1.2. 5/28/2018 Austin(1): 65 of 87. Austin(2): 2 of 29.

1.2. 5/28/2018 Austin(1): 65 of 87. Austin(2): 2 of 29.
cleveland clinic October 2004: 21 of 88. For Reza Dec 2004: 58 of 123. Hawaii_March2005_final: 30 of 156. India_1_Dec2004: 17 of 82. Intercol-2 Aug2005: 34 of 34. JSM-1 Aug2005: 1 of 10. NIST_Oct2004: 21 of 102. NYC_NISS_NOV2004: 17 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Spatial Temporal Surveillance
5/28/2018 Spatial Temporal Surveillance Austin(2): 1 of 29 For Reza Dec 2004: 55 of 123. George Mason University April 2004: 17 of 96. Hawaii_March2005_final: 26 of 156. India_1_Dec2004: 14 of 82. NIST_Oct2004: 17 of 102. NYC_NISS_NOV2004: 14 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Center for Statistical Ecology and Environmental Statistics
5/28/2018 Geographic Surveillance and Hotspot Detection for Homeland Security: Cyber Security and Computer Network Diagnostics Securing the nation's computer networks from cyber attack is an important aspect of Homeland Security. Project develops diagnostic tools for detecting security attacks, infrastructure failures, and other operational aberrations of computer networks. Geographic Surveillance and Hotspot Detection for Homeland Security: Tasking of Self-Organizing Surveillance Mobile Sensor Networks Many critical applications of surveillance sensor networks involve finding hotspots. The upper level set scan statistic is used to guide the search by estimating the location of hotspots based on the data previously taken by the surveillance network. Geographic Surveillance and Hotspot Detection for Homeland Security: Drinking Water Quality and Water Utility Vulnerability New York City has installed 892 drinking water sampling stations. Currently, about 47,000 water samples are analyzed annually. The ULS scan statistic will provide a real-time surveillance system for evaluating water quality across the distribution system. Geographic Surveillance and Hotspot Detection for Homeland Security: Surveillance Network and Early Warning Emerging hotspots for disease or biological agents are identified by modeling events at local hospitals. A time-dependent crisis index is determined for each hospital in a network. The crisis index is used for hotspot detection by scan statistic methods Geographic Surveillance and Hotspot Detection for Homeland Security: West Nile Virus: An Illustration of the Early Warning Capability of the Scan Statistic West Nile virus is a serious mosquito-borne disease. The mosquito vector bites both humans and birds. Scan statistical detection of dead bird clusters provides an early crisis warning and allows targeted public education and increased mosquito control. Geographic Surveillance and Hotspot Detection for Homeland Security: Crop Pathogens and Bioterrorism Disruption of American agriculture and our food system could be catastrophic to the nation's stability. This project has the specific aim of developing novel remote sensing methods and statistical tools for the early detection of crop bioterrorism. Geographic Surveillance and Hotspot Detection for Homeland Security: Disaster Management: Oil Spill Detection, Monitoring, and Prioritization The scan statistic hotspot delineation and poset prioritization tools will be used in combination with our oil spill detection algorithm to provide for early warning and spatial-temporal monitoring of marine oil spills and their consequences. Geographic Surveillance and Hotspot Detection for Homeland Security: Network Analysis of Biological Integrity in Freshwater Streams This study employs the network version of the upper level set scan statistic to characterize biological impairment along the rivers and streams of Pennsylvania and to identify subnetworks that are badly impaired. Austin(2): 3 of 29. cleveland clinic October 2004: 25 of 88. For Reza Dec 2004: 60 of 123. George Mason University April 2004: 24 of 96. India_1_Dec2004: 19 of 82. NIST_Oct2004: 25 of 102. NYC_NISS_NOV2004: 19 of 82. Center for Statistical Ecology and Environmental Statistics G. P. Patil, Director 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Predictability of Surface Water Pollution Loadings AWRA Best Paper Award
5/28/2018 Goal: Evaluate relationship between watershed-wide landscape characteristics and two measures of surface water quality in Pennsylvania: Monthly exported nitrogen mass estimated from field measurements GIS-modeled pollution potential index After removing physiographic province effects, dominant predictors were: Proportion of “annual herbaceous” land for nitrogen loading Proportion of “total herbaceous” land for pollution potential index Important finding because these simple landscape metrics are easily and cheaply obtained from remotely sensed data Optimal set of predictors also included several indicators of spatial pattern in the land cover Landscape metrics, obtained solely from remotely sensed data, account for much of the water quality variability (R2 = 0.75) within Pennsylvania watersheds NSF Oct. 15 Final, 2002: 100 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Landscape Metrics Studied and Optimal Predictors of Water Quality
5/28/2018 Landscape Metrics Studied and Optimal Predictors of Water Quality Optimal Model p-value Landscape Metric Nitrogen Loadings Pollution Potential Patch Density Mean Patch Size Patch Size Coefficient of Variation Edge Density Landscape Shape Index .03 Area-Weighted Mean Shape Index Double Log Fractal Dimension .08 .02 Area-Weighted Mean patch Fractal Dimension Shannon Evenness Index Interspersion and Juxtaposition Index Contagion Total Forest Cover Total Herbaceous Cover .0000 Annual Herbaceous Cover .0005 Terrestrial Unvegetated Cover Conditional Entropy A Conditional Entropy B .01 NSF Oct. 15 Final, 2002: 101 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Map of Pollution Potential Index
5/28/2018 Map of Pollution Potential Index NSF Oct. 15 Final, 2002: 102 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Stone-Age Space-Age Syndrome
5/28/2018 Stone-Age Space-Age Syndrome Stone-age data Space-age data Stone-age analysis Space-age analysis cleveland clinic October 2004: 3 of 88. Geoinformatics Seminar March 2003: 3 of 133 George Mason University April 2004: 3 of 96 Huck Institute: 4 of 48. NIEHS: 3 of 85. NIST_Oct2004: 3 of 102. NJ DHHS CES SEER: 3 of 105. NSF Oct. 15 Final, 2002: 3 of 111. NYC: 4 of 23. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Data Sharing, Interoperable Middleware
5/28/2018 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 cleveland clinic October 2004: 5 of 8 cleveland clinic October 2004: 88 of 88. Conservation International 2003: 90 of 91. DGSeattle04_large: 2 of 41 For Reza Dec 2004: 3 of 123. For Reza Dec 2004: 123 of 123. Geoinformatics Seminar March 2003: 132 of 133. George Mason University April 2004: 5 of 96. George Mason University April 2004: 96 of 96. Huck Institute: 5 of 48. India_1_Dec2004: 2 of 82. India_1_Dec2004: 82 of 82. India_2_Dec2004: 33 of 34. NIST_Oct2004: 5 of 102. NIST_Oct2004: 102 of 102. NSF_ITR_Figures_CT: 1 of 5. NYC_NISS_NOV2004: 3 of 82. NYC_NISS_NOV2004: 82 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

1.2. 5/28/2018 cleveland clinic October 2004: 6 of 88.
DGSeattle04_large: 3 of 41. For Reza Dec 2004: 4 of 123. George Mason University April 2004: 6 of 96 India_1_Dec2004: 4 of 82. NIST_Oct2004: 6 of 102. NYC_NISS_NOV2004: 4 of 82. Parma-March2006: 9 of 374. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Spatial Scan Statistic: Properties
5/28/2018 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). cleveland clinic October 2004: 9 of 88. DGSeattle04_large: 6 of 41. Geoinformatics Seminar March 2003: 19 of 133. George Mason University April 2004: 9 of 96. Kulldorff-JSM2002: 11 of 49. NCI Oct. 16, 2002 Final: 17 of 101. NIEHS: 26 of 85. NIST_Oct2004: 9 of 102. NJ DHHS CES SEER: 19 of 105. Parma-March2006: 11 of 374. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

