E.G. please always turn off your cell phone or put it on vibrate mode for class

1
A kind reminder on distractions such as phone
use, noise (see syllabus). Please allow fellow
students to focus.
E.G. please always turn off your cell phone or
put it on vibrate mode for class
GIS for environmental management Dr. William
James Smith, Jr. Assistant Professor of
Environmental Studies, UNLV Adjunct Assistant
Professor in Geography, The University of Iowa
2
Development and population animations http//www.
nrel.colostate.edu/davet/animations.html
3
Maps as numbers
MAPS AS NUMBERS Data structures, basic
statistics and queries, and topology
Legend
Mixed conifer
Douglas fir
Oak savannah
Theme
Time
Space
Grassland
Points
Lines
Areas
Volumes
Nominal
Raster representation. Each color represents a
different value of a nominal-scale field denoting
land cover class.
Ordinal
Interval
Ratio

• Using Arcview software

4
But that is not the main topic of
discussion for this type of class We will focus
on data structures again and then connect them
to topology Topology The property that describes
adjacency and connectivity of features. A
topological data structure encodes topology with
geocoded features.
5
We require a mental model of how the physical
data represent a geographic figure.
Traditionally there have been two types of data
model for map data. 1. Raster format 2. Vector
format
Standards. Attributes. Formats. Topology.
Key terms
6
Raster model Uses a grid, like coordinate systems
we discussed do, to hold the data. Each grid cell
in the grid is one map unit, often selected so
that each cell shows on the GIS map as one
screen display point or pixel, or on the ground
as a whole number increment in the coordinate
system.
5
7
When we capture a map into the raster model we
have to assign a value to every cell in the grid.
The value we assign can be an actual number
such as terrain elevation in a DEM, or an index
value standing for an attribute stored in a
database.
8
The cell size determines the resolution of the
data

and the cell size has
both a ground and map expression. On a map we
may use several pixels to display the grid cell,
or on paper we may use a dot of a certain size
in a given color.
9
Resolution

• Resolution increases as the size of the cell
  decreases however, coding, database storage,
  and processing speed for analysis are more costly.

10
Composition of a Raster data set

• Raster is made up of cells
• Cells have coordinates
• Cells have attributes
• Integers categorical or nominal values
• Floating point

11
Composition of a Raster data set

• Each cell is assigned a specific value to
  identify or describe the class, category, or
  group the cell belongs to (categorical data).
• Examples of categorical data include soil type,
  soil texture, landuse class, water body type,
  road class, and housing type.

12
Data dictionary The part of a database
containing information about the files, records,
and attributes rather than just the
data. Attributes have a database dictionary of
sorts, this lists of attributes and their
characteristics. Sometimes its is a separate
file(s) and other times it is written in to the
top or header of the file before the data begin.
Digital Chart of the World hard copy data
dictionary E.G. DEM hack if time
13
We often speak of Landsat imagery being 30 meter.
This means that each cell in the data is 30
meters by 30 meters on the ground. YOU CAN
ZOOM IN ALL YOU WANT BUT IT DOES NOT IMPROVE
BEYOND THAT! E.G. people do not show up.
Foreshadow - FSM 30, 4, 1 at same spot
different things show up. Zoom in on volcano
tiff from last class to look for trash
14
Composition of a Raster data set

• Raster organized in rows and columns
• Upper left corner defines origin
• Spatial extent by origin and cell size
• Again, resolution is a matter of cell size

15
Discrete v Continuous Models

• Discrete

• Continuous

16
Representing features in a raster dataset - areas

• The accuracy of the raster representation is
  dependent on the scale of the data and the size
  of the cell. The finer the cell resolution and
  the greater the number of cells that represent
  small areas, the more accurate the representation.

