Session 3A, Presentation 1

1. Spatially Clustered Associations in Health GIS “mash-ups” Didier Leibovici 1 , Lucy Bastin 2 , Suchith Anand 1 , Jerry Swan 1 , Gobe Hobona 1 and Mike Jackson 1 1 Centre for Geospatial Sciences, University of Nottingham, UK 2 School of Engineering & Applied Science, Aston University, UK Tel. (+44(0)115 846 8408) Fax (+44(0)115 951 5249) [email_address] Spatial Statistics, Multiway Data, Marked Point Process, Spatial Pattern, Spatial Interaction, Multi-Scale Analysis Co-Occurrences,

3. advanced conflation/analysis

6. a simple example star dot square 3 spatial patterns from 3 point processes focus is on: “ associations” , local profiles ??? ??? ??? e.g. contagion factors

10. > library(spatstat) > source(“kOO.R”) ={“star”,“dot”,”square”} dot square star 47 46 50 1/HSu HSu Min Q1 Median Q3 Max 0.51 0.85 0.92 0.97 0.99 1/HsSu HsSu Min Q1 Median Q3 Max 0.13 0.59 0.86 0.94 1

11. chi2 minimum ... “ “ independence” ”

12. - + 1450 428 -1 - 3 - 5 + 1 +3 + 5 571 478 401 55 138 235 Epidemiological study Infectious disease dataset somewhere in UK 626 616 636 1000 878

13. more -1 and less +5 9.2% profile with same +3 and +5

14. relatively more +3 and less +5 reds higher odd - for 1 higher odd + for 3 than blacks

16. SOOk analysis (global)

17. /21

18. 150 subjects 0 Sept04-Feb05 3 5 - 59 7 + 5 4 / Mar05-Aug05 3 5 - 50 8 + 12 5 <45 45-75 >75 3 5