Spatially Clustered Associations in Health GIS “mash-ups” Didier Leibovici 1 , Lucy Bastin 2 , Suchith Anand 1  , Jerry Sw...
an historical perspective <ul><li>data acquisition /  storage /  exchange </li></ul><ul><li>conflation / data analysis / g...
advanced conflation/analysis
Cluster detection  vs  Clustered associations <ul><li>Detection of  clusters </li></ul><ul><ul><li>Epidemiology Ecology He...
Cluster detection  vs   Clustered associations <ul><li>Clustered  associations </li></ul><ul><ul><li>Epidemiology Ecology ...
a simple example  star   dot   square 3 spatial patterns from  3 point processes focus is on:  “ associations” ,  local pr...
spatial associations <ul><li>talking about labels </li></ul><ul><ul><li>presence of all labels, uniformity,  </li></ul></u...
<ul><li>chi2 </li></ul><ul><li>entropy </li></ul>spatial associations /  co-occurrences counts global & local The  Co-o cc...
<ul><li>chi2 / entropy  local  statistics </li></ul><ul><ul><ul><ul><ul><li>at each  neighbourhood </li></ul></ul></ul></u...
> library(spatstat) > source(“kOO.R”) ={“star”,“dot”,”square”} dot  square  star  47  46  50  1/HSu HSu Min  Q1  Median  Q...
chi2 minimum  ... “ “ independence” ”
-  +   1450    428   -1  - 3  - 5   + 1  +3   + 5   571  478   401   55  138   235  Epidemiological study Infectious   dis...
more  -1  and less  +5   9.2% profile with same +3 and +5
relatively more +3 and less +5  reds higher odd  -  for 1 higher odd +  for 3  than blacks
 
SOOk analysis (global)
/21
150 subjects  0  Sept04-Feb05 3   5   -  59   7  +   5  4   /   Mar05-Aug05 3   5   -   50  8   +   12   5 <45  45-75  >75...
conclusions and further work <ul><li>scankOO (local view) </li></ul><ul><li>+  statistics from local co-occurrences </li><...
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3A_1_spatially clustered associations

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  • Data mashups
  • Efficient geoprocessing service vs pertinent statistical method /pertinent model
  • Cluster detection /cluster association
  • Cluster detection /cluster association
  • 3A_1_spatially clustered associations

    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,
    2. 2. an historical perspective <ul><li>data acquisition / storage / exchange </li></ul><ul><li>conflation / data analysis / geocomputation </li></ul><ul><li>for general public / for practitioners / for researchers </li></ul>
    3. 3. advanced conflation/analysis
    4. 4. Cluster detection vs Clustered associations <ul><li>Detection of clusters </li></ul><ul><ul><li>Epidemiology Ecology Health Geography ... </li></ul></ul><ul><li>GAM Openshaw et al. (1987) </li></ul><ul><li>Besag-Newell (areals) Besag & Newell (1991) DCluster </li></ul><ul><li>SaTScan Kulldorf & Nagarwalla (1995) www.SaTScan.org </li></ul><ul><li> spatclus </li></ul><ul><li>cases / observed, expected, </li></ul><ul><li>non cases / background population, population at risk </li></ul>
    5. 5. Cluster detection vs Clustered associations <ul><li>Clustered associations </li></ul><ul><ul><li>Epidemiology Ecology Health Geography ... </li></ul></ul><ul><li>“ multiple” cases / observed, (expected) </li></ul><ul><li>risk factors / attributes, areal attributes </li></ul><ul><li>functions/package & web service </li></ul><ul><li> ... </li></ul>detecting clusters of multivariate associations between attributes of one or more populations localised spatially
    6. 6. a simple example star dot square 3 spatial patterns from 3 point processes focus is on: “ associations” , local profiles ??? ??? ??? e.g. contagion factors
    7. 7. spatial associations <ul><li>talking about labels </li></ul><ul><ul><li>presence of all labels, uniformity, </li></ul></ul><ul><ul><li>dominance of one label, particular profiles </li></ul></ul><ul><li>talking about variables </li></ul><ul><li>independence, correlation </li></ul><ul><li>co-occurrences counts </li></ul><ul><li>uniformity / non-uniformity of spatial profiles | entropy </li></ul><ul><li>independence, lack of independence of variables | chi2 </li></ul>in a vicinity
    8. 8. <ul><li>chi2 </li></ul><ul><li>entropy </li></ul>spatial associations / co-occurrences counts global & local The Co-o ccurrences at a chosen order , of attributes from the same or different processes , build multinomial distributions at the root of spatial organisation and interactions of processes according to: the collocation d istance, and the order of collocation . see Leibovici et al. (2008) (2009) CAkOO and SOOk methods for global / overall “ ”
    9. 9. <ul><li>chi2 / entropy local statistics </li></ul><ul><ul><ul><ul><ul><li>at each neighbourhood </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>a) “condition of sufficiency” for </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>N bp points (any points) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>N bc cases (list of cases labels) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>b) co-occurrences counts </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>- at “ d ” chosen, “ all ” ( d =d N ), “ opti mal” (best d ) </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>c) statistical map, chi2 and entropy to </li></ul></ul></ul></ul></ul><ul><ul><ul><ul><ul><li>d) choice of growing </li></ul></ul></ul></ul></ul>spatial associations / co-occurrences counts in a vicinity x S V x S V x S V x S x S ScankOO package kOO (to be finalised with CAkOO, SOOk, selSOOk) d x “ ” d N x x S x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x V x S x x
    10. 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. 11. chi2 minimum ... “ “ independence” ”
    12. 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. 13. more -1 and less +5 9.2% profile with same +3 and +5
    14. 14. relatively more +3 and less +5 reds higher odd - for 1 higher odd + for 3 than blacks
    15. 16. SOOk analysis (global)
    16. 17. /21
    17. 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
    18. 19. conclusions and further work <ul><li>scankOO (local view) </li></ul><ul><li>+ statistics from local co-occurrences </li></ul><ul><li>+ neighourhood control (points, cases) </li></ul><ul><li>+ map of local spatial entropies / spatial chi2 </li></ul><ul><li>+ more tests needed on d ( opti) and Nbp Nbc (grows) </li></ul><ul><li>+ monte- carlo, random fields p-values for local maximum (or minimum) </li></ul><ul><li>other exploratory analysis based on co-occurrences (global view) </li></ul><ul><li>+ SOOk, SelSOOk and CAkOO </li></ul><ul><li>+ scale analysis </li></ul><ul><li>+ needs correction edge effects </li></ul><ul><li>integration in an OGC Web Processing Service (using R) </li></ul><ul><li>+ R wp server, Rapache, parallel processing </li></ul>package kOO (to be finalised with CAkOO, SOOk, selSOOk)

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