17
Earth at night
18
There must be a link between the raster or vector
and the flat file. -For a raster grid we could
store index numbers in the grid and any number of
attributes for the index numbers of attributes in
the flat file. LAND USE MAP 1FOREST 2FARMLAND 3
URBAN See DCW -- if raster data O.K. there
19
Raster data structures Each pixel or grid cell
contains either a data value (e.g.
brightness) for an attribute, or an index
number that points to a reference in the
attribute database. Again, pixel s 2 and then
2 always oak forest anywhere on the map, then
that pixel is oak -- and patterns
manifest. To write numbers to a file
start with writing attribute codes, the number
of rows and columns and maximum size of value
possible, then convert to binary in all rows and
columns.
20
Advantages - An operation such as comparing a
grid cell with its neighbors can be performed by
looking at the values in the next and preceding
row and column of the grid cells in question. -
TIN structure in vector fit easily in raster. -
Remote sensing or scanning.
Disadvantages - Not good at representing lines or
points since each become a whole cell in the grid
-- making lines disconnected or fat if they cross
the grid at a shallow angle. - Mixed pixels are
a problem page 76 CLARKE, as sometimes more
than one type of attribute occupies a cell, and
then if there are two classes an edge pixel is
assigned, but when lots of types exist in the
cell we need to assign computational rules, such
as the majority attribute takes the whole cell
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Composition of a Raster data set

• Raster data can be organized into zones of cells
  with similar values.
• Each group of connected cells in a zone is
  considered a region.

23
Legend
Mixed conifer
Douglas fir
Oak savannah
Grassland
Raster representation. Each color represents a
different value of a nominal-scale field denoting
land cover class.
24
We need to decide what patterns are legal, e.g.
diagonal vs. cardinal. Each grid cell can
usually only be owned by one feature, that is,
the one whose attribute it holds. But that is
not the real world!
Soils for example, are often listed by percent of
sand, silt and clay. And the grid may be 49
percent water and 51 percent land, but the cell
must be one. DEMO Lat/long over counties to
make grid of land water See why lots of cells at
high resolution is best? Each cell must be
able to hold maximum data possible, so as the
rows X columns space is eaten up fast.
25
Representing features in a raster dataset - points

• Point data
• Point data have area depending on resolution or
  cell size (centroid)

26
Representing features in a raster dataset - lines

• As with point data, linear features will become
  the width of the cell.

DEMO CENTROIDS FOR BASINS AND TAX PARCELS LINE
TO RASTER ELE exts in topology pjr
27
Last word, you can convert from vector to raster
easily, but the other way makes is more
complicated. Either way you need to avoid
problems like lines overlapping (e.g. streams).
28
Digital Orthophoto Quad
Typical sources of local data you might use.
29
Digital Raster Graphic
30
Surfaces -- some examples
31
What are TINs? TIN A vector topological data
structure designed to store the attributes,
usually geographic surfaces. Uses contiguous
non-overlapping triangle facets. Not as
efficient as grids, but good for detailed large
scale apps and better for position of roads, etc.
(as triangle edges) than grids. But needs
relatively better data to be worth using (e.g.
not for DEMs at U.S. scale). See Using AV 3D
pp 24-25 figs
32
Digital Elevation Models grid elevation sources
Definitions More TINs and other models later in
the semester -- not on your midterm.
DEMO ELE TO GRID TO TIN
33
Volumes (surfaces) are structured with the TIN
model, including edge or triangle topology. TINs
use an optimal Delaunay triangulation of a set of
irregularly distributed points. TINs are popular
in CAD and surveying packages. Chuuk
Demo in topology vector only view with DEM
34
Disadvantages include high costs and processing
power / time
More on using such data to drape, for flow,
aspect, slope, visibility/view-sheds,
watersheds, etc. later And more on querying and
exploring their relationships and making
calculations to produce maps later
35
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36
Continuous Surface Models

• Concentration

• Diffusion

37
Satellite Imagery
38
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39
Satellite Imagery
40
Online rastser only GISgrid outputs with cells
user defined

• http//www.ngdc.noaa.gov/mgg/gdas/gd_designagrid.h
  tml

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46

• http//www.nrel.colostate.edu/davet/animations.ht
  ml

47
Positive aspects -They are easy to
understand. -Capable of rapid retrieval and
analysis -Easy to draw on a screen and devices
that display pixels. Chuuk or NV topo
48
Ways around the raster data hog problem
Run-length encoding It looks at CHANGES between
attributes and the numbers of them stored. If a
whole row is a class, then it is stored as such,
and not lots of cells individually. Not much
help if the raster image is quite varied!
49
Quad tree can help too Divides the grid into 4
quadrants, saving a reference to a given quadrant
only if it hosts the data desired. And that
quadrant is also divided, and so on till one
quadrant has all of the same pixels desired.
50
Less resource intensive technique utilizing
vector topology, rather than raster topology, to
get at a very similar environmental issue
Interpolation
51

• Interpolation predicts values for cells in a
  raster from a limited number of sample data
  points. It can be used to predict unknown values
  for any geographic point data elevation,
  rainfall, chemical concentrations, noise levels,
  and so on.

