1. Health GeoInformatics
Symposium G. P. Patil December 2005
Bangkok
Health_GeoInformatics_Bangkok_Dec2005.ppt

2. Infectious Diseases
Use of remote sensing data and other available geospatial data on a continental scale to help evaluate landscape characteristics that may be precursors for vector-borne diseases leading to early warning systems involving landscape health, ecosystem health, and human health
Water-Borne Diseases
Air-Borne Diseases
Emerging Infectious Diseases

4. Dead Bird Clusters as an Early Warning System for West Nile Virus Activity Mostashari, Kulldorff, Hartman, Miller, Kulasekara, Emerging Infectious Diseases 9,
Prospective geographic cluster analysis of dead bird reports may provide early warning of increasing viral activity in birds and mosquitoes, allowing jurisdictions to triage limited mosquito-collection and laboratory resources and more effectively prevent human disease caused by the virus… We adapted the spatial scan statistic for prospective detection of infectious disease outbreaks in New York City… This adaptation of the spatial scan statistic could also be useful in other infectious disease surveillance systems, including those for bioterrorism.

5. Geographic Prediction of Human Onset of West Nile Virus Using Dead Crow Clusters: A Quantitative Assessment of Year 2002 Data in New York State Johnson, Eidson, Schmit, Ellis, Kulldorff (2005)
The risk of becoming a West Nile Virus (WNV) case in New York State, excluding New York City, was evaluated for individuals whose town of residence was proximal to spatial clusters of dead American crows (Corvus brachyrhynchos). Weekly clusters were delineated for June-October 2002 by the binomial spatial scan statistic and by kernel density smoothing. Relative risk of a human case was estimated for different spatial-temporal exposure definitions after adjusting for population density and age distribution using Poisson regression, adjusting for week and geographic region, and Cox proportional hazards modeling, where the week of a human case was treated as the failure time and baseline hazard was stratified by region.

6. The risk of becoming a WNV case was positively associated with living in towns that are proximal to dead crow clusters. The highest risk was consistently for towns associated with a cluster in the current or prior 1-2 weeks Weaker, but positive, associations were found for towns associated with a cluster in just the 1-2 prior weeks, indicating an ability to predict onset in a timely fashion. These results are relevant to spatial-temporal surveillance for zoonotic diseases in general, including those of bioterrorism concern.

8. National Applications
Biosurveillance
Carbon Management
Coastal Management
Community Infrastructure
Crop Surveillance
Disaster Management
Disease Surveillance
Ecosystem Health
Environmental Justice
Environmental Management
Environmental Policy
Homeland Security
Invasive Species
Poverty Policy
Public Health
Public Health and Environment
Robotic Networks
Sensor Networks
Social Networks
Syndromic Surveillance
Tsunami Inundation
Urban Crime
Water Management

9. Examples of Hotspot Analysis
Spatial
Disease surveillance
Biodiversity: species-rich and species-poor areas
Geographical poverty analysis
Network
Water resource impairment at watershed scales
Water distribution systems, subway systems, and road transport systems
Social Networks/Terror Networks
Health_GeoInformatics_Bangkok_Dec2005.ppt

10. Issues in Hotspot Analysis
Estimation: Identification of areas having unusually high (or low) response
Testing: Can the elevated response be attributed to chance variation (false positive) or is it statistically significant?
Explanation: Assess explanatory factors that may account for the elevated response
Health_GeoInformatics_Bangkok_Dec2005.ppt

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

12. A small sample of the circles used

13. Detecting Emerging Clusters
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 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.
Health_GeoInformatics_Bangkok_Dec2005.ppt

15. Hospital Emergency Admissions in New York City
Hospital emergency admissions data from a majority of New York City hospitals.
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
If an alarm, a local investigation is conducted

16. Issues

17. Geospatial Surveillance

18. Spatial Temporal Surveillance

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

20. Syndromic Crisis-Index Surveillance

21. Hotspot Prioritization

23. 421 Thomas Building, Penn State University, University Park, PA 16802
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) (4) http: //
(2) (5)
(3) (6)
Project address: Penn State Center for Statistical Ecology and Environmental Statistics
421 Thomas Building, Penn State University, University Park, PA 16802
Telephone: (814) ;