West Nile Virus Surveillance in New York City
5/28/2018 West Nile Virus Surveillance in New York City 2000 Data: Simulation/Testing of Prospective Surveillance System 2001 Data: Real Time Implementation of Daily Prospective Surveillance cleveland clinic October 2004: 11 of 88. For Reza Dec 2004: 49 of 123. Geoinformatics Seminar March 2003: 21 of 133. George Mason University April 2004: 11 of 96. Hawaii_March2005_final: 20 of 156. India_1_Dec2004: 8 of 82. Kulldorff-JSM2002: 31 of 49. NCI Oct. 16, 2002 Final: 19 of 101. NIEHS: 28 of 85. NIST_Oct2004: 11 of 102. NJ DHHS CES SEER: 21 of 105. NSF Oct. 15 Final, 2002: 31 of 111. NYC_NISS_NOV2004: 8 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Major epicenter on Staten Island
5/28/2018 Major epicenter on Staten Island Dead bird surveillance system: June 14 Positive bird report: July 16 (coll. July 5) Positive mosquito trap: July 24 (coll. July 7) Human case report: July 28 (onset July 20) cleveland clinic October 2004: 12 of 88 DGSeattle04_large: 8 of 41. For Reza Dec 2004: 50 of 123. Geoinformatics Seminar March 2003: 25 of 133. George Mason University April 2004: 12 of 96. Hawaii_March2005_final: 21 of 156. Huck Institute: 16 of 48. India_1_Dec2004: 9 of 82. Kulldorff-JSM2002: 38 of 49. NCI Oct. 16, 2002 Final: 23 of 101. NIEHS: 32 of 85. NIST_Oct2004: 12 of 102. NJ DHHS CES SEER: 25 of 105. NSF Oct. 15 Final, 2002: 35 of 111. NYC_NISS_NOV2004: 9 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

1.2. 5/28/2018 cleveland clinic October 2004: 13 of 88.
DGSeattle04_large: 9 of 41. For Reza Dec 2004: 51 of 123. George Mason University April 2004: 13 of 96. Hawaii_March2005_final: 22 of 156. India_1_Dec2004: 10 of 82. NIST_Oct2004: 13 of 102. NYC_NISS_NOV2004: 10 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Issues 5/28/2018 cleveland clinic October 2004: 15 of 88.
For Reza Dec 2004: 53 of 123. George Mason University April 2004: 15 of 96. Hawaii_March2005_final: 24 of 156. India_1_Dec2004: 12 of 82. NIST_Oct2004: 15 of 102. NYC_NISS_NOV2004: 12 of 82. Parma-March2006: 13 of 2006. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Spatial Temporal Surveillance
5/28/2018 Spatial Temporal Surveillance cleveland clinic October 2004: 17 of 88. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Syndromic Crisis-Index Surveillance
5/28/2018 Syndromic Crisis-Index Surveillance cleveland clinic October 2004: 18 of 88. For Reza Dec 2004: 56 of 123. George Mason University April 2004: 18 of 96. India_1_Dec2004: 15 of 82. NIST_Oct2004: 18 of 102. NYC_NISS_NOV2004: 15 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Prioritization
5/28/2018 Hotspot Prioritization cleveland clinic October 2004: 19 of 88. For Reza Dec 2004: 57 of 123. George Mason University April 2004: 19 of 96. Hawaii_March2005_final: 29 of 156. India_1_Dec2004: 16 of 82. NIST_Oct2004: 19 of 102. NYC_NISS_NOV2004: 16 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

421 Thomas Building, Penn State University, University Park, PA 16802
You Are Invited NSF DGP PROJECT 5/28/2018 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) (2) (3) Project address: Penn State Center for Statistical Ecology and Environmental Statistics 421 Thomas Building, Penn State University, University Park, PA 16802 Telephone: (814) ; cleveland clinic October 2004: 20 of 88. DGSeattle04_large:11 of 41. George Mason University April 2004: 20 of 96. Hawaii_March2005_final: 31 of 156. NIST_Oct2004: 20 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

National Applications
5/28/2018 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 Syndromic Surveillance Urban Crime Water Management cleveland clinic October 2004: 22 of 88 DGSeattle04_large: 12 of 41. For Reza Dec 2004: 59 of 123. George Mason University April 2004: 21 of 96. India_1_Dec2004: 18 of 82. NIST_Oct2004: 22 of 102. NYC_NISS_NOV2004: 18 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

MARMAP System Partnership
5/28/2018 MARMAP System Partnership A primary purpose of MARMAP System Partnership is to develop sound methodology and appropriate software for the quantitative analysis and interpretation of multi-categorical raster maps and cellular surfaces (inferential geospatial informatics) involving landscape pattern analysis, multiscale landcover landuse change detection, accuracy assessment, critical area detection and delineation, disease mapping and geographic surveillance, prioritization and ranking without having to integrate multiple indicators, and a few more. It will be nice to see you participate in one capacity or the other. The following websites may be of particular interest at this time, giving recent publications together with current exciting events. Please feel free also to share this material with your potentially interested friends and colleagues. MARMAP and MARMAP Prospectus 1, 2, 3, 4, 5, 6, 7. 2. Multiscale Advanced Raster Map Analysis System: Definition, Design, and Development. Invited Paper for Joint Statistical Meetings (New York City), Portuguese Statistical Congress, International Environmetrics Society, Brazilian Ecological Congress, and Italian Ecological Society. 3. Project MARMAP System Partnership Collaboration with EPA STAR Grant Atlantic Slope Consortium for Development, Testing, and Application of Ecological and Socioeconomic Indicators for Integrated Assessment of Atlantic Slope in the mid-Atlantic states. Website: 4. Project MARMAP System Partnership Collaboration with UNEP Division of Early Warning and Assessment on Human Environment Index based on Countrywide Land, Air, and Water Indicators. 5. Project MARMAP Show and Tell Seminar series: EPA ORD NCEA, EPA ORD NERL, EPA OEI, NASA HQ, NASA GSFC, NCHS, NYSDEH; UMD, GWU, UCB, MSU, UM, SUNY SPH. Powerpoint Presentations cleveland clinic October 2004: 23 of 88. George Mason University April 2004: 22 of 96. NIST_Oct2004: 23 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 6. Ecosystem Health and Its Measurement at Landscape Scale: Towards the Next Generation of Quantitative Assessments. Ecosystem Health, 7(4):307—316. 7. Multiscale Advanced Raster Map Analysis System for Measurement of Ecosystem Health at Landscape Scale: A Novel Synergistic Consortium Initiative. In Managing for Healthy Ecosystems, D. Rapport, W. Lasley, D. Rolston, O. Nielsen, C. Qualset, and A. Damania, eds. CRC Press/Lewis Press pp. 567—576. 8. Detection and Delineation of Critical Areas Using Echelon and Spatial Scan Statistics with Synoptic Cellular Data. Environmental and Ecological Statistics, 2004 (to appear). 9. Use of landscape and land use parameters for classification and characterization of watersheds in the Mid-Atlantic across five physiographic provinces. Healthy Ecosystems, Healthy People Conference, International Society for Ecosystem Health, Washington, DC. Environmental and Ecological Statistics, 2004 (to appear). 10. Finding upper level sets in cellular surface data using echelons and SaTScan. Environmental and Ecological Statistics, 2004 (to appear). cleveland clinic October 2004: 24 of 88. George Mason University April 2004: 23 of 96. NIST_Oct2004: 24 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