52
On white board - show points, leading to
interpolation, leading to points to lines to
raster or Demo vector only topology view add
points add lat/long for wq testing
or DEMO interpolation later when discussing
renewable energy
53
Some positive aspects of the vector model
include -Very good at representing features
shown on maps as lines e.g. roads -Unlike
raster, we do not have to store grid cells of no
interest. Size! -We only place the amount of
data really necessary to make the shape e.g. more
for bends in a river, less where straight.
54
Some disadvantages -Not good at representing
continuous field variables e.g.
topography, though Triangulated Irregular
Networks get around this.
55
Practical environmental application
56
Threats Posed by Perchlorate ContaminationThe
Hills Site, Johnson County, Iowa

• Warren Pierce
• University of Iowa Student
• Dr. Smiths
• Socio-Political Aspects of Water Resources
  Management GEOG 605
• 12/15/03

57
Geographic Information Systems

• Plume size
• Plume concentration
• Aerial photographs
• Field samples
• Ground/surface water
• Determining areas at risk
• Locating point source

58
The Plume Has A Volume of Approximately 2,750,000
Cubic Meters

• A home on Main Street in Hills
• Very nearly in the center of the plume
• Is that a for sale sign?

59
Aerial Photo of Hills

• Triangle
• 1000m base
• 1100m length
• 5m height

60
This could be done in 3-D with depth of plume
11/05/2003 o\hills_ia\projects\figures\round2\r2
_15_to_18ft.ppt
Legend
Hills Perchlorate Site Hills, Iowa
0.01 to 4.00 ppb
Figure 1
4.01 to 11.00 ppb
15 ft. to 18 ft. Below Ground Surface
11.01 to 18.00 ppb
Greater than 18.00 ppb
Source ArcScene 3D Interpolation Snapshot
Date 11/05/2003 Drawn By Kurt Cholak
Project No G9011.L.03.0131.00
61
11/05/2003 o\hills_ia\projects\figures\round2\r2
_18_to_21ft.ppt
Legend
Hills Perchlorate Site Hills, Iowa
0.01 to 4.00 ppb
Figure 2
4.01 to 11.00 ppb
18 ft. to 21 ft. Below Ground Surface
11.01 to 18.00 ppb
Greater than 18.00 ppb
Source ArcScene 3D Interpolation Snapshot
Date 11/05/2003 Drawn By Kurt Cholak
Project No G9011.L.03.0131.00
62
11/05/2003 o\hills_ia\projects\figures\round2\r2
_21_to_24ft.ppt
Legend
Hills Perchlorate Site Hills, Iowa
0.01 to 4.00 ppb
Figure 3
4.01 to 11.00 ppb
21 ft. to 24 ft. Below Ground Surface
11.01 to 18.00 ppb
Greater than 18.00 ppb
Source ArcScene 3D Interpolation Snapshot
Date 11/05/2003 Drawn By Kurt Cholak
Project No G9011.L.03.0131.00
63
A pound of perchlorate is distributed throughout
the plume

• Photo taken at the Atrium Village
• The plume engulfs this area
• Hopefully there is close monitoring

64
11/05/2003 o\hills_ia\projects\figures\round2\r2
_24_to_27ft.ppt
Legend
Hills Perchlorate Site Hills, Iowa
0.01 to 4.00 ppb
Figure 4
4.01 to 11.00 ppb
24 ft. to 27 ft. Below Ground Surface
11.01 to 18.00 ppb
Greater than 18.00 ppb
Source ArcScene 3D Interpolation Snapshot
Date 11/05/2003 Drawn By Kurt Cholak
Project No G9011.L.03.0131.00
65
Downstream Communities

