1. 4.1.1 Multiscale Advanced Raster Map System

2. 4.1.2 The Value of Mapping Maps provide an efficient and unique method of demonstrating distributions of phenomena in space. Though [maps are] constructed primarily to show facts, to show spatial distributions with an accuracy which cannot be attained in pages of description or statistics, their prime importance is as research tools. They record observations in succinct form; they aid analysis; they stimulate ideas and aid in the formation of working hypotheses; they make it possible to communicate findings; they assist in research and policy research.

3. 4.1.3 Disease Mapping Disease Mapping is about the use and interpretation of maps showing the incidence or prevalence of disease. Disease data occur either as individual cases or as groups (or counts) of cases within census tracts. Any disease map must be considered with the appropriate background population which gives rise to the incidence. Maps answer the question: where? They can reveal spatial patterns not easily recognized from lists of statistical data. Maps showing infectious diseases can help elucidate the cause of disease. Maps showing non-infectious diseases may be used to generate hypotheses of disease causation.

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7. 7 National Mortality Maps and Health Statistics Health Service Areas, Counties, Zip Codes, … Geographical Patterns for Health Resource Allocation Study Areas for Putative Sources of Health Hazard –Balance between dilution effect and edge effect Case Event Analysis and Ecological Analysis –Thresholds, contours, corresponding data Regional Comparisons and Rankings with Multiple Indicators/Criteria Choices of Reference/Control Areas

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11. 4.1.11 Baltimore Asthma Project Interdisciplinary Analysis of Childhood Asthma in Baltimore, MD 1.Collect and integrate in-situ measurements, remotely sensed measurements and clinical records that have possible relationships to the occurrence of asthma in the Baltimore, Maryland region. 2.Identify key trigger variables from the data to predict asthma occurrence on a spatial and temporal basis. 3.Organize a multidisciplinary team to assist in model design, analysis and interpretation of model results. 4.Develop tools for integrating, accessing and manipulating relevant health and remote sensing data and make these tools available to the scientific and health communities. Partners: Baltimore City Health Department Baltimore City School System Baltimore City Planning Council, Mayor’s Office State of Maryland Department of the Environment State of Maryland Department of Health and Human Services University of Maryland Asthma Assessment from GIS techniques Inner Harbor Time Aerosol Size The impact of asthma is escalating within the U.S. and children are particularly impacted with hospitalization increasing 74% since 1979. This study is investigating climate and environmental links to asthma in Baltimore, Maryland, a city in the top quintile for children’s asthma in the U.S.

12. 4.1.12 Urban Heat Islands Use of aircraft and spacecraft remote sensing data on a local scale to help quantify and map urban sprawl, land use change, urban heat island, air quality, and their impact on human health (e.g. pediatric asthma)

13. 4.1.13 Malaria in Chiapas, Mexico

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16. 4.1.16 Mekong Malaria and Filariasis Projects To develop a predictive model for assessing risk areas of malaria transmission in the Greater Mekong Sub- region, and To make risk maps for filariasis  map breeding sites for major vector species  explore the linkage between vector population density and disease transmission intensity with environmental variables Anticipated Benefits –Reduce malaria and filariasis transmission rates –Minimize environmental damage by strategically using larvicides and insecticides –Improve the health status and economic activity of populations affected by malaria and filariasis in the Greater Mekong Sub-region Source: Southeast Asian Journal of Tropical Medicine and Public Health, volume 30 supplement 4, 1999.

17. 4.1.17 African Dust Quantities of African dust transported by winds across the Atlantic have been increasing due to prolonged and agricultural practices in North Africa Recent studies show dust carries microbes and pollutants that have been detected in the US and Caribbean Islands Objectives of new studies are to determine harmful effects, e.g., childhood asthma in Puerto Rico

18. 4.1.18 Vector-Borne Disease Detection Using NASA Satellite Data NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event Research program on the relationships between environmental parameters (e.g vegetation), climate ( e.g. rainfall) and outbreaks of diseases such as: Rift Valley Fever (RVF) St. Louis Encephalitis Fever (EHF) Dengue Fever Ebola Fever Hanta Virus and others BENEFITS Map and monitor Eco-climatic patterns associated with disease outbreaks from satellite platforms Better understanding the dynamics of climate-disease interactions Advance warning of disease outbreaks would enable preventive measures (vaccination, vector control, etc.) to be undertaken Provide disease surveillance tools to public health authorities  Using near real-time climate data and satellite imagery, scientists have discovered environmental triggers for Rift Valley Fever and other diseases  Prediction of Rift Valley Fever outbreaks may be made up to 5 months in advance in Africa  Using near real-time climate data and satellite imagery, scientists have discovered environmental triggers for Rift Valley Fever and other diseases  Prediction of Rift Valley Fever outbreaks may be made up to 5 months in advance in Africa

