An Equal Opportunity University Geographic Surveillance of Hotspot Detection, Prioritization, and Early Warning By G. P. Patil1, R. Modarres2, P. Patankar3, S. L. Rathbun1, and C. Taillie1 1Center for Statistical Ecology and Environmental Statistics Department of Statistics, Penn State University 2Department of Statistics, George Washington University 3 Department of Computer Science and Engineering, Penn State University This material is based upon work supported by the National Science Foundation under Grant No. 0307010. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This project is funded, in part, under a grant with the Pennsylvania Department of Health using Tobacco Settlement Funds. The Department specifically disclaims responsibility for any analyses, interpretations or conclusions. [ Invited paper for The National Conference on Digital Government Research, Atlanta, GA] Technical Report Number 2005-0201 TECHNICAL REPORTS AND REPRINTS SERIES February 2005 Center for Statistical Ecology and Environmental Statistics Department of Statistics The Pennsylvania State University University Park, PA 16802 G. P. Patil Distinguished Professor and Director Tel: (814)865-9442 Fax: (814)865-1278 Email: [email protected] http: //www.stat.psu.edu/~gpp http://www.stat.psu.edu/hotspots DGOnline NewsGeoinformatic Surveillance of Hotspot Detection, Prioritization, and Early Warning G.P. PATIL, P. PATANKAR, S.L. RATHBUN, AND C. TAILLIE Penn State University and REZA MODARRES George Washington University ________________________________________________________________________ A Geoinformatic Hotspot Surveillance System (GHS) will be demonstrated. This system is comprised of an upper level set scan statistic system for hotspot delineation, and a poset prioritization and ranking system for hotspot prioritization. Additional Key Words and Phrases: Upper level set scan statistic, Posets ________________________________________________________________________ Government agencies often require concise summaries of georeferenced data to support their decisions regarding the geographic allocation of resources. Geoinformatic surveillance for spatial and spatiotemporal hotspot detection and prioritization is a critical need for the 21st century. A hotspot can mean an unusual phenomenon, anomaly, aberration, outbreak, or critical area. Hotspot delineation and prioritization may be required for etiology, management, or early warning. With support from the NSF/DG program, an interdisciplinary team has developed a prototype Geoinformatic Hotspot Surveillance (GHS) system for hotspot delineation and prioritization. Our efforts are driven by a wide variety of case studies of potential interest to Federal agencies and 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. The prototype system is comprised of modules for (1) hotspot detection and delineation, and (2) hotspot prioritization. SpatiallydistributedresponsevariablesHotspotanalysisPrioritizationDecisionsupportsystemsGeoinformatic spatio-temporal data from a variety of dataproducts and data sources with agencies, academia, and industryMasks, filtersIndicators, weightsMasks, filtersGeoinformatic Surveillance SystemSpatiallydistributedresponsevariablesHotspotanalysisPrioritizationDecisionsupportsystemsGeoinformatic spatio-temporal data from a variety of dataproducts and data sources with agencies, academia, and industryMasks, filtersIndicators, weightsMasks, filtersGeoinformatic Surveillance System Our approach employs a novel upper level set scan statistic to delineate arbitrarily shaped hotspots in both spatial and spatiotemporal dimensions [Patil and Taillie 2004a].It features maximum likelihood estimation of candidate hotspots, an upper level set tree, and confidence sets for assessing uncertainty in hotspot delineation. Intensity Ggg′4Z5Z6Z1Z2Z3ZSchematicintens ity “surface”ABCIntensity Ggg′4Z5Z6Z1Z2Z3ZSchematicintens ity “surface”ABC We propose a novel prioritization scheme based on multiple indicators that does not require reduction of the data to a single index. This poset prioritization and ranking system features Haase diagrams describing the partial ordering of the data, linear extension decision trees enumerating admissible rankings among hotspots, and cumulative rank functions for hotspot prioritization [Patil and Taillie 2004b]. Poset B(Hasse Diagram)abdcefLinear extension decision treeacebbdffdedfeefcfdedfeefdfeefcfeefccfdedfeefdfeefcbabadPoset B(Hasse Diagram)abdcefPoset B(Hasse Diagram)abdcefLinear extension decision treeacebbdffdedfeefcfdedfeefdfeefcfeefccfdedfeefdfeefcbabadLinear extension decision treeacebbdffdedfeefcfdedfeefdfeefcfeefccfdedfeefdfeefcbabad For additional information regarding our project, see http://www.stat.psu.edu/hotspots/ and http://www.stat.psu.edu/~gpp/ . REFERENCES Patil, G.P., and Taillie, C. 2004a. Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environmental and Ecological Statistics 11, 183-197. Patil, G.P., and Taillie, C. 2004b. Multiple indicators, partially ordered sets, and linear extensions: Multi-criterion ranking and prioritization. Environmental and Ecological Statistics 11, 199-228. This material is based upon work supported by the National Science Foundation under Grant No. 0307010 and by the Pennsylvania Department of Health using Tobacco Settlement Funds. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the view of the National Science Foundation or of the Pennsylvania Department of Health. Authors' addresses: G.P. Patil, S.L. Rathbun, and C. Taillie, Department of Statistics, Penn State University, University Park, PA 16802; P. Patankar, Department of Computer Science and Engineering, Penn State University, University Park, PA 16802; R. Modarres, Department of Statistics, George Washington University, Washington, D.C. 20052. Permission to make digital/hard copy of part of this work for personal or classroom use is granted without fee provided that the copies