1. 1 Fukuoka Conference, Japan G. P. Patil November 2005

2. 2 This report is very disappointing. What kind of software are you using?

3. 3 Stone-Age Space-Age Syndrome Stone-age data Space-age data Stone-age analysis Space-age analysis

4. 4

5. 5

6. 6

7. 7 The Spatial Scan Statistic Move a circular window across the map. Move a circular window across the map. Use a variable circle radius, from zero up Use a variable circle radius, from zero up to a maximum where 50 percent of the population is included.

8. 8 A small sample of the circles used

9. 9 Spatial Scan Statistic: Properties – Adjusts for inhomogeneous population density. – Simultaneously tests for clusters of any size and any location, by using circular windows with continuously variable radius. – Accounts for multiple testing. – Possibility to include confounding variables, such as age, sex or socio-economic variables. – Aggregated or non-aggregated data (states, counties, census tracts, block groups, households, individuals).

10. 10 Detecting Emerging Clusters Instead of a circular window in two dimensions, we use a cylindrical window in three dimensions. Instead of a circular window in two dimensions, we use a cylindrical window in three dimensions. The base of the cylinder represents space, while the height represents time. The base of the cylinder represents space, while the height represents time. The cylinder is flexible in its circular base and starting date, but we only consider those cylinders that reach all the way to the end of the study period. Hence, we are only considering ‘alive’ clusters. The cylinder is flexible in its circular base and starting date, but we only consider those cylinders that reach all the way to the end of the study period. Hence, we are only considering ‘alive’ clusters.

11. 11 West Nile Virus Surveillance in New York City 2000 Data: Simulation/Testing of Prospective Surveillance System 2000 Data: Simulation/Testing of Prospective Surveillance System 2001 Data: Real Time Implementation of Daily Prospective Surveillance 2001 Data: Real Time Implementation of Daily Prospective Surveillance

12. 12 Major epicenter on Staten Island Dead bird surveillance system: June 14 Dead bird surveillance system: June 14 Positive bird report: July 16 (coll. July 5) Positive bird report: July 16 (coll. July 5) Positive mosquito trap: July 24 (coll. July 7) Positive mosquito trap: July 24 (coll. July 7) Human case report: July 28 (onset July 20) Human case report: July 28 (onset July 20)

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14. 14 Hospital Emergency Admissions in New York City Hospital emergency admissions data from a majority of New York City hospitals. Hospital emergency admissions data from a majority of New York City hospitals. At midnight, hospitals report last 24 hour of At midnight, hospitals report last 24 hour of data to New York City Department of Health A spatial scan statistic analysis is performed every morning A spatial scan statistic analysis is performed every morning If an alarm, a local investigation is conducted If an alarm, a local investigation is conducted

