1. Spatio – Temporal Cluster Detection Using AMOEBA
Jimmy Kroon
Pennsylvania State University
Advisor: Dr. Frank Hardisty

2. This is a parody – Original Art: http://projectswordtoys. blogspot

3. Outline Introduction – Clustering and Project Direction
The Spatial Scan Statistic and SatScan
AMOEBA
Proposed Spatio-Temporal AMOEBA Method
Software, Data, and Progress

4. Cluster Detection
Cluster: “a geographically and/or temporally bounded group of occurrences of sufficient size and concentration to be unlikely to have occurred by chance” (Knox, 1989)
Two Typical Uses
Disease Surveillance
Week of 2/7/2010
Data: Google Flu Trends – Analysis: GeoDa
Epidemiological Studies
Brain Cancer in NM
Kulldorff et al. 1998
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

5. Time in Spatial Analysis
Time Matters:
Many geographic phenomena are dynamic.
Spatial patterns we see probably change over time
The American Association of Geographers describes temporal geography as a ‘frontier’ of GIScience.
Spatio-temporal clusters may exhibit behaviors not seen in purely spatial clusters.
Growth
Movement
Splits / Joins
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

6. Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress
Research Problem
Primary: No method exists for the determining the true extent of irregularly shaped clusters in spatio-temporal datasets.
Secondary: Spatial AMOEBA has not been implemented in R
Project Goals
A demonstration of spatio-temporal cluster detection based on the AMOEBA procedure.
R scripts for running spatial and spatio-temporal AMOEBA will be contributed to the R community.
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

7. The Spatial Scan Statistic
Scan data with a moving ‘window’, calculating local autocorrelation for spatial units that fall within the window.
Select the window(s) with the highest calculated autocorrelation value as possible cluster(s).
The spatial scan statistic is by far the most popular cluster detection technique, largely due to the availability of SaTScan software by Martin Kulldorff.
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

8. The Spatial Scan Statistic
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

9. Drawbacks of the Spatial Scan Statistic
Clusters that are not similar in shape to the scanning window can produce errors.
False inclusions
False exclusions
Identify thin clusters as multiple small clusters
Cannot detect holes in clusters
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

10. The Elliptical Spatial Scan Statistic
Must choose shapes a priori to avoid pre-selection bias
See Kulldorff et al. 2006
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

12. Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress
Ecotope-Based – Regions of contiguous spatial units that are related in terms of z-value
Multidirectional – Search in all directions.
Optimum – Procedure takes place at the finest spatial scale possible and is capable of revealing all spatial association present in the dataset (Aldstadt and Getis, 2006).
AMOEBA Clusters
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

13. Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress
Defining an Ecotope
Add a seed location (one polygon) to the ecotope
Calculate Gi* (Getis-Ord local autocorrelation statistic)
Search in all directions for contiguous polygons
Those that increase Gi* are added to the growing ecotope for that seed location
Keep searching for more neighbors, growing the ecotope until Gi* no longer increases
Repeat – creating ecotopes for each polygon in the dataset
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

14. Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress
The R Neighbor Object
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

15. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

16. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

17. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

18. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

19. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

20. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

21. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

22. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

23. Finding an Ecotope with AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

24. Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress
From Ecotopes to Clusters
Rank ecotopes by final Gi*
Select that with the highest Gi* as a cluster
Eliminate intersecting ecotopes
Select the ecotope with the next highest Gi* as a second cluster
Repeat
Probability of clusters can be tested using Monte Carlo simulation
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

25. Incorporating Time into AMOEBA
Remember - Spatio-temporal clusters may exhibit behaviors not seen in purely spatial clusters.
Growth
Movement
Splits / Joins
Visualize temporal data as layers of data with time extending vertically through the layers.
Each spatio-temporal unit has spatial neighbors and temporal neighbors
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

26. The Spatio-Temporal Scan Statistic
See Kulldorff et al. 1998
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

27. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

28. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

29. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

30. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

31. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

32. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

33. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

34. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

35. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

36. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

37. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

38. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

39. Spatio-Temporal AMOEBA
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

40. Software Environment and Test Data
The R Project
Free, open source statistical software
Extendable with user contributed packages
Google Flu Trends
Estimates flu incidence levels using aggregated data about user searches for certain keywords
90% accurate compared to CDC data
State-level data - updated daily
SEER (Surveillance Epidemiology and End Results)
National Cancer Institute incidence, survival, and mortality data
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

41. AMOEBA ArcToolbox for ArcGIS
Python Scripts by Jared Aldstadt and Yeming Fan (Aldstadt, 2010)
Google Flu Trends – Feb 1, 2009
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

42. Spatio-Temporal AMOEBA in Python: 2009 Flu Epidemic
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

43. Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress
Hmmm…
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

44. R Programming Progress
Compete …
Geoprocessing tasks
Create spatio-temporal
neighbor list
Delineate ecotopes
Sort and eliminate intersecting ecotopes
Returns primary cluster PolyID’s that match the Python results
To Do …
Monte Carlo simulation
Process results and add to the output shapefile
Test, test, test
Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

45. References
Aldstadt, Jared, and Arthur Getis Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters. Geographical Analysis 38: Aldstadt, Jared Spatial Analysis Tools (ArcGIS). Spatial Analysis Tools. Bellec, S, D Hémon, J Rudant, A Goubin, and J Clavel Spatial and space–time clustering of childhood acute leukaemia in France from 1990 to 2000: a nationwide study. British Journal of Cancer Duczmal, Luiz, Martin Kulldorff, and Lan Huang Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters. Journal of Computational and Graphical Statistics 15(2): Knox, G Detection of Clusters. In Methodology of Enquiries into Disease Clustering, ed. P Elliott, London: Small Area Health Statistics Unit. Kulldorff, Martin, Athas, William, Feuer, Eric, Miller, Barry, and Key, Charles Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health 88(9): Kulldorff, Martin, Lan Huang, Linda Pickle, and Luiz Duczmal An elliptic spatial scan statistic. Statistics in Medicine 25(22): Kulldorff, Martin Geographic Information Systems (GIS) community health: Some statistical issues. Journal of Public Health Management and Practice 5(2):
Original artwork for parody title slide: