• Journal of Korea Multimedia Society
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• v.9 no.11
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• pp.1395-1409
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• 2006

A spatial data warehouse has spatial data cube composed of multi-dimensional data for efficient OLAP(On-Line Analytical Processing) operations. A spatial data cube supporting concept hierarchies holds huge amount of data so that many researches have studied a incremental update method for minimum modification of a spatial data cube. The Cube, however, compressed by eliminating prefix and suffix redundancy has coalescing paths that cause update inconsistencies for some updates can affect the aggregate value of coalesced cell that has no relationship with the update. In this paper, we propose incremental batch update method of a spatial data cube. The proposed method uses duplicated nodes and extended node structure to avoid update inconsistencies. If any collision is detected during update procedure, the shared node is duplicated and the duplicate is updated. As a result, compressed spatial data cube that includes concept hierarchies can be updated incrementally with no inconsistency. In performance evaluation, we show the proposed method is more efficient than other naive update methods.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2786-2796
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• 1998

Extraction of interesting and general knowledge from large spatial database is an important task in the development of geographical information system and knowledge-base systems. In this paper, we propose a spatial data mining system using generalization method; In this system, we extend an existing generalization mining and design a rulebase to support deriving new spatial knowledge. For this purpose, we propose an interleaved method which integrates spatial data dominated and nonspatial data dominated mining and construct a rulebase to extract topological relationship between spatial objects.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2713-2719
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• 2012

According to increasing of various location-based services, distributed data stream processing techniques have been widely studied to provide high scalability and availability. In previous researches, in order to balance the load of distributed nodes, the geographic characteristics of spatial data stream are not considered. For this reason, distributed operations for adjacent spatial regions increases the overall system load. We propose a operation allocation scheme considering the characteristics of spatial operations to effectively processing spatial data stream in distributed computing environments. The proposed method presents the efficient share maximizing approach that preferentially distributes spatial operations sharing the common query regions to the same node in order to separate the adjacent spatial operations on overlapped regions.

• Spatial Information Research
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• v.13 no.2 s.33
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• pp.119-128
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• 2005

Most spatial data handled in GIS is two-dimensional. These two-dimensional data is established by selecting 2D aspects form 3D, or by projecting 3D onto 2D space. During this conversion, without user's intention, data are abstracted and omitted. This unwanted data loss causes disadvantages such as restrictingof the range of data application and describing inaccurate real world. Recently, three dimensional data is getting wide interests and demands. One of the examplesis Database Management System which can store and manage three dimensional spatial data. However, this DBMS does not support spatial query which is the essence of the database management system. So, various studies are needed in this field. This research designs spatial relationship that is defined in space database standard using the three-dimension space model. The spatial data model, which is used in this research, is the one defined in OGC for GMS3, and designing tool is DE-9IM based on Point-Set Topology blow as the best method for topological operation.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.337-340
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• 2008

As a 'Construction of Information System on GIS based and Resource Map', establish the strategic of design about construction of Spatial Data Warehouse for New & Renewable Energy For Construction of comfortable Spatial Data Warehouse, It suggested The Construction of Spatial Data Warehouse on Block(Grid) Based with Analyze into the old Data & Method of Study. For Decide the Block(Grid) Size, We need The Study of Data & Method. Also, we expect Standardize The Process of Change & Apply with Data. make the best use of New & Renewable Energy Part

• Journal of Korea Spatial Information System Society
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• v.7 no.3 s.15
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• pp.107-118
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• 2005

Various spatial data are utilized, through geographic information system, for the process to make a decision related to space. Generally, spatial data is large in quantity and it costs high and takes quite a long time for producing and maintaining it. An existing spatial data management system, tile-based one, for digital map manages spatial data being separated by a uniform data unit called tile. These systems can be implemented easily but have many problems such as they can directly store and manage feature included in tile. Therefore, in this paper, we suggest a feature-based spatial data management system for digital map. The proposed system is able to store and manage spatial data in the unit of feature directly. Hence this system is able to immediately update any change in the data and to supply users with the updated data without any delay. The proposed system can not only support a function of data input, management, supplying and update but also support unity origin coordinate conversion, UFID creation, feature unifying, feature dividing and metadata input which is not supported by the existing tile-based system. The proposed system can easily manage spatial data and can increase efficiency in processing and application.