15. 15 Issues

16. 16 Geospatial Surveillance

17. 17 Spatial Temporal Surveillance

18. 18 Syndromic Crisis-Index Surveillance

19. 19 Hotspot Prioritization

20. 20 You Are Invited NSF DGP PROJECT Geoinformatic Surveillance: Hotspot Detection and Prioritization Across Geographic Regions and Networks for Digital Government in the 21st Century Geoinformatic surveillance for spatial and temporal hotspot detection and prioritization is a critical need for the 21st century Digital Government. A hotspot can mean an unusual phenomenon, anomaly, aberration, outbreak, elevated cluster, or critical area. The declared need may be for monitoring, etiology, management, or early warning. The responsible factors may be natural, accidental or intentional, with relevance to both infrastructure and homeland security. This project describes a multi-disciplinary research program based on novel methods and tools for hotspot detection and prioritization, driven by a wide variety of case studies of direct interest to several government agencies. These case studies deal with critical societal issues, such as carbon budgets, water resources, ecosystem health, public health, drinking water distribution system, persistent poverty, environmental justice, crop pathogens, invasive species, biosecurity, biosurveillance, remote sensor networks, early warning and homeland security. The geosurveillance provides an excellent opportunity, challenge, and vehicle for synergistic collaboration of computational, technical, and social scientists. Our methodology involves an innovation of the popular circle-based spatial scan statistic methodology. In particular, it employs the notion of an upper level set and is accordingly called the upper level set scan statistic, pointing to the next generation of a sophisticated analytical and computational system, effective for the detection of arbitrarily shaped hotspots along spatio-temporal dimensions. We also propose a novel prioritization scheme based on multiple indicator and stakeholder criteria without having to integrate indicators into an index, using revealing Hasse diagrams and partially ordered sets. Responding to the Government’s role and need, we propose a cross-disciplinary collaboration among federal agencies and academic researchers to design and build the prototype system for surveillance infrastructure of hotspot detection and prioritization. The methodological toolbox and the software toolkit developed will support and leverage core missions of federal agencies as well as their interactive counterparts in the society. The research advances in the allied sciences and technologies necessary to make such a system work are the thrust of this five year project. The project will have a dual disciplinary and cross-disciplinary thrust. Dialogues and discussions will be particularly welcome, leading potentially to well considered synergistic case studies. The collaborative case studies are expected to be conceptual, structural, methodological, computational, applicational, developmental, refinemental, validational, and/or visualizational in their individual thrust. For additional information, see the webpages: (1) http://www.stat.psu.edu/~gpp/PDFfiles/Prospectus%2016.pdfhttp://www.stat.psu.edu/~gpp/PDFfiles/Prospectus%2016.pdf (2) http://www.stat.psu.edu/~gpp/PDFfiles/Prospectus%2016%20overview.pdfhttp://www.stat.psu.edu/~gpp/PDFfiles/Prospectus%2016%20overview.pdf (3) http://www.stat.psu.edu/~gpp/PDFfiles/Prospectus-15-Case%20Studies.pdfhttp://www.stat.psu.edu/~gpp/PDFfiles/Prospectus-15-Case%20Studies.pdf Project address: Penn State Center for Statistical Ecology and Environmental Statistics 421 Thomas Building, Penn State University, University Park, PA 16802 Telephone: (814)865-9442; Email: gpp@stat.psu.edu

21. 21 National Applications Biosurveillance Biosurveillance Carbon Management Carbon Management Coastal Management Coastal Management Community Infrastructure Community Infrastructure Crop Surveillance Crop Surveillance Disaster Management Disaster Management Disease Surveillance Disease Surveillance Ecosystem Health Ecosystem Health Environmental Justice Environmental Justice Environmental Management Environmental Management Environmental Policy Environmental Policy Homeland Security Homeland Security Invasive Species Invasive Species Poverty Policy Poverty Policy Public Health Public Health Public Health and Environment Public Health and Environment Syndromic Surveillance Syndromic Surveillance Urban Crime Urban Crime Water Management Water Management

22. 22 Attractive Features Identifies arbitrarily shaped clusters Identifies arbitrarily shaped clusters Data-adaptive zonation of candidate hotspots Data-adaptive zonation of candidate hotspots Applicable to data on a network Applicable to data on a network Provides both a point estimate as well as a confidence set for the hotspot Provides both a point estimate as well as a confidence set for the hotspot Uses hotspot-membership rating to map hotspot boundary uncertainty Uses hotspot-membership rating to map hotspot boundary uncertainty Computationally efficient Computationally efficient Applicable to both discrete and continuous syndromic responses Applicable to both discrete and continuous syndromic responses Identifies arbitrarily shaped clusters in the spatial-temporal domain Identifies arbitrarily shaped clusters in the spatial-temporal domain Provides a typology of space-time hotspots with discriminatory surveillance potential Provides a typology of space-time hotspots with discriminatory surveillance potential Hotspot Detection Innovation Upper Level Set Scan Statistic

