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Geographic Information Systems Applications in Natural Resource Management. Chapter 2 GIS Databases: Map Projections, Structures, and Scale. Michael G. Wing & Pete Bettinger. Chapter 2 Objectives. Definition of a map projection, and the components that comprise a projection

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geographic information systems applications in natural resource management

Geographic Information SystemsApplications in Natural Resource Management

Chapter 2

GIS Databases:

Map Projections, Structures, and Scale

Michael G. Wing & Pete Bettinger

chapter 2 objectives
Chapter 2 Objectives
  • Definition of a map projection, and the components that comprise a projection
  • Components and characteristics of a raster data structure
  • Components and characteristics of a vector data structure
  • The purpose and structure of metadata
  • Likely sources of GIS databases that describe natural resources within North America
  • Types of information available on a typical topographic map and
  • Definition of scale and resolution as they relate to GIS databases
big questions
Big questions…
  • What is the size and shape of the earth?
  • Geodesy: the science of measurements that account for the curvature of the earth and gravitational forces
  • We are still refining our approximation of the earth’s shape but are relying on GPS measurements for much of this work
slide4

Vertical

Alexandria

Rays parallel

to the sun

712’

Earth

Syene

712’

360 / 712’ = 1/50

500 miles * 50 = 25,000

Figure 2.1. Eratosthenes’ (276-194 BC) approach to determining the Earth’s circumference.

slide5

North Pole

b

Equator

a

Ellipsoid

Figure 2.2. The ellipsoidal shape of the Earth deviates from a perfect circle by flattening at the poles and bulging at the equator.

Isaac Newton (end of the 17th century) theorized this shape.

Field measurements, beginning in 1735, confirmed it.

earth measurements models
Earth measurements & models
  • Datums
    • Horizontal and vertical control measurements
  • Ellipsoids (spheroids)
    • The big picture
  • Geoids
    • Gravity and elevation
horizontal datums
Horizontal Datums
  • Geodetic datums define orientation of the coordinate systems used to map the earth
  • Hundreds of different datums exist
  • Referencing geodetic coordinates to the wrong datum can result in position errors of hundreds of meters.
  • NAD27 and NAD83 common in US
    • NAD83 preferred
    • Uses center of the earth as starting point
    • Many GPS systems use WGS84
vertical datums
Vertical Datums
  • For use when elevation is of interest
  • National Geodetic Vertical Datum of 1929
    • NGVD29
    • 26 gaging stations reading mean sea level
  • North American Vertical Datum of 1988
    • NAVD88
    • 1.3 million readings
    • Has become the preferred datum in the US
spheroids
Spheroids
  • Newton defined the earth as an ellipse rather than a perfect circle (1687)
    • Spheroids are all called ellipsoids
  • Represents the elliptical shape of the earth
  • Flattening of the earth at the poles results in about a twenty kilometer difference at the poles between an average spherical radius and the measured polar radius of the earth
  • Clarke Spheroid of 1866 and Geodetic Reference System (GRS) of 1980 are common
  • WGS84 has its own ellispoid
geoids
Geoids
  • Attempts to reconcile the non-spherical shape of the earth
  • Earth has different densities depending on where you are and gravity varies
  • A geoid describes earth’s mean sea-level perpendicular at all points to gravity
    • Coincides with mean sea level in oceans
    • Geoid is below ellipsoid in the conterminous US
  • Important for determining elevations and for measuring features across large study areas
coordinate systems
Coordinate Systems
  • Used to describe the location of an object
  • Many basic coordinate systems exist
    • Instrument (digitizer) Coordinates
    • State Plane coordinates
    • UTM Coordinates
    • Geographic
  • Rene Descartes (1596-1650) introduced systems of coordinates
  • Two and three-dimensional systems used in analytical geometry are referred to as Cartesian coordinate systems
slide13

9

8

7

6

5

4

3

2

1

2,6

y

6,1

0

1 2 3 4 5 6 7 8 9

x

Figure 2.4. Example of point locations as identified by Cartesian coordinate geometry.

slide14

90° North latitude

W E

30°N, 30°W

N

S

0° latitude

30°S, 60°E

Prime

meridian

Equator

90° South latitude

Figure 2.5. Geographic coordinates as determined from angular distance from the center of the Earth and referenced to the equator and prime meridian.

geographic coordinates
Geographic coordinates
  • Longitude, latitude (degrees, minutes, seconds)
map projections
Map Projections
  • Map projections are attempts to portray the surface of the earth or a portion of the earth on a flat surface
    • Earth features displayed on a computer monitor or on a map
    • Earth is not round, has a liquid core, is not static, and has differing gravitational forces
  • Distortions of conformality, distance, direction, scale, and area always result
  • Many different projection types exist:
    • Lambert, Albers, Mercator
map projection process 2 steps
Map projection process: 2 steps
  • Measurements from the earth are placed on a globe or curved surface that reflects the reduced scale in which measurements are to be viewed or mapped
    • This is the reference globe
  • Measurements placed on the three-dimensional reference globe are then transformed to a two-dimensional surface
envisioning map projections
Envisioning map projections
  • Transforming three-dimensional earth measurements to a two-dimensional map sheet
  • Visualize projecting a light from the middle of the earth and shining the earth’s features onto a map
  • The map sheet may be:
    • Planar
    • Cylindrical
    • Conic
slide19

Figure 2.6. The Earth’s graticule projected onto azimuthal, cylindrical, and conic surfaces.