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

25. Candidate Zones Z Want zones to be connected Maximize L(Z) for Z in 
Tessellated study area
Want zones to be connected
Maximize L(Z) for Z in 
Allowable zone
Not a zone
Health_GeoInformatics_Bangkok_Dec2005.ppt

26. A confidence set of hotspots on the ULS tree
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

27. Hotspot Delineation and Hotspot Rating
Determine a confidence set for the hotspot
Each member of the confidence set is a zone which is a statistically plausible delineation of the hotspot at specified confidence
Confidence set lets us rate individual cells a for hotspot membership
Rating for cell a is percentage of zones in confidence set that contain a. (More generally, use weighted proportion.)
Map of cell ratings:
Inner envelope = cells with 100% rating
Outer envelope = cells with positive rating

28. Space-Time Detection and Early Warning ULS Scan Statistic
The traditional space-time scan statistic employs cylinders as the candidate zones in the reduced parameter space . In many instances, the cylindrical shape may be a poor approximation to actual space-time hotspots, whereas the ULS approach is able to adapt its shape to the actual hotspot.
Since the ULS tree is derived from the adjacency matrix, the same software will work once the notion of adjacency has been specified for space-time cells.

29. Hotspot Detection for Continuous Responses
Human Health Context:
Blood pressure levels for spatial variation in hypertension
Cancer survival (censoring issues)
Environmental Context:
Landscape metrics such as forest cover, fragmentation, etc.
Pollutant loadings
Animal abundance

30. Hotspot Model for Continuous Responses
Simplest distributional model:
Additivity with respect to the index parameter k suggests that we model k as proportional to size:
Scale parameter  takes one value inside Z and another outside Z
Other distribution models (e.g., lognormal) are possible but are computationally more complex and applicable to only a single spatial scale

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

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

33. Syndromic Surveillance
(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).

34. Symbolization: Network Sensor Readings to Symbolic Dynamics
Phase-Space Trajectory
String of Symbols h a b g d c f e
……f c g h d a d c……
Sensor 3
The first step is to convert the continuous, multidimensional sensor network data into a stream of symbols.
This is done by dividing the phase space of the system into n dimensional cubes where n is the dimension of the system. A symbol is output when the phase space trajectory of the system enters the corresponding cube.
For realistic systems, this would result in too large an alphabet and too many states in the resulting automaton.
The effective dimension can often be reduced using techniques from nonlinear dynamics or other areas.
Sensor 2
Sensor 1
Health_GeoInformatics_Bangkok_Dec2005.ppt

35. Ocean SAmpling MObile Network OSAMON

36. Scalable Wireless Geo-Telemetry with Miniature Smart Sensors
Geo-telemetry enabled sensor nodes deployed by a UAV into a wireless ad hoc mesh network: Transmitting data and coordinates to TASS and GIS support systems

37. Wireless Sensor Networks for Habitat Monitoring
Health_GeoInformatics_Bangkok_Dec2005.ppt

38. Video Surveillance and Data Streams

39. Prioritization Innovation Partial Order Set Ranking
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.

40. HUMAN ENVIRONMENT INTERFACE LAND, AIR, WATER INDICATORS
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
RANK COUNTRY
LAND
AIR
WATER
Sweden
Finland
Norway
Iceland
Austria
Switzerland
Spain
France
Germany
Portugal
Italy
Greece
Belgium
Netherlands
Denmark
United Kingdom
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 82

41. Hasse Diagram (all countries)

42. Hasse Diagram (Western Europe)

43. Ranking Partially Ordered Sets – 5
Poset (Hasse Diagram)
Linear extension decision tree a b a b c d c b a d e b c d c d a e f b e d e d c e d c c d d e f d e f e d e f e f e f f f f f e f f e f e f f e f e f e
Jump Size:

44. 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
Element 1 2 3 4 5 6 a 9 14 16 b 7 12 15 c 10 d e f
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

45. Cumulative Rank Frequency Operator – 6 An Example of the Procedure16
The curves are stacked one above the other and the result is a linear ordering of the elements: a > b > c > d > e > f

