1. Topology
    spatial relationships between features, such as which are connected or adjacent to each other.
  2. Data exploration

    Chapter 10
  3. Sorting tables
    Has no effect on original data
  4. Geo databases
    Can have nearly unlimited number of feature data sets, tables, raster images and photos

    Difficult to determine patterns within and between data layers

    Turning data layers on and off may be the first step in spatial analysis
  5. Data Queries
    • Extract certain records from a map or table
    • Records meet certain criteria
    • Aspatial queries~All parcels with value > $100,000
    • Spatial queries~All parcels that lie completely within a flood plain.
  6. Queries
    • Redefine a layer to include subset of actual features
    • Only selected features appear on map and in table
    • Has no effect on data stored on disk
    • Temporarily treats a layer as being smaller than it actually is
    • Does not require an additional data set to be stored.
  7. 3 Ways to Create Queries
    • Interactive selection
    • Select By Attribute
    • Select By Location
  8. Valid queries

    • “POP1990” > 1000000
    • “STATE_NAME” = ‘Alabama’
    • "POP2000" >= "POP1990“
  9. Multiple Criteria Queries

    Multiple criteria queries using AND/OR

    “STATE_NAME” = ‘Alabama’ OR “STATE_NAME” = ‘Texas’

    Note that the field name must be repeated for each condition
  10. Intersect
    lets you select features from one or more layers based on where they are located in relation to the features in another layer
  11. When to use AND vs OR


    “Pop2000” ≥ 5000 OR “Pop2000” < 9000


    Pop2000” ≥ 5000 AND “Pop2000” < 9000
  12. The Like Operator
    • “NAME”  LIKE ‘%(D)%’
    • Finds all of the (D)  Democrats
    • % is wildcard
    • Ignores Don or Danforth

    • “NAME”  LIKE ‘%New %’
    • Would find New Hampshire and New York, but not Newcastle or Kennewick
  13. Vector Data Analysis and Map Overlays

    Chapter 11
  14. Problem with Spatial Joins
    • Associate land use type with the road
    • BUT . . . the road does not stop at land use boundary.
  15. Map Overlay
    A map overlay forces features to split at polygon boundaries.

    • Map overlay operations are powerful spatial analysis tools
    • Important driving force behind development of GIS technologies
    • They involve combining spatial and attribute data from two or more spatial data layers
  16. Map Overlay Examples
    • Find inexpensive houses in good school districts
    • Whale feeding grounds that overlap with proposed oil drill areas
    • Location of farm fields that are on highly erodible soils
  17. Map Overlay - Intersects
    • has combined attribute data of features from two inputs
    • only contains features that fall within spatial extent of overlay polygons.

    finds common areas

    Both union and intersect combine attributes from each table in output layer.
  18. Map Overlays - Unions
    • has combined attribute data of the polygons in two inputs
    • contains all the polygons from inputs, whether or not they overlap.

    combines all possible areas

    • Both union and intersect combine attributes from each table in output layer.
  19. Appending Attributes
    Combines feature classes without overlay

    • To bring attributes along, tables of input features classes must match exactly.
    • same classes, same order, same definitions
  20. Buffering
    Constructs polygon areas within a specified distance of features.
  21. Efficient Overlay
    • Overlay is time intensive
    • Minimize number of features
    • Intersect geology and elevation first
    • Dissolve vegetation before intersecting with other layers
  22. Raster Data Analysis

    Chapter 12
  23. Raster Data Analysis
    • Based on cells and raster data layers.
    • Can be performed for individual cells, groups of cells, or all cells within the layer.
    • Some raster data operations use a single raster; others use two or more raster data layers.
    • Also depends on the type of cell value (numeric or categorical values).
  24. 4 Levels of Analysis
    • Local – works on a cell-by-cell basis
    • Focal – works on a neighborhood basis
    • Zonal – works on a block basis or uses entire raster layer
    • Global – works on the entire raster layer(s)
  25. Neighborhood Operations
    • Involves a focal cell and set of surrounding cells.
    • Surrounding cells are chosen for their distance and/or directional relationship to the focal cell.
    • Common neighborhoods include rectangles, circles, annuluses, and wedges. 

    4 common types

    • rectangle
    • circle
    • annulus
    • wedge
  26. Zonal Operations
    • Works with groups of cells called zones of same values or like features. Zones may be contiguous or noncontiguous.
    • May work with a single raster or two rasters.
    • With input raster, zonal operations measure geometry of each zone in the raster, such as area, perimeter, thickness, and centroid.
  27. Reclassification Serves 3 Purposes
    • Creates a simplified raster
    • Creates a new raster that contains a unique  category or value
    • Can create a new raster with a ranking of cell values from most suitable to least
  28. Change Detection
    can add/subtract or combine values from two or more layers to make comparisons
  29. Physical Distance Measure Operations
    Measures the straight-line or euclidean distance away from cells designated as source cells. 
  30. Cost Distance Measure Operations
    Measures costs for traversing the physical distance. 
  31. Other Raster Data Operations
    • Clip and Mosaic.
    • Operations for raster data extraction include use of a graphic object, mask, or a query expression to create a new raster by extracting data from an existing raster.
  32. Normalized Difference Vegetation Index (NDVI)
    • Map “greenness” (biomass).
    • Used for:
    • Drought Monitor
    • Agriculture Production
    • Desert  
    • Encroