“Alternative” Data Structures. Information Spaces / Spatialization .

“Alternative” Data Structures

Information Spaces / Spatialization

www.smartmoney.com

Information Spaces / Spatialization

Chen et al. 1998

Information Spaces / Spatialization

Information Spaces / Spatialization

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

Thiessen (Voronoi) Polygonsand 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

• 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.

Sampled locations and values Thiessen polygons

Daniel P. Ames, Dept. of Geosciences (Geology), Idaho State University

Visualization of Theissen Concept

Arthur J Lembo, Jr., Bowne

Inverse Distance Weighting

Arthur J Lembo, Jr., Bowne

Kriging

Arthur J Lembo, Jr., Bowne

Perspective Plot from TIN

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

Contours from TIN(triangles can be many and extremely small with a

good sampling of points)

• Computers love rasters• A cell on 1 map is at same position on all others

• Easy query, neighborhood ops., etc.

Storage/Scan Orders

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 704 a 6 b 4 a 6 b 4 a 1 c 4 b 3 a 2 c 5 b 3 a 4 c 3 b 2 a 5 c 3 b 8 c 2 b 8 c 2 b

a a a a b b b b b b

a a a a b b b b b b

a a a a c b b b b b

a a a c c c c b b b

a a c c c c c b b b

c c c c c c c c b b

c c c c c c c c b b

• way of encoding irregularity of vector in raster form

• step beyond run-length-encoding compression• compress in row AND column directions

Raster to Quadtree

Divide into sub-quadrantsfocusing on irregularity

Quadtrees of Chloropleth Raster Map

NW NE SW SE

NW NE

SW SE

Marc van Kreveld, U. of Utrecht

Multiple resolution storage

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.

Gateway to the Literature“information spaces”

Reitsma, R. and Trubin, S., Information space partitioning Information space partitioning using adaptive Voronoi diagrams, using adaptive Voronoi diagrams, Information Information VisualizationVisualization, http://www.palgrave-journals.com/ivs/, 2006., http://www.palgrave-journals.com/ivs/, 2006.

Dodge, M., and R. Kitchin, Code and the transduction of Dodge, M., and R. Kitchin, Code and the transduction of space, space, Annals AAGAnnals AAG, , 9595 (1), 162-180, 2005. (1), 162-180, 2005.

Fabrikant, S.I., and B.P. Buttenfield, Formalizing semantic Fabrikant, S.I., and B.P. Buttenfield, Formalizing semantic spaces for information access, spaces for information access, Annals AAGAnnals AAG, , 9191 (2), 263- (2), 263-280, 2001.280, 2001.

Skupin, A., On Geometry and Transformation in Map-Like Skupin, A., On Geometry and Transformation in Map-Like Information Visualization. In: Börner, K., Chen, C (Eds.) Information Visualization. In: Börner, K., Chen, C (Eds.) Visual Interfaces to Digital LibrariesVisual Interfaces to Digital Libraries. Lectures in Computer . Lectures in Computer Science 2539. Springer Verlag, Berlin. 161-170, 2002Science 2539. Springer Verlag, Berlin. 161-170, 2002.

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

Dynamic Segmentationmultiple attributes to a single arc...

attribute to a portion of an arc...

DynSeg: Measures & “Events”

DynSeg: Point Events

DynSeg: Single Arc, Multiple Attributes

Heceta Bank, Oregon

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?

EM 300 Multibeam Bathymetry

Depth Range:– 60-1000 m

Gridded to 5 and 10 m

Nasby, 2000; Whitmire, 2003

Dives

28 ROV dives 5 submersible dives 6 historical stations

Nasby, 2000; Whitmire, 2003

Heceta Bank Fish Habitats

Seabed Classification– Mud– Sand– Pebble– Cobble– Boulder– Flat Rock– Rock Ridge

Nasby, 2000; Whitmire, 2003

M = Mud S = Sand P = Pebble C = Cobble B= Boulder F = Flat Rock R = Rock Ridge

12671269

1268

ID TO HABITAT TRANSECT DELTA88#1 104.85 RR 1267A 102 146.79 CC 1267A 103 251.64 RR 1267A 104 293.58 BR 1267A 105 356.49 BB 1267A 106 377.46 BB 1267A 107 419.4 CR 1267A 108 440.37 BB 1267A 109 482.31 RR 1267A 1010 482.31 SC 1267A 10

Mud

Sand

Pebble

Cobble

Boulder

Flat rock

Rock ridge

Nasby, 2000

Bottom Type

Whitmire, 2003

Species TypeDensity of Dover Sole

Nasby, 2000

Other Fish Species

Greenstripe rockfish

Sablefish

Yellowtail rockfish

Shortspine thornyhead

Rex Sole

Lingcod

Pygmy rockfish

Nasby, 2000

3 3

2

0

0.5

1

1.5

2

2.5

3

Rock Ridge Pebble/Cobble/Boulder

Mud

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

Segue to Terrain Analysis

Whitmire, 2003

Thesis Downloads

Nicole Nasby, 2000dusk.geo.orst.edu/djl/theses/nasby_lucas.html

(also published in 2002 issue of Fisheries Bulletin)

Curt Whitmire, 2003dusk.geo.orst.edu/djl/theses/whitmire_abs.html