• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.405-408
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• 2006

사용자들의 특성에 맞게 개인화되고 세분화된 위치 기반 서비스를 제공하기 위해서는 방대한 이동객체의 위치 이력 데이터로부터 유용한 패턴을 추출하기 위한 시간 패턴 탐사가 필요하다. 기존의 시간 패턴 탐사 기법들은 이동 객체의 시간에 따른 공간 속성들의 변화를 충분히 고려하지 못하거나, 시공간 속성을 동시에 고려한 패턴 탐사는 가능하나 제약을 가진 공간 정보를 포함하는 패턴 탐사 문제에는 적용하기 어렵다. 따라서 이동 객체의 위치 이력 데이터들에 대한 시공간적 속성들을 동시에 고려하여 다양한 이동 패턴들 중 공간 제약을 만족하는 패턴들을 추출하기 위한 새로운 이동 패턴 탐사 기법이 요구된다. 이러한 패턴 탐사 기법의 개발을 위해서는 상세 수준의 위치 이력 데이터들을 공간 영역 정보 형태로 변환하는 위치 일반화 접근법이 필요하다. 이에 본 논문에서는 객체의 위치값과 공간 영역간의 위상 관계를 고려하여 이동 객체의 위치 속성에 대한 공간영역으로의 일반화 방법을 제안한다. 이동 객체의 상세 수준의 위치 정보에서는 의미있는 패턴을 찾기가 어렵기 때문에 데이터 전처리 과정을 통해 일반화된 데이터 집합을 형성함으로써 효율적인 이동 객체의 시간 패턴 마이닝을 유도할 수 있다.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.833-841
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• 1998

The ann of this study is 10 propose all eff'tcient mclhod for partition of multi-dimensIOnal feature space into pattern subspace for automated generation of fuzzy rule. The suggested mclhod predicates on sequential subdivision of the fuzzy subspacc. and the size of construc1cd pattern space is variable. Under this procedure, n-dimensional pattern space, after considering the distributional characteristic patterns, is partitioned into two different pattern subspaces. From the two subspaces, the pattern space for further subdivision is chosen; then, this subdivision procedure recursively repeats itself until the stopping condition is fulfilled. The result of this study is applied to 2, 4, 7 band of satellite Landsat TM and satisfac10ry result is acquired.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.632-647
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• 2007

Lots of geographic phenomena have dynamic spatial patterns with time changes, and there have been lots of researches on exploring these dynamic spatial patterns. However, most of these researches focused on the static pattern analysis in a given period, rather than dealing with dynamic changes in the spatial pattern over time with the continual or cumulative perspective. For this reason, investigation of the inertia of spatial process in terms of temporal changes is needed. From this background, the purpose of this paper is to propose the methodology to explore the changes in spatial pattern cumulatively by considering the inertia of the spatial statistics over time, and to apply it to the case study That is, we introduce the new spatial statistic, and produce the z-values of the statistic using Monte Carlo Simulation, and then to explore the changes in spatial patterns over time cumulatively. To do this, the method to combine the J statistic with CUSUM statistic for exploring spatial patterns, and to apply it to the changes in the commercial landuse in Erie County, New York State. Through the proposed method for spatio-temporal Patterns, we could explore continual changes effectively in the spatial patterns reflecting the statistics by temporal spot cumulatively.

• Journal of the Korean Geographical Society
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• v.43 no.1
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• pp.104-116
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• 2008

Overheated speculation areas which have high potential of becoming speculative are the target of many real estate policies. This paper proposes a model for spatial patterns of house price volatility and suggests a spatial pattern of overheated speculation areas. House prices are determined by economic behaviors of sellers and buyers who have rational or adaptive expectations. Spatial patterns of house price volatility are formed by tendencies of their economic behavior. If there is a majority of adaptive sellers and buyers in an area, it may appear as a "hotspot" by showing high volatility of house prices and simultaneous price increases. Overheated speculation areas are formed by adaptive sellers and buyers who want to realize maximum expectation profit, therefore these areas patterns are defined as hotspot patterns of price volatility.

• Journal of Korea Spatial Information System Society
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• v.12 no.1
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• pp.49-56
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• 2010

Many researches of frequent pattern mining technique for detecting unknown patterns on spatial data have studied actively. Existing data structures have classified into tree-structure and array-structure, and those structures show the weakness of performance on dense or sparse data. Since spatial data have obtained the characteristics of dense and sparse patterns, it is important for us to mine quickly dense and sparse patterns using only single algorithm. In this paper, we propose novel data structure as compressed patricia frequent pattern tree and frequent pattern mining algorithm based on proposed data structure which can detect frequent patterns quickly in terms of both dense and sparse frequent patterns mining. In our experimental result, proposed algorithm proves about 10 times faster than existing FP-Growth algorithm on both dense and sparse data.

• Journal of the Korean Association of Geographic Information Studies
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• v.10 no.4
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• pp.60-74
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• 2007

For quantitative analysis of spatio-temporal distribution pattern on marine environment, spatial autocorrelation statistics on the both global and local aspects was applied to the observed data obtained from Gwangyang Bay in South Sea of Korea. Global indexes such as Moran's I and General G were used for understanding environmental distribution pattern in the whole study area. LISAs (local indicators of spatial association) such as Moran's I ($I_i$) and $G_i{^*}$ were considered to find similarity between a target feature and its neighborhood features and to detect hot spot and/or cold spot. Additionally, the significance test on clustered patterns by Z-scores was carried out. Statistical results showed variations of spatial patterns quantitatively in the whole year. Then all of general water quality, nutrients, chlorophyll-a and phytoplankton had strong clustered pattern in summer. When global indexes showed strong clustered pattern, the front region with a negative $I_i$ which means a strong spatial variation was observed. Also, when global indexes showed random pattern, hot spot and/or cold spot were/was found in the small local region with a local index $G_i{^*}$. Therefore, global indexes were useful for observing the strength and time series variations of clustered patterns in the whole study area, and local indexes were useful for tracing the location of hot spot and/or cold spot. Quantification of both spatial distribution pattern and clustering characteristics may play an important role to understand marine environment in depth and to find the reasons for spatial pattern.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5608-5618
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• 2015

This study conducted a spatial characterization of the public space using the pattern language of Christopher Alexander. It is the place which the city offers the human various experience. Urban public space has played an important role in flourishing public life through history. It has been a place for communication, cultural events, commercial exchange recreation, etc. among people in the city. But today, various behavior in public space and public life are declined. One of the reason in neglecting the user's need when urban space are planned and managed. This study examined the role and types of urban public space through literature survey and found the reason for its decline. For this public space spatial characteristics and indicators by FGI(Focus Group Interview) derived through. After those, case study was examined Jongro in Seoul and analyzed the spatial characteristics of the urban public space in a pattern language. Consequently, this study presents suitable a space structure direction of the public space and do for rather abundant a city space furtherance.

• Proceedings of the Acoustical Society of Korea Conference
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• 1998.06e
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• pp.11-14
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• 1998

근접 4점법을 이용하여 음원으로부터 측정점까지 음파가 전달되어 오는 공간상의 반사음 패턴을 구할 수 있다. 그러나 차량과 같이 협소한 공간에서는 반사음간의 시간차가 매우 작게 되고 따라서 단지 몇 개의 초기 반사음을 제외하고는 측정이 어렵게 된다. 본 연구에서는 측정에 사용된 스피커의 특성을 역필터링하여 차실 내의 펄스 반사음 자체 특성만을 추출함으로써 좁은 공간에서도 반사음 패턴의 측정이 가능하도록 하였다. 실내 반사음 패턴은 음파의 경로를 보여 주는 것이므로 본 측정법은 차량의 실내 음향 특성 파악과 음질 개선에 중요한 도구로 활용될 수 있다.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.241-256
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• 2015

The purpose of this paper is to verify the spatial distribution difference between formation and dissolution of start-up firms related to life-cycle. For this purpose, a Korean business directories and information on business closures were used, and location information, starting time, and closure time of start-up firms in the Seoul metropolitan region from 2007 to 2009 were generated in this study. It applied The spatial distribution analysis methods on the formation and dissolution of the start-up firms included Barcki measurements and global spatial autocorrelation. The total number of start-up firms was 5,810, and their five-year survival rate was 77.25%. The dissolution pattern of the start-up firms was dispersed more than their formation pattern in each of the Seoul metropolitan regions, the city of Seoul, and the city of Incheon-Gyeonggi province. In addition, differences between the formation and dissolution patterns according to size and industry category of the firms were confirmed.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.11a
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• pp.413-416
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• 2006

최근 위치 측위 기술의 발달 및 GPS 기술의 상용화로 인해 무선 통신 기기의 보급이 증가하면서 다양한 위치 기반 서비스 개발을 위한 노력이 활발히 진행되고 있다. 사용자들의 특성에 맞게 개인화되고 세분화된 위치 기반 서비스를 제공하기 위해서는 방대한 이동 객체의 위치 이동 데이터로부터 의미있는 지식인 유용한 패턴을 추출하기 위한 시간 패턴 탐사가 필요하다. 기존의 시간 패턴 탐사 기법들 중 일부는 이동 객체의 시간에 따른 공간 속성들의 변화를 충분히 고려하지 못하거나 또는 시공간 속성을 동시에 고려한 패턴 탐사는 가능하나 전체 이동 패턴들 중 추출하고자 하는 패턴에 반드시 포함되어야 하는 공간 정보에 대한 제약이 없어 특정 지점들 사이의 최적 이동 경로 탐색 문제나 단위기간 동안 이동 객체가 순회해야 지점들에 대한 스케줄링 경로 예측 문제 등에 적용하기 어렵다. 따라서 본 논문에서는 이동 객체의 위치 이력 데이터들에 대한 시공간 속성들을 고려하여 다양한 이동 패턴들 중 객체의 최적 이동 경로에 해당하는 패턴을 탐색하기 위한 새로운 시간 패턴 마이닝 알고리즘을 제안한다. 제안된 알고리즘은 특정한 지점들 사이를 이동한 객체의 위치 데이터들 중 객체가 가장 빈번하게 이동한 경로를 탐색하여 최적 경로를 결정하는 알고리즘으로, 공간 추상 계층의 각 계층별 영역 내 포함여부를 고려한 위치 일반화를 수행하여 보다 효과적으로 이동 패턴을 탐색할 수 있다.