• The KIPS Transactions:PartD
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• v.10D no.4
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• pp.567-576
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• 2003

Spatial data mining, i.e., discovery of interesting characteristics and patterns that may implicitly exists in spatial databases, is a challenging task due to the huge amounts of spatial data. Clustering algorithms are attractive for the task of class identification in spatial databases. Several methods for spatial clustering have been presented in recent years, but have the following several drawbacks increase costs due to computing distance among objects and process only memory-resident data. In this paper, we propose an efficient grid cell based spatial clustering method for spatial data mining. It focuses on resolving disadvantages of existing clustering algorithms. In details, it aims to reduce cost further for good efficiency on large databases. To do this, we devise a spatial clustering algorithm based on grid ceil structures including cell relationships.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.767-785
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• 2021

With the rapid development of information technology, it is now possible to analyze the spatial patterns of cultivated land and its evolution by combining GIS, geostatistical analysis models and spatiotemporal big data for the dynamic monitoring and management of cultivated land resources. The spatial pattern of cultivated land and its evolutionary patterns in Luoyang City, China from 2009 to 2019 were analyzed using spatial autocorrelation and spatial autoregressive models on the basis of GIS technology. It was found that: (1) the area of cultivated land in Luoyang decreased then increased between 2009 and 2019, with an overall increase of 0.43% in 2019 compared to 2009, with cultivated land being dominant in the overall landscape of Luoyang; (2) cultivated land holdings in Luoyang are highly spatially autocorrelated, with the 'high-high'-type area being concentrated in the border area directly north and northeast of Luoyang, while the 'low-low'-type area is concentrated in the south and in the municipal area of Luoyang, and being heavily influenced by topography and urbanization. The expansion determined during the study period mainly took place in the Luoyang City, with most of it being transferred from the 'high-low'-type area; (3) elevation, slope and industrial output values from analysis of the bivariate spatial autocorrelation and spatial autoregressive models of the drivers all had significant effects on the amount of cultivated land holdings, with elevation having a positive effect, and slope and industrial output having a negative effect.

• Journal of Cadastre & Land InformatiX
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• v.45 no.1
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• pp.1-15
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• 2015

Korea's Spatial Information Policy has been institutionalized based by "National Spatial Data Infrastructure Act", "Act on Land Survey, Waterway Survey and Cadastral Records", "Spatial Data Industry Promotion Act". In year 2015, These acts were modified with new names and contents. It is critical that the definition of spatial information ought to be generalized. In addition, it is of paramount importance to review factors those contribute to the systematic development on coherent relation between Spatial Information Act and other acts. Examples of improvement area may include: Categorization of Spatial Information Concept, Systematization of Spatial Data Related Legislation, and Enhancement of Organization Specializing in Spatial Information. Furthermore, Legislation & Policy case of Germany, France and the United States were used as a basis for improvement.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.625-635
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• 2011

This paper investigates the effects of spatial autocorrelation structures in low resolution data on downscaling without ground measurements or secondary data, as well as the potential of geostatistical downscaling. An advanced geostatistical downscaling scheme applied in this paper consists of two analytical steps: the estimation of the point-support spatial autocorrelation structure by variogram deconvolution and the application of area-to-point kriging. Point kriging of block data without variogram deconvolution is also applied for a comparison purpose. Experiments using two low resolution thematic maps derived from remote sensing data showing very different spatial patterns are carried out to discuss the objectives. From the experiments, it is demonstrated that the advanced geostatistical downscaling scheme can generate the downscaling results that well preserve overall patterns of original low resolution data and also satisfy the coherence property, regardless of spatial patterns in input low resolution data. Point kriging of block data can produce the downscaling result compatible to that by area-to-point kriging when the spatial continuity in block data is strong. If heterogeneous local variations are dominant in input block data, the treatment of the low resolution data as point data cannot generate the reliable downscaling result, and this simplification should not be applied to donwscaling.