GIS 1 Final exam

Takes measured values at points and distributes them across a raster, estimating the values in between the measurements

• Inverse Distance Weighted (IDW)
• Splining
• Kriging

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

• 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

• 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

• 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

• 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

• Simple joins: One-to-one, many-to-one cardinality
• Summarized joins (many records from the source): One-to-many, many-to-many

• 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

• Combine both the features and the tables
• Includes intersect and union

• Extracts features within the boundary
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• Keeps features outside the boundary
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• 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

• 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

• Union-combines and keeps all features
• Intersect-combines features and keeps what is common to both

• 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

• 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.
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• 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

• 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

• 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

• Inverse distance weighted
• Kriging
• Splining

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

• 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

• 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

• 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
• Tension

• 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

• 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

• Build statistics
• Build pyramids
• Convert to vector
• Convert from vector
• Resampling
• Reclassification

• 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.
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• 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.
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• 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
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• 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

• 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

• 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