“Alternative” Data Structures
Longley et al.,
chs. 14 and 15
Zeiler
Ch. 8 (for Lab 5 on Network Analysis)
Chs. 9-10
Increased processing speeds/storage allow
for alternatives
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
Data Structure
the way in which the data model is represented in
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

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
Thiessen neighbors of point A share a common
boundary. Delauney triangles are formed by
joining points to its Thiessen neighbors.
Thiessen Polygons
A
Delaunay Triangles
A
• 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.
Thiessen Polygon
Start:
1)
3
1
1. Draw lines
connecting the
points to their
nearest neighbors.
2
5
4
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
2)
3)
Sampled locations and values
Daniel P. Ames, Dept. of Geosciences (Geology), Idaho State University
Thiessen polygons
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

a
a
a
a
a
c
c
a
a
a
a
a
c
c
a
a
a
a
c
c
c
a
a
a
c
c
c
c
b
b
c
c
c
c
c
b
b
b
c
c
c
c
b
b
b
c
c
c
c
b
b
b
b
b
c
c
b
b
b
b
b
b
b
data entered as pairs
– run length & value

40 items instead of 70
4a6b4a6b4a1c4b
3a2c5b3a4c3b2a
5c3b8c2b8c2b
b
b
b
b
b
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-quadrants
focusing on irregularity
Quadtrees of Chloropleth Raster Map
NW
NE
SW
SE
NW
Marc van Kreveld, U. of Utrecht
NE
SW
SE
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
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), 263280, 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.
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 Segmentation
multiple 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 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?
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
Mud
1267
1269
Sand
Pebble
1268
M = Mud S = Sand P = Pebble C = Cobble B= Boulder
F = Flat Rock R = Rock Ridge
ID
1
2
3
4
5
6
7
8
9
10
TO
104.85
146.79
251.64
293.58
356.49
377.46
419.4
440.37
482.31
482.31
HABITAT
RR
CC
RR
BR
BB
BB
CR
BB
RR
SC
TRANSECT DELTA88#
1267A
10
1267A
10
1267A
10
1267A
10
1267A
10
1267A
10
1267A
10
1267A
10
1267A
10
1267A
10
Cobble
Boulder
Flat rock
Rock ridge
Nasby, 2000
Bottom Type
Whitmire, 2003
Species Type
Density of
Dover Sole
Nasby, 2000
Other Fish Species
Pygmy rockfish
Shortspine thornyhead
Greenstripe rockfish
Rex Sole
Sablefish
Lingcod
Yellowtail rockfish
Nasby, 2000
3
Habitat
Characterization
Summary
3
3
2.5
2
1.5
1
0.5
0
2
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
Nasby, 2000
Segue to Terrain Analysis
Whitmire, 2003
Thesis Downloads
 Nicole
Nasby, 2000
dusk.geo.orst.edu/djl/theses/nasby_lucas.html
(also published in 2002 issue of Fisheries Bulletin)
 Curt
Whitmire, 2003
dusk.geo.orst.edu/djl/theses/whitmire_abs.html