cleveland clinic October 2004: 26 of 88.
5/28/2018 Website Links 1. Prospectus 8: Synoptic Surveillance 2. Prospectus 11: Network-Based Surveillance Prospectus 10: Classification and Prioritization Prospectus 9:Crop Surveillance Prospectus Abstract Syndromic Surveillance 6. Poster for Geographic and Network Surveillance for Hotspots 7. Proof-of-Concept Paper Proof-of-Concept Paper-2 9. Proof-of-Concept Paper-3 10. Background Biographics Background Biographics 2 cleveland clinic October 2004: 26 of 88. George Mason University April 2004: 25 of 96. NIST_Oct2004: 26 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Detection Innovation Upper Level Set Scan Statistic
5/28/2018 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 cleveland clinic October 2004: 27 of 88. Conservation International June 2003: 40 of 88. DGSeattle04_large: 13 of 41. For Reza Dec 2004: 61 of 123. Geoinformatics Seminar March 2003: 11 of 133. George Mason University April 2004: 26 of 96. Hawaii_March2005_final: 33 of 156. Huck Institute: 12 of 48. India_1_Dec2004: 20 of 82. NCI Oct. 16, 2002 Final: 11 of 101. NIST_Oct2004: 27 of 102. NJ DHHS CES SEER: 11 of 105. NYC_NISS_NOV2004: 20 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Candidate Zones for Hotspots
5/28/2018 Candidate Zones for Hotspots 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 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 cleveland clinic October 2004: 28 of 88. DGSeattle04_large: 14 of 41. For Reza Dec 2004: 62 of 123. Geoinformatics Seminar March 2003: 33 of 133. George Mason University April 2004: 27 of 96. India_1_Dec2004: 21 of 82. NCI Oct. 16, 2002 Final: 31 of 101. NIEHS: 38 of 85. NIST_Oct2004: 28 of 102. NJ DHHS CES SEER: 33 of 105. NSF Oct. 15 Final, 2002: 58 of 111. NYC_NISS_NOV2004: 21 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Scan Statistic Zonation for Circles and Space-Time Cylinders
5/28/2018 Scan Statistic Zonation for Circles and Space-Time Cylinders cleveland clinic October 2004: 29 of 88. DGSeattle04_large: 15 of 41. For Reza Dec 2004: 63 of 123. Geographic Surveillance Decision Support System: 17 of 33. George Mason University April 2004: 28 of 96. Hawaii_March2005_final: 35 of 156. India_1_Dec2004: 22 of 82. NIST_Oct2004: 29 of 102. NYC_NISS_NOV2004: 22 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 ULS 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? 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. cleveland clinic October 2004: 30 of 88. Conservation International June 2003: 48 of 91. DGSeattle04_large: 16 of 41. For Reza Dec 2004: 64 of 123. Geoinformatics Seminar March 2003: 42 of 133. George Mason University April 2004: 29 of 96. Huck Institute: 24 of 48. India_1_Dec2004: 23 of 82. NCI Oct. 16, 2002 Final: 40 of 101. NIEHS: 45 of 85. NIST_Oct2004: 30 of 102. NJ DHHS CES SEER: 42 of 105. NSF Oct. 15 Final, 2002: 65 of 111. NYC_NISS_NOV2004: 23 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 ULS Scan Statistic Data-adaptive approach to reduced parameter space 0 Zones in 0 are connected components of upper level sets of the empirical intensity function Ga = Ya / Aa Upper level set (ULS) at level g consists of all cells a where Ga  g Upper level sets may be disconnected. Connected components are the candidate zones in 0 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 cleveland clinic October 2004: 31 of 88. Conservation International June 2003: 49 of 91. DGSeattle04_large: 17 of 41. For Reza Dec 2004: 65 of 123. Geoinformatics Seminar March 2003: 43 of 133. George Mason University April 2004: 30 of 96. India_1_Dec2004: 24 of 82. NCI Oct. 16, 2002 Final: 41 of 101. NIST_Oct2004: 31 of 102. NJ DHHS CES SEER: 43 of 105. NYC_NISS_NOV2004: 24 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Upper Level Set (ULS) of Intensity Surface
5/28/2018 Upper Level Set (ULS) of Intensity Surface Hotspot zones at level g (Connected Components of upper level set) cleveland clinic October 2004: 32 of 88. Conservation International June 2003: 50 of 91. DGSeattle04_large: 18 of 41. For Reza Dec 2004: 66 of 123. Geoinformatics Seminar March 2003: 44 of 133. George Mason University April 2004: 31 of 96. Hawaii_March2005_final: 44 of 156. Huck Institute: 25 of 48. India_1_Dec2004: 25 of 82. NCI Oct. 16, 2002 Final: 42 of 101. NIEHS: 47 of 85. NIST_Oct2004: 32 of 102. NJ DHHS CES SEER: 44 of 105. NYC_NISS_NOV2004: 25 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Changing Connectivity of ULS as Level Drops
5/28/2018 Changing Connectivity of ULS as Level Drops g cleveland clinic October 2004: 33 of 88. Conservation International June 2003: 51 of 91. DGSeattle04_large: 19 of 41. For Reza Dec 2004: 67 of 123. Geoinformatics Seminar March 2003: 45 of 133. George Mason University April 2004: 32 of 96. Hawaii_March2005_final: 45 of 156. Huck Institute: 26 of 48. India_1_Dec2004: 26 of 82. Interface St. Louis June 2005: 14 of 23. NCI Oct. 16, 2002 Final: 43 of 101. NDGC Atlanta_May_2005_Final: 8 of 65. NIEHS: 48 of 85. NIST_Oct2004: 33 of 102. NJ DHHS CES SEER: 45 of 105. NSF Oct. 15 Final, 2002: 63 of 111. NYC_NISS_NOV2004: 26 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 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 cleveland clinic October 2004: 34 of 88. Conservation International June 2003: 52 of 91. DGSeattle04_large: 20 of 41 For Reza Dec 2004: 68 of 123. Geoinformatics Seminar March 2003: 46 of 133. George Mason University April 2004: 33 of 96. Hawaii_March2005_final: 52 of 156. Huck Institute: 27 of 48. India_1_Dec2004: 27 of 82. Interface St. Louis June 2005: 15 of 23. Interface St. Louis 2 June 2005: 11 of 20. NCI Oct. 16, 2002 Final: 44 of 101. NIEHS: 49 of 85. NIST_Oct2004: 34 of 102. NJ DHHS CES SEER: 46 of 105. NSF Oct. 15 Final, 2002: 64 of 111. NYC_NISS_NOV2004: 27 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 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 cleveland clinic October 2004: 35 of 88. DGSeattle04_large: 21 of 41. For Reza Dec 2004: 69 of 123. Geographic Surveillance Decision Support System: 19 of 33. George Mason University April 2004: 34 of 96. Hawaii_March2005_final: 53 of 156. India_1_Dec2004: 28 of 82. NIST_Oct2004: 35 of 102. NYC_NISS_NOV2004: 28 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

New York City Water Distribution Network
5/28/2018 New York City Water Distribution Network cleveland clinic October 2004: 37 of 88. For Reza Dec 2004: 71 of 123. Geographic Surveillance Decision Support System: 25 of 33. George Mason University April 2004: 36 of 96. India_1_Dec2004: 30 of 82. NIST_Oct2004: 37 of 102. NSF Oct. 15 Final, 2002: 6 of 111. NYC_and_other: 2 of 10. NYC_NISS_NOV2004: 30 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

