Objectives of lecture:
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The transformational view of cartography is the basis for 'analytical cartography' (Waldo Tobler and others). [An alternative to the 'communication' school.]
Transformations presented first as projections (alter spatial measurements), then as transformations between primitives (point, line area).
The operations discussed so far in this course work for a particular measurement framework. There have been hints of a need to convert from one framework to another, and variations of the operations already discussed will do exactly this job.
The question, as it has been all quarter, is how an operation can "create" new information? The secret is to exploit relationships, to generate new ways to compare attributes through the spatial structure of the geographic information.
Surfaces provide a relatively confined example of the transformation process. A matrix gives the procedure used to convert from one measurement framework (rows) to another (columns - same list).
Demonstrations: working with the LIDAR data for Bainbridge
Island (bi_patch)
These procedures can be reclassified according to the combination
of neighborhood relationships and attribute assumptions in Table 9-3.
Table 9-2: Scheme for Transformations | ||
---|---|---|
Attribute Assumptions | ||
Neighborhood Construction | Implicit | External |
Implicit | Case 0 | Case 1A |
Discovered | Case 1N | Case 2 |
source contains information required for create target. Eg. topological structure contains 'spaghetti'.
geometry remains, attributes changed. Simplest case: Groupings; add information; rules do not have to remain local. Remotely sensed imagery: multiple continuous measures=> clusters (classes)
(to construct neighborhood): Eg. TIN operations to locate point in triangle
requires geometry and attribute assumptions. Eg. areal
interpolation John K. Wright, Cape Cod
dasymetric map (image copyright by American Geographical Society,
1936)