2005 Fiscal Year Final Research Report Summary
Development of the method of multivariate spatial analysis and its application in spatial information science
| Project/Area Number |
15310112
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Social systems engineering/Safety system
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| Research Institution | THE UNIVERSITY OF TOKYO |
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Principal Investigator |
SADAHIRO Yukio The University of Tokyo, Graduate School of Engineering, ASSOCIATE PROFESSOR, 大学院・工学系研究科, 助教授 (10240722)
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| Co-Investigator(Kenkyū-buntansha) |
OKUNUKI Keiichi NAGOYA UNIVERSITY, DEPARTMENT OF GEOGRAPHY, ASSOCIATE PROFESSOR, 大学院・文学研究科, 助教授 (90272369)
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| Project Period (FY) |
2003 – 2005
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| Keywords | multivariate spatial analysis / statistical analysis / GIS |
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| Research Abstract |
When we analyze mare than one set of spatial data in a region, we use multivariate analytical methods including statistical methods. In this case, spatial dimension is treated equivalently as other attribute dimensions, which causes both conceptual and technical problems in analysis. In regression analysis, for instance, attributes of neighborhoods are often included as independent variables. This makes the probability distribution of error terms quite complicated so that it is impossible to obtain the maximum likelihood analytically. Spatial aggregation across spatial units causes the Modifiable Areal Unit Problem, which refers to the dependency of the results of analysis on the definition of spatial units used for aggregation.
To resolve the problems, it is necessary to introduce the concept of space explicitly in analysis of the relationship among variables-multivariate analysis with a view of spatial analysis. Regarding the attributes defined over a space as a set of functions of location, we analyze them by a spatial multivariate analysis method.
To this end, we develop a new method of multivariate spatial analysis. The method should 1)explicitly incorporate the concept of space, 2)be applicable to exploratory spatial analysis, and 3)have wide applications in GIS. The first requirement is recognized to resolve the problems mentioned earlier. The second requirement is put to answer the demand for exploratory spatial data analysis that has emerged rapidly with the spread of massive spatial data. The third reqirement assures us to apply the method not only to spatial analysis but also mare general multivariate analysis that contains spatial dimensions as a part of space where variables are defined. Time series variables distributed over a space and error distribution measured over a space are also functions of location. They can be treated in the same way as the attribute variables discussed in this research.
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