Gis Test 1

Front 1

GIS

Back 1

System of computer software, hardware, data, and personnel which enables the manipulation, analysis and presentation of information that is tied to a spatial location

Front 2

Map Scale

Back 2

Ratio of distance on the map to distance on the ground

Front 3

Small scale map

Back 3

A large denominator gives a small fraction

Front 4

Large scale map

Back 4

A small denominator gives a larger fraction

Front 5

Types of Maps

Back 5

–Thematic maps
–Topographic maps
–Physical maps
–Network maps
–Surface maps

Front 6

Thematic maps

Back 6

Land cover, population, politics, etc

Front 7

Network maps

Back 7

Roads, rivers/streams

Front 8

Surface maps

Back 8

-Topographic maps
-Digital Elevation Model (DEM)

Front 9

Topographic maps

Back 9

Front 10

Digital Elevation Model (DEM)

Back 10

Front 11

Survey Instruments

Back 11

-Transit
-Theodolite
-Total Station
-RTK GPS

Front 12

Components of spatial data

Back 12

–Geometry: locations, shapes, and sizes
–Attributes: tables of information about features

Front 13

Vector

Back 13

–points, lines, polygons
–discrete objects

Front 14

Raster

Back 14

-grid (matrix) of cells
–each cell has a value

Front 15

Examples of Raster Data

Back 15

-Elevation
Digital Elevation Models

-Air photos
orthophotographs

-Satellite data
visible, infrared, gravity

Front 16

Vector characteristics

Back 16

-Usually complex
-Small for most datasets
-Simple
-Preferred for network analysis
-Limited only by positional measurements (scale)

Front 17

Raster characteristics

Back 17

-Usually simple
-Large for most data
-May be slow and require resampling
-Easy for continuous data, combining layers
-Floor set by cell size

Front 18

Types of attributes

Back 18

–Nominal (text)
–Ordinal (rank)
–Interval (numeric)

Front 19

Nominal

Back 19

Location name; address; description

Front 20

Ordinal

Back 20

Suitability (high, medium, low); importance

Front 21

Interval/Numeric

Back 21

Area, population, temperature, elevation

Front 22

Large Scale Map

Back 22

More detail, covers smaller area

Front 23

Small Scale Map

Back 23

Less detail, covers larger area

Front 24

Scale Bars

Back 24

Front 25

Points

Back 25

–Street address
–Earthquake epicenter

Front 26

Lines

Back 26

–Roadways
–Stream network

Front 27

Polygons

Back 27

–Political Boundaries
–watersheds

Front 28

Coordinate systems

Back 28

–Cartesian
–Geographic

Front 29

Datums

Back 29

–Horizontal
–Vertical

Front 30

Map projections

Back 30

–Azimuthal
–Cylindrical
–Conical

Front 31

Cartesian Coordinates

Back 31

–2-dimensional (X, Y)
–Origin at (0,0)
–Negative X values to the left of the Y axis
–Negative Y values below the X axis

Front 32

Geographic

Back 32

–Earth is approximately spherical
–Locations measured in degrees of latitude and longitude
–DMS: 0°0’0”
–Decimal: 0.0000°

Front 33

Latitude (lat)

Back 33

•Equator is 0° latitude
•Measure degrees north and south
•Also called parallels
•North Pole is 90°N (+)
•South Pole is 90°S (-)
•Tropics/Circles

Front 34

Longitude (long)

Back 34

•Prime Meridian is 0°
•Measure degrees east and west
•Known as meridians
•All lines of longitude are “great circles”
•180° E (+) or W (-)

Front 35

Hemispheres

Back 35

Front 36

Datums

Back 36

Models of the Earth

Front 37

Type of Datums

Back 37

–Sphere
–Ellipsoid
–Geoid

Front 38

Common US horizontal (2D) datums

Back 38

North American Datum (NAD) 1927 or 1983
World Geodetic System of 1984 – U.S. DOD (used worldwide)

Front 39

Horizontal Datums

Back 39

Ellipsoids

Front 40

Ellipsoids

Back 40

-Bulge at the equator
-Flattened at the poles
-A theoretical surface which fits the Earth best (globally/regionally

Front 41

Vertical Datum

Back 41

Geoid

Front 42

Geoid

Back 42

• The mean sea surface level
• Varies with the Earth’s gravity (larger when Earth’s crust is thicker)
• A detailed 3D model of the surface

Front 43

Ellipsoid vs Geoid

Back 43

• Ellipsoids are idealized (mathematical) models
• Geoids are more complex and representative (of the Earth surface)
• Different ellipsoids work better in certain parts of the world

Front 44

Map Projections

Back 44

Projecting a 3D surface onto a 2D surface

Front 45

Types of Projections

Back 45

Planar (Azimuthal)
Cylindrical (Mercator)
Conical

Front 46

Projection Properties

Back 46

– Area: equal area or equivalent projection
– Shape: conformal
– Direction: conformal, azimuthal
– Distance: equidistant
• Distortion (unavoidable)
• The least distortion is along the tangent line (s)

Front 47

More Projections

Back 47

Front 48

Projected Coordinate Systems

Back 48

• Once projected, data still needs coordinates
• Different systems depending on the scale and orientation of the map you’re trying to make
• Most common are UTM (worldwide) and State Plane (for US)

Front 49

Universal Transverse Mercator

Back 49

Based on a cylindrical projection cutting through the globe. The zero point for the x axis is located on the equator.