• The plume has reached the Iowa River
• The first town downstream is River Junction
• A second river then runs past Riverside

66
11/05/2003 o\hills_ia\projects\figures\round2\r2
_27_to_31ft.ppt
Legend
Hills Perchlorate Site Hills, Iowa
0.01 to 4.00 ppb
Figure 5
4.01 to 11.00 ppb
27 ft. to 31 ft. Below Ground Surface
11.01 to 18.00 ppb
Greater than 18.00 ppb
Source ArcScene 3D Interpolation Snapshot
Date 11/05/2003 Drawn By Kurt Cholak
Project No G9011.L.03.0131.00
67
Bioaccumulation

• Plant biota readily absorbs perchlorate
• Concentrations increase
• Biomagnification occurs moving up trophic levels
• MCL levels for drinking water does not account
  for ingestion through food

68
Lucky FarmsSan Bernardino, California

• Lockheed Martin (aerospace and defense
  contractor)
• Contaminated much of Southern California
• Irrigation contains perchlorates
• Concentrations in vegetables 65 times greater
  than irrigation water

69
Detrimental to Farming

• Products not safe to sell
• Decreased income from ruined produce
• Money lost from product input
• Labor costs
• FDA

70
Bioaccumulation in Aquatic Biota

• Plants
• Fish
• Amphibians

71
Microbial (algal and bacterial) and fish tissues
accumulated perchlorate to high levels and the
magnitude of accumulation corresponded well to
exposure levels. Therefore, it is likely that
even exposures to relatively low concentrations
of perchlorate(lt20 ppm) will result in the
bioaccumulation of perchlorate throughout the
food chain. ---U.S. Air Force
Space and Missile Center report
72
Stakeholders
Hills Residents
Stutsman, Inc. Employees
Downstream Communities
Environment
Government
Developers
Farmers
Stutsman, Inc Owners
73
Development

• Six homes and two lots for sale
• Property values drop
• New development stymied
• Lose tax revenue
• Environmental justice

74
Conclusion

• The EPA must set MCL levels for perchlorate
• Regulation standards for wells
• Chance findings of contaminations not good enough
• Water treatment facility needed
• General safety and welfare top priority
• Those responsible held accountable
• GIS excellent tool
• Bioaccumulation must be of concern

75
Do ch 13 and 14c demo, if time, come back start
topology.
76
VECTOR TOPOLOGY
Topology The property that describes adjacency
and connectivity of features. A topological data
structure encodes topology with geocoded features.
77
Vector topology diagram
e.g. river or not
78
There are lots of formats for GIS data such as
TIGER, DLG, DFX, etc. But we do not have time to
get into this now. You will have to learn
them if you get into advanced use of them. Data
exchange by translation (export and import) can
lead to significant errors in attributes and in
geometry.
79
This includes exchanging data between formats.
The Spatial DataTransfer Standard (SDTS) is
the national standard. SDTS became a federal
standard (FIPS 173) in 1992. SDTS contains a
terminology, a set of references, a list of
features, a transfer mechanism, and an accuracy
standard.
80

• Efficient data exchange is important for the
  future of GIS.
• The history of GIS data exchange is chaotic and
  has been wasteful.