NYC Drinking Water Quality Within-City Sampling Stations
5/28/2018 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 cleveland clinic October 2004: 38 of 88. For Reza Dec 2004: 72 of 123. Geographic Surveillance Decision Support System: 26 of 33. George Mason University April 2004: 37 of 96. India_1_Dec2004: 31 of 82. NIST_Oct2004: 38 of 102. NSF Oct. 15 Final, 2002: 7 of 111. NYC_and_other: 3 of 10. NYC_NISS_NOV2004: 31 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Sampling Station Locations
5/28/2018 Sampling Station Locations City-Wide Manhattan cleveland clinic October 2004: 39 of 88. Geographic Surveillance Decision Support System: 27 of 33. George Mason University April 2004: 38 of 96. NIST_Oct2004: 39 of 102. NSF Oct. 15 Final, 2002: 8 of 111. NYC_and_other: 4 of 10. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Water-Related Synoptic Hotspot Analyses
5/28/2018 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 cleveland clinic October 2004: 40 of 88. Geographic Surveillance Decision Support System: 28 of 33. George Mason University April 2004: 39 of 96. NIST_Oct2004: 40 of 102. NSF Oct. 15 Final, 2002: 16 of 111. NYC_and_other: 5 of 10. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Mapping Priority Hotspots of Vegetative Disturbance for Carbon Budgets
5/28/2018 Mapping Priority Hotspots of Vegetative Disturbance for Carbon Budgets cleveland clinic October 2004: 45 of 88. DGSeattle04_large:26 of 41. Geographic Surveillance Decision Support System: 10 of 33. George Mason University April 2004: 44 of 96. NIST_Oct2004: 64 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Carbon Transformations from Disturbance
5/28/2018 Carbon Transformations from Disturbance Carbon transformations from disturbance must be included in developing integrated carbon budgets at national, continental, and global scales Data at high spatial resolution can characterize the types and intensities of disturbance to estimate carbon releases Rapid identification and prioritization of disturbance hotspots would facilitate: Timely follow-up imaging to estimate carbon transformations Identifying policies that may have contributed to the disturbance or that might mitigate its effects on the global carbon budget Developing a time-series of measurements to estimate ecosystem responses and recovery rates cleveland clinic October 2004: 46 of 88. George Mason University April 2004: 45 of 96. NSF Oct. 15 Final, 2002: 26 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Data Sources, Methods, and Indicators
5/28/2018 Data Sources, Methods, and Indicators Disturbance Hotspot Detection Data products from MODIS instruments on Terra and Aqua Corrected and 8-day composited surface reflectance values at red and near-infrared wavelengths (bands 1 & 2, MOD09_L3) will be used to identify vegetated pixels showing both a significant increase in red reflectance and a decrease in near-infrared reflectance ULS scan statistic applied to pixellated adjacency network of detected vegetation change using a sequential image pair Hotspot Prioritization based on: hotspot area statistical significance type of land cover vegetation index before the change magnitude of reflectance changes hotspot geographical context cleveland clinic October 2004: 47 of 88. George Mason University April 2004: 46 of 96. NSF Oct. 15 Final, 2002: 27 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Upper Midwest, Siberian Boreal Forest and Carbon Dynamics
5/28/2018 Upper Midwest, Siberian Boreal Forest and Carbon Dynamics Relatively Undisturbed Carbon Sinks Top down LCLUC Modeling with MARMAP Methods Socio-economic Drivers and Regional Carbon Dynamics State-Controlled Soviet Era and Post Soviet Transitioning Era cleveland clinic October 2004: 48 of 88. George Mason University April 2004: 47 of 96. NSF Oct. 15 Final, 2002: 28 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Early Detection of Biological Invasions
5/28/2018 Early Detection of Biological Invasions cleveland clinic October 2004: 52 of 88. Geographic Surveillance Decision Support System: 13 of 33. George Mason University April 2004: 51 of 96. NIST_Oct2004: 68 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Mid-Atlantic Invasive Species
5/28/2018 Mid-Atlantic Invasive Species 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 cleveland clinic October 2004: 53 of 88. George Mason University April 2004: 52 of 96. Kluza_ReVa MAIA conf: 6 of 29. NIST_Oct2004: 69 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

What is a better way ? Predictive Modeling
5/28/2018 What is a better way ? Predictive Modeling Accumulate occurrence data on native range Build ecological niche model Project niche model to areas of actual or potential invasion cleveland clinic October 2004: 54 of 88. George Mason University April 2004: 53 of 96. Kluza_ReVa MAIA conf: 7 of 29. NIST_Oct2004: 70 of 102. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Predictive Modeling: Genetic Algorithm for Rule-set Prediction (GARP)
5/28/2018 Predictive Modeling: Genetic Algorithm for Rule-set Prediction (GARP) Uses a genetic algorithm, an artificial intelligence application, for choosing rules Uses multiple rule types (BIOCLIM, logistic regression, etc.) Different decision rules may apply to different sectors of species’ distributions Extensive testing indicates excellent predictive ability cleveland clinic October 2004: 55 of 88. George Mason University April 2004: 54 of 96. Kluza_ReVa MAIA conf: 8 of 29. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Ecological Niche Modeling
5/28/2018 Ecological Niche Modeling Native range locality data Specimen records Temperature Precipitation Solar radiation Snow cover Frost-free days Ecological data cleveland clinic October 2004: 56 of 88. George Mason University April 2004: 55 of 96. Kluza_ReVa MAIA conf: 11 of 29. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Ecological Niche Modeling
5/28/2018 Ecological Niche Modeling Native range locality data Ecological data Distributional prediction GARP Invasive species projection cleveland clinic October 2004: 57 of 88. George Mason University April 2004: 56 of 96. Kluza_ReVa MAIA conf: 13 of 22. Kluza_ReVa MAIA conf: 20 of 29.. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Sudden Oak Death (Phytophthora ramorum)
5/28/2018 Sudden Oak Death (Phytophthora ramorum) cleveland clinic October 2004: 58 of 88. George Mason University April 2004: 57 of 96. Kluza_ReVa MAIA conf: 16 of 29. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Sudden Oak Death (Phytophthora ramorum)
5/28/2018 Sudden Oak Death (Phytophthora ramorum) cleveland clinic October 2004: 59 of 88. George Mason University April 2004: 58 of 96. Kluza_ReVa MAIA conf: 17 of 29. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Sudden Oak Death Native Range?
5/28/2018 Sudden Oak Death Native Range? cleveland clinic October 2004: 60 of 88. George Mason University April 2004: 59 of 96. Kluza_ReVa MAIA conf: 19 of 29. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Prioritization Innovation Partial Order Set Ranking
5/28/2018 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. cleveland clinic October 2004: 65 of 88. Conservation International June 2003: 41 of 91. For Reza Dec 2004: 101 of 123. Geoinformatics Seminar March 2003: 12 of 133. George Mason University April 2004: 64 of 96. Hawaii_March2005_final: 79 of 156. Huck Institute: 13 of 48. India_1_Dec2004: 60 of 82. India_2_Dec2004: 2 of 34. NCI Oct. 16, 2002 Final: 12 of 101. NIST_Oct2004: 71 of 102. NJ DHHS CES SEER: 12 of 105. NYC_NISS_NOV2004: 60 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

