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Takes measured values at points and distributes them across a raster, estimating the values in between the measurements
Three different interpolation methods are common:
- Inverse Distance Weighted (IDW)
An area of specific size and shape around a cell
- Regions of a raster or a feature class that share the same attribute/integer value
- Need not be contiguous
- Values converted to a raster with a different cell size
- Must also occur anytime two rasters with different cell sizes are analyzed together
3 methods of resampling
- Nearest neighbor: the new cell is given the value of the old cell that falls at or closest to the center of the new cell. Best for categorical rasters
- Bilinear resampling: a distance-weighted average is taken from the four nearest cell centers. Works better for continuous rasters, like elevation.
- Cubic Raster Analysis convolution: determines a new value by a curve fit through the nearest 16 cell centers.
Digital Elevation Model
Table has three fields that are always present
- ObjectlD field: containing a unique ID for the table rows
- Value field: showing each unique cell value
- Count field: indicating how many cells contain that value
Spatial Joins Criteria
- Join two tables based on a common spatial relationship
- One feature inside another
- One feature closest to another
- Spatial joins always create a new, permanent data layer, rather than being temporary like attribute joins
- Can join points to points, polygons to polygons, lines to points, and nearly any combination of the three types of data
Distance join units
- Given in stored map units
- Decimal degrees cannot be easily converted to miles or km because the conversion factor varies with latitude
- Better to use a projected coordinate system…
- Can choose from several statistics, such as minimum, maximum, average, and so on
- All numeric attributes in the source table are summarized using the statistics you choose and placed in the output table.
- String fields cannot be summed or averaged, of course, so they are not included in the output table.
- won't work with categorical data
- Ex: group the cities according to the airport they served, sum the population, makes a table. Now every airport has one record
Spatial Join Cardinality
- Simple joins: One-to-one, many-to-one cardinality
- Summarized joins (many records from the source): One-to-many, many-to-many
Point to point joins
- Which attraction is the closest to each hotel? Enforces a one to one cardinality…
- How many attractions are best accessible from each hotel? One to many cardinality…must use summarize
- Forces the road features to split at the land use boundaries.
- Each new segment falls inside one land use category.
- The tables can be now joined, providing a basis for calculating total road lengths in each land use category.
- Two major types of overlay: Extraction functions & Overlay with attributes
- Extraction functions combine the features but do not combine the tables.
- Includes clip and erase
Overlay with attributes
- Combine both the features and the tables
- Includes intersect and union
- Extracts features within the boundary
- Keeps features outside the boundary
- Temporary clip applied to a map layout
- Does not create new layers or affect lengths or areas of the source layers
- Can be performed on many layers simultaneously
- Can be removed when no longer needed
- Set as a data frame property
Overlay with attributes
- Joins features based on common location
- Forces features to split when they overlap each other, creating new features
- Enforces one-to-one cardinality between features in order to join attributes
- Similar to an spatial join
- Combines attributes based on common location (inside join)
- Enforces one-to-one cardinality between features
Types of overlay
- Union-combines and keeps all features
- Intersect-combines features and keeps what is common to both
Results from overlay
- Combine features spatially, producing all possible new features
- Combine attribute tables, bringing original values from each table and assigning to each new feature
- New spatial data set is created with features and attribute table
- Tiny polygons created during geoprocessing
- Result of slight differences in boundaries
- Can build up as a result of multiple operations
Solutions for slivers
- use the tolerance setting has some problems
- The shape of the polygons are changed
- It might not be acceptable for many projects
- Use Eliminate to get rid of slivery polygons
- Eliminates all of the attributes in the table except the dissolved one
- Can choose to summarize the other attributes
- Create models built from sequences of tools
- Store processing steps for later reference
- Execute models repeatedly with different inputs
- Share models with others
- A way to string functions together to create a work flow
- Useful for grouping sets of related functions for repeated use
- Models are created inside your toolboxes
- Models can be shared with others
- Models can be converted to scripts for more advanced looping and control development.
Ground control points (GCPs)
- Location on the surface of the Earth
- Can be identified on the imagery and map
- Coordinate system (UTM, StatePlane)
Control must be visible on layers
The National Standard for Spatial Data Accuracy (NSSDA)
- Implements a statistical approach to estimate the positional accuracy of points on maps and in digital data
- The accuracy is relative to georeferenced ground positions of higher accuracy
National Map Accuracy Standards (NMAS)
- Specifies that 90% of the well-defined points that are tested must fall within a specified tolerance:
- For map scales larger than 1:20,000, the NMAS horizontal tolerance is 1/30 inch
- For map scales of 1:20,000 or smaller, the NMAS horizontal tolerance 1s 1/50 inch
Estimate gridded values between known points
Three methods of Interpolation in ArcGIS
- Inverse distance weighted
A computational procedure of estimating (calculating/predicting) the surface values for a continuous geo-spatial variable at unsampled locations within the area where a sample of surface values given.
Interpolating irregularly distributed points to a regular grid is the most frequently performed type of spatial interpolation in GIS
Inverse Distance Weighted (IDW) Interpolation
- Due to surface autocorrelation, the closer sample values, the more similar the surface values.
- The weight of a sample point is assigned according to the inverse of its distance to the point being estimated.
- The closer the sample value, the greater the weight is assigned.
- Size of exponent m affects the shape of the surface
- Larger m will result in higher peaks while lower m will give a more gentle surface
Search for neighboring points
- Fixed radius: regardless the number of point, take all the points in the radius range
- Fixed number of neighbors: regardless the search radius, and find the closest neighboring points until the condition is satisfied
- Quadrant or octant searching: Distribute the searching efforts into four or eight directions equally
Minimum Curvature Spline Interpolation
- Used to interpolate along a smooth curve.
- Force a smooth line to pass through a desired set of points
- Uses a piecewise polynomial to provide a series of patches resulting in a surface that has continuous first and second derivatives
- Incorporates the third derivative terms into minimization
- Ensures a smooth surface together with smooth first-derivative surfaces.
- It is useful if the second derivative is needed
- Incorporates the first derivative terms to the minimization
- Surface is smooth but the first derivative is not smooth
Kriging Interpolation Method
- Similar to Inverse Distance Weighting (IDW)
- Uses the minimum variance method to calculate the weights rather than applying an arbitrary or less precise weighting scheme
- Computationally intensive;
- Provide an estimate of the potential amount of error for the output
Common operations about raster data
- Build statistics
- Build pyramids
- Convert to vector
- Convert from vector
Nearest neighbor resampling
- Grabs the value from the old cell that falls at the center of the new cell
- It preserves the original value and should always be used with categorical data, or when the original data values need to be preserved.
- It is the fastest method.
- Calculates a new value from the four cells that fall closest to the center of the new cell
- Uses a distance-weighted algorithm based on the old cell centers
- Best used with continuous data such as elevation.
Cubic convolution resampling
- Calculates a new value from the sixteen cells that fall closest to the center of the new cell
- Uses a distance-weighted algorithm based on the old cell centers
- best used with continuous data such as elevation
- most time-consuming method
Zonal statistics of lines
- Zones defined by the zone layer (watersheds)
- Generates statistics for each zone from the value grid (slope)
- Output is either a raster, or a table
Application that is part of ArcGIS that is meant for actually drawing, editing, and tracing map images
Digital Elevation Model (DEM)
- Used to refer specifically to a regular grid of spot heights
- Simplest and most common form of digital representation of topography
simplest way is to use a 3x3 window centered on the point. The maximum slope on the basis of a comparison of a central target cell with its neighbors
- The deepest downslope direction
- Vegetation, crops, fruits between slopes facing north and south
- Wind generators: face the prevailing winds rather than be sheltered from them.
- The aspect at each location determines the direction of water flow over the terrain surface.
- N = 0 with degrees increasing clockwise
Image Simulation - Hillshaded Relief Map
- Hill shading, relief shading, plastic shading, shaded relief.
- each pixel's illumination computed from its slope relative to a hypothetic "sun".
- Assume that light source infinitely far away from surface and the light is coming from a constant direction and elevation angle.
- The continuous tonal variations give an impression of shadow produced by the interaction of the sun with topographic surface, thus making the map looks more like a photograph.
- Areas visible from a set of observation points
- region that is visible from a given vantage point in the terrain
Calculate from point distributions
- Changing the values of the grid by applying some scheme set up by the user
- Ended up with fewer classes, which is also a kind of data aggregation process
- An extension to ArcGIS
- Installed under Complete or Custom option
- Requires purchase of additional license to use
- Operates within ArcMap or ArcCatalog
- Analyzes raster data