23. 23 Candidate Zones for Hotspots Goal: Identify geographic zone(s) in which a response is significantly elevated relative to the rest of a region Goal: Identify geographic zone(s) in which a response is significantly elevated relative to the rest of a region A list of candidate zones Z is specified a priori A list of candidate zones Z is specified a priori –This list becomes part of the parameter space and the zone must be estimated from within this list –Each candidate zone should generally be spatially connected, e.g., a union of contiguous spatial units or cells –Longer lists of candidate zones are usually preferable –Expanding circles or ellipses about specified centers are a common method of generating the list

24. 24 Scan Statistic Zonation for Circles and Space-Time Cylinders

25. 25 ULS Candidate Zones Question: Are there data-driven (rather than a priori) ways of selecting the list of candidate zones? Question: Are there data-driven (rather than a priori) ways of selecting the list of candidate zones? Motivation for the question: A human being can look at a map and quickly determine a reasonable set of candidate zones and eliminate many other zones as obviously uninteresting. Can the computer do the same thing? Motivation for the question: A human being can look at a map and quickly determine a reasonable set of candidate zones and eliminate many other zones as obviously uninteresting. Can the computer do the same thing? A data-driven proposal: Candidate zones are the connected A data-driven proposal: Candidate zones are the connected components of the upper level sets of the response surface. The candidate zones have a tree structure (echelon tree is a subtree), which may assist in automated detection of multiple, but geographically separate, elevated zones. Null distribution: If the list is data-driven (i.e., random), its variability must be accounted for in the null distribution. A new list must be developed for each simulated data set. Null distribution: If the list is data-driven (i.e., random), its variability must be accounted for in the null distribution. A new list must be developed for each simulated data set.

26. 26 Data-adaptive approach to reduced parameter space  0 Data-adaptive approach to reduced parameter space  0 Zones in  0 are connected components of upper level sets of the empirical intensity function G a = Y a / A a Zones in  0 are connected components of upper level sets of the empirical intensity function G a = Y a / A a Upper level set (ULS) at level g consists of all cells a where G a  g Upper level set (ULS) at level g consists of all cells a where G a  g Upper level sets may be disconnected. Connected components are Upper level sets may be disconnected. Connected components are the candidate zones in  0 These connected components form a rooted tree under set inclusion. These connected components form a rooted tree under set inclusion. –Root node = entire region R –Leaf nodes = local maxima of empirical intensity surface –Junction nodes occur when connectivity of ULS changes with falling intensity level ULS Scan Statistic

27. 27 Upper Level Set (ULS) of Intensity Surface Hotspot zones at level g (Connected Components of upper level set)

28. 28 Changing Connectivity of ULS as Level Drops g

29. 29 ULS Connectivity Tree Schematic intensity “surface” N.B. Intensity surface is cellular (piece-wise constant), with only finitely many levels A, B, C are junction nodes where multiple zones coalesce into a single zone A B C

30. 30 A confidence set of hotspots on the ULS tree. The different connected components correspond to different hotspot loci while the nodes within a connected component correspond to different delineations of that hotspot

31. 31 Network Analysis of Biological Integrity in Freshwater Streams

32. 32 NYC Drinking Water Quality Within-City Sampling Stations 892 sampling stations Each station about 4.5 feet high and draws water from a nearby water main Sampling frequency increased after 9-11 Currently, about 47,000 water samples analyzed annually Parameters analyzed:  Bacteria  Chlorine levels  pH  Inorganic and organic pollutants  Color, turbidity, odor  Many others

33. 33 Sampling Station Locations City-WideManhattan

34. 34 Water-Related Synoptic Hotspot Analyses Due to the nature of water flow, most hotspot investigations of water resources are network-based. Some possibilities for synoptic investigations:  Lakes and other large bodies of water.  Remotely sensed parameters.  Surface thermal characteristics  Shorelines and coastal regions  Algal blooms ?  Groundwater  Sampling/data collection issues