HUMAN ENVIRONMENT INTERFACE LAND, AIR, WATER INDICATORS
5/28/2018 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 cleveland clinic October 2004: 67 of 88. Conservation International June 2003: 61 of 91. DGSeattle04: 5 of 10. DGSeattle04_large: 36 of 41. For Reza Dec 2004: 102 of 123. Geoinformatics Seminar March 2003: 61 of 133. George Mason University April 2004: 66 of 96. Hawaii_March2005_final: 80 of 156. Huck Institute: 34 of 48. India_1_Dec2004: 61 of 82. India_2_Dec2004: 11 of 34. Intercol-1 Aug2005: 22 of 26. NCI Oct. 16, 2002 Final: 58 of 101. NIEHS: 64 of 85. NIST_Oct2004: 72 of 102. NJ DHHS CES SEER: 61 of 105. NSF Oct. 15 Final, 2002: 80 of 111. NYC_NISS_NOV2004: 61 of 82. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Mid-Atlantic Highlands Watershed Prioritization Model (WPM)
5/28/2018 Mid-Atlantic Highlands Watershed Prioritization Model (WPM) MAH Region: Most of Pennsylvania West Virginia Western Maryland Western Virginia cleveland clinic October 2004: 75 of 88. Geographic Surveillance Decision Support System: 30 of 33. George Mason University April 2004: 83 of 96. NIST_Oct2004: 89 of 102. NSF Oct. 15 Final, 2002: 76 of 111. NYC_and_other: 7 of 10. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Watershed Prioritization Model (WPM)
5/28/2018 Watershed Prioritization Model (WPM) Classify watersheds according to: Disturbance — observed stressors Vulnerability — physical characteristics and natural features Feasibility — economic, social, and political costs as well as technical limitations of protection and restoration cleveland clinic October 2004: 76 of 88. Geographic Surveillance Decision Support System: 31 of 33. George Mason University April 2004: 84 of 96. NIST_Oct2004: 90 of 102. NSF Oct. 15 Final, 2002: 77 of 111. NYC_and_other: 8 of 10. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Watershed Prioritization Model (WPM) Schematic View
5/28/2018 Watershed Prioritization Model (WPM) Schematic View cleveland clinic October 2004: 77 of 88. Geographic Surveillance Decision Support System: 32 of 33. George Mason University April 2004: 85 of 96. NIST_Oct2004: 91 of 102. NSF Oct. 15 Final, 2002: 78 of 111. NYC_and_other: 9 of 10. Feasibility is an optional third axis 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Watershed Prioritization Model (WPM) Primary Variables for MAH
5/28/2018 Watershed Prioritization Model (WPM) Primary Variables for MAH Disturbance (observed stressors) Excess sediment Riparian degradation Mine drainage Acid deposition Excess nutrients Exotic species Agriculture (esp. on slopes) Road crossings Forest fragmentation Biological impairment (IBI) Vulnerability (physical characteristics and natural features) Hydrogeomorphology (HGM) Climate Aspect Slope Stream sinuosity Soil type Bedrock Water Source cleveland clinic October 2004: 78 of 88. Geographic Surveillance Decision Support System: 33 of 33. George Mason University April 2004: 86 of 96. NIST_Oct2004: 92 of 102. NSF Oct. 15 Final, 2002: 79 of 111. NYC_and_other: 10 of 10. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Composite Index: 6 of 11. DC March 2003: 3 of 33. Figure. With two indicators, each object a divides indicator space into four quadrants. Objects in the second and fourth quadrants are ambiguous in making comparisons with a. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Composite Index: 7 of 11. DC March 2003: 4 of 33. India_2_Dec2004: 8 of 34. Figure. Contour of index H passing through object a. A linear index is shown on the left and a non-linear index on the right. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Composite Index: 8 of 11. DC March 2003: 5 of 33. India_2_Dec2004: 9 of 34. Figure. The top two diagrams depict valid contours while the bottom two diagrams depict invalid contours. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Composite Index: 9 of 11. DC March 2003: 6 of 33. India_2_Dec2004: 10 of 34. Figure. The tradeoff or substitutability between height and weight in assessing the size of a person. The tradeoff is constant with a linear index (left) but varies across indicator space with a nonlinear index (right). 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Composite Index: 10 of 11. DC March 2003: 17 of 33. Figure. 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. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Composite Index: 11 of 11. DC March 2003: 18 of 33. Figure. 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. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Ridge and Valley Hotspots
5/28/2018 Hotspots within Hotspots Conservation International June 2003: 34 of 91. Geoinformatics Seminar March 2003: 32 of 133. NCI Oct. 16, 2002 Final: 30 of 101. NIEHS: 37 of 85. NJ DHHS CES SEER: 32 of 105. NSF Oct. 15 Final, 2002: 22 of 111. Ridge and Valley Hotspots Ridge and Valley hotspots within hotspots. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 PROJECT GEOINFORMATIC SURVEILLANCE DECISION SUPPORT SYSTEM Geographic and Network Surveillance for Arbitrary Shaped Hotspots -Next Generation of Geographic Hotspot Detection, Prioritization, and Early Warning System- Conservation International June 2003: 35 of 91. Geoinformatics Seminar March 2003: 6 of 133. Huck Institute: 9 of 48. NCI Oct. 16, 2002 Final: 6 of 101. NJ DHHS CES SEER: 6 of 105. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Geographical Surveillance
5/28/2018 Geographical Surveillance Discrete response Hotspot detection and upper level set scan Hotspot delineation and hot-spot rating Multiple hotspot detection and delineation Hotspot prioritization and poset ranking Space-time detection and early warning Continuous response User friendly software and downloadable website Conservation International June 2003: 36 of 91. Geoinformatics Seminar March 2003: 7 of 133. Hawaii_March2005_final: 32 of 156. NCI Oct. 16, 2002 Final: 7 of 101. NJ DHHS CES SEER: 7 of 105. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Areas of Application Biodiversity, species-rich, and species-poor areas Water resources at watershed scales Power lines and their effects Networks of water distribution systems, subway systems, and road transport systems Urban and regional planning Disease epidemiology Medical imaging Reconnaissance Astronomy Archaeology Conservation International June 2003: 37 of 91. Geoinformatics Seminar March 2003: 8 of 133. NCI Oct. 16, 2002 Final: 8 of 101. NJ DHHS CES SEER: 8 of 105. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

