2007 55 1 113 124 c 2007 1 1 2,3 Jung Jin Lee 4 2006 8 2 2007 3 2 Jasp Jasp 1., 2005 Chang, 2006 1 1 769 2193 1314 1 2 106 8569 4 6 7 3 106 8569 4 6 7 4 Department of Statistics, Soong Sil University, 1 1 Sangdo-5 Dong, Dongjak-Gu, Seoul 156 743, Korea
114 55 1 2007 choropleth map conditioned choropleth map Jasp Nakano et al., 2000 Jasp Java 2 3 2 4 5 2. 2.1 1 Jasp CMap(mapObject, dataobject, vname, col) 1 mapobject 2 dataobject 3 vname dataobject 4 col dataobject mapobject vname col vname cdata 1 vname col Jasp CMap 2 CMap http://www.stat.go.jp/data/ssds/index.htm
115 50 72 65 1,000 1997 7 Jasp K-Means 3 4 3 Jasp tokushima = read("ssds_tokushima.jdt") dataobject = tokushima[,!"id"] mapobject = read("maptokushima.jdt") km3 = wekakmeansmodel(dataobject, 3) col3 = clusterdata(km3, dataobject) kmeansresult3 = CMap(mapObject, col3) kmeansresult3 1 3 4 K-Means col3 2 4 4 3 4 Jasp GUI Window 1 1 3 1 4 3 2 4 1.
116 55 1 2007 Jasp C4.5, 2006 2.2 2 2 Carr et al., 2000 Jasp CCMaps(mapObject, dataobject) 1 mapobject 2 2 CMap 2 50 2.
117 2 3 2 2 2 2 2 2 3 2 3 65 1,000 suicide revenue medicaldoctor 2 Jasp 1 2 3 2 3 getselectedregions(ccmobject) 1 ccmobject CCMaps CMap 2.3 Parallel Coordinate Plot, PCP 4 Inselberg, 1985
118 55 1 2007 3. Jasp PCP(dataObject) dataobject Jasp 3 50 50 65 65 1,000 3 1,000 1,000 1,000 12 73 12 12 3.
119 4. 2 2 0.71 2.83 0.18 2 3 3 4 17 industproduct 6 17 65 popover65 primaryschool postoffice hospital localtax 3 employee3rdindust crimerate 5 5 1 suicide 2 65 popover65 3 industproduct localtax 4 hospital
120 55 1 2007 5. 5 6. 5 6 5 6 5 6 65 popover65
121 7. 5 hospital 2 3 6 3 2 65 30.14 38.89 0.18 5 Jasp 2 Jasp 7 2 7 5 2
122 55 1 2007 4. Geographic Information System, GIS GIS, 2005 GIS ESRI ArcView GIS GIS GIS R Ihaka and Gentleman, 1996 GIS Bivand, 1999 R iplots http://www.iplots.org/ Cleveland 1993 S Chambers and Hastie, 1992 Trellis Graphics R coplot Murrell, 2005 Edsall 2003 Edsall Edsall 3 5. 2 3
123 Jasp Jasp http://jasp.ism.ac.jp/ Bivand, R. S. 1999. Integrating GRASS 5.0 and R: GIS and modern statistics for data analysis, Proceedings 7th Scandinavian Research Conference on Geographical Information Science, 111 127, Aalborg, Denmark. Carr, D. B., Wallin, J. F. and Carr, D. A. 2000. Two new templates for epidemiology applications: Linked micromap plots and conditioned choropleth maps, Statistics in Medicine, 19, 2521 2538. Chambers,J.andHastie,T. eds. 1992. Statistical Models in S, Chapman Hall, New York. Chang, K. 2006. Introduction to Geographic Information Systems 3rd Edition, McGraw-Hill, New York. Cleveland, W. S. 1993. Visualizing Data, Hobart Press, Summit, New Jersey. Edsall, R. M. 2003. The parallel coordinate plot in action: Design and use for geographics visualization, Computational Statistics and Data Analysis, 43, 605 619., 2005. GIS, Ihaka, R. and Gentleman, R. 1996. R: A language for data analysis and graphics, Journal of Computational and Graphical Statistics, 5, 299 314. Inselberg, A. 1985. The plane with parallel coordinates, Visual Computer, 1, 69 91. 2006. Java 18 1 15 25. 2005. Murrell, P. 2005. R Graphics, Chapman & Hall/CRC, Boca Raton. Nakano, J., Fujiwara, T., Yamamoto, Y. and Kobayashi, I. 2000. A statistical package based on Pnuts, COMPSTAT2000 Proceedings in Computational Statistics eds. J. G. Bethlehem and P. G. M. van der Heijden 361 366, Physica-Verlag, Heidelberg.
124 Proceedings of the Institute of Statistical Mathematics Vol. 55, No. 1, 113 124 (2007) Multivariate Geographical Data Visualization Using Linked Statistical Graphics Ikunori Kobayashi 1, Yoshikazu Yamamoto 1, Junji Nakano 2 and Jung Jin Lee 3 1 Faculty of Engineering, Tokushima Bunri University 2 The Institute of Statistical Mathematics 3 Department of Statistics, Soong Sil University Multivariate geographical data have become widely available and are being used for various purposes because of the development of information technologies. As geographical data include locations of regions, map-based graphics such as dot maps and choropleth maps are suitable for expressing them. Map-based graphics, however, are not suitable for expressing high dimensional data. This paper proposes the use of linked statistical graphics, especially conditioned choropleth maps (CCmaps) and parallel coordinate plots (PCPs), for visualizing and analyzing multivariate geographical data. The CCmaps is an extension of a choropleth map and arranges several choropleth maps according to conditions defined by two other variables. The PCP is appropriate for describing multivariate data in a single graph, but are not good at showing location information. We illustrate that linked views and interactive operations on these graphics are effective for geographical data analysis. The proposed functions are implemented in a statistical analysis system Jasp. Key words: Choropleth map, conditioned choropleth map, parallel coordinate plot, Jasp.