46. Space-Time Poverty Hotspot Typology
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 ?

47. Growing poverty

48. Persistent poverty

49. Shifting poverty Oakland 1970 Poverty data Oakland 1980 Poverty data

50. Concentrated poverty Camden 1970 Poverty data Camden 1980 Poverty data

51. Hotspot Persistence Persistence is a property of space-time hotspots
Space (census tract)
1970
1980
1990
2000
Time (census year)
Persistent
Hotspot
Long Duration
Persistence is a property of space-time hotspots
Persistence can be assessed by the projection of the space-time hotspot onto the time axis
Space (census tract)
1970
1980
1990
2000
Time (census year)
One-shot
Hotspot
Duration
Short

52. Typology of Persistent Space-Time Hotspots-1
Space (census tract)
1970
1980
1990
2000
Time (census year)
Stationary
Hotspot
Space (census tract)
1970
1980
1990
2000
Time (census year)
Expanding
Hotspot
Space (census tract)
1970
1980
1990
2000
Time (census year)
Shifting
Hotspot
Space (census tract)
1970
1980
1990
2000
Time (census year)
Contracting
Hotspot

53. Typology of Persistent Space-Time Hotspots-2
Space (census tract)
1970
1980
1990
2000
Time (census year)
Bifurcating
Hotspot
These hotspots are connected in space-time
However, certain time slices of the hotspot are disconnected in space
Space (census tract)
1970
1980
1990
2000
Time (census year)
Merging
Hotspot
Spatially disconnected time slice

54. Trajectory of a Persistent Space-Time Hotspot
A space-time hotspot is a three-dimensional object
Visualization can be done by displaying the sequence of time slices---called the trajectory of the hotspot
Time slices of
space-time hotspot
Space (census tract)
1970
1980
1990
2000
Time (census year)
Merging
Hotspot

55. Application of the Scan Statistic to Wetland and Stream Monitoring: Testing the Method in the Upper Juniata Watershed, Pennsylvania, USA
Denice Heller Wardrop, Charles Taillie,
Kristen Hychka, G.P. Patil, and Wayne Myers
Penn State University, University Park, PA

56. Potential advantages of critical areas as an indicator of stress
Delineation of contributing areas is labor intensive
Can critical areas give us a first cut as to wetlands in poor condition?
Can they serve as an early warning?

58. Restoration Targeting with SaTScan
Can effectively target areas for restoration activities or best management practices
Can be scaled so that critical area matches management area

59. Spatial-Temporal Surveillance for Infectious Disease: Where are we and where can we go?
Glen D. Johnson, PhD,MS, MA
Zoonoses Program
Bureau of Communicable Disease Control
New York State Department of Health
and
The State University of New York
University at Albany School of Public Health
Department of Environmental Health and Toxicology

60. Ability of Dead Crow Clusters to Predict Human WNV
2002 data in NYS, excluding NYC
“exposed” if town of residence within one mile of spatial cluster of crows within previous two weeks
Estimate risk of exposed vs. unexposed by
- Poisson regression, using cases within town/week units as response and adjusting for region, week, town population density, density squared and proportion of town > 50 years old Proportional Hazards modelling, using week until onset as the response, stratifying by region and adjusting for town population density, density squared and proportion of town > 50 years old

61. So, What Next?
Much data not currently being exploited geo-spatial and spatial-temporal much is free and readily available some that may require development
How use such data? via GIS, compile with respect to geographic units of concern, such as townships via Generalized Linear Mixed Models (GLMM), model outcome (i.e. # dead crows or human cases) as a function of predictors obtained across multiple spatial-temporal scales

62. Atlantic Slope Consortium Watershed Classification
Modeling ecological condition in upstream watersheds and downstream estuaries in the Atlantic Slope Region
D.H. Wardrop, R.P. Brooks, G.P. Patil,
W. Myers, M.M. Easterling, and
C. Taille

63. Resulting Process Informative Define Watershed Types Representative
Compile
Frequency
Distributions
Consensus
Designate
Sampling
Design
Individual Judgement
Distribute
Candidate
Watersheds

64. Ternary Plot Description
Require a fuller description of departure from reference than “disturbed”
Describes land use via the three main land cover types
Can be used at any spatial extent (as can land cover %)

65. Results Six watershed classes identified: Forested, high slope
Forested, low slope
Agricultural
Urban
Mixed, low nodal variance
Mixed, high nodal variance

68. Logo for Statistics, Ecology, Environment, and Society