1.2. 5/28/2018 Conservation International June 2003: 38 of 91.
Geoinformatics Seminar March 2003: 9 of 133. Huck Institute: 10 of 48. NCI Oct. 16, 2002 Final: 9 of 101. NJ DHHS CES SEER: 9 of 105. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Spatial Scan Statistic Limitations and Needs
5/28/2018 Spatial Scan Statistic Limitations and Needs Circles capture only compactly shaped clusters Want to identify clusters of arbitrary shape Circles handle only synoptic (tessellated ) data Want to handle data on a network Circles provide point estimate of hotspot Want to assess estimation uncertainty (hotspot confidence set) Conservation International June 2003: 39 of 91. Geoinformatics Seminar March 2003: 10 of 133. Hawaii_March2005_final: 60 of 156. Huck Institute: 11 of 48. NCI Oct. 16, 2002 Final: 10 of 101. NJ DHHS CES SEER: 10 of 105. NSF Oct. 15 Final, 2002: 57 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Crime Rate Hotspots Conservation International June 2003: 43 of 91 Geoinformatics Seminar March 2003: 39 of 133. Huck Institute: 21 of 48. NDGC Atlanta_May_2005_Final: 12 of 65. NIEHS: 41 of 85. NSF Oct. 15 Final, 2002: 54 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Cylindrical space-time scan statistic zonation
5/28/2018 Cylindrical space-time scan statistic zonation Outbreak expanding in time Small cylinders miss much of the outbreak Large cylinders include many unwanted cells Space Time Conservation International June 2003: 44 of 91. Geoinformatics Seminar March 2003: 40 of 133. Huck Institute: 22 of 48. NCI Oct. 16, 2002 Final: 38 of 101. NIEHS: 43 of 85. NJ DHHS CES SEER: 40 of 105. NSF Oct. 15 Final, 2002: 61 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Some Space-Time Hotspots and Their Cylindrical Approximations
5/28/2018 Some Space-Time Hotspots and Their Cylindrical Approximations Hotspot Cylindrical approximation Cylindrical approximation sees single hotspot as multiple hotspots Space Time Conservation International June 2003: 45 of 91. Geoinformatics Seminar March 2003: 41 of 133. Hawaii_March2005_final: 57 of 156. Huck Institute: 23 of 48. NCI Oct. 16, 2002 Final: 39 of 101. NIEHS: 44 of 85. NJ DHHS CES SEER: 41 of 105. NSF Oct. 15 Final, 2002: 62 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Poor Hotspot Delineation by Circular Zones
5/28/2018 Poor Hotspot Delineation by Circular Zones Hotspot Circular zone approximations Circular zones may represent single hotspot as multiple hotspots Conservation International June 2003: 46 of 91. Geoinformatics Seminar March 2003: 34 of 133. Hawaii_March2005_final: 34 of 156. Huck Institute: 18 of 48. NCI Oct. 16, 2002 Final: 32 of 101. NIEHS: 39 of 85. NJ DHHS CES SEER: 34 of 105. NSF Oct. 15 Final, 2002: 59 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Circular spatial scan statistic zonation
5/28/2018 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 Conservation International June 2003: 47 of 91. Geoinformatics Seminar March 2003: 35 of 133. Huck Institute: 19 of 48. NCI Oct. 16, 2002 Final: 33 of 101. NIEHS: 40 of 85. NJ DHHS CES SEER: 35 of 105. NSF Oct. 15 Final, 2002: 60 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Confidence Region on ULS Tree
5/28/2018 Confidence Region on ULS Tree Conservation International June 2003: 53 of 91. Geoinformatics Seminar March 2003: 53 of 133. Huck Institute: 28 of 48. NCI Oct. 16, 2002 Final: 51 of 101. NIEHS: 56 of 85. NJ DHHS CES SEER: 53 of 105. NSF Oct. 15 Final, 2002: 67 of 111. A hotspot confidence set with two connected components is shown on the ULS tree. The connected components correspond to different hotspot loci while the nodes within a connected component correspond to different delineations of that hotspot – all at the appropriate confidence level. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Estimation Uncertainty in Hotspot Delineation
5/28/2018 Estimation Uncertainty in Hotspot Delineation Outer envelope MLE Conservation International June 2003: 54 of 91. Geoinformatics Seminar March 2003: 54 of 133. Hawaii_March2005_final: 55 of 156. Huck Institute: 29 of 48. NCI Oct. 16, 2002 Final: 52 of 101. NDGC Atlanta_May_2005_Final: 14 of 65. NIEHS: 57 of 85. NJ DHHS CES SEER: 54 of 105. NSF Oct. 15 Final, 2002: 68 of 111. Inner envelope 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Delineation and Hotspot Rating
5/28/2018 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 Conservation International June 2003: 55 of 91. Geoinformatics Seminar March 2003: 55 of 133. Hawaii_March2005_final: 54 of 156. NCI Oct. 16, 2002 Final: 53 of 101. NIEHS: 58 of 85. NJ DHHS CES SEER: 55 of 105. NSF Oct. 15 Final, 2002: 69 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 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 Conservation International June 2003: 58 of 91. Geographic Surveillance Decision Support System: 21 of 33. Geoinformatics Seminar March 2003: 58 of 133. India_2_Dec2004: 7 of 34. NJ DHHS CES SEER: 58 of 105. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Prioritization and Poset Ranking
5/28/2018 Hotspot Prioritization and Poset Ranking 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. Multiple attributes, multiple indicators Ranking without having to integrate the multiple indicators into a composite index Conservation International June 2003: 59 of 91. DGSeattle04: 4 of 10. DGSeattle04_large: 35 of 41. Geoinformatics Seminar March 2003: 59 of 133. Huck Institute: 32 of 48. India_2_Dec2004: 6 of 34. NCI Oct. 16, 2002 Final: 56 of 101. NIEHS: 62 of 85. NJ DHHS CES SEER: 59 of 105. NSF Oct. 15 Final, 2002: 73 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Conservation International June 2003: 71 of 91. Geoinformatics Seminar March 2003: 72 of 133. Huck Institute: 41 of 48. NCI Oct. 16, 2002 Final: 67 of 101. NDGC Atlanta_May_2005_Final: 13 of 65. NIEHS: 73 of 85. NJ DHHS CES SEER: 71 of 105. NSF Oct. 15 Final, 2002: 10 of 111. Figure 1. Upper Juniata river network with superimposed network of Pennsylvania Department of Environmental Protection (DEP) sampling stations. In Phase 1, scan statistics methods will be used to identify hotspots of biological impairment. Phase 2 incorporates potential explanatory factors and identifies residual hotspots that are subjected to detailed network modeling in Phase 3. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Environmental Justice
5/28/2018 Environmental Justice Is environmental degradation worse in poor and minority communities? Identification of persistent poverty and its trajectories Identification of pollution proximity and vulnerability Co-location research hampered by the lack of methods and tools of investigation Conservation International June 2003: 72 of 91. Geoinformatics Seminar March 2003: 109 of 133. Huck Institute: 45 of 48. NCI Oct. 16, 2002 Final: 79 of 101. NIEHS: 8 of 85. NJ DHHS CES SEER: 83 of 105. NSF Oct. 15 Final, 2002: 36 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Needed Downloadable geoinformatic analysis system
5/28/2018 Needed Downloadable geoinformatic analysis system Identification of arbitrary shape hotspots, coldspots, midspots Multi-criteria prioritization and ranking Synoptic and network-based surveillance Multiple scales and aggregation levels Space, time, and space-time Zonal tree scan system Conservation International June 2003: 87 of 91. Geoinformatics Seminar March 2003: 125 of 133. NCI Oct. 16, 2002 Final: 94 of 101. NIEHS: 78 of 85. NJ DHHS CES SEER: 98 of 105. NSF Oct. 15 Final, 2002: 108 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Penn State Involvements
5/28/2018 Penn State Involvements Center for Statistical Ecology and Environmental Statistics/NSF,EPA Poverty Research Center/Ford Foundation GeoVista Center/NCI, NSF Geovisualization and Spatial Analysis of Cancer Data NCI Program For: Geographic-based Research in Cancer Control and Epidemiology Appalachia Cancer Network/NCI Environmental Consortium on Biosurveillance Cooperative Wetlands Center/EPA Center for Remote Sensing of Earth Resources/NASA, EPA Conservation International June 2003: 88 of 91. Geoinformatics Seminar March 2003: 130 of 133. NCI Oct. 16, 2002 Final: 100 of 101. NIEHS: 84 of 85. NJ DHHS CES SEER: 104 of 105. NSF Oct. 15 Final, 2002: 110 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 7 of 33. India_2_Dec2004: 15 of 34. 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}. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 8 of 33. India_2_Dec2004: 16 of 34. Figure 6. Bottom-up Hasse diagrams for the posets of Figure 5. Hasse diagrams for Posets A and B are unchanged. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 9 of 33. India_2_Dec2004: 17 of 34. Figure 7. Hasse diagram for the four countries of Table 1. Note that it has the same structure as Poset A in Figure 5. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 11 of 33. DGSeattle04: 6 of 10. DGSeattle04_large: 37 of 41. For Reza Dec 2004: 104 of 123. Geoinformatics Seminar March 2003: 63 of 133. George Mason University April 2004: 68 of 96. Hawaii_March2005_final: 82 of 156. Huck Institute: 35 of 48. India_1_Dec2004: 63 of 82. India_2_Dec2004: 13 of 34. NCI Oct. 16, 2002 Final: 59 of 101. NIEHS: 65 of 85. NIST_Oct2004: 74 of 102. NJ DHHS CES SEER: 62 of 105. NSF Oct. 15 Final, 2002: 81 of 111. NYC: 18 of 23. NYC_NISS_NOV2004: 63 of 82. Figure 9. Hasse diagram for the countries of Western Europe. The diagram is connected. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 13 of 33. India_2_Dec2004: 18 of 34. Figure 10. Hasse diagram for Latin America. There are four connected components. Three of these components are isolates; the remaining component contains 13 countries. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 14 of 33. India_2_Dec2004: 19 of 34. Figure 11. Hasse diagram for the 52 watersheds in the primary component. Labels are (arbitrary) row numbers in the data matrix. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 15 of 33. India_2_Dec2004: 19 of 34. Figure 12. Map of the Mid-Atlantic region showing the primary Hasse component (shaded). Geographically, there are three connected components of which two are small and located near the periphery of the region. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC: 16 of 33. India_2_Dec2004: 21 of 34. Figure 13: Hasse diagrams (right) of the two possible rankings for the poset on the left. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 19 of 33. India_2_Dec2004: 24 of 34. 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. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 20 of 33. DGSeattle04: 7 of 10. DGSeattle04_large: 38 of 41. India_2_Dec2004: 25 of 34. 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. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Figure 18. Histograms of the rank-frequency distributions for Poset B.
5/28/2018 DC March 2003: 21 of 33. India_2_Dec2004: 26 of 34. Figure 18. Histograms of the rank-frequency distributions for Poset B. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Figure 19. Cumulative rank-frequency distributions for Poset B.
5/28/2018 DC March 2003: 22 of 33. Figure 19. Cumulative rank-frequency distributions for Poset B. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 DC March 2003: 23 of 33. Geographic Surveillance Decision Support System: 24 of 33. Figure 20. (Left) Two iterations of the CRF operator are required to transform this poset into a linear ordering. (Right) A poset for which the CRF operator produces ties in the final linear ordering. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Data Sharing, Interoperable Middleware
5/28/2018 NSF Digital Government surveillance geoinformatics project, federal agency partnership and national applications for digital governance. Agency Databases Thematic Databases Other Databases Homeland Security Disaster Management Public Health Ecosystem 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 Federal Agency Partnership Survellance Geoinformatics of Hotspot Detection, Prioritization and CDC DOD Early Warning NSF Digital Government Project # EPA NASA PI: G. P. Patil NIH NOAA USFS USGS National Applications NDGC Atlanta May05 Overview_OneSlide: 1 of 1. DemoExample: 1 of 10. NDGC Atlanta_May_2005_Final: 1 of 65. NDGC Atlanta_May_2005_Final: 47 of 65. Parma-March2006: 2 of 374. • Biosurveillance • Environmental Management • Carbon Management • Environmental Policy • Coastal Management • Homeland Security • Community • Invasive Species Infrastructure • Poverty Policy • Crop Surveillance • Public Health Public Health and • Disaster Management • Environment • Disease Surveillance • Robotic Networks • Ecosystem Health • Sensor Networks • Environmental Justice • Social Networks Websites: • Syndromic Surveillance • Tsunami Inundation • Urban Crime 1.2. • Water Management 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Geographic and Network Surveillance for Arbitrarily Shaped Hotspots Center for Statistical Ecology and Environmental Statistics G.P. Patil, R. Acharya, W.L. Myers, P. Patankar, Y. Cai, and S.L. Rathbun The Penn State University, University Park, PA 16802 R. Modarres George Washington University, Washington, D.C. Overview Geospatial Surveillance Upper Level Set Scan Statistic System Spatial-Temporal Surveillance Typology of Space-Time Hotspots Hotspot Prioritization Ranking Without Having to Integrate Multiple Indicators Surveillance Geoinformatics for Hotspot Detection, Prioritization, Early Warning and Sustainable Management Upper Level Set Scan System Definition: A hotspot is that portion of the study region with an elevated risk of an adverse outcome Example: West Nile Virus First isolated in 1937, this mosquito born disease, indigenous to north Africa, the Middle East and west Asia was first introduced into the United States in 1999. Example: Lyme Disease Infections from the bacterium Borelia burgdorfei vectored by ticks from the genus Ixodes. Example: Human-environment indicator values for 16 European countries. Changing Connectivity of ULS as Level Drops g Comparison of ULS Scan with Cylindrical Scan ULS Scan Disease Count Quintiles Population Quintiles Year Disease Rates Cylindrical Scan 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 1997 1998 Haase Diagram Poset Prioritization System Objective: Prioritize or rank hotspots based on multiple indicator and stakeholder criteria without having to integrate indicators into an index, using Haase diagrams and partially ordered sets. Example: Prioritization of disease clusters with Multiple Indicators Disease Rate Quintiles Likelihood Quintiles 1999 2000 There are a total of 3,764,448 admissible linear extensions. The cumulative rank function for Sweden exceeds that of all remaining countries. The crf’s of all countries dominate that of Ireland. The remaining countries cannot be uniquely ordered based on their crf’s. Belgium, Netherlands and United Kingdom have identical crf’s. Comparison of ULS Scan with Circular Scan 2001 ULS Scan Circular Scan Admissible linear extensions are comprised of rankings compatible with the rankings of all indicators. Treating each linear extension as a voter, the cumulative rank function is obtained from the frequencies at which each object receives each rank. DemoExample: 3 of 10. Interface St. Louis June 2005: 8 of 23. NDGC Atlanta May05 Poster: 1 of 16. NDGC Atlanta_May_2005_Final: 31 of 65. NDGC Atlanta_May_2005_Final: 49 of 65. Parma-March2006: 3 of 374. 2002 2003 The crf’s also form a partially ordered set. There are only 182 admissible linear extensions for this poset, yielding the cumulative rank function: Federal Agency Partnerships CDC DOD EPA NASA NIH NOAA USFS USGS Confidence set for ULS Hotspot Hotspot Membership Rating National Applications and Case Studies Biosurveillance Carbon Management Costal Management Community Infrastructure Crop Surveillance Disaster Management Disease Surveillance Ecosystem Health Environmental Justice Sensor Networks Robotic Networks Environmental Management Environmental Policy Homeland Security Invasive Species Poverty Policy Public Health Public Health and Environment Syndromic Surveillance Social Networks Stream Networks 1.2. One more iteration yields the rankings in the data table. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Demonstration Example Data
5/28/2018 Demonstration Example Data Population Cases DemoExample: 4 of 10 NDGC Atlanta_May_2005_Final: 50 of 65. 1.2. Disease Rate Likelihood 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Demonstration Example Upper Level Set Tree: Cells
5/28/2018 Demonstration Example Upper Level Set Tree: Cells 12 9 2 1 3 4 5 6 7 8 13 14 15 16 17 18 19 11 10 DemoExample: 5 of 10. NDGC Atlanta_May_2005_Final: 51 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Demonstration Example Upper Level Set Tree: Zones
5/28/2018 Demonstration Example Upper Level Set Tree: Zones 12 9 2 1 3 4 5 6 7 8 13 14 15 16 17 18 19 11 10 [3] [3,18] [3,18,0] [8] [3,18,0,4] [17] [8,7] [14] [3,18,0,4;8,7;19] [17,16] [17,16;14;15] DemoExample: 6 of 10. NDGC Atlanta_May_2005_Final: 52 of 65. [3,18,0,4,8,7,19,5;17,16,14,15;11] 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

