1. JalaSRI Consortium Delhi – Jalgaon Workshop TERI U G.P. Patil June 1, 2009

2. Federal Agency Partnership CDC DOD EPA NASA NIH NOAA USFS USGS Agency Databases Thematic Databases Other Databases Homeland Security Disaster Management Public Health Ecosystem Health Other Case Studies Statistical Processing: Hotspot Detection, Prioritization, etc. Data Sharing, Interoperable Middleware Standard or De Facto Data Model, Data Format, Data Access Arbitrary Data Model, Data Format, Data Access Application Specific De Facto Data/Information Standard Agency Databases Thematic Databases Other Databases Homeland Security Disaster Management Public Health Ecosystem Health Other Case Studies Statistical Processing: Hotspot Detection, Prioritization, etc. Data Sharing, Interoperable Middleware Standard or De Facto Data Model, Data Format, Data Access Arbitrary Data Model, Data Format, Data Access Application Specific De Facto Data/Information Standard SurvellanceGeoinformaticsof Hotspot Detection, Prioritization and Early Warning NSF Digital Government Project #0307010 PI: G. P. Patil gpp@stat.psu.edu Websites: http://www.stat.psu.edu/~gpp/ http://www.stat.psu.edu/hotspots/ http://www.stat.psu.edu/%7Egpp/DGOnlineNews2006.mht NSF Digital Government surveillance geoinformatics project, federal agency partnership and national applications for digital governance. Cellular Surface National and International 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

3. Spatially distributed response variables Hotspot analysis Prioritization Decision support systems Geoinformatic spatio-temporal data from a variety of data products and data sources with agencies, academia, and industry Masks, filters Indicators, weights Masks, filters Geoinformatic Surveillance System

4. 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

5. 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

6. 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

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. A small sample of the circles used

9. 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

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. West Nile Virus, NY City, Success Story. West Nile Virus, NY City, Success Story.

11. Conclusions The space-time scan statistic can serve as an important tool in prospective systematic time-periodic geographical surveillance for the early detection of disease outbreaks. The space-time scan statistic can serve as an important tool in prospective systematic time-periodic geographical surveillance for the early detection of disease outbreaks. It is possible to detect emerging clusters, and we can adjust for the multiple tests performed over the years. It is possible to detect emerging clusters, and we can adjust for the multiple tests performed over the years. The method can be used for different diseases. The method can be used for different diseases.

12. West Nile Virus in New York City 2000 Data - Dead birds reported by the public - Simulation of a daily prospective surveillance system - Start date: June 1, 2000.

14. Spatial Scan Statistic Limitations and Needs Circles capture only compactly shaped clusters Circles capture only compactly shaped clusters Want to identify clusters of arbitrary shape Want to identify clusters of arbitrary shape Circles handle only synoptic (tessellated ) data Circles handle only synoptic (tessellated ) data Want to handle data on a network Want to handle data on a network Circles provide point estimate of hotspot Circles provide point estimate of hotspot Want to assess estimation uncertainty (hotspot confidence set) Want to assess estimation uncertainty (hotspot confidence set)

15. Crime Rate Hotspots

16. Circular spatial scan statistic zonation Cholera outbreak along a river flood plain Small circles miss much of the outbreak Large circles include many unwanted cells

17. Poor Hotspot Delineation by Circular Zones Hotspot Circular zone approximations Circular zones may represent single hotspot as multiple hotspots

18. Outbreak expanding in time Small cylinders miss much of the outbreak Large cylinders include many unwanted cells Space Time Cylindrical space-time scan statistic zonation

19. 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

20. Demonstration Example Data Cases Disease Rate Population Likelihood

21. Confidence Set for ULS Hotspot Hotspot membership rating

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

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

24. Features of ULS Scan Statistic: Identifies arbitrarily shaped hotspots Applicable to data on a network Confidence sets and hotspot ratings Computationally efficient Generalizes to space-time scan

25. 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 ?

26. Hotspot Persistence Hotspot Persistence Space (census tract) 1970 1980 1990 2000 Time (census year) Persistent Hotspot Long Duration Space (census tract) 1970 1980 1990 2000 Time (census year) One-shot Hotspot Short 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

27. 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) Shifting Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Expanding Hotspot Space (census tract) 1970 1980 1990 2000 Time (census year) Contracting Hotspot

28. 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

29. 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

30. Trajectory of a Merging Hotspot 19701980 1990 2000

31. Trajectory of a Shifting Hotspot 19701980 1990 2000

32. Network Analysis of Biological Integrity in Freshwater Streams

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

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. 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 New York City New York City Pennsylvania Pennsylvania

