하는 가치와 의미는 사람마다 다르고 따라서 행동도 다를 것이라는 가정하에, 싸이월드에서 나타나는 다양한 이용행동과 활동들을 유형으로 분류하고 그 유형에 따라 싸이월드에서의 의사소통 패턴과 경험, 활동에서의 차이를 살펴보고자 했다. 사전 연구로 진행되었던 사용자 인터뷰를 통해 정리된 행동문항을 전체 설문을 통해 분석한 결과 싸이월드에서 보이는 개인의 행동적 특성을 의미하는 5 개 요인(적극성.몰입, 낯선 사람과의 연결과 정보 추구, 사생활 보호, 단순한 연락 수단, 폐쇄성)이 확인되었고, 이렇게 추출된 행동 요인을 토대로 싸이월드에서 나타나는 3 개의 행동 유형(적극적 활용형, 폐쇄적. 다이어리 추구형, 새로운 관계.정보 추구형)이 구분되었다. 행동 요인의 차이로 표현되는 3 개의 행동 유형에 따른 싸이월드에서의 의사소통 패턴과 싸이월드에서의 활동 그리고 싸이월드 경험에 대한 평가를 분석한 결과 행동 유형간에 유의미한 차이가 있는 것으로 나타났다. 본 연구는 동일한 싸이월드라는 공간이 사용자가 추구하는 가치와 목적에 따라 각기 다른 공간으로 기능하고 있음을 보여주었다는 데에 의의가 있으며, 가치의 차이로 나타나는 행동 유형에 따라 싸이월드에서 소통하는 대상과 방식이 달라지고 경험의 의미 또한 달라질 수 있음을 시사하고 있다.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1367-1375
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• 2017

Recently, the use of land cover maps has been continuously increasing to analyze spatial patterns such as spatial compositions, functions and changes of landscape mosaics. In this paper, we propose a landscape analysis system that extracts patches, which is an element of landscape mosaics, in the land cover map using region-based image processing technique, and computes patch-based measures at patch level and class level. Also we propose a patch-based spatial pattern that can represent spatial relations using the computed measures. To validate the proposed system's effectiveness, we apply to Gwangju metropolitan city and analyze Gwangju's land use and spatial patterns.

• Journal of the Korean Association of Geographic Information Studies
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• v.11 no.1
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• pp.155-166
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• 2008

The quantitative bivariate spatial pattern analysis was applied for the water quality and nutrients data of Masan Bay, and for this analysis Pearson's r as aspatial correlation measurement, Moran's I as spatial association measurement and L index as integration of aspatial and spatial measurement methods were used. To understand the aspatial and spatial characteristics implicated in L index, Pearson's r as well as Moran's I were classified into 3 types respectively, and Pearson's r and Moran's I were combined with 9 types, and also quantile of L index value was used for each of those 9 types. Finally, these types were defined as 5 groups having not overlapped L index range. According to the application result of L index groups, bivariate water quality and nutrients showed no aspatial correlation regardless of spatial association in February and July, but they showed aspatial correlation having clustered spatial pattern in May and November. The result of this study providing the guideline for the interpretation of aspatial correlation and spatial association using L index is expected to be helpful for the marine environment pattern analysis using quantitative index for further study.

• Journal of the Korean Geographical Society
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• v.30
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• pp.62-76
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• 1984

이 글에서는 다음의 내용을 다루었다. 1. 연구지역과 분석방법, (1) 연구지역, (2) 분석지표와 자료, (3) 분석방법 2. 공간적 자기상관의 측도와 검정, (1) 공간적 자기상관의 개념, (2) 공간적 자기상관의 측도, (3) 공간적 자기상관의 검정 3. 공간적 패턴의 형성과정, (1) 제1기 (1960-1966년), (2) 제2기 (1966-1970년), (3) 제3기(1970-1975년), (4) 제4기(1975-1980년)

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.478-487
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• 2004

We had proposed the RPA(Recursive Partition Averaging) method in order to improve the storage requirement and classification rate of the Memory Based Reasoning. That algorithm worked not bad in many area, however, the major drawbacks of RPA are it's partitioning condition and the way of extracting major patterns. We propose an adaptive RPA algorithm which uses the FPD(feature-based population densimeter) to stop the ARPA partitioning process and produce, instead of RPA's averaged major pattern, optimizing resulting hyperrectangles. The proposed algorithm required only approximately 40% of memory space that is needed in k-NN classifier, and showed a superior classification performance to the RPA. Also, by reducing the number of stored patterns, it showed an excellent results in terms of classification when we compare it to the k-NN.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.139-147
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• 2009

The memory based reasoning just stores in the memory in the form of the training pattern of the representative pattern. And it classifies through the distance calculation with the test pattern. Because it uses the techniques which stores the training pattern whole in the memory or in which it replaces training patterns with the representative pattern. Due to this, the memory in which it is a lot for the other machine learning techniques is required. And as the moreover stored training pattern increases, the time required for a classification is very much required. In this paper, We propose the EAS(Evaluation And Selection) algorithm in order to minimize memory usage and to improve classification performance. After partitioning the training space, this evaluates each partitioned space as MDL and PM method. The partitioned space in which the evaluation result is most excellent makes into the representative pattern. Remainder partitioned spaces again partitions and repeat the evaluation. We verify the performance of Proposed algorithm using benchmark data sets from UCI Machine Learning Repository.