ULS Scan ULS Scan Disease Rate Hotspots using ULS 133.78 120.90 0.19
5/28/2018 ULS Scan 133.78 120.90 31.55 31.22 19.33 Disease Rate Hotspots using ULS DemoExample: 7 of 10. NDGC Atlanta_May_2005_Final: 53 of 65. ULS Scan Hotspot Hotspot 2 (red) (orange) Log Likelihood 133.78 120.90 p-value 0.19 0.395 Relative Risk 2.05 2.37 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Confidence Set for ULS Hotspot Hotspot membership rating
5/28/2018 Confidence Set for ULS Hotspot Hotspot membership rating DemoExample: 8 of 10. NDGC Atlanta_May_2005_Final: 54 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

ULS Scan (With modified rates)
5/28/2018 ULS Scan (With modified rates) 216.82 3.09 1.58 0.12 Disease Rate Hotspots using ULS DemoExample: 9 of 10. NDGC Atlanta_May_2005_Final: 55 of 65. ULS Scan Hotspot 1 (red) Log Likelihood 216.82 p-value 0.03 Relative Risk 15.46 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Confidence Set for ULS Hotspot Hotspot membership rating DemoExample: 10 of 10. NDGC Atlanta_May_2005_Final: 56 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Prioritization Innovation Partially Ordered Set Ranking
5/28/2018 Prioritization Innovation Partially Ordered Set Ranking We present a prioritization innovation: Ability to prioritize and rank hotspots Based on multiple indicator and stakeholder criteria without integrating indicators into an index Employs Hasse diagrams and partially ordered sets Leads to Early warning systems Selection of areas for focused investigation DGSeattle04: 2 of 10. DGSeattle_large: 33 of 41. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Recommendations Research, education, and outreach initiative
5/28/2018 Recommendations Research, education, and outreach initiative National Center Multi-focal multi-disciplinary thrust Concepts, methods, tools, validations Solid, sound, sophisticated and yet user-friendly Priority activities and regional projects Downloadable user-friendly software system Innovative Synergistics Geoinformatics Seminar March 2003: 129 of 133. NCI Oct. 16, 2002 Final: 99 of 101. NIEHS: 83 of 85. NJ DHHS CES SEER: 103 of 105. NSF Oct. 15 Final, 2002: 109 of 111. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

1.2. 5/28/2018 NDGC Atlanta May05 Poster: 7 of 16.
NDGC Atlanta_May_2005_Final: 67 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Introduction Lyme Disease Lyme disease bacterium: Borrelia burgdorferi
5/28/2018 Introduction Lyme Disease Lyme disease bacterium: Borrelia burgdorferi Vector: ticks of the genus Ixodes Natural reserviors: Ticks, small rodents, deer, and other vertebrates NDGC Atlanta_May_2005_Final: 57 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Ohio Lyme Disease Space Time Data (1997-2003)
5/28/2018 Ohio Lyme Disease Space Time Data ( ) Disease Rates 1997 1998 1999 2000 2001 2002 2003 Hotspots (ULS) NDGC Atlanta_May_2005_Final: 58 of 65. 1997 1998 1999 2000 2001 2002 2003 Hotspots (Circular Scan) 1997 1999 2000 1998 1.2. 2001 2002 2003 1.2 Digital Governance and Hotspot Geoinformatics.ppt

