the nature of geographic data based in part on longley et al. chapters 3 and 4
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The Nature of Geographic Data
Based in part on Longley et al. Chapters 3 and 4
What are Geographic Data?
“Location, location, location!”to map, to link based on the same place, to measure distances and areas
Attributes physical or environmental soci-economic (e.g., population or income)
Timeheight above sea level (slow?) Sea surface temperature (fast)
Problems w/ Representing Geographic Data
Digital EarthEntire Earth into single digital representation
Infinite complexityWhat to leave in, what to leave outRepresentations are partial (data models)
Discrete Objects and Continuous Fields( Vector and Raster Structures)
DISCRETEWell-defined boundaries in empty space“Desktop littered w/ objects”World littered w/ cars, houses, etc.Counts49 houses in a subdivision
Dimensionality of Objects:A way of identifying them
0-D
1-D
2-D
Example of representation of geographic information as a table. The locations and attributes are for each of four grizzly bears in the Kenai Peninsula of Alaska. Locations, in degrees of longitude and latitude, have
been obtained from radio collars. Only one location is shown for each bear, at noon on July 31, 2000.
The discrete object view leads to a powerful way of representing geographic information about objects
Fields:Not points, lines, areas, but what varies and how smoothly…. “What occurs everywhere”
care to count every peak, valley, ridge, slope???
Object/Vector Feature Types
Data Models: fields and objects are no more than conceptualizations, or ways in which we think about geographic phenomena. They are NOT always designed to deal with the limitations of computers.
Field & Object Data Models
Data Structures: methods of representing the data model in digital form w/in the computer
Raster and Vector Data Structures
Data Models and Data Structures
Raster Data Structure
Mixed Pixels
Examples of the largest share rule, where a cell's value is on the value that occupies the largest share of the cell's area, and the central point rule, where a cell's value is based on the value that occupies the
central point of the cell.
Vector Data Structure:Lines vs. Polygons
An area (red line) and its approximation by a polygon (blue line).
Slivers due to double digitizing and overlay
can be eliminated.
Sliver
Rasters and VectorsVector-based line
4753456 6234124753436 6234244753462 6234784753432 6234824753405 6234294753401 6235084753462 6235554753398 623634
Flat File
Raster-based line
00000000000000000001100000100000101010000101000011001000010100000000100010001000000010001000010000010001000000100010000100000001011100100000000100001110000000000000000000000000
Flat File
Now YOU!
TopologyScience and mathematics of geometric relationships
Simple features + topological rulesConnectivityAdjacencyShared nodes / edges
Topology needed byData validationSpatial analysis (e.g. network tracing, polygon adjacency)
An Object Model uses a Vector (Arc/Node) Data Structure
Object data model evolved into the arc/node variation in the 1960s. Points in sequence build lines.Lines have a direction - nodes or ordering of the points. Lines in sequence build polygons.
Vectors (Arcs) and TopologyVectors without topology are spaghetti structures.Points, lines, and areas
stored in their own files, with links between them.stored w/ topology (i.e. the connecting arcs and left and right polygons).
Relationships are computed and stored
A
C B
1
2
3
4
5
0
D6
ab
c
d
e7
Arc
ID
L e f t
Poly
R t
Poly
From
n o d e
T o
n o d e
1 A 0 c a
2 A B b c
3 C A b a
4 0 C d a
5 C B d b
6 B D e e
7 B 0 d c
Poly
ID
No. of
arcs
List of
arcs
A 3 - 1 , - 2 , 3
B 4 2, -7, 5, -6
C 3 - 3 , - 5 , 4
D 1 6
2, -7, 5, 6
Connectedness, Adjacency, Contiguity, Geo-Relational
Topology, GIS, and YouTopological data structures very important in GIS software.Must BUILD topology from unconnected arcs
rarely are maps topologically clean when digitized, imported, or “GPSed.”
“Tolerances” important - features can move or disappear
“snapping”, elimination, merging, etc.
Nodes that are close together are snapped.
The bounding rectangle(xmax, ymax)
(xmin, ymin)
Why Topology Matters
allows automated error detection and elimination. allows many GIS operations to be done without accessing the (x,y) files.makes map overlay feasible. makes spatial analysis possible.
Issues w/ Raster & Vector
Issue Raster Vector
Volume of Data Depends on cell size Depends on density
of vertices
Sources of data Remote sensing,
imagery
Socio-economic,
environ. sampling
Applications Resources,
enviromental
Socio-economic,
administrative
Software Raster GIS, image
processing
Vector GIS, autom.
Cartography
Resolution Fixed Variable
TIN: Triangulated Irregular Network
Based on the Delaunay triangulation model of a set of irregularly distributed points. Way to handle raster data with the vector data structure.Common in most GISs.More efficient than a grid.
triangulation
Courtesy www.ian-ko.com/resources/triangulated_irregular_network.htm
TIN surface
pseudo 3D
Spatial Autocorrelation
Arrangements of dark and light colored cells exhibiting negative, zero, and positive spatial autocorrelation.
Tobler’s 1st Law of Geography: everything is related to everything else, but near things are more related than distant things
S. autocorrelation: formal property that measures the degree to which near and distant things are related.
Close in space
Dissimilar in attributes
Attributes independent
of location
Close in space
Similar in attributes
Sampling: The Quest to Represent the Real World
a spatially random sample
a spatially systematic (stratified)
sample
a stratified random sample
a sampling scheme with
periodic random changes in the grid width of a
spatially systematic
sample
Field - selecting discrete objects from a continuous surface
Object - selecting some discrete objects, discarding others
Spatially systematic sampling presumes that each observation is of equal importance in building a representation.
Spatial Interpolation:“Intelligent Guesswork”
the process of filling in the gaps between sample observations. attenuating effect of distance between sample observations selection of an appropriate interpolation functionTobler’s law - nearer things are key, in a smooth, continuous fashion
Pollution from an oil spillNoise from an airport, etc.
(Artificial) Smooth & Continuous Variation:contours equally spaced, along points of equal elevation
Is Variation in Nature Always Smooth and Continuous?
Graduate Student’s Corollary to Tobler’s 1st Law of Geography
“The real world is infinitely complex, so why bother?”
For true nature of geographic data, use other interpolation methods and functionsIDW - nearer points given more importanceSampling still important!!!
An Example from ArcGIS
Examine Attributes of Points
Choose Interpolation Parameters
IDW Interpolation
Hillshade ( hypothetical illumination ) to Better Visualize
Another set of sample points
Examine Attributes
Same Interpolation Parameters
Same IDW Interpolation( but higher elevations skewed to right )
Hillshade
Comparison
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