Front 50

UTM Coordinate System

Back 50

• Best for features with North-South orientation
• 60 zones, each of which is 6° of latitude wide
• Origin at equator, 500,000 m west of the central meridian
• Best for small scale maps

Front 51

State Plane Coordinate System

Back 51

• Each state has one or more
• Usually one of two types:
–Transverse Mercator
•North-south states
– Lambert Conformal Conic
•East-west states

Front 52

Northing and Easting

Back 52

• Origin far to the south and west
• Y values = northings
• X values = eastings
• Prevents negatives

Front 53

Mass. State Plane

Back 53

NAD 1983 State Plane Massachusetts (m or ft.)
Projection: Lambert Conformal Conic
Spheroid: GRS 80

Front 54

Azimuthal/planar

Back 54

Front 55

Azimuthal/planar

Back 55

Front 56

Conic

Back 56

Front 57

Conic

Back 57

Front 58

Cylindrical (Mercator)

Back 58

Front 59

Cylindrical (Mercator)

Back 59

Front 60

Unprojected (GCS)

Back 60

– Geographic coordinate system
– Based on spherical coordinates
– Degrees of latitude and longitude

Front 61

Projected

Back 61

– Converts spherical coordinates to planar
– Set of mathematical equations
– Projects 3D coordinates to 2D map

Front 62

Avoid GCS when mapping

Back 62

-A map using a Geographic Coordinate system (GCS) appears distorted.
-Always use a projected coordinate system for mapping.

Front 63

Map Units

Back 63

UTM (meters)
GCS (degrees)
State Place (feet)

Front 64

Units Terminology

Back 64

• Map units are determined by the data frame coordinate system.
• Display units can be set by the user, so that the coordinates may be viewed in any desired unit, such as miles.
• Page units show the location on the map page layout, usually in inches or cm.

Front 65

Map Basics

Back 65

Front 66

Thematic Map

Back 66

• Feature Map
• Choropleth
• Dot Density
• Isopleth/Contour

Front 67

Feature Map

Back 67

Front 68

Choropleth

Back 68

Front 69

Dot Density

Back 69

Front 70

Isopleth/Contour

Back 70

Front 71

Symbology Basics

Back 71

• Symbols can indicate type or importance
• Can be based on nominal or numeric attributes

Front 72

Classification Methods

Back 72

• Common:
– Manual
– Equal Interval
– Quantile
– Natural Breaks

Front 73

Classification Methods

Back 73

• Uncommon:
–Geometrical Interval
–Standard Deviation

Front 74

Choosing Class Breaks

Back 74

• For normally distributed data: Equal Interval
• Skewed data: Quantile or Natural Breaks
• Most of the time, Manual may work better

Front 75

Symbolizing Class Breaks

Back 75

Graduated Colors
Graduated Symbols
Proportional Symbols
Dot Density

Front 76

Data Collection Techniques

Back 76

• Digitizing (tracing features)
–Scanned maps
–Raster data
• Surveying data points using GPS, surveying equipment
• Remote sensing
• Drawing files (CAD)

Front 77

Surveying

Back 77

•The technique, profession, and science of accurately determining the terrestrial or three-dimensional position of spatial features

Front 78

Surveying Tools

Back 78

-Total Stations
-Theodolite
-GPS

Front 79

Global Navigation Satellite Systems (GNSS)

Back 79

• Global positioning system (GPS) is the first deployed set of GNSS for positioning. It was developed by DoD.
• Russia has been developing GLONASS
• Galileo is planned by a consortium of European governments and industries
• The fourth system is under development is the Chinese Compass Satellite Navigation System

Front 80

Global Positioning System

Back 80

• system (constellation) of 24 satellites in high altitude orbits
• coded satellite signals that can be processed in a GPS receiver to compute position, velocity, and time

Front 81

Segments of GPS

Back 81

Control
Space
User

Front 82

GPS Key Concepts

Back 82

: using satellite ranging
: measuring distance from satellite
: getting perfect timing
: knowing where a satellite is in space
: identifying errors

Front 83

Receiver Position is Based on Time

Back 83

The Global Positioning System allows a GPS receiver to determine its position by using a simple formula: Velocity x Time = Distance

Front 84

Measuring TIme

Back 84

•Satellites have atomic clocks
– Very expensive: $100K
•Receivers have “ordinary” clocks
– Inexpensive and not as accurate as satellite’s clocks

Front 85

How many satellites are needed for positing?