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. Crisis-Index Surveillance

44. 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

45. Hotspot Prioritization and Poset Ranking Multiple hotspots with intensities significantly elevated relative to the rest of the region Multiple hotspots with intensities significantly elevated relative to the rest of the region Ranking based on likelihood values, and additional attributes: raw intensity values, socio-economic and demographic factors, feasibility scores, excess cases, seasonal residence, atypical demographics, etc. Ranking based on likelihood values, and additional attributes: raw intensity values, socio-economic and demographic factors, feasibility scores, excess cases, seasonal residence, atypical demographics, etc. Multiple attributes, multiple indicators Multiple attributes, multiple indicators Ranking without having to integrate the multiple indicators into a composite index Ranking without having to integrate the multiple indicators into a composite index

47. First stage screening First stage screening Significant clusters by SaTScan and/or 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 Multicriteria noteworthy clusters by partially ordered sets and Hass diagrams Final stage screening Final stage screening Follow up clusters for etiology, intervention 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

48. Regions of comparability and incomparability for the inherent importance ordering of hotspots. Hotspots form a scatterplot in indicator space and each hotspot partitions indicator space into four quadrants

49. 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

50. Hasse Diagram (all countries)

51. Hasse Diagram (Western Europe)

52. Figure 5. Hasse diagrams for four different posets. Poset D has a disconnected Hasse diagram with two connected components {a, c, e} and {b, d}.

53. Figure 13: Hasse diagrams (right) of the two possible rankings for the poset on the left.

54. Figure 14. Rank-intervals for all 106 countries. The intervals (countries) are labeled by their midpoints as shown along the horizontal axis. For each interval, the lower endpoint and the upper endpoint are shown vertically. The length of each interval corresponds to the ambiguity inherent in attempting to rank that country among all 106 countries.

55. Figure 15. Rank-intervals for all 106 countries, plotted against their HEI rank. The HEI rank appears as the 45-degree line. The HEI tends to be optimistic (closer to the lower endpoint) for better-ranked countries and pessimistic (closer to the upper endpoint) for poorer-ranked countries.

56. Rank range run sequence for the 4-index data set. The bottom of each vertical line represents the minimum rank and the top of the line is the maximum rank for the indicators.

57. (End-member Elimination results for the 4-index data set. Maintenance and Restoration Guidance.)

58. Figure 16. A ranking of a poset determines a linear Hasse diagram. The numerical rank assigned to each element is that element’s depth in the Hasse diagram.

59. Figure 17. Hasse diagram of Poset B (left) and a decision tree enumerating all possible linear extensions of the poset (right). Every downward path through the decision tree determines a linear extension. Dashed links in the decision tree are not implied by the partial order and are called jumps. If one tried to trace the linear extension in the original Hasse diagram, a “jump” would be required at each dashed link. Note that there is a pure-jump linear extension (path a, b, c, d, e, f) in which every link is a jump.

60. Figure 18. Histograms of the rank-frequency distributions for Poset B.

61. 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 a91416 b7121516 c041016 d0261216 e00141016 f00006 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

62. 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

63. 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

64. Cumulative Rank Frequency Operator – 8 An example where F results in ties Original Poset (Hasse Diagram) a cb d a b, c (tied) d F Ties reflect symmetries among incomparable elements in the original Hasse diagram Elements that are comparable in the original Hasse diagram will not become tied after applying F operator

65. Digital Government Research NSF Digital Government Research Project NSF Digital Government Research Project Methods, Tools, and Software Methods, Tools, and Software Hotspot Detection: PULSE Software Development Hotspot Detection: PULSE Software Development Poset Prioritization: RAPID Software Development Poset Prioritization: RAPID Software Development Digital Governance and Hotspot GeoInformatics Decision Support System Digital Governance and Hotspot GeoInformatics Decision Support System

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

68. Application of the Scan Statistic to Wetland and Stream Monitoring: Testing the Method in the Upper Juniata Watershed, Pennsylvania, USA

69. Figure 1. Hotspot areas of agricultural activity on high slopes, identified using the SatScan statistic, in the Upper Juniata watershed, Pennsylvania, USA.

70. A Case Study of Hotspot Geoinformatics and Digital Governance for the Map of Italian Nature in the Presence of Multiple Indicators of Ecological Value, Ecological Sensitivity, and Anthropic Pressure for the Study Area of Oltrepò Pavese in Italy by Angelo Pecci 1, Sham Bhat 2, Wayne Myers 3, Ganapati Patil 2, Orazio Rossi 1, and Pierfranchesca Rossi 4 1 Dipartimento di Scienze Ambientali, Universita di Parma, Parma, Italy 2 Center for Statistical Ecology and Environmental Statistics, Department of Statistics 3 School of Forest Resources and Office for Remote Sensing for Spatial Information Resources, and Penn State Institutes of Environment, The Pennsylvania State University, University Park, PA, 16802 4 Italian Environmental Protection Agency, Milan, Italy C enter for S tatistical E cology and E nvironmental S tatistics

71. A Case Study of Hotspot Geoinformatics and Digital Governance for the Map of Italian Nature in the Presence of Multiple Indicators of Ecological Value, Ecological Sensitivity, and Anthropic Pressure for the Study Area of Oltrepò Pavese in Italy