19. 4.1.19 A New NASA Initiative... To apply Space-based capabilities to examine environmental conditions that affect human health To enable easy use of and timely access to Earth science data and models To help our health community partners to develop practical early warning systems

20. 4.1.20 Examples of Relevant Research Early Warning Systems –Vector-borne disease risk maps using remote sensing technologies – Rift Valley Fever –Risk mapping of Malaria and Filariasis in S.E. Asia Baltimore Childhood Asthma Study African Dust Cities: Air Pollution and Heat Fire Management & Smoke Dispersal Source: Southeast Asian Journal of Tropical Medicine and Public Health, volume 30 supplement 4, 1999.

21. 4.1.21 Infectious Diseases Use of remote sensing data and other available geospatial data on a continental scale to help evaluate landscape characteristics that may be precursors for vector-borne diseases leading to early warning systems involving landscape health, ecosystem health, and human health Water-Borne Diseases Air-Borne Diseases Emerging Infectious Diseases

22. 4.1.22 Statistical Ecology, Environmental Statistics, Health Statistics—1 Sampling, Monitoring, and Observational Economy Initiatives — Twentieth Century— Capture-Mark-Recapture Composite, Ranked Set Adaptive with Clusters and Networks Transect, Selection Bias, Meta-Analysis Partnerships:

23. 4.1.23 Geospatial Patterns and Pattern Metrics –Landscape patterns, disease patterns, mortality patterns Surface Topology and Spatial Structure –Hotspots, outbreaks, critical areas –Intrinsic hierarchical decomposition, study areas, reference areas –Change detection, change analysis, spatial structure of change Statistical Ecology, Environmental Statistics, Health Statistics—2 Multiscale Advanced Raster Map Analysis System Initiative—1

24. 4.1.24 Statistical Ecology, Environmental Statistics, Health Statistics—3 Multiscale Advanced Raster Map Analysis System Initiative—2 Partially Ordered Sets and Hasse Diagrams –Multiple indicators, comparisons, fuzzy rankings –Intrinsic hierarchical groups, reference areas –Performance measures, composite indices System Design and Development –BAT, BPT, and synergistic collaboration –Bilateral and multilateral partnerships

25. 4.1.25 Mortality rate due to a specific cause of death Elevated rates areas, patterns Ordinal thematic maps Transition pattern, transitionogram Transition matrices; spatial association with varying distance Comparatives with different causes of death National Mortality Maps and Statistics Geographic Patterns—1

26. 4.1.26 Surface topology and spatial structure High mortality area delineation –Hotspots, clusters, outbreaks, corridors Surface smoothing Masking of true geographic patterns? Echelon analysis, original surface, smoothed surface National Mortality Maps and Statistics Geographic Patterns—2

27. 4.1.27 Study areas for response and explanatory variables relationships Response proximity –Hotspots, thresholds, contours, counter strips Spatial proximity –Buffers, putative hazards Dilution effect and edge effect National Mortality Maps and Statistics Relationships—1

28. 4.1.28 Intrinsic study areas Intrinsic hierarchical decomposition Consistent vertical and horizontal balance Echelons and echelon trees Urban heat islands and pediatric asthma Infectious and vector-borne diseases UV radiation National Mortality Maps and Statistics Relationships—2

29. 4.1.29 Multiscale Advanced Raster Map System MARMAP SYSTEM Design and Development PARTNERSHIP NSF Digital Government Research Program Proposal for Invited Re-Submission

30. 4.1.30 Multiscale Raster Map Analysis for Sustainable Environment and Development A Research and Outreach Prospectus of Advanced Mathematical, Statistical and Computational Approaches Using Remote Sensing Data. Development and Implementation of a Prototype and user-friendly MARMAP system. Remote Sensing Application, Technology and Education for Multiscale Advanced Raster Map Analysis Program.

31. 4.1.31 Consider a 21st century digital government scenario of the following nature: What message does a remote sensing-derived land cover land use map have about the large landscape it represents? And at what scale and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change?

32. 4.1.32 Consider a 21st century digital government scenario of the following nature: How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? Of the change map over time for change detection?

33. 4.1.33 Consider a 21st century digital government scenario of the following nature What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, or early warning, or others. And with what confidence, even with a single map/change-map?

34. 4.1.34 The needed partnership research is expected to find answers to these questions and a few more that involve multicategorial raster maps based on remote sensing and other geospatial data. It is also expected to design a prototype and user-friendly advanced raster map analysis system for digital governance.

35. 4.1.35 MARMAP SYSTEM Partnership communication of June 11, 2001. NSF Partnership Proposal. Review and Response-1 Review and Response-2 Review and Response-3

36. 4.1.36 MARMAP SYSTEM Partnership Research and Outreach Prospectus: http://www.stat.psu.edu/~gpp/PDFfiles/prospectus 8-00.pdf http://www.stat.psu.edu/~gpp/PDFfiles/prospectus 8-00.pdf Our web page for raster map analysis: http://www.stat.psu.edu/~gpp/newpage11.htm http://www.stat.psu.edu/~gpp/newpage11.htm Our web page for raster map monographs: http://www.stat.psu.edu/~gpp/raster.htm http://www.stat.psu.edu/~gpp/raster.htm Our web page for UNEP HEI http://www.stat.psu.edu/~gpp/unephei.htm

37. 4.1.37 MARMAP System Partnership EPA STAR Grant EaGLes Program Atlantic Slope Consortium Ford Foundation project on poverty research in Appalachian Region and Mississippi Delta DARPA MURI project on surveillance networks Map of Italian Nature of Italian Govt Additional synergistic partners

38. 4.1.38 This report is very disappointing. What kind of software are you using?

39. 4.1.39 Space Age and Stone Age Syndrome Data:Space Age/Stone Age Analysis:Space Age/Stone Age DataSpace AgeStone Age Analysis Space Age++ Stone Age+

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42. 4.1.42 Partnership Research Issues

43. 4.1.43 Geospatial Cell-based Data Kinds of Data Cell as a Unit (Regular grid layout) –Categorical –Ordinal –Numerical –Multivariate Numerical Cell as an Object (Irregular cell sizes and shapes) –Partially Ordered –Ordinal –Numerical –Multivariate Numerical

44. 4.1.44 Approaches to Research Issues

45. 4.1.45 Data Compression Issue Spectral data can be compressed via hypercluster methods, replacing spectral measurements by within-cluster means and other summary statistics, e.g., ISODATA, PHASES, etc. Compression methods include image segmentation (PHASES, hidden MC), dimensionality reduction (e.g., PCA), wavelets. This entails a loss of information. Research issue is to assess the impact of this information loss.

46. 4.1.46 PROGRESSIVELY SEGMENTED IMAGES PROTOCOL PSI PROTOCOL To be prepared

47. 4.1.47 Classification for Thematic Map Making Fuzzy classification approaches Storage of pixel-by-pixel membership functions is a major obstacle for fuzzy classification of satellite imagery. Segment classification approaches (classify segments instead of pixels).With fuzzy classification of segments instead of spectral data, membership functions can be stored in separate lookup tables. Anything else ?

48. 4.1.48 MARMAP SYSTEM Data Procurement, Data Management, Data Processing NASA, USGS, EPA, PAGAP, DGPCSG, USFS UMIACS-GLCF, NPACI:SDSC Data Resources; Large Datasets; Data Cutter Middleware Infrastructure; Active Data Repository Storage; Retrieval, Processing; Data Intensive Computing; Program Tools and Environments.

49. 4.1.49 Landscape Pattern Extraction Spectral data Empirical extraction Thematic data Empirical extraction Spectral data Model-based extraction Thematic data Model-based extraction

50. 4.1.50 Pattern Extraction by Image Segmentation Spectral Data Empirical (PHASES, neural networks) PHASES: Compression by hyperclustering, replacing pixel information by within- cluster and between-cluster statistics. Model-based (hidden MC, continuous HMTM).

51. 4.1.51 Empirical Pattern Extraction Thematic Data Pattern = Spatial variability in thematic maps Proposed research limited to raster maps Empirical Pattern Extractors: –Landscape metrics (e.g., FRAGSTATS) –Multiscale fragmentation profiles (entropy-based) –Patch structure metrics Scaling domain detection

52. 4.1.52 WHAT IS A WATERSHED? A watershed is an area of land, which drains water (and everything the water carries) to a common outlet. The critical thing to remember about watersheds is that the streams and rivers, the hills, and the bottom lands are all part of an inter-connected system. Every activity on the land, in the water or even in the air has the potential to affect a watershed.

