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1 SDAM SDAM SDAM SDAM SPSS SAS Microsoft Excel ArcView GIS 2002 ArcExplorer 1) MANDARA 2) GIS 2000 TNTLite 3) GIS

2 100 SDAM SDAM Windows2000/XP 4) SDAM TIN ESDA K G G GWR SDAM GUI

3 101 SDAM ESRI JAPAN 5) 6) 7 GIS SDAM 8) containsintersect GIS 20,000 Exploratory Spatial Data Analysis; ESDA ESDA 2003 SDAM 65

4 102 ESDA

5 103 SDAM K km km km 100m

6 104 SDAM 1992 Microsoft Excel

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8 SDAM SDAM SDAM

9 km 13 TIN TIN Triangulated Irregular Network 10 TIN TIN SDAM SDAM km 16

10 Geographically Weighted Regression; GWR GWR SDAM GWR GWR 17 GWR GWR GWR GWR

11 TIN 15

12 GWR

13 111 SDAM SDAM SDAM SDAM GIS GIS GIS SDAM A GIS 17B SDAM ESRI GIS http: // www5f.biglobe.ne.jp/ ~ ktani/ GIS ~ mandara/ MicroImage GIS GIS jp/ TNTMips a/sdam/ html htm contains contained cross disjoint equals intersects

14 112 overlaps touches within 2002 GIS GIS 65A GIS p Availability of SDAM in Quantitative Geography MURAYAMA Yuji and KOMAKI Nobuhiko Beginning in the 1960s, we saw the development of quantitative and theoretical geography and a research shift away from spatial structure toward spatial processes. Today, we are reaching the point where we will move away from spatial processes toward research that emphasizes spatial forecast, control, and management. Sophisticated methods to support spatial decision-making, for example, genetic algorithms, hedonic approaches, hierarchical analysis (AHP), multi-standard evaluation methods, etc., are being developed in rapid succession and are being incorporated into GIS. An intensive effort must be made to using the techniques to make this a powerful tool and to become intricately involved in quantitative and theoretical geography. Given this background, this paper discusses the availability of the Spatial Data Analysis Machine (SDAM) in the course of quantitative geography at the undergraduate level. SDAM was developed in the Division of Spatial Information Science, University of Tsukuba. The functions of this system built by only open sources, include the mapping, spatial search, TIN, overlay, point pattern analysis, spatial autocorrelation, multivariate analysis such as regression analysis, factor analysis and cluster analysis, neural network, tessellation, and so on. Especially spatial interaction model and geographical weighted regression analysis are useful for analytical human geography. Key words: Quantitative Geography, Spatial Data Analysis Machine, SDAM, Education, Teaching Method, Software Graduate student, doctoral program in life and environmental sciences

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untitled Studies in Human Geography 32 Geoenvironmental Sciences, Graduate School of Life and Environmental Sciences, University of Tsukuba, Japan ArcGIS Geography Network ArcGIS ArcGIS ArcGIS Geography Network

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