81
Topology Matters Rarely are maps topologically
clean when digitized or imported. A GIS has to
be able to build topology from unconnected arcs.
82
The tolerances controlling snapping, elimination,
and merging must be considered carefully, because
they can move features, or, e.g. set polygon size
too big and lose small features. Complete
topology makes map overlay feasible.
83
Nodes that are close together are snapped --
averaging their x,y coordinates and closing
polygons .
84
Slivers due to double digitizing and overlay are
eliminated.
85
Mathematical topology assumes that geographic
features occur on a two-dimensional plane. We
can ensure spatial features can be represented
through 1) Nodes (0-dimensional cells) 2)
Edges, sometimes called arcs (one-dimensional
cells) or 3) Polygons (two-dimensional
cells). Because features can exist only on a
plane, lines that cross are broken into separate
lines that terminate at nodes representing
intersections rather than simple vertices.
86
skip
87
This is more complicated than raster topology,
so I will come back to my points multiple ways
and times in this PPT to get the info across.
Vector data arc/node model emerged in the 1960s
as a nested hierarchy. In the arc/node model,
an area/polygon consists of lines -- and a line
consists of points. Points, lines, and areas
can each be stored in their own files, with links
between them. At least 2 dimensions of x
left-right and y up-down.
pen
88
The topological vector model uses the line (arc)
as a basic unit of storage. Areas (polygons) are
built up from arcs as needed. The endpoint of a
line (arc) is called a node. Arc junctions are
only at nodes. Stored with the arc is the
topology. For a point or set of
points, vectors just use a list of coordinate.
89
The file of arcs has a skeleton of just the
first and last points in the arc (end nodes),
and information as to its neighbors, so it can
identify the of the next connecting arc and
polygon numbers to the left and right. This
means a polygon can be drawn by extracting all
the arcs of its neighbor. This avoids the
sliver problem when digitizing info. FIG
3.5 P 74 Clark and P 79 FIG 310
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91
Data structure as the theory evolved
92
In GIS, topology is implemented through data
structure. E.G. ArcInfo coverage explicitly
stores topological relationships among
neighboring polygons in the Arc Attribute Table
by storing the adjacent polygon IDs in the LPoly
and RPoly fields. Adjacent lines are connected
through nodes, and this information is stored in
the arc-node table. Not every GIS approaches
this the same way -- E.G. how you build
topology.
93
Topological data structures are advantageous
because they 1) Provide an automated way to
handle digitizing and editing errors and
artifacts 2) Reduce data storage for polygons
because boundaries between adjacent polygons are
stored only once 3) Enable advanced spatial
analyses such as adjacency, connectivity, and
containment. Another important consequence is
that a map that has topology contains
space-filling, nonoverlapping polygons.
94
Automated method to clean up data entry errors
and verify data. The typical digitizing
procedure is to 1) Digitize all lines 2) Build
topology 3) Label polygons 4) Clean up
slivers, dangles, and under- and overshoots
and 5) Build topology again, repeating the clean
and build phases as many times as necessary
95
Government has made its contributions, e.g. The
U.S. Census Bureau, while preparing for the 1970
census, pioneered the application of mathematical
topology to maps to reduce the errors in
tabulating massive amounts of census data.
Today, topology in GIS is generally defined as
the spatial relationships b/n adjacent or
neighboring features.
96
Moreover, polygon features in shapefile format
can contain one or more parts. E.G, an
individual parcel that is split by a road can be
represented alternatively as two separate
polygons with two shapes (rings) and two records
in the attribute table or as one polygon with
two parts and one record in the attribute table.
Same theme covered, but attribute table
organized differently
97
Why this form? 1) Fast drawing (used to be like
ArcExplorer 2) Easy to copy 3) No complex
import/export concerns 4) Easy to support in
other software programs
98
skip
By approaching digitizing from a feature-centric
perspective and enforcing planar topology when
each feature boundary is digitized and labeled,
sliver polygons, dangling nodes, missing labels,
and multilabeled features would be eliminated.
(Computer hardware was not always powerful
enough to support a feature-centric digitizing
approach that requires on-the-fly calculation of
geometric intersections.)
99
Your volcano digitizing! Today's computers are
powerful enough to support feature-centric
digitizing for most GIS users. ArcView GIS
supports feature-centric digitizing through the
Append Polygon, Split Polygon, and Split Line
tools. With these tools users can add a polygon
(or line) adjacent to an existing polygon and
have boundaries match perfectly. ArcView GIS
also supports topological editing of shared
boundaries or nodes through the manipulation of
vertices.