35. 35 Network-Based Surveillance Subway system surveillance Subway system surveillance Drinking water distribution system surveillance Drinking water distribution system surveillance Stream and river system surveillance Stream and river system surveillance Postal System Surveillance Postal System Surveillance Road transport surveillance Road transport surveillance Syndromic Surveillance Syndromic Surveillance

36. 36 Syndromic Surveillance Symptoms of disease such as diarrhea, respiratory problems, headache, etc Symptoms of disease such as diarrhea, respiratory problems, headache, etc Earlier reporting than diagnosed disease Earlier reporting than diagnosed disease Less specific, more noise Less specific, more noise

37. 37 (left) The overall procedure, leading from admissions records to the crisis index for a hospital. The hotspot detection algorithm is then applied to the crisis index values defined over the hospital network. (right) The -machine procedure for converting an event stream into a parse tree and finally into a probabilistic finite state automaton (PFSA). Syndromic Surveillance

38. 38 Experimental Validation Pressure sensitive floor Formal Language Events: a – green to red or red to green b – green to tan or tan to green c – green to blue or blue to green d – red to tan or tan to red e – blue to red or red to blue f – blue to tan or tan to blue Wall following Random walk Analyze String Rejections Target Behavior

39. 39 Mapping Priority Hotspots of Vegetative Disturbance for Carbon Budgets

40. 40 Key Crop Areas Crops NOAA Weather Threat Locations Plants Infected Non-infected Sentinel Ground Cameras Air/Space Platforms Hyperspectral Imagery Signature Library Data Processing Anomaly Report Crop Attack Decision Support System Ground Truthing Site Identification Module Signature Development Module

41. 41 Crop Biosurveillance/Biosecurity

42. 42 Hyperspectral Imagery Signature Library Image Segmentation (hyperclustering) Proxy Signal (per segment) Disease Signature Similarity Index (per segment) Tessellation (segmentation) of raster grid Signature Similarity Map Hotspot/ Anomaly Detection Crop Biosurveillance/Biosecurity Data Processing Module

43. 43 Early Detection of Biological Invasions

44. 44 Significant, but often overlooked problem Existing strategies are reactive Action taken only after detection of invasion/collateral damage Due to time-lag, species may already be established Such an approach is environmental catch-up Mid-Atlantic Invasive Species

45. 45 Emergent Surveillance Plexus (ESP) Surveillance Sensor Network Testbed Autonomous Ocean Sampling Network Types of Hotspots Hotspots due to multiple, localized, stationary sources Hotspots due to multiple, localized, stationary sources Hotspots corresponding to areas of interest in a stationary mapped field Hotspots corresponding to areas of interest in a stationary mapped field Time-dependent, localized hotspots Time-dependent, localized hotspots Hotspots due to moving point sources Hotspots due to moving point sources

46. 46 Ocean SAmpling MObile Network OSAMON

47. 47 Ocean SAmpling MObile Network OSAMON Feedback Loop Network sensors gather preliminary data Network sensors gather preliminary data ULS scan statistic uses available data to estimate hotspot ULS scan statistic uses available data to estimate hotspot Network controller directs sensor vehicles to new locations Network controller directs sensor vehicles to new locations Updated data is fed into ULS scan statistic system Updated data is fed into ULS scan statistic system

48. 48 SAmpling MObile Networks (SAMON) Additional Application Contexts Hotspots for radioactivity and chemical or biological agents to prevent or mitigate the effects of terrorist attacks or to detect nuclear testing Hotspots for radioactivity and chemical or biological agents to prevent or mitigate the effects of terrorist attacks or to detect nuclear testing Mapping elevation, wind, bathymetry, or ocean currents to better understand and protect the environment Mapping elevation, wind, bathymetry, or ocean currents to better understand and protect the environment Detecting emerging failures in a complex networked system like the electric grid, internet, cell phone systems Detecting emerging failures in a complex networked system like the electric grid, internet, cell phone systems Mapping the gravitational field to find underground chambers or tunnels for rescue or combat missions Mapping the gravitational field to find underground chambers or tunnels for rescue or combat missions