Back 85

Three can be enough but four is best and necessary because of clock errors associated with receivers

Front 86

Sources of Errors When Positioning with GPS

Back 86

• Tropospheric water vapor
• Multipath: reflected signals from surfaces near receiver
• Noise: receiver noise
• Satellite clock errors
• Blunders: human error
• Dilution of precision (DOP): satellite geometry
• Ionosphere: electrically charged particles

Front 87

Differential GPS (DGPS)

Back 87

Corrects errors at one location using measured errors at a known position (base station)

Front 88

DGPS modes of measurement

Back 88

• Real time
• Post-process

Front 89

"Heads-up” digitizing

Back 89

Also known as on screen digitizing

Front 90

Georeferencing

Back 90

–The process of converting a map or an image from one coordinate system to another by using a set of control points and a transformation equation.

Front 91

Editor Toolbar

Back 91

Tools for creating and modifying features

Front 92

Geocoding

Back 92

Converting street address to x y coordinates

Front 93

Rematching

Back 93

Fixing the unmatched addresses

Front 94

Space borne remote sensing

Back 94

– CORONA
– IKONOS / Geoeye (high spatial res.)
– Quickbird / WorldView (high spatial res.)
– Landsat/ SPOT (medium spatial res.)
– MODIS/VIIRS/AVHRR (low spatial res.)

Front 95

Airborne remote sensing (UAV)

Back 95

– AVIRIS
– Predator
– Global Hawk

Front 96

Concept or Resolution

Back 96

Spatial
Spectral
Temporal
Radiometric

Front 97

Spectral Resolution

Back 97

Panchromatic (one single band, e.g. CORONA, old aerial photographs, IKONOS/Quickbird Pan band)
Multispectral (several bands, e.g. Landsat, MODIS)
Hyperspectral (many bands, e.g. AVIRIS)

Front 98

Spectral Resolution

Back 98

Derived by the width and height of the resolution bands and the number of spectral bands

Front 99

Airborne remote sensing

Back 99

• Collected by cameras mounted on planes
• Multiple passes over a short time period
• Orthorectified once images are joined
• Perspective view

Front 100

LiDAR

Back 100

Light detection and Ranging - laser elevations

Front 101

Topology

Back 101

– The arrangement for how point, line, and polygon features share geometry
– Or knowledge about relative spatial positioning

Front 102

Query

Back 102

A question posed to a database

Front 103

Organizing attribute tables

Back 103

• Flat Files
• Hierarchical
• Relational (databases)
• Object-oriented (database)

Front 104

Flat files

Back 104

Spreadsheets

Front 105

Relational

Back 105

Various tables (databases) are linked through unique identifiers

Front 106

Query Selection

Back 106

– Select by Attribute: specify matching criteria
– Select by Location: based on spatial proximity

Front 107

SQL

Back 107

Structured Query Language

Front 108

One-to-one relationships

Back 108

• each record in one table has only one matching record in another table

Front 109

Many-to-one relationships

Back 109

• multiple records in the table match to one record in another table

Front 110

Relating tables

Back 110

Used when tables have a one-to-many or many-to-many relationship

Front 111

Steps of Georeferencing

Back 111

– Coordinate transformation (scaling, rotating, skew)
– Resamping

Front 112

Coordinate Transformation Methods

Back 112

– First-order polynomial (Affine)
– 2nd Order polynomial
– 3rd order polynomial

Front 113

Vector Models

Back 113

- Geo-relational Vector Model
- Object-based Vector Model

Front 114

Geo-relational Vector Model

Back 114

- Arc Coverage (has topology) >>> format: binay
- Shape files (no topology) >>>> format: *.shp, *.shx, *dbf, etc

Front 115

Object-based Vector Model

Back 115

Includes classes and geodatabases >>> format: *.mdb

Front 116

Satellite Broadcast two types of data

Back 116

Almanac data- not very precise
Ephemeris- by comparison very precise

Front 117

Remote sensing

Back 117

Sensing/Taking measurements from a distance away from objects

Front 118

Remote sensing data collection

Back 118

– Optical/Thermal Cameras (e.g. Landsat)
– Laser (e.g. LiDAR)
– Radar Transmitters/Receivers (e.g. SAR)

Front 119

Radiometric resolution

Back 119

Tells us about the dynamic range of pixel numbers in an image

Front 120

Shapefiles

Back 120

• dbf = attribute table
• .prj = projection file
• .shp = contains geometry information