Geography Reference
In-Depth Information
one or two dimensions is quite small compared with other dimension(s), we may be able to safely
ignore a dimension or two. For example, we tend to think of a single sheet of paper as a two-
dimensional object, but of course it has thickness as well. We might think of a fire hydrant (depicted
on a map as a dot—just a geometric point) as a zero-dimensional entity, but it is, of course, a
three-dimensional artifact. (Ask the engineer who designed it, the workpeople who installed it, the
firefighters who use it, or yourself, should you try to lift it.) Just as we idealize objects depicted on
maps, we do so in a GIS. We say that the fire hydrant exists at a location specified by a single latitude
and longitude pair, when in fact parts of it exist at an infinite number of latitude-longitude pairs—all,
admittedly, close together but different nonetheless.
Aggregation.
Entities having similar characteristics are put together. For example, saying that an
area has
x
acres where corn is grown and
y
acres where soybeans are grown is a statement of aggre-
gation. Information about where respective acreages of crops are located may or may not be detailed.
Interpolation and extrapolation.
We probable-ize. We assume. Data points with a believed high
degree of accuracy are interpolated or extrapolated to obtain new information. If we know that the
altitude of a certain point on the Earth's surface is 900 feet and that the altitude of another point very
close by is at 910 feet, we might interpolate between the two to say that the altitude of a point half-
way between them is 905 feet. To get a better estimate, we might also consider the 890-foot contour
and the 920-foot contour. In any event, the elevation of such an unknown point is probably known
to be not less than 900 feet nor more than 910 feet. Thus, in some cases, there are bounds on the error
introduced by the process of probablization.
Categorization.
We categorize when we break up a continuous set into a number of discrete sets.
For example, we might subsume slopes of 0
o
to 1
o
in category A, slopes of greater than 1
o
up to 3
o
in
category B, and so on.
Storage Paradigms for Areal Data
Now we turn to looking at the specifics of the different data structures used by ArcGIS. Representing
“almost zero-dimensional objects” (e.g., parking meters) and “essentially one-dimensional objects”
(e.g., narrow streams) is relatively simple. If an object is, for our practical purposes, just a point then a
simple, single coordinate pair will suffice. If a feature can be represented by a sequence of line segments,
then just a sequence of coordinate pairs does the job. Representing areas, however, is a much less
straightforward problem.
Fundamental Bases of Geographic Data Mode
Figure 4-2 is a orthophotoquad showing a picture of a piece of Earth's surface. It shows houses, green
space, warehouses, roads, trees, railroad, parking lots, a horse race track, and so on. Suppose that you
have been given the task of determining the area occupied by each of the feature types:
x
square feet
of housing,
y
square feet of highway, and so on. Information about where these various land uses exist
is also desired. Suppose further that the year is 1960 and you have a computer available to use for the
project. If you use the computer, your employer insists that you store the information so that whatever
you do can be verified by someone else.
What approach would you take? Basically, to use the computer, you would have to transform the
“picture” into numbers and symbols (which the computer would transform into bits). For a given theme
(such as land cover) these numbers and symbols must answer two questions at the same time:
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