2002 Fiscal Year Final Research Report Summary
Study on the structure of the remote sensing data and spatial data
| Project/Area Number |
12680316
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | OKAYAMA UNIVERSITY |
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Principal Investigator |
KURIHARA Koji Okayama University Faculty of Envi. Sci. and Tech. Professor, 環境理工学部, 教授 (20170087)
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| Co-Investigator(Kenkyū-buntansha) |
TARUMI Tomoyuki Okayama University Faculty of Envi. Sci. and Tech. Professor, 環境理工学部, 教授 (50033915)
TANAKA Yutaka Okayama University Faculty of Envi. Sci. and Tech. Professor, 環境理工学部, 教授 (20127567)
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| Project Period (FY) |
2000 – 2002
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| Keywords | spatial data / remote sensing data / regional data / hotspots / contingency table / echelon analysis / クリギング |
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| Research Abstract |
The research results based on this Grant-in-Aid for Scientific Research are shown as follows.
1. The study of spatial structure in remote sensing data based on echelon analysis : The relationship between density of human residence and sparseness of visitation computed from remote sensing data is investigated. (Kurihara et al. 2000a)
2. The study of spatial structure in mass data based on echelon tree : Echelon tree is too complicated for visual study as dendrograms. Thus characterization and comparison of echelon tree is done with four profiles based on pruning process. (Kurihara etal. 2000b, Ishioka and Kurihara 2002)
3. The investigation of spatial structure in contingency table based on echelon analysis : Although contingency tables constitute a pseudo-space rather than a geo-space, we can use echelon techniques in a statistical context. (Kurihara et al. 2001)
4. Hotspots detection based on echelon analysis and spatial scan statistics : Regional, features such as hotspots and trends are shown hi an echelon dendrogram, as well as in maps and tables. The candidates of hotspots are given as the top echelon in the dendrogram. Therefore we can detect the hotspots for spatially aggregated regional data based on spatial statistics. (Kurihara et al. 2002)
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Research Products
(15 results)