Azimuthal

Cylindrical

Conic

slide20

Figure 2.7. Examples of secant azimuthal, cylindrical, and conic map projections.

Azimuthal

Cylindrical

Conic

map projections within gis
Map projections within GIS
  • There are several components that make up a projection:
    • Projection classification (the strategy that drives projection parameters)
    • Coordinates
    • Datum
    • Spheroid or Ellipsoid
    • Geoid
  • Most full-featured GIS can project coordinate systems to represent earth measurements on a flat surface (map)
    • ArcGIS handles most projections
map projection importance
Map projection importance
  • GIS analysis relies strongly on covers being in the same coordinate system or projection
    • Do not trust “projections on the fly”
    • This is the visual referencing of databases in different projections to what appears to be a common projection
  • Failure to ensure this condition will lead to bad analysis results
  • You should always try to get information about the projection of any spatial themes that you work with
    • Metadata- Data about data
the classification of map projections according to how they address distortion
The classification of map projections according to how they address distortion
  • Conformal
    • Useful when the determination of distances or angles is important
    • Navigation and topographic maps
  • Equal area
    • Will maintain the relative size and shape of landscape features
  • Azimuthal
    • Maintains direction on a mapped surface
slide24

Figure 2.8. The orientation of the Mercator and transverse Mercator to the projection cylinder.

transverse Mercator

Mercator

common map projections
Common Map Projections
  • Lambert (Conformal Conic)- Area and shape are distorted away from standard parallels
    • Used for most west-east State Plane zones
    • There is also a Lambert Azimuthal projection that is planar-based
  • Albers Equal Area (Secant Conic)-
    • Maintains the size and shape of landscape features
    • Sacrifices linear and distance relationships
  • Mercator (Conformal Cylindrical)- straight lines on the map are lines of constant azimuth, useful for navigation since local shapes are not distorted
    • Transverse Mercator is used for north-south State Plane zones
planar coordinate systems
Planar coordinate systems
  • Used for locating features on a flat surface
  • Universal Transverse Mercator
    • The most popular coordinate system in the U.S. and Canada
    • Even used on Mars
    • 1:2,500 accuracy
  • State plane coordinate system
    • Housed within a Lambert conformal conic or Tranverse Mercator projection
    • 1:10,000 accuracy
slide27

126°W

120°W

114°W

108°W

102°W

96°W

90°W

84°W

78°W

72°W

66°W

10 11 12 13 14 15 16 17 18 19

Figure 2.9. UTM zones and longitude lines for the U.S.

where do coordinates come from
Where do coordinates come from?
  • Many measurements
  • Initially these are manual measurements
    • The Public Land Survey System (PLSS) within the U.S.
    • The Dominion Land Survey within Canada
  • GPS is now used to refine measurements and support coordinate systems
public lands survey system
Public Lands Survey System
  • The mapping of the U.S. and where U.S. coordinates originated from
  • Thomas Jefferson proposed the Land Ordinance of 1785
    • Begin surveying and selling (disposing) of lands to address national debt
  • Surveying begins in Oregon in 1850
  • U.S. not linked by the PLS until 1903!
    • Provided first quantitative measurement from coast-to-coast
slide30

First Standard Parallel North

First Guide Meridian East

T2N R3E

Baseline

Initial Point

Principal Meridian

First Guide Meridian West

First Standard Parallel South

NW ¼ NE 1/4

NE ¼ NE 1/4

SE ¼ NE 1/4

NW 1/4

SW ¼ NE 1/4

6

5

4

3

2

1

N1/2 SW 1/4

7

8

9

10

11

12

W1/2 SE 1/4

E1/2 SE 1/4

18

17

16

15

14

13

S1/2 SW 1/4

19

20

21

22

23

24

30

29

28

27

26

25

31

32

33

34

35

36

Figure 2.10. Origin, township, and section components of the Public Land Survey System.

