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“Alternative” Data Structures

Longley et al., chs. 14 and 15 Zeiler Ch. 8 (for Lab 5 on Network Analysis) Chs. 9-10. “Alternative” Data Structures. Increased processing speeds/storage allow for alternatives. Review.

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“Alternative” Data Structures

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  1. Longley et al., chs. 14 and 15 Zeiler Ch. 8 (for Lab 5 on Network Analysis) Chs. 9-10 “Alternative” Data Structures

  2. Increased processing speeds/storage allow for alternatives

  3. Review • Model = representation of something in the real world, of a process in the real world - how the world WORKS • Data Model = representation of data or information ABOUT that something or process - how the world LOOKS

  4. Data Structure • the way in which the data model is representedin the GIS • concerned simply with what can be computed and what can’t • not tied to process in nature at all • DEM/grid/raster for field model • coverage/shapefile for ESRI geo-relational (object model) • TIN for Voronoi (Thiessen)/Delauney Triangulation

  5. they divide the space between the points as ‘evenly’ as possible market area delimitation, rain gauge area assignment, VIPs DTs are as near equiangular as possible, thus minimizes distances for interpolation elevation, slope and aspect of triangle calculated from heights of its three corners A A Thiessen (Voronoi) Polygonsand Delaunay Triangles Thiessen Polygons Delaunay Triangles Thiessen neighbors of point A share a common boundary. Delauney triangles are formed by joining points to its Thiessen neighbors.

  6. • partition areas based on “influence” of sample points (Thiessen polys)• all sample points connected w/ 2 nearest neighbors to form triangles• connect centroids of Thiessen polygons market area delimitation, rain gauge area assignment, trusted elevation benchmarks or VIPs, etc.

  7. 3 1 2 Start: 1) 5 4 2) 3) Thiessen Polygon • Draw lines connecting the points to their nearest neighbors. • Find the bisectors of each line. • Connect the bisectors of the lines and assign the resulting polygon the value of the center point

  8. Sampled locations and values Thiessen polygons Daniel P. Ames, Dept. of Geosciences (Geology), Idaho State University

  9. Visualization of Theissen Concept Arthur J Lembo, Jr., Bowne

  10. Inverse Distance Weighting Arthur J Lembo, Jr., Bowne

  11. Kriging Arthur J Lembo, Jr., Bowne

  12. Perspective Plot from TIN

  13. TIN(Triangulated Irregular Network) • avoids redundancy of raster while still producing a continuous surface • more efficient than raster for some terrain analysis • slope and aspect (faces of triangles) • contouring • Measurements are irregularly spaced with more sampling in areas of greater complexity • requires fewer points or grid cells

  14. Contours from TIN(triangles can be many and extremely small with a good sampling of points)

  15. • Computers love rasters• A cell on 1 map is at same position on all others• Easy query, neighborhood ops., etc.

  16. Storage/Scan Orders

  17. Compression:Run Length Encoding • based on spatial autocorrelation • nearby things tend to be more similar than distant things • data entered as pairs • run length & value • 40 items instead of 70

  18. • way of encoding irregularity of vector in raster form• step beyond run-length-encoding compression• compress in row AND column directions

  19. Raster to Quadtree

  20. Divide into sub-quadrantsfocusing on irregularity

  21. Quadtrees of Chloropleth Raster Map NW NE SW SE NW NE SW SE Marc van Kreveld, U. of Utrecht

  22. Multiple resolution storage

  23. Adaptive MWVD solution Rene Reitsma, OSU CoB • Vector solution: infinite precision, difficult computing. • Raster solution: limited precision, easy computing. • Resolution increases allow higher precision. • Boundary-only, quadtree resolution increases.