53. 4.1.53 Figure 4. River basins, watersheds, and stream order. One watershed within the Patapsco River Basin is that of Herring Run. The numbers beside the streams indicate each stream’s order. The smallest permanently flowing stream is termed first order, and the union of two first order streams creates a second order stream. A third order stream is formed where two second order streams join.

54. 4.1.54 Selected landscape metrics for the medium-delineated watersheds MetricNameDefinition PSCV Patch Size Coefficient of Variation Variability in patch size, or the size of homogeneous land cover areas, relative to the mean patch size DFLD Double Log Fractal Dimension 2 divided by the slope of the regression line calculated by regressing the log of the patch area against the log of patch perimeter IJI Interspersion and Juxtaposition Index Measures the unevenness in patch types across a watershed CONTAG Contagion Index Measures the unevenness in patch types across all pixels in a watershed

55. 4.1.55 MetricNameDefinition LPI Largest Patch Index Percentage of watershed comprised by the largest continuous patch of homogeneous land cover type PSCV Patch Size Coefficient of Variation Variability in patch size, or the size of homogeneous land cover areas, relative to the mean patch size DFLD Double Log Fractal Dimension 2 divided by the slope of the regression line calculated by regressing the log of the patch area against the log of patch perimeter CONTAG Contagion Index Measures the unevenness in patch types across all pixels in a watershed Selected landscape metric for the large-delineated watersheds

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64. 4.1.64 Model-based Pattern Extraction Pattern = Spatial variability in thematic maps Proposed research limited to raster maps Possible Parametric Models: –Geostatistics (Multi-indicator) –Markov Random Fields –Hierarchical Markov Transition Matrix models (HMTM)

65. 4.1.65 Dual Use of the Parametric Models Pattern Extraction –Parameters of the fitted model summarize pattern across the map –Efficient model fitting procedures needed Uncertainty Assessment and Confidence Statement –Repeated map simulation with fitted model, followed by calculation of landscape metric provides an uncertainty cloud for that metric.

66. 4.1.66 Model Assessment and Comparison Pattern extraction effectiveness and discriminatory capability of fitted model parameters on actual landscapes Failure of uncertainty cloud to enclose the empirical value of a landscape metric is indicative of model inadequacy with respect to that aspect of pattern as summarized by that particular metric Computer execution time comparison of models for both fitting and simulation

67. 4.1.67 MARMAP SYSTEM Software Design and Development Data Resource Partners Active Data Repository Data Intensive Computing Programming Tools and Environments Visualization Tools

68. 4.1.68 MARMAP SYSTEM Software Design and Development Algorithm development Computer programming/Coding User interface design and implementation User output/Visualization Documentation/On-line help Other considerations: –Supported platforms (Windows, UNIX ?, LINUX ?) –Programming languages ? (C/C++, Java, Visual Basic, Delphi, etc.) –Software distribution (CD, Website)

69. 4.1.69 CENTER FOR GEOSPATIAL INFORMATICS AND STATISTICS -proposed federal partnership- NSF: DGP, FRG, ITR, SDSC-NPACI NASA, USGS, EPA, USFS, DOT, NCHS, CENSUS, NIMA, DOD

70. 4.1.70 Case Study – NASA - PSU Issues Involved: Landcover classification –with available spectral image(s) –with a previous map and current spectral image –with fine or coarse segmentation Multi-period change detection Data Integration

71. 4.1.71 Case Study – EPA – PSU Issues Involved: Indicators of Watershed Ecosystem Health Multiple Landscape Fragmentation Analysis Echelon Analysis of Spatial Structure and Behavior Multiscale Bivariate Raster Map Analysis Regional Human Environment Index: Formulation, Visualization, Evaluation, and Validation

72. 4.1.72 Case Study –UNEP - PSU Nationwide Human Environment Index worldwide Construction and Evaluation of HEI Multiple Indicators and Comparisons without Integration of Indicators Hasse Diagrams, fuzzy rankings, and visualizations Handbook Interactive Queries

73. 4.1.73 Partnership Synergistics Concept Prototype Software Implementation Pilot Tests Feedback Case Studies

74. 4.1.74 Partnership Synergistics PI/CO-PI MG- PG CG CSG

75. 4.1.75 Partnership Synergistics Methodology Group Concepts, Issues, Approaches, Methods Prototype Group Techniques, Algorithms, Routines Methodology Group Refinement, Adaptation, Development MARMAP SYSTEM VALIDATION MG, PG, CG, CSG Computational Group Data Management, Software Design and Development Case Studies Data Resources Issues Answers

76. 4.1.76 Logo for Statistics, Ecology, Environment, and Society