T2N R3E

NW 1/4, NE 1/4, Section 17

arcgis has projection capabilities
ArcGIS has Projection Capabilities
  • ArcGIS Projection Abilities
    • ArcView will allow you to project shapefiles
    • ArcEditor and ArcInfo will allow you to project shapefiles and coverages
projection challenges
Projection challenges
  • Within several miles of Oregon State University, you would find several different map projections being applied to spatial databases
  • Siuslaw National Forest
    • UTM (Universal Transverse Mercator) zone 10, NAD27
  • Benton County Public Works & McDonald Forest
    • Oregon State Plane North, NAD83/91
  • OSU Departments
    • Various map projections
  • A potential solution
    • Create a map projection customized for Oregon
    • Oregon Centered Lambert, NAD83
oregon s projection
Oregon’s Projection
  • Selected by state leaders in 1996
    • Designed to centralize projections used by state agencies
  • Projection: LAMBERT
  • Datum: NAD83
  • Units INTERNATIONAL FEET, 3.28084 units = 1 meter (.3048 Meters)
  • Spheroid GRS1980
oregon s projection specifics
Oregon’s Projection Specifics
  • 43 00 00.00 /* 1st standard parallel
  • 45 30 00.00 /* 2nd standard parallel
  • -120 30 0.00 /* central meridian
  • 41 45 0.00 /* latitude of projection's origin
  • -400,000.00 /* false easting (meters), (1,312,335.958 feet)
  • 0.00 /* false northing (meters)
finding projection information
Finding projection information
  • Should always be part of the metadata document
    • Example: the Oregon Geospatial Enterprise Office
    • http://www.oregon.gov/DAS/EISPD/GEO/alphalist.shtml
  • Can also be stored as part of an ArcInfo coverage or an ArcView shapefile
    • You can return projection information in workstation ArcInfo with the DESCRIBE command
    • The .prj part of the shapefile will contain projection information but it is not created automatically- a user must create the file
projection information
Projection information
  • Within ArcGIS, you can examine projection information (if it exists) by examining a layer’s properties
  • Without the projection information, you’ll need to do detective work
    • Probability for success: low
two primary gis data structures raster vector
Two primary GIS data structures: Raster & Vector
  • Two different approaches to capturing and storing geographic data
  • “Yes raster is faster, but raster is vaster, and vector just seems more corrector.” C. Dana Tomlin 1990
  • Decision to use one or both structures will be based on project objectives, existing data, available data, and monetary resources
slide40

Figure 2.11. Generic raster data structure.

Rows

Raster or grid cell

Columns

raster data
Raster data
  • Many different types of raster data
    • Satellite imagery
      • Landsat TM, IKONOS, AVHRR, SPOT
    • Aerial imagery
      • LIDAR, color and infrared digital photographery
    • Digital raster graphics (DRGs)
    • Digital orthophoto quadrangles (DOQS)
slide42

Figure 2.12. Landsat 7 satellite image captured using the Enhanced Thematic Mapper Plus Sensor that shows the Los Alamos/Cerro Grande fire in May 2000. This simulated natural color composite image was created through a combination of three sensor bandwidths (3, 2, 1) operating in the visible spectrum. Image courtesy of Wayne A. Miller, USGS/EROS Data Center.

slide46

Figure 2.17

Figure 2.19

Figure 2.16. Corvallis Quadrangle with neatlines around map areas to be described in detail.

slide48

124°

123°

45°

H

G

F

E

D

C

B

A

Figure 2.18. Ohio code location of the Corvallis Quadrangle

E3

44°

8 7 6 5 4 3 2 1

slide51

Scale Standard

1:1,200 ± 3.33 feet

1:2,400 ± 6.67 feet

1:4,800 ± 13.33 feet

1:10,000 ± 27.78 feet

1:12,000 ± 33.33 feet

1:24,000 ± 40.00 feet

1:63,360 ± 105.60 feet

1:100,000 ± 166.67 feet

Table 2.1. Map scales and associated National Map Accuracy Standards for horizontal accuracy.

vector data structure
Vector data structure
  • In contrast to raster, not necessarily organized in a pattern
  • Vector data are usually irregular in shape and represent precise locations
  • The vector world is organized using three basic shapes
    • points, lines, and polygons
    • referred to as the GIS feature model
slide53

Line

Polygon

Point

Figure 2.21. Point, line, and polygon vector shapes.

topology
Topology
  • The spatial relationships between points, lines, and polygons
  • Determines
    • Distance between points
    • Whether lines intersect
    • Whether points, lines, or polygons are within the extent of a polygon
slide55

Connected stream

network

Adjacent polygons

One polygon contained inside

another polygon

Figure 2.22. Examples of adjacency, connectivity, and containment.

topological building blocks
Topological building blocks
  • Topology needs to be coded and described mathematically
  • Most GIS programs will have database tables that describe topology
  • All vector shapes must be isolated and located using a coordinate system
slide57

Link

Vertex

Node

Figure 2.23. Examples of nodes, links, and vertices.