49. 49 We also present a prioritization innovation. It lies in the ability for prioritization and ranking of hotspots based on multiple indicator and stakeholder criteria without having to integrate indicators into an index, using Hasse diagrams and partial order sets. This leads us to early warning systems, and also to the selection of investigational areas. Prioritization Innovation Partial Order Set Ranking

50. 50 First stage screening First stage screening –Significant clusters by SaTScan and/or upper level sets upper level sets Second stage screening Second stage screening –Multicriteria noteworthy clusters by partially ordered sets and Hass diagrams Final stage screening Final stage screening –Follow up clusters for etiology, intervention based on multiple criteria using Hass diagrams based on multiple criteria using Hass diagrams Multiple Criteria Analysis, Multiple Indicators and Choices, Health Statistics, Disease Etiology, Health Policy, Resource Allocation

51. 51 HUMAN ENVIRONMENT INTERFACE LAND, AIR, WATER INDICATORS RANK COUNTRYLANDAIRWATER 1Sweden 2Finland 3Norway 5 Iceland 13 Austria 22 Switzerland 39 Spain 45 France 47 Germany 51 Portugal 52 Italy 59 Greece 61 Belgium 64 Netherlands 77 Denmark 78 United Kingdom 81 Ireland 69.01 76.46 27.38 1.79 40.57 30.17 32.63 28.34 32.56 34.62 23.35 21.59 21.84 19.43 9.83 12.64 9.25 35.24 19.05 63.98 80.25 29.85 28.10 7.74 6.50 2.10 14.29 6.89 3.20 0.00 1.07 5.04 1.13 1.99 100 98 100 82 100 98 100 for land - % of undomesticated land, i.e., total land area-domesticated (permanent crops and pastures, built up areas, roads, etc.) for air - % of renewable energy resources, i.e., hydro, solar, wind, geothermal for water - % of population with access to safe drinking water

52. 52 Hasse Diagram (all countries)

53. 53 Hasse Diagram (Western Europe)

54. 54 Ranking Partially Ordered Sets – 5 Linear extension decision tree a b dc e f a c e b b d ff d ed f e e f c f d ed f e e f d f e e f c f e e f c c f d ed f e e f d f e e f c b a b a d Jump Size: 1 3 3 2 3 5 4 3 3 2 4 3 4 4 2 2 Poset (Hasse Diagram)

55. 55 Cumulative Rank Frequency Operator – 5 An Example of the Procedure In the example from the preceding slide, there are a total of 16 linear extensions, giving the following cumulative frequency table. Rank Element123456 a91416161616 b71215161616 c0410161616 d026121616 e00141016 f0000616 Each entry gives the number of linear extensions in which the element (row label) receives a rank equal to or better that the column heading

56. 56 Cumulative Rank Frequency Operator – 6 An Example of the Procedure 16 The curves are stacked one above the other and the result is a linear ordering of the elements: a > b > c > d > e > f

57. 57 Cumulative Rank Frequency Operator – 7 An example where F must be iterated Original Poset (Hasse Diagram) a f eb c g d h a f e b ad c h g a f e b ad c h g F F 2

58. 58 Incorporating Judgment Poset Cumulative Rank Frequency Approach Certain of the indicators may be deemed more important than the others Such differential importance can be accommodated by the poset cumulative rank frequency approach Instead of the uniform distribution on the set of linear extensions, we may use an appropriately weighted probability distribution , e.g.,

59. 59 Space-Time Poverty Hotspot Typology Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty Federal Anti-Poverty Programs have had little success in eradicating pockets of persistent poverty Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved location- specific anti-poverty policy ? Can spatial-temporal patterns of poverty hotspots provide clues to the causes of poverty and lead to improved location- specific anti-poverty policy ?

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