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Coordinate systems and data types in GIS
- 1 Datum
- 2 Geographic coordinate systems
- 3 Projected coordinate systems
- 4 Conformality vs. equivalency
- 5 Universal transverse Mercator (UTM)
- 6 State plane coordinate systems
- 7 Vector data
- 8 Raster data
- 9 Raster vs. vector
- 10 Triangulated irregular network (TIN)
- 11 Terrain dataset
- 12 Common GIS file formats
- 13 Exercise: State plane and Universal Transverse Mercator (UTM) projections
1 Datum
A datum describes the relationship between a spheroid model of the Earth and its real shape.
There are hundreds of datums and each one uses different methods to measure the Earth.
- WGS84 (World Geodetic System of 1984)
- NAD83 (North American Datum of 1983)
- NAD27 (North American Datum of 1927)
2 Geographic coordinate systems
A geographic coordinate system (GCS) uses a three-dimensional spherical surface.
- $a$: Semi-major axis
- $b$: Semi-minor axis
- $1/f$: Inverse flattening
3 Projected coordinate systems
3.1 Projection types
Secant conformal conic projection
Meridians are lines of longitude (vertical lines) that end at the North and South poles.
Parallels are circles of latitude (horizontal circles).
Tangent conformal conic projection
4 Conformality vs. equivalency
Conformality
- Correct shape and angle (for smaller areas, e.g., coast lines)
- Incorrect size
- Conformal projections
Equivalency
- Correct size and area
- Incorrect shape
- Equal-area projections
5 Universal transverse Mercator (UTM)
- Each 6 degrees of longitude
- Large north-south extent with low distortion
6 State plane coordinate systems
Conformal projections with an accuracy of one part in 10,000
7 Vector data
7.1 Points
Points are zero-dimensional objects and represent geographic features such as wells, sample locations, or trees.
7.2 Point data examples
- Soil samples
- Type
- PH
- Contaminants
- Utility poles
- Owner
- Height
- Attachments
- Spill locations
- Accident number
- Type of spill
- Extent
- Parcel centroids
- Section/block/lot no.
- Address
- Owner
- Assessment data
7.3 Point data: Light poles
7.4 Point data: Parcel centroids
7.5 Lines
Lines represent one-dimensional objects or linear features such as road and stream centerlines.
Lines are made up of a series of interconnected points.
A line typically starts and ends with a special point called a node, and the points that make up the rest of a line are called vertices.
7.6 Line data examples
- Street centerlines
- Street name
- Address ranges
- Water mains
- Pipe size
- Pipe material
- Date installed
- Streams
- Flow rate
- Cross-sectional area
- Depth
- Water quality
7.7 Line data: Street centerlines
7.8 Polygons
Polygons represent two-dimensional objects such as the boundaries of a field or property, or the outline of a building or lake.
Polygons are made up of a series of connected lines where the starting point of a polygon is the same as the ending point.
7.9 Polygon data examples
- Parcel
- Parcel ID
- Dimensions and area
- Soil boundaries
- Type
- Permeability
- Flood zones
- Flood depth
- Flood frequency
7.10 Polygon data: Parcels
7.11 Polygon data: Building footprints
8 Raster data
Geographic data sets
- Land use/land cover
- Vegetation index
- Soil stability
Digital photography
- Building photos
- Accident scenes
- Crop damages
- Full motion videos
Digital orthophotography
- Rectified aerial photos
8.1 Raster data structure
Matrix of equal-area cells
8.2 Issues with resolution
9 Raster vs. vector
What is the best format to represent geographic objects in a GIS, raster or vector?
9.1 Shoreline example
In this example, the vector data source representing the shoreline appears to have greater detail and possibly greater accuracy.
9.2 When to use vector or raster?
Vector data can often store information in a more compact format than raster data and also work well with linear objects such as stream networks. Vector data is not suitable for representing terrain surfaces.
Raster data models are much better at representing information that is continuous in nature such as temperature where the value of temperature may be different between neighbors. Raster data is suitable for representing terrain surfaces.
9.3 Vector: Advantages and disadvantages
Advantages
- Good representation of reality
- Compact data structure
- Topology can be described in a network
- Accurate graphics
Disadvantages
- Complex data structure
- Simulation may be difficult
- Some spatial analysis is difficult or impossible to perform
9.4 Raster: Advantages and disadvantages
Advantages
- Simple data structure
- Easy overlay
- Various kinds of spatial analysis
- Uniform size and shape
- Cheaper technology
Disadvantages
- Large amount of data
- Less “pretty”
- Projection transformation is difficult
- Different scales between layers can be a nightmare
- May lose information due to generalization
10 Triangulated irregular network (TIN)
A triangulated irregular network (TIN) is a data model that is used to represent three-dimensional objects.
While the TIN model is somewhat more complex than the simple point, line, and polygon vector models, or the raster model, it is actually quite useful for representing elevations.
10.1 Issues with TIN
Can be too heavy and slow!
11 Terrain dataset
Multi-resolution TIN
12 Common GIS file formats
12.1 Shapefile
Primary vector data file format used by many GIS programs
Consists of
- Shape file (*.shp): geometries
- Shape index (*.shx): shape indices
- Database table (*.dbf): attributes
Optionally
- Projection file (*.prj): projection information
- Ancillary files (*.sbn, *.sbx, etc.): spatial indices and other information used by ArcGIS
- Metadata files (*.htm, *.xml, *.txt): metadata
12.1.1 Shape index
Stores the following two fields
- Record offset
- Record length
Makes reading geometries from a shapefile efficient and faster
12.1.2 Spatial index
12.2 GeoTiff
Primary raster data file format used by many GIS programs
*.tif or *.tiff
Yes, it’s an image file format with some geospatial metadata embedded, such as projections, extents, resolutions, etc.
13 Exercise: State plane and Universal Transverse Mercator (UTM) projections
In this exercise, we will import the polygons of state plane projections and UTM zones.