  24. Gateway to the Literature“information spaces” • Reitsma, R. and Trubin, S., Information space partitioning using adaptive Voronoi diagrams, Information Visualization, http://www.palgrave-journals.com/ivs/, 2006. • Dodge, M., and R. Kitchin, Code and the transduction of space, Annals AAG, 95 (1), 162-180, 2005. • Fabrikant, S.I., and B.P. Buttenfield, Formalizing semantic spaces for information access, Annals AAG, 91 (2), 263-280, 2001. • Skupin, A., On Geometry and Transformation in Map-Like Information Visualization. In: Börner, K., Chen, C (Eds.) Visual Interfaces to Digital Libraries. Lectures in Computer Science 2539. Springer Verlag, Berlin. 161-170, 2002.

  25. Gateway to the Literature“natural spaces” • Chen, J., C. Li, Z. Li, and C. Gold, A Voronoi-based 9-intersection model for spatial relations, Int. J. Geog. Inf. Sci., 15 (3), 201-220, 2001. - voronoi_ijgis.pdf • Chen, J., C. Qiao, and R. Zhao, A Voronoi interior adjacency-based approach for generating a contour tree, Comp. Geosci, 30, 355-367, 2004. • voronoi_contour_tree.pdf • Gold, C.M., and A.R. Condal, A spatial data structure integrating GIS and simulation in a marine environment, Mar. Geod., 18 (3), 213-228, 1995. • Mostafavi, M.A., C. Gold, and M. Dakowicz, Delete and insert operations in Voronoi/Delauney methods and applications, Comp. Geosci, 29, 523-530, 2003. - voronoi_2003.pdf • Zhang, H., and C. Thurber, Adaptive mesh seismic tomography based on tetrahedral and Voronoi diagrams: Application to Parkfield, California, J. Geophys. Res., 110 (B04303), doi:10.1029/2004JB003186, 2005. - seismic_mesh.pdf

  26. Dynamic Segmentationmultiple attributes to a single arc...attribute to a portion of an arc...

  27. DynSeg: Measures & “Events”

  28. DynSeg: Point Events

  29. DynSeg: Single Arc, Multiple Attributes

  30. Heceta Bank, Oregon

  31. Heceta Bank Fisheries InvestigationsM.S. Theses: Nasby, 2000; Whitmire, 2003 • At what scales are there quantifiable relationships between groundfish populations and seafloor morphology/texture? • What are the factors that control these relationships? • What changes may have occurred in the fish populations after a decade? • What are the characteristics and extent of natural refugia?

  32. EM 300 Multibeam Bathymetry • Depth Range: • 60-1000 m • Gridded to 5 and 10 m Nasby, 2000; Whitmire, 2003

  33. Dives • 28 ROV dives • 5 submersible dives • 6 historical stations Nasby, 2000; Whitmire, 2003

  34. Heceta Bank Fish Habitats • Seabed Classification • Mud • Sand • Pebble • Cobble • Boulder • Flat Rock • Rock Ridge Nasby, 2000; Whitmire, 2003

  35. Mud Sand Pebble Cobble Boulder Flat rock Rock ridge 1267 1269 1268 M = Mud S = Sand P = Pebble C = Cobble B= Boulder F = Flat Rock R = Rock Ridge Nasby, 2000

  36. Bottom Type Whitmire, 2003

  37. Species TypeDensity of Dover Sole Nasby, 2000

  38. Other Fish Species Pygmy rockfish Shortspine thornyhead Greenstripe rockfish Rex Sole Sablefish Lingcod Yellowtail rockfish Nasby, 2000

  39. Habitat Characterization Summary Rock ridge: yellowtail rockfish and juvenile rockfish Pebble/cobble/boulder: sharpchin rockfish, rosethorn rockfish, greenstripe rockfish and pygmy rockfish Mud: Dover sole, rex sole, sablefish and shortspine thornyhead Nasby, 2000

  40. Segue to Terrain Analysis Whitmire, 2003

  41. Thesis Downloads • Nicole Nasby, 2000 dusk.geo.orst.edu/djl/theses/nasby_lucas.html (alsopublished in 2002 issue ofFisheries Bulletin) • Curt Whitmire, 2003 dusk.geo.orst.edu/djl/theses/whitmire_abs.html

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