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“Alternative” Data Structures. Information Spaces / Spatialization

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Presentation on theme: "“Alternative” Data Structures. Information Spaces / Spatialization"— Presentation transcript:

1 “Alternative” Data Structures

2 Information Spaces / Spatialization

3 Information Spaces / Spatialization Chen et al. 1998

4 Information Spaces / Spatialization


6 Alternative Data Structures (especially w/ increased processing speeds, storage)

7 Thiessen (Voronoi) Polygons and Delaunay Triangles  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 Thiessen neighbors of point A share a common boundary. Delauney triangles are formed by joining points to its Thiessen neighbors. A Thiessen Polygons Delaunay Triangles

8 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.

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

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

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

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

13 Kriging Arthur J Lembo, Jr., Bowne


15 Perspective Plot from TIN


17 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

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

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

20 Storage/Scan Orders

21 Consistency/Uniformity great for computers but what about human psyche? Can we bear such generalization? Cartographic Heresy?

22 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

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

24 Raster to Quadtree

25 Divide into sub-quadrants focusing on irregularity



28 Quadtrees of Chloropleth Raster Map NWNESWSE NWNE SWSE Marc van Kreveld, U. of Utrecht

29 Multiple resolution storage

30 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.

31 Gateway to the Literature “information spaces” Information space partitioning using adaptive Voronoi diagrams, Information Visualization,, 2006.  Reitsma, R. and Trubin, S., Information space partitioning using adaptive Voronoi diagrams, Information Visualization,, 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  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.

32 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

33 Dynamic Segmentation multiple attributes to a single arc... attribute to a portion of an arc...

34 DynSeg: Measures & “Events”

35 DynSeg: Point Events

36 DynSeg: Single Arc, Multiple Attributes

37 Heceta Bank, Oregon

38 Heceta Bank Fisheries Investigations M.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?

39 EM 300 Multibeam Bathymetry  Depth Range: –60-1000 m  Gridded to 5 and 10 m Nasby, 2000; Whitmire, 2003

40 Dives  28 ROV dives  5 submersible dives  6 historical stations Nasby, 2000; Whitmire, 2003

41 Heceta Bank Fish Habitats  Seabed Classification –Mud –Sand –Pebble –Cobble –Boulder –Flat Rock –Rock Ridge Nasby, 2000; Whitmire, 2003

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

43 Bottom Type Whitmire, 2003

44 Species Type Density of Dover Sole Nasby, 2000

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

46 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 Habitat Characterization Summary Nasby, 2000

47 Segue to Terrain Analysis Whitmire, 2003

48 Thesis Downloads  Nicole Nasby, 2000 (also published in 2002 issue of Fisheries Bulletin)  Curt Whitmire, 2003

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