1. INTEGRATION OF THE NATIONAL MAP 21 July 2004 Michael P. Finn E. Lynn Usery Michael Starbuck Bryan Weaver Gregory M. Jaromack mfinn@usgs.gov U.S. Department of the Interior U.S. Geological Survey

2. Outline Goals and Objectives Goals and Objectives Approach and Data Approach and Data Test Sites Test Sites Methods (& design illustration) Methods (& design illustration) Conclusions Conclusions

3. Goals and Objectives The National Map will consist of integrated datasets The National Map will consist of integrated datasets Current USGS digital products are single layer and not vertically-integrated Current USGS digital products are single layer and not vertically-integrated The objective is to develop procedures for automated integration based on metadata The objective is to develop procedures for automated integration based on metadata Framework for layer integration based on metadata Framework for layer integration based on metadata Framework for feature integration Framework for feature integration Example results for Atlanta and St. Louis Example results for Atlanta and St. Louis

4. Approach Integrating disparate networks Integrating disparate networks Conceptual data flow -> federated database design (schema mapping) Conceptual data flow -> federated database design (schema mapping) Physical integration processes -> vertical & horizontal Physical integration processes -> vertical & horizontal Layer-based (vertical) Layer-based (vertical) Use existing seamless datasets Use existing seamless datasets Determine feasibility based on resolution and accuracy Determine feasibility based on resolution and accuracy Feature-based Feature-based Implement integration on feature by feature basis using developed feature library Implement integration on feature by feature basis using developed feature library

5. Data “best available” – focus on 5 layers Orthoimages from 133 priority cities of the Homeland Security Infrastructure Program Orthoimages from 133 priority cities of the Homeland Security Infrastructure Program National Hydrography Dataset (NHD) National Hydrography Dataset (NHD) National Elevation Dataset (NED) National Elevation Dataset (NED) Transportation (DLG, TIGER, State DOT, others) Transportation (DLG, TIGER, State DOT, others) National Land Cover Dataset (NLCD, others) National Land Cover Dataset (NLCD, others)

6. Test Sites St. Louis, Missouri St. Louis, Missouri Initially the Manchester and Kirkwood quadrangles Initially the Manchester and Kirkwood quadrangles Atlanta Atlanta Initially the Chamblee and Norcross quadrangles Initially the Chamblee and Norcross quadrangles

10. Challenges Facing The National Map Institutional (Masser and Campbell, 1995) Variation in participant priorities Variation in GIS experience among participants Differences in spatial data handling Technical Most datasets are outdated and inaccurate Vertical & horizontal data integration

11. Technical Factors Complicating Integration:  Total length of coincident participant boundaries  Road network feature density at the participant boundaries  Complexity (attribute precision) of the global schema

12. Methods Layer integration Layer integration Determine compatible resolutions and accuracies and use metadata to automatically combine appropriate datasets Determine compatible resolutions and accuracies and use metadata to automatically combine appropriate datasets Determine transformations possible that integrate datasets of incompatible resolutions and accuracies Determine transformations possible that integrate datasets of incompatible resolutions and accuracies Determine limits of integration based on resolution and accuracy Determine limits of integration based on resolution and accuracy

13. Cartographic Transformations from Keates Sphere to plane coordinates – projection Sphere to plane coordinates – projection Mathematical, deterministic, correctable Mathematical, deterministic, correctable Three-dimensional to two-dimensional surface Three-dimensional to two-dimensional surface Mathematical, deterministic, correctable Mathematical, deterministic, correctable Generalization Generalization Non-mathematical, scale dependent, humanistic, not correctable Non-mathematical, scale dependent, humanistic, not correctable

14. Scale and Resolution Matching (Mathematical Transformations) Working postulate: If data meet NMAS (or NSSDA), then integration can be automated based on the scale ratios Working postulate: If data meet NMAS (or NSSDA), then integration can be automated based on the scale ratios If linear ratios of scale denominators are >= ½, then integration is possible through mathematical transformations (12 – 24 K = 0.5) If linear ratios of scale denominators are >= ½, then integration is possible through mathematical transformations (12 – 24 K = 0.5) For ratios < ½, generalization results in incompatible differences (12 – 48 K = 0.25) For ratios < ½, generalization results in incompatible differences (12 – 48 K = 0.25)

15. Generalization Issues Selection – common features may not appear on data layers to be integrated (Topfer’s Radical Law) Selection – common features may not appear on data layers to be integrated (Topfer’s Radical Law) Simplification – lines may contain reduced numbers of points and have different shapes Simplification – lines may contain reduced numbers of points and have different shapes Symbolization – for map sources, symbolization may result in areas shown as lines or points Symbolization – for map sources, symbolization may result in areas shown as lines or points Induction – features may have been interpolated and appear differently on different sources Induction – features may have been interpolated and appear differently on different sources

16. NHD on Ortho

23. GA DOT on Ortho (12K)

24. USGS DLG on Ortho (12K)

25. Feature Integration Metadata exists on a feature basis Metadata exists on a feature basis Accuracy, resolution, source are documented by feature Accuracy, resolution, source are documented by feature Use Feature Library with an integration application Use Feature Library with an integration application

27. Conflating Vector Data with Orthoimagery USGS grant partially funding work of Ching-Chien Chen, Cyrus Shahabi, & Craig A. Knoblock USGS grant partially funding work of Ching-Chien Chen, Cyrus Shahabi, & Craig A. Knoblock University of Southern California University of Southern California Department of Computer Science & Information Sciences Institute Department of Computer Science & Information Sciences Institute Approach to identifying road intersections from orthoimagery Approach to identifying road intersections from orthoimagery Classify pixels as on-road/ off-road Classify pixels as on-road/ off-road Compare to road network nodes (intersections) Compare to road network nodes (intersections) Filter algorithm to eliminate inaccurate pairs Filter algorithm to eliminate inaccurate pairs

28. Technique To Automatically Identify Road Intersections

29. Example of Localized Template Matching

30. Areas Where Conflation Technique Was Applied

31. Vector Intersections (circles) & Corresponding Imagery Intersections (rectangles)

32. MO-DOT and Orthoimagery Conflation (red: MO-DOT; yellow: conflated roads)

33. MO-DOT and Orthoimagery Conflation

34. Evaluation of Conflation Result

35. Conclusions Data integration of layers for The National Map can only be accomplished with datasets that are compatible in resolution and accuracy Data integration of layers for The National Map can only be accomplished with datasets that are compatible in resolution and accuracy Mathematical transformation can automate integration with limited ranges of scales, but cannot correct generalization differences Mathematical transformation can automate integration with limited ranges of scales, but cannot correct generalization differences The National Map road data will leverage partners data BUT technical and institutional integration present many challenges to partnering The National Map road data will leverage partners data BUT technical and institutional integration present many challenges to partnering Illustrated a design of an integration approach (conflating vector data with orthoimagery) for geospatial datasets Illustrated a design of an integration approach (conflating vector data with orthoimagery) for geospatial datasets Design should support generalization to a variety of data sources Design should support generalization to a variety of data sources

36. INTEGRATION OF THE NATIONAL MAP 21 July 2004 Michael P. Finn E. Lynn Usery Michael Starbuck Bryan Weaver Gregory M. Jaromack U.S. Department of the Interior U.S. Geological Survey http://mcmcweb.er.usgs.gov/carto_research