This preview shows page 1. Sign up to view the full content.
Unformatted text preview: Maps as Numbers
Maps Geographical Features
s A GIS dataset (often referred to as a
layer) consists of
Spatial (geometrical data capturing location
and form of a geographical feature)
s Attribute data (textual information describing
key characteristics of associated
s Maps as Numbers
s The data are stored in data structures and as
files on storage devices (e.g. hard drives,
USB drives, DVDs).
Files can be written in binary or as ASCII text.
Binary is faster to read, requires less disk
space, and is typically more efficient
(software allows us to read and edit binary),
ASCII can be read by humans and a variety
of software packages (e.g., word processing
software) but uses more space. ASCII- American Standard Code for Information Interchange “Machine code” This is a
challenge! Two Main Data Structures in GIS
GISs have traditionally used either
raster or vector data structure for
capturing geographical features.
s LEARN RASTER and VECTOR!
s “Reality” is in
the middle and
are to the left
and When selecting a data storage
technique you should try to:
1. Maximize accuracy
2. Minimize storage requirements
3. Minimize processing time
4. Facilitate analyses Some objectives conflict
When striking a compromise among
competing objectives you must consider
the particular problem you are trying to
What level of data accuracy is required?
What are your available resources?
Can you solve your problem given
required accuracy with available
resources? The raster data structure is based on
a simple grid.
s s One grid cell holds one attribute (usually).
Every cell has a value, even if it is “missing.”
A cell can hold a number or an index value
standing for an attribute (e.g., 300m above
msl, 4 = residential development).
A cell has a resolution, that is- the cell size in
ground units. All cells in a raster dataset have
the same resolution.
the Rasters are conceptually simple and often fast
(computationally)... A grid or raster maps directly onto a
programming computer memory
structure called an array.
s Grids are poor at representing points,
lines and areas, but good at surfaces.
s Surfaces? What are these? Surface (fields) vs. objects Rasters are conceptually simple and often fast
s Grids are good only at very localized topology, and
s Grids are a natural for scanned or remotely sensed
Grids suffer from the “mixed pixel” problem. s Grids must often include redundant or missing data.
redundant s Grid compression techniques are often used in GIS
to reduce memory requirements (e.g., run-length
encoding and quad trees).
encoding Generic structure for a grid
min x, min y
Max y, max y
Figure 3.1 Generic structure for a grid. Binary (0,1 or present, absent) grid
using a matrix representation Redundancy Forest
Urban 35 cells
“forest” A multi-valued (n=3) grid in matrix form
multi-valued This is a “non-matrix”
complex, not very
efficient- but more
flexible if additional
variables need to be
added. One grid cell holds one
attribute (usually- but
not always- you will see
a refinement of this
later). …jjust add
General idea- more efficient techniques exist. Problems with representing objects defined by points lines
and polygons Not really a point “Line” has width Not stored as
unconnected cells Cell Resolution Is Important
Which size do you pick?
s Too large and you miss intra-cell
s Too small and you record too much
s Same data, different cell
sizes and very different
heterogeneity is the
problem. Problems occur when assigning
values to cells
Different methods for assigning
values can be used
s Again, problem is: different
methods yield different results
methods The mixed pixel problem
The Water dominates Winner takes all Edges separate WW G WG G WE G WW G WW G WE G WW G WG G E G E 5 dominates- e.g.,
a cover type of
Winner takes all Different method =
Different results Problems can
occur is you
you use this?
this? False connections
Mostly water Mostly land
Lost elements Vector map to
all” Storage considerations:
When you increase resolution (or double
the extent), you increase data storage
requirements exponentially 2
n Double the resolution- four times the space 1 TB 10MB The 23rd cell
counting rowwise from upper
left to lower right row column Raster in
matrix form is
used. Value Count Variable 2 Variable 3 9 6 13 .23 6 19 5 .65 … … … … For integer grids,
attribute can be
Why just integer? Bolstad Quadtree is a hierarchical (tree)
representation of space.
2 B2 B
E GD 1
A D 1234 2 1 1234 0
3 4 A B C C From Bolstad Given a diagram
like this, could you
quadtree? Each cell is 4km2,
12 cells= 48km2 Value Count Variable 2 Variable 3 9 6 13 .23 6
Raster measurement: area = .65 (cells *
resolution… 6 * 4 = 24 x 2 km/side
= km perimeter
64 64 km perimeter Perimeter = Σ (external edges* resolution) Raster analysis using multiple layers
Raster Integration requires all datasets (layers) to use a consistent raster framework
i.e., cells are geographically “coregistered” (e.g same projection, same datum)
and have the same resolution. A quick Review
What are the benefits of the raster data
What are some of the disadvantages of the
raster data structure?
What is the mixed pixel/cell problem?
What is the relationship between resolution
and data storage requirements?
What techniques are available to reduce
Examples of the kinds of geographical features
are best represented by the raster data
structure are? The Triangulated Irregular Network (TIN) Bolstad 2002 Bolstad 2002 The Delaunay
Triangulation Bolstad 2002 ...
View Full Document
This note was uploaded on 04/01/2012 for the course 044 005 taught by Professor Davidbennett during the Fall '11 term at University of Iowa.
- Fall '11