slide58

1

5

C

4

4

D

2

3

6

2

Y

1

5

3

E

7

B

A

6

X

a. Network of nodes, links, and polygons

b. node coordinate file

c. topological relationship file

Figure 2.24. Vector topological data. Network of nodes, links, and polygons (a), node coordinate file (b), and topological relationship file.

slide59

a. An un-closed polygon

b. An extraneous polygon

Figure 2.25. Examples of topological errors. In the first (a), an undershoot has occurred and instead of a closed figure creating a polygon, a line has been created. In the second (b), a small loop has been formed extraneously adjacent to a polygon. This might represent a digitizing error or the result of a flawed overlay process.

raster vector must i choose one
Raster & vector: must I choose one?
  • These are complimentary data structures and you will use both if you conduct GIS analysis
  • More commonly, GIS software will allow you to read both data types
    • Some software (ArcGIS) will allow you to analyze both types simultaneously
    • Demonstrated in chapters 13 and 14
  • Not uncommon for some analysts to convert from one structure to another
slide61

.

.

.

.

.

Figure 2.26. Point, line, and polygon features in a raster or grid

slide62

(a)

(b)

(c)

A forest stand boundary in vector format (a) scanned or converted to a raster format using 25 m grid cells (b), then converted back to vector format (c) by connecting lines to the center of each grid cell.

slide63

Summary of Raster and Vector Characteristics

CharacteristicRaster Vector

Structure complexity simple complex

Location specificity limited not limited

Computational efficiency high low

Data volume high low

Spatial resolution limited not limited

Representation of topology difficult not difficult

among features

other types of data structures models
Other types of data structures/models
  • Other hybrid forms of data structures exist
    • May help you solve spatial analysis challenges
  • TINs
    • Landforms
  • Regions
    • Area
  • Routes
    • Linear features
triangular irregular network
Triangular Irregular Network
  • Triangles to represent earth surfaces
  • A generic (not dominated by a specific software manufacturer ) reference to a data structure that is similar to vector yet possesses
  • An alternative to raster in representing continuous surfaces
  • May help reduce some of the ambiguities presented by a raster structure
regions
Regions
  • Not all GIS software can accommodate regions
  • ArcGIS can
  • Allows for overlapping polygons
  • May reduce database complexity
  • Large woody debris study
slide68

Figure 2.28. Large Woody Debris

  • One record for each log possible
  • Reduces data storage needs
routes
Routes
  • Dynamic Segmentation
    • Traditionally linked to ESRI products
  • For linear networks but can also handle points
  • Stream habitat
  • Delivery / emergency routing
  • Recreation use patterns
slide70

Routes Example: Aquatic Habitat Database

  • One record to represent the many links that make up a primary channel
  • Simplify structures
  • Reduce data storage redundancy
map scale and resolution
Map scale and resolution
  • GIS databases are often described in terms of their scale or resolution
  • These terms make reference to the source data that was used to create the GIS database
  • Scale and resolution will often provide guidance in the application of a GIS database for specific purposes
map scale
Map Scale
  • Scale is the relationship between a linear feature on a map or photograph and the actual distance on the ground
  • Scale is usually expressed as a representative fraction
    • 1:24,000
      • One inch on the map or photograph represents 24,000 inches (or 2000 feet) of on the ground distance
    • 1:200,000
      • One cm on the map or photograph represents 200,000 cm (or 2000 m) of on the ground distance
  • Scale plays a strong role in determining the proper use of a spatial database
map scale73
Map Scale
  • Do not confuse the scale of a spatial data layer with how closely you are zoomed in on it
    • Scale is tied to on the ground measurements
  • Most scale measurements of vector data usually come to us from the scale derived from aerial photograph measurements
    • A relationship between the focal length of the camera lens and the height of the lens above terrain
resolution
Resolution
  • With raster data, scale is often expressed in terms of resolution or the amount of ground that each side of a pixel, or cell, covers
    • Measurements typically are the same on all sides of a pixel or cell
    • 1 meter, 10 meter, 30 meter
      • These measurements are squared to represent area
map scale75
Map Scale
  • Relative size of scales is dependent on the representative fraction
    • 1:24,000 is considered a large-scale map
    • 1:100,000 is usually considered a small scale map, at least in comparison to 1:24,000
  • You may also use the terms fine and coarse-scale resolution to avoid confusion
slide76

Figure 2.29. Map of stream network displayed at scales of 1:100,000 and 1:24,000.

1:100,000 map

1:24,000 map

map scale and resolution77
Map Scale and Resolution
  • Be wary of mixing data themes that are drawn from widely different scales or resolutions
    • Mixing 1:24,000 scale data with 1:250,000 is probably not a good idea
    • Sometimes, there may be no alternatives and you’ll have to take “your best shot”