100
File Sizes No Longer an Issue An oft-cited
advantage of topological data structures is
smaller file sizes because shared vertices of
adjacent polygons are not stored twice.
Attribute tables are often a large proportion
of the overall file size but are the same size
regardless of how feature geometry is stored.
Moreover, although storage was often an
important consideration in the past, the current
low cost of storage means that for most GIS users
storage space is not a constraint.
101
A misunderstanding about shapefiles is that
because topology is not explicitly stored,
adjacent features cannot be found. However,
adjacent features can easily be found by
intersecting target polygons with other polygons
in the same map and identifying the points of
intersection of polygons that touch boundaries or
overlap. The geometric intersections of
adjacent features are calculated on the fly by
comparing the vertices of adjacent features
rather than looking up adjacent features in a
table
skip
102
E.G. Demo using Ch. 13 parcel data Neighboring
parcels of a parcel 1) Select the parcel 2)
Choose Theme 3) Choose Select by Theme from the
View menu 4) Choose "intersect" from the
drop-down box and 5) Click on New Set to select
all the parcels immediately adjacent to the
originally selected parcel. More complex
adjacency analyses can be accomplished by
combining the selection by theme with a query for
specific attributes Weakness (Some of the more
complex adjacencies that involve direction (e.g.,
find the adjacent parcels to the east of a given
road) are much more difficult to accomplish
without stored topology.)
103
Although analytical operations that require
adjacency information can be performed in AV
through the interface, performance requirements
many necessitate building a table to store
adjacency information. Algorithms for building
lists of adjacent features, described here, could
be incorporated in an Avenue script. E.G.
Although the representation of topological
spatial relationships traditionally has been
restricted to exactly adjacent neighbors,
adjacency can be extended to include features
that are within some distance (D) rather than
exactly adjacent (D 0).
skip
104
E.G. Although the representation of topological
spatial relationships traditionally has been
restricted to exactly adjacent neighbors,
adjacency can be extended to include features
that are within some distance (D) rather than
exactly adjacent (D 0). One advantage can be
defined in relation to the spatial precision of
the coordinates. These algorithms can find
adjacency for polylines and polygons. If D gt 0,
adjacent points can also be identified. One
algorithm creates an adjacency list using a
"brute-force" approach. In this algorithm, for
every pair of features, it determines if these
features intersect and stores the adjacent index
values
105
A planar-enforced shapefile can be created as
described above or derived from a coverage.
However, if nontopological editing methods are
used, a shapefile can lose its planar topology
during editing. E.G. If volcano digitizing
features overlap each other and create gaps in
your! Planar topology, based on hand heads-up
digitizing can be enforced on shapefiles with the
assistance of some Avenue scripts that close gaps
between features etc. using algorithms (The
logic and the scripts can be downloaded from
ArcUser Online or http//www.nrel.colostate.edu/d
avet/.)
106
Some purists might be offended at the notion that
there are advantages to using shapefiles that
violate planar assumptions (i.e., shapefiles that
have overlaps and/or gaps). Many useful
analyses do not require data with precise planar
topology, but these analyses are never conducted
because it is assumed that base data must have
topology. E.G. city and county governments find
it extremely time-consuming and difficult to
build parcel coverages because parcel boundary
descriptions rarely match cleanly with adjacent
parcels. Resolving boundary disputes is very
time-consuming, often fraught with complicated
legal issues. However, a standard query of
parcel data performed with reasonably coincident
boundaries (i.e., submeter accuracy) can be used
to find landowners wn a certain distance of a
location for notification purposes.
107
The first step in enforcing planar topology in a
shapefile is to remove twisted or
self-intersecting polygon rings and to ensure
that the "inside" of the polygon is on the
correct side of the polygon boundary. Next, gaps
are identified by creating a rectangle that
encompasses all the polygons of interest and
serves as a backdrop. The polygons are subtracted
from the rectangle containing all polygons. The
remaining areas are gaps. A gap polygon is
removed by merging it into an adjacent polygon or
by making it a legitimate polygon. Overlaps are
found by intersecting each polygon with all other
polygons. If an intersection is found, then the
polygon representing the overlap is created.
Overlaps can be removed by deleting the
overlapping area from one of the involved
polygons. Once boundary changes have been made,
the area and perimeter of each polygon should be
recalculated. Conclusion The standard notion of
