30 99 112 2006 SDAM SDAM SDAM SDAM 1950 1960 1970 SPSS SAS Microsoft Excel ArcView GIS 2002 ArcExplorer 1) MANDARA 2) GIS 2000 TNTLite 3) GIS
100 SDAM SDAM Windows2000/XP 4) SDAM TIN ESDA K G G GWR SDAM GUI
101 SDAM ESRI JAPAN 5) 6) 7 GIS SDAM 8) containsintersect GIS 20,000 Exploratory Spatial Data Analysis; ESDA ESDA 2003 SDAM 65
102 ESDA
103 SDAM K km km km 100m
104 SDAM 1992 Microsoft Excel
105 10 10
106 2 1 SDAM SDAM SDAM 11 65 10 11
107 12 km 13 TIN TIN Triangulated Irregular Network 10 TIN TIN 14 15 SDAM SDAM km 16
108 12 13 Geographically Weighted Regression; GWR GWR SDAM GWR GWR 17 GWR GWR GWR GWR
109 14 TIN 15
110 16 17 GWR
111 SDAM SDAM SDAM SDAM GIS GIS GIS SDAM A GIS 17B 16 17 SDAM ESRI http://www.esrij.com GIS http: //www.esrij.com/products/arcexplorer/index.shtml http:// www5f.biglobe.ne.jp/ ~ ktani/ GIS http://www5c.biglobe.ne.jp/ ~ mandara/ MicroImage http://www.microimages.com/ GIS GIS http://www.opengis.co. jp/ TNTMips http://www.microimages.com/tntlite/ http://giswin.geo.tsukuba.ac.jp/teacher/murayam a/sdam/ http://www.esrij.com/ http://www.esrij.com/gis_data/japanshp/japanshp. html http://www.toukei.metro.tokyo.jp/ssihyou/ss-index. htm contains contained cross disjoint equals intersects
112 overlaps touches within 2002 GIS GIS 65A 395-418 26 125-149 2000 GIS 2003 202p 1992 24 77-97 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