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

Spatio – Temporal Cluster Detection

Using AMOEBA

Jimmy KroonPennsylvania State University

Advisor: Dr. Frank Hardisty

This is a parody – Original Art: http://projectswordtoys.blogspot.com/2009/05/project-sword-annual-1967.html

Outline

• Introduction – Clustering and Project Direction

• The Spatial Scan Statistic and SatScan

• AMOEBA

• Proposed Spatio-Temporal AMOEBA Method

• Software, Data, and Progress

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)

Disease SurveillanceWeek of 2/7/2010

Data: Google Flu Trends – Analysis: GeoDa

Clusters : SaTScan : AMOEBA : ST AMOEBA : Progress

Epidemiological StudiesBrain Cancer in NM

Two Typical Uses

Kulldorff et al. 1998

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


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.


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.

The Spatial Scan Statistic


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


The Elliptical Spatial Scan Statistic


• Must choose shapes a priori to avoid pre-selection bias

See Kulldorff et al. 2006

AMOEBA

AMOEBA Clusters

• 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

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


The R Neighbor Object


Finding an Ecotope with AMOEBA


















AMOEBA

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


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


The Spatio-Temporal Scan Statistic

Clusters : SaTScan : AMOEBA : ST AMOEBA : ProgressSee Kulldorff et al. 1998

Spatio-Temporal AMOEBA


























Software Environment and Test Data

The R Project•Free, open source statistical software•Extendable with user contributed packages•www.r-project.org

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•www.google.org/googleflu

SEER (Surveillance Epidemiology and End Results)•National Cancer Institute incidence, survival, and mortality data


AMOEBA ArcToolbox for ArcGIS Python Scripts by Jared Aldstadt and Yeming Fan (Aldstadt, 2010)


Google Flu Trends – Feb 1, 2009

Spatio-Temporal AMOEBA in Python: 2009 Flu Epidemic


Hmmm…


R Programming Progress


Compete …Geoprocessing tasks

• Create spatio-temporalneighbor 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

Aldstadt, Jared, and Arthur Getis. 2006. Using AMOEBA to Create a Spatial Weights Matrix and Identify Spatial Clusters. Geographical Analysis 38: 327-343.

Aldstadt, Jared. 2010. Spatial Analysis Tools (ArcGIS). Spatial Analysis Tools. http://www.acsu.buffalo.edu/~geojared/tools.htm.

Bellec, S, D Hémon, J Rudant, A Goubin, and J Clavel. 2006. 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. 2006. Evaluation of Spatial Scan Statistics for Irregularly Shaped Clusters. Journal of Computational and Graphical Statistics 15(2): 428-442.

Knox, G. 1989. Detection of Clusters. In Methodology of Enquiries into Disease Clustering, ed. P Elliott, 17-22. London: Small Area Health Statistics Unit.

Kulldorff, Martin, Athas, William, Feuer, Eric, Miller, Barry, and Key, Charles. 1998. Evaluating cluster alarms: A space-time scan statistic and brain cancer in Los Alamos, New Mexico. American Journal of Public Health 88(9): 1377-1380.

Kulldorff, Martin, Lan Huang, Linda Pickle, and Luiz Duczmal. 2006. An elliptic spatial scan statistic. Statistics in Medicine 25(22): 3929.

Kulldorff, Martin. 1999. Geographic Information Systems (GIS) community health: Some statistical issues. Journal of Public Health Management and Practice 5(2): 100-106.

References

Original artwork for parody title slide: http://projectswordtoys.blogspot.com/2009/05/project-sword-annual-1967.html

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

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