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Interpolation
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)
 Splining
 Kriging

Neighborhood
An area of specific size and shape around a cell

Zones
 Regions of a raster or a feature class that share the same attribute/integer value
 Need not be contiguous

Resampling
 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 distanceweighted 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.

DEM
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…

Summarized join
 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: Onetoone, manytoone cardinality
 Summarized joins (many records from the source): Onetomany, manytomany

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

Map Overlay
 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
 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

Clip
 Extracts features within the boundary

Erase
 Keeps features outside the boundary

Onthefly clipping
 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 onetoone cardinality between features in order to join attributes
 Similar to an spatial join
 Combines attributes based on common location (inside join)
 Enforces onetoone cardinality between features

Types of overlay
 Unioncombines and keeps all features
 Intersectcombines 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

Slivers
 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

Dissolve
 Eliminates all of the attributes in the table except the dissolved one
 Can choose to summarize the other attributes

Model Builder
 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)

Registered Image
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 welldefined 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

Interpolation
Estimate gridded values between known points

Three methods of Interpolation in ArcGIS
 Inverse distance weighted
 Kriging
 Splining

Spatial interpolation
A computational procedure of estimating (calculating/predicting) the surface values for a continuous geospatial variable at unsampled locations within the area where a sample of surface values given.

Gridding
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.

Exponent m
 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


Regularized Spline
 Incorporates the third derivative terms into minimization
 Ensures a smooth surface together with smooth firstderivative surfaces.
 It is useful if the second derivative is needed

Tension Spline
 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
 Resampling
 Reclassification

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.

Bilinear resampling
 Calculates a new value from the four cells that fall closest to the center of the new cell
 Uses a distanceweighted 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 distanceweighted algorithm based on the old cell centers
 best used with continuous data such as elevation
 most timeconsuming 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

ArcMap
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

Slope
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

Aspect
 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.

Viewshed
 Areas visible from a set of observation points
 region that is visible from a given vantage point in the terrain

Density
Calculate from point distributions

Reclassification
 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

Spatial Analyst
 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