72. DATA BASE  CORINE Biotopes habitat map  Hydrographic network map  Street network map  Built-up map  Administrative boundaries map  Regional and National Parks map  Natural Regional Reserves map  Map of Sites of Communitary importance for the Nature Conservation  Special Protection Zones map  Ramsar Zones map (wetlands map)  Geographic range of distribution of italian vertebrates  Suitability map of italian vertebrates  Digital Elevation Model (DEM)  Landsat 5 TM images

73. CORINE Biotopes Map of Oltrepò Pavese and Ligure – Emiliano Appennine Most Frequent C.B. habitats are: Lowland hay meadows, unbroken intensive croplands, Hop-hornbeam woods, Northern Italian Quercus pubescens woods. Total C.B. units: 25,318 Natural C.B. Units: 21,010 Area Size: 321,815 hectares

74.  HABITAT RARITY WITHIN THE AREA (H_rarity)  INVOLVEMENT OF THE HABITAT IN THOSE WHICH HOST RARE VERTEBRATES (V_rarity)  INVOLVEMENT OF THE HABITAT IN THOSE WHICH ARE SUITABLE FOR VERTEBRATES AT RISK (Vr_suit)  % OF HABITAT INCLUDED IN PROTECTED AREA DEVOTED TO EDUCATIONAL AIMS (Perc_prot)  NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)  HABITAT SIZE (Size_ha)  SOIL ROUGHNESS (Soil_rough)  HYDROLOGICAL NETWORK DENSITY (Hydro_dens)  SPECIES RICHNESS VERTEBRATES (Vert_rich)  INVOLVEMENT OF THE HABITAT IN CONSERVATION AREAS (Cons_areas): 1. THE LIST OF THE SITES OF COMMUNITY IMPORTANCE FOR NATURE CONSERVATION (SIC) 2. THE LIST OF SPECIAL PROTECTION ZONES (ZPS) 3. THE LIST OF RAMSAR ZONES (WETLANDS OF INTERNATIONAL IMPORTANCE) CRITERIA AND CORRESPONDING INDICATORS FOR THE COMPARATIVE ESTIMATE OF THE ECOLOGICAL VALUE OF A CORINE BIOTOPES HABITAT BIODIVERSITY RARITY PROTECTIVE ASPECTS HUMAN BENEFITS INSTITUTIONAL ASPECTS INDICATORS CRITERIA

75.  FRACTAL COEFFICIENT OF THE HABITAT PERIMETER (H_convol)  CIRCULARITY RATIO OF THE HABITAT AREA (H_compact)  HABITAT SIZE (Size_ha)  HABITAT AVERAGE SLOPE (Slope)  VERTEBRATES SPECIES DECLARED AT RISK BY IUCN AND PRESENT IN THE HABITAT (Vert_IUCN)  VEGETAL SPECIES DECLARED AT RISK BY IUCN AND PRESENT IN THE HABITAT (Veg_IUCN)  NEAREST NEIGHBOUR INDEX (NN_index)  LANDSLIDE INDEX (Landslide)  FIRE POTENTIAL INDEX (FPI)  HABITAT ORIENTATION COMPARED TO THE MAIN WIND DIRECTION (Wind) ISOLATION COMPOSITIONAL ASPECTS (species at risk) ABIOTIC RISKS STRUCTURAL ASPECTS CRITERIA AND CORRESPONDING INDICATORS FOR THE COMPARATIVE ESTIMATE OF THE ECOLOGICAL SENSITIVITY OF A CORINE BIOTOPES HABITAT INDICATORS CRITERIA

76. Taking into account simultaneously all the indicators of the Communes containing HSEA, it’s possible, by a Cluster Analysis Tecnique, to divide the 64 Communes in three different sub-groups: N = NUMBERS OF COMMUNES 1 = POPULATION DENSITY 2 = MEAN AGE 3 = AGEING RATE 4 = DEPENDENCY RATIO 5 = POPULATION RATE OF NATURAL INCREASE 6 = NET MIGRATION RATE

77. Wireless Sensor Networks for Habitat Monitoring 'GAURI RANE'; 'Sanjay Pawde'; Anil Rao; 'Nandkumar Bendale'

78. Incidence of Dengue and Chikungunya in Jalgaon District (2006-2007) EcoHealth Hotspot GeoInformatics Why Jalgaon? In MS? In India? Why these Talukas?

84. Guidance Factors Cross-Disciplinary Cross-Institutional Cross- National Collaboration Cross-Disciplinary Cross-Institutional Cross- National Collaboration In House Thrust: Synergize, Agonize Together, and Advance In House Thrust: Synergize, Agonize Together, and Advance Each one a Professor and a Student at the Same Time Each one a Professor and a Student at the Same Time Doctoral / Post-Doctoral Students Doctoral / Post-Doctoral Students Tenured Cross-Disciplinary Faculty Tenured Cross-Disciplinary Faculty Graduate Degree Programs Graduate Degree Programs Triangular ACA Collaboration Triad Triangular ACA Collaboration Triad