ULS vs. Circular Scan ULS Scan Circular Scan 90.436 17.645 0.001 4.035
5/28/2018 ULS vs. Circular Scan ULS Scan Circular Scan Hotspot 1 (Red) (red) Log Likelihood 90.436 17.645 p-value 0.001 Relative Risk 4.035 3.545 NDGC Atlanta_May_2005_Final: 59 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Confidence Set for ULS Hotspot Hotspot membership rating 1997 1998 1999 2000 NDGC Atlanta_May_2005_Final: 60 of 65. 1.2. 2001 2002 2003 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Forecast Ratings
5/28/2018 Hotspot Forecast Ratings NDGC Atlanta_May_2005_Final: 61 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Introduction West Nile Virus First isolated in 1937;
5/28/2018 Introduction West Nile Virus First isolated in 1937; Indigenous to Africa, West Asia, and the Middle East; Not documented in the western hemisphere until the 1999 outbreak in the New York City metropolitan area. In 2003, west nile virus was found in 46 states, and caused illness in over 9,800 people. NDGC Atlanta_May_2005_Final: 62 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Ohio West Nile Virus Yr.2003 Data
5/28/2018 Ohio West Nile Virus Yr.2003 Data Population NDGC Atlanta_May_2005_Final: 63 of 65. Cases Disease Rate 1.2. Likelihood 1.2 Digital Governance and Hotspot Geoinformatics.ppt

ULS vs. Circular Scan ULS Scan Circular Scan Disease Rate ULS Scan
5/28/2018 ULS vs. Circular Scan Disease Rate ULS Scan Circular Scan NDGC Atlanta_May_2005_Final: 64 of 65. ULS Scan Circular Scan Hotspot Hotspot 2 (red) (orange) Hotspot 1 (red) Log Likelihood 17.99 9.312 15.748 p-value 0.001 0.015 0.004 Relative Risk 3.189 3.391 1.625 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Confidence Set for ULS Hotspot Hotspot membership rating NDGC Atlanta_May_2005_Final: 65 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

NDGC Atlanta_May_2005_Final: 17 of 65. NDGC Atlanta_May_2005_Final: 43 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Ranking Without Having to Integrate Multiple Indicators 5/28/2018 NDGC Atlanta May05 Poster: 14 of 16. NDGC Atlanta_May_2005_Final: 44 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

The primary thrust of the proposed work:
5/28/2018 The primary thrust of the proposed work: a) To formulate and develop statistical methodology and computational technology for geoinformatic surveillance of hotspot detection and prioritization using upper level set detection and partially ordered set prioritization methods, software tools, and visualization capabilities. b) To formulate and initiate individual case study/application area project proposals that will have stronger and speedier performance, utilizing the detection and prioritization methods and software tools of (a) above. c) To work toward a National Center for Geoinformatic Surveillance, utilizing (a) and (b) above as a synergistic springboard. NDGC Atlanta_May_2005_Final: 3 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Detection Component
5/28/2018 Hotspot Detection Component The hotspot component of the geoinformatic surveillance project is concerned with the question of identifying, delineating, and assessing the significance of hotspots. Our approach to hotspot detection is based on the spatial scan statistic (Kulldorff and Nagarwalla 1995; Kulldorff 1997), which has been widely adopted in the health sciences for disease surveillance. This tends to produce zones that are relatively compact and roughly circular in shape. We propose a novel development of an upper level set (ULS) algorithm for selecting the candidate zones in an adaptive (date-driven) manner. The ULS approach allows for arbitrarily shaped hotspot candidate zones. NDGC Atlanta_May_2005_Final: 4 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Hotspot Detection Component
5/28/2018 Hotspot Detection Component Our efforts are driven by a wide variety of case studies of potential interest to Federal agencies involving critical society issues, such as public health, ecosystem health, biosecurity, biosurveillance, robotic networks, social networks, sensor networks, video mining, homeland security, and early warning. For additional information regarding our project, see demo and poster at this conference, and NDGC Atlanta_May_2005_Final: 5 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Prioritization Component
5/28/2018 Prioritization Component The prioritization component of the geoinformatic surveillance project is concerned with the question of ranking a finite collection of objects when a suite of indicator values is available for each member of the collection. The goal of the prioritization system is to canonically transform a partial order into a linear order of the objects. We propose a novel prioritization scheme based on multiple indicators that does not require reduction of the data to a single index. NDGC Atlanta_May_2005_Final: 6 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Method Upper Level Set (ULS) Scan Statistic for Arbitrarily Shaped Cluster Penn State/NSF/Digital Government NDGC Atlanta_May_2005_Final: 7 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Geospatial Surveillance
5/28/2018 Geospatial Surveillance Hotspot Identification and Uncertainty Assessment Penn State/EPA/Atlantic Slope Consortium NDGC Atlanta_May_2005_Final: 11 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Spatio-Temporal Surveillance
5/28/2018 Spatio-Temporal Surveillance Typology of Space-Time Hotspots Penn State/Ford Foundation/Poverty Research NDGC Atlanta_May_2005_Final: 15 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Crisis-Index Surveillance
5/28/2018 Crisis-Index Surveillance Behavior-Event Streams Automata Characterization Penn State/NSF/Digital Government NDGC Atlanta_May_2005_Final: 19 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Hotspot Prioritization Ranking Without Having to Integrate the Multiple Indicators Penn State/EPA/Impairment Consortium NDGC Atlanta_May_2005_Final: 23 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Prioritization of Disease Clusters with Multiple Indicators
5/28/2018 Prioritization of Disease Clusters with Multiple Indicators Data Matrix NDGC Atlanta_May_2005_Final: 24 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 ABSTRACT The five year NSF DGP project has been instrumental to conceptualize surveillance geoinformatics partnership among several interested cross-disciplinary scientists in academia, agencies, and private sector. A declared need is around for statistical geoinformatics and software infrastructure for spatial and spatiotemporal hotspot detection. Our efforts are driven by a wide variety of case studies of potential interest to federal agencies involving critical society issues, such as public health, ecosystem health, biosurveillance, biosecurity, sensor networks, robotic networks, social networks, video mining, homeland security, early warning, and disaster management. NDGC Atlanta_May_2005_Final: 27 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 PARTNERSHIPS CoPrincipal Investigator(s): Raj Acharya; Amy K Glasmeier; Wayne L Myers; Shashi Phoha. Senior personnel: Robert Brooks; Denice Wardrop; Lance Waller; Elizabeth Middleton; James Shortle; Reza Modarres; Stephen Rathbun; Charles Taillie. Other collaborators: Howard Burkom; Lawrence Cox; John Kelmelis; Martin Kulldorff; Bo Ranneby; Phil Ross; and others. NDGC Atlanta_May_2005_Final: 28 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

Training and Development Component
5/28/2018 Training and Development Component Crossdisciplinary classroom for surveillance geoinformatics and multiscale advanced raster map analysis (Ecometrics and Environmetrics). Instructor: G. P. Patil, PI. An advanced crossdisciplinary graduate course across the academic year for graduate students from different programs on the campus, but with common interest in surveillance geoinformatics and multiscale advanced raster map analysis with emphasis on ecometrics and environmetrics. NDGC Atlanta_May_2005_Final: 29 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

5/28/2018 Outreach Component 1. Surveillance Geoinformatics Forum: A partnership in the making. 2. Multiscale Advanced Raster Map Analysis System Partnership: A partnership in the making. 3. Synergistic Outreach NDGC Atlanta_May_2005_Final: 30 of 65. 1.2. 1.2 Digital Governance and Hotspot Geoinformatics.ppt

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