Kevin Byrne's Conspectus on Paracoords Using GeoDa Offered as a Software Workshop, 2008

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This is part of a workshop prepared for WorldComp’08 titled “Why and How GeoDa Software Works as a Powerful and Intuitive Method for Geovisualizing Demographic Data” held in Las Vegas, Nevada in July 2008. Funded by fellowship from the Center for Spatially Integrated Social Science, University of California, Santa Barbara.
http://www.world-academy-of-science.org/worldcomp08/ws/tutorials/tutorial_byrne

J. Kevin Byrne is a Professor in the Science Baccalaureate Program in Visualization + Creative Management and Adjunct Professor, Sustainable Design Program, Minneapolis College of Art and Design, Minneapolis, Minnesota USA.
http://kevinbyrne.info

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Kevin Byrne's Conspectus on Paracoords Using GeoDa Offered as a Software Workshop, 2008

  1. 1. Geodemographics Geodemographics is ‘the analysis of people by where they live’ (Sleight, 2004) or, more precisely, by a data based classification of residential location (although classifications have also been produced for workplace, financial services and CYBERSPACE). The origins of geodemographics include Charles Booth’s Poverty Maps of London (1898-9, see http://booth.lse.ac.uk)and the 1920s-30s CHICAGO SCHOOL of Urban Sociology. During the twentieth century, the increasing availability of national census data and the development of computation permitted multivariate summaries of census zones to be produced, and for those areas to be grouped together on a like with like basis using clustering techniques (see CLASSIFICATION AND REGIONALISATION).
  2. 2. Data Table Format: cases x variables Case, instance, element Variable, factor, trait Variable1 Variable2 Variable3 Case1 • • • Case2 • (Values) • Case3 • • •
  3. 3. Example: Home buyer’s table ft2 Asking price Year built Home1 • • • Home2 • (Values) • Home3 • • • Sample values: 1, 10.5, 500
  4. 4. Example: Car salesperson’s table MPG # of Cylinders Weight Car1 • • • Car2 • (Values) • Car3 • • • Sample values: 35, 4, 4000
  5. 5. Bivariate Analysis-Presentation: Car Scatterplot cases: Car1, Var1: 2, MPG 3 Var2: Weight
  6. 6. Another Bivariate Analysis: Homes Scatterplot cases: Home1, Var1: 2, asking 3 price Var2: ft2
  7. 7. Trivariate Analysis-Presentation: 3D Scatterplot MPG Weight rs de in yl C
  8. 8. “It was in 1977 while giving a Linear Algebra course that I was challenged by my students to ‘show some multi-dimensional spaces.’ This was the catalyst leading to subsequent development of the methodology: How do multi-dimensional lines, planes, curves, surfaces look in (two) coordinates?” Alfred Inselberg, inventor of Parallel Coordinates* http://www.math.tau.ac.il/~aiisreal/ *He calls them //-coords.
  9. 9. Hypervariate analysis? A guitar metaphor… Fret1 Fret2 Fret3 String1 • • • String2 • (notes/chords?!) • String3 • • •
  10. 10. Parallel Coodinates best for multivariate analysis: Guitar neck as metaphor... ‘Strings’ are cases (1,2,3) but they can ‘cross’ ‘Fret’ is Fret is Fret is Fret is Var1 Var2 Var3 Var4 Think of ‘notes’ as data values here
  11. 11. Guitar neck as Parallel Coodinates And, yes, cases (our ‘strings’) can be strummed Fret is Fret is Fret is Fret is Var1 Var2 Var3 Var4 Guitars make music, parallel coordinates reveal patterns!
  12. 12. Car data as Parallel Coodinates one case is one car Var1: Var2: Var3: Var4: MPG Cylin- Horse- Weight ders power
  13. 13. WTMCC ASA Data Exposition data set. The data set contains 392 a6 a5 a1 b3 b2 b4 a7 automobiles. These automobiles are described by 7 attributes: a1: MPG, a2: Cylinders, a3: Horsepower, a4: Weight, a5: Acceleration, a6: Year and a7: Origin. The results are shown in Figure 4 and Table III. We can see that the number of crosses between lines decreases much after using the proposed algorithms. Fig. 4. Cars data set. TABLE III
  14. 14. Scanner Data for Breakfast Cereals
  15. 15. Conspectus credits (slide #, author/s, affiliation, source) Slide 3, R. Harris, University of Bristol, article Slide 4, S. Sweeney, CSISS, ESDA lecture Slide 11, A. Inselberg, Tel-Aviv University, website photo Slide 12, A. Inselberg, Tel-Aviv University, book cover Slide 17, C. Yang and J. Zhou, Tsinghou University, article Slide 18, R. Edsall, Arizona State University, article Slides 19-20, A. MacEachren, GeoVISTA Center / Penn. State University, G. & N. Andrienko, Fraunhofer Institute AIS; article Slide 21, E. Wegman, George Mason University, Powerpoint presentation

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