topology in GIS centers around explicit
representation of adjacent spatial relations and
involves planar enforcement of geographic
features. Although shapefiles do not explicitly
store spatial relations, they can conform to
planar enforcement. If, during map production or
editing, planar enforcement is violated, then
statistical summations that assume space-filling
polygons could be inaccurate. Although this may
be heresy to many users, there are advantages to
using shapefiles that violate planar assumptions
(i.e., shapefiles that have overlaps and/or
gaps). Many useful analyses do not require data
with precise planar topology, but these analyses
are never conducted because it is assumed that
base data must have topology. For instance, city
and county governments find it extremely
time-consuming and difficult to build parcel
coverages because parcel boundary descriptions
rarely match cleanly with adjacent parcels.
Resolving boundary disputes is a very
time-consuming process, often fraught with
complicated legal issues. However, a standard
query of parcel data performed with reasonably
coincident boundaries (i.e., submeter accuracy)
can be used to find landowners within a certain
distance of a given location for notification
purposes. Though the advantages previously
attributed to topological data structures have
become less clear, in large part because of
improvements in computer performance, the bottom
line is that GIS users need to adequately
understand the data structures and use them
appropriately.
Skip I will not go over how to enforcing planar
topology in AV GIS at this stage
108
Conclusion The standard notion of topology in
GIS centers around explicit representation of
adjacent spatial relations and involves planar
enforcement of geographic features. Although
shapefiles do not explicitly store spatial
relations, they can conform to planar
enforcement. If, during map production or
editing, planar enforcement is violated
statistical summations that assume space-filling
polygons may be inaccurate. Although this may be
heresy to many users, there are advantages to
using shapefiles that violate planar assumptions
(i.e., shapefiles that have overlaps and/or
gaps). Many useful analyses do not require data
with precise planar topology, but these analyses
are never conducted because it is assumed that
base data must have topology. For instance, city
and county governments find it extremely
time-consuming and difficult to build parcel
coverages because parcel boundary descriptions
rarely match cleanly with adjacent parcels.
Resolving boundary disputes is a very
time-consuming process, often fraught with
complicated legal issues. However, a standard
query of parcel data performed with reasonably
coincident boundaries (i.e., submeter accuracy)
can be used to find landowners wn a certain
distance of a given location for notification
purposes. Though the advantages previously
attributed to topological data structures have
become less clear, in large part because of
improvements in computer performance, the bottom
line is that GIS users need to adequately
understand the data structures and use them
appropriately.
skip
109
References with an emphasis on ArcView -- not
including the last few years of ArcGIS Further
Reading Cooke, Donald F., and William H.
Maxfield. "The Development of a Geographic Base
File and Its Uses for Mapping," Proceedings of
the Fifth Annual Conference of the Urban and
Regional Information Systems Association, pp.
207-218, 1967. Corbett, James P.
Topological Principles in Cartography, Technical
Paper 48, United States Department of Commerce,
Bureau of the Census Washington, D.C., 1979.
ESRI Shapefile Technical Description White
Paper, Environmental Systems Research Institute,
Inc. Redlands, CA, 1998. Reed, Carl. "GIS
Users Shouldn't Forget About Topology," GeoWorld,
Vol. 12, No. 4, p. 12, April 1999. Strand,
Eric J. "Shapefiles Shape GIS Data Transfer
Standards," GIS World, Vol. 11, No. 5, p. 28, May
1998. Theobald, David M. Understanding
Topology and Shapefiles. Colorado State
University, ArcUser April-June 2001
.http//www.esri.com/news/arcuser/0401/topo.html
110
We will discuss advances in the new ArcGIS system
in this area if we have time at the end of the
course END
111
Composition of a Raster data set

• Integer (discrete or categorical) raster datasets
  usually have an attribute table associated with
  them.
• Floating-point (continuous) raster datasets
  usually do not have a table associated with them
  because most, if not all cell values are unique,
  and the nature of continuous data excludes other
  associated attributes.

112
Spatial Autocorrelation
113
GISs have to store digital maps somehow!
The map, like the attributes, is eventually
broken down into a sequence of numbers stored in
the computer files. We will not d discuss this
as computer programmers would - but we need
general awareness at this stage.
In general 2 ways are used to store. 1. Each
number is saved in the file encoded into binary
digits or bits. 2. Treating each number like
humans do -- one decimal digit at a time, this
format is called text or ASCII files. These
files can be hacked, printed and read without a
program.