GIS 1 Final exam

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  1. Interpolation
    Takes measured values at points and distributes them across a raster, estimating the values in between the measurements
  2. Three different interpolation methods are common:
    • Inverse Distance Weighted (IDW)
    • Splining
    • Kriging
  3. Neighborhood
    An area of specific size and shape around a cell
  4. Zones
    • Regions of a raster or a feature class that share the same attribute/integer value
    • Need not be contiguous
  5. Resampling
    • Values converted to a raster with a different cell size
    • Must also occur anytime two rasters with different cell sizes are analyzed together
  6. 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.
  7. DEM
    Digital Elevation Model
  8. 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
  9. 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
  10. 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…
  11. 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
  12. 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
  13. 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
  14. 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
  15. Extraction functions
    • Extraction functions combine the features but do not combine the tables.
    • Includes clip and erase
  16. Overlay with attributes
    • Combine both the features and the tables
    • Includes intersect and union
  17. Clip
    • Extracts features within the boundary
    • Image Upload
  18. Erase
    • Keeps features outside the boundary
    • Image Upload
  19. On-the-fly 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
  20. 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
  21. Types of overlay
    • Union-combines and keeps all features
    • Intersect-combines features and keeps what is common to both
  22. 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
  23. Slivers
    • Tiny polygons created during geoprocessing
    • Result of slight differences in boundaries
    • Can build up as a result of multiple operations
  24. 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
  25. Dissolve
    • Eliminates all of the attributes in the table except the dissolved one
    • Can choose to summarize the other attributes
  26. 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.
    • Image Upload
  27. Ground control points (GCPs)
    • Location on the surface of the Earth
    • Can be identified on the imagery and map
    • Coordinate system (UTM, StatePlane)
  28. Registered Image
    Control must be visible on layers
  29. 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
  30. 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
  31. Interpolation
    Estimate gridded values between known points
  32. Three methods of Interpolation in ArcGIS
    • Inverse distance weighted
    • Kriging
    • Splining
  33. Spatial interpolation
    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.
  34. Gridding
    Interpolating irregularly distributed points to a regular grid is the most frequently performed type of spatial interpolation in GIS
  35. 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.
  36. 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
  37. 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
  38. 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
  39. Interpolation options
    • Regularized
    • Tension
  40. Regularized Spline
    • 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
  41. Tension Spline
    • Incorporates the first derivative terms to the minimization
    • Surface is smooth but the first derivative is not smooth
  42. 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
  43. Common operations about raster data
    • Build statistics
    • Build pyramids
    • Convert to vector
    • Convert from vector
    • Resampling
    • Reclassification
  44. 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.
    • Image Upload
  45. Bilinear resampling
    • 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.
    • Image Upload
  46. 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
    • Image Upload
  47. 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
  48. ArcMap
    Application that is part of ArcGIS that is meant for actually drawing, editing, and tracing map images
  49. 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
  50. 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
  51. 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
  52. 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.
  53. Viewshed
    • Areas visible from a set of observation points
    • region that is visible from a given vantage point in the terrain
  54. Density
    Calculate from point distributions
  55. 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
  56. 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
Card Set:
GIS 1 Final exam
2014-12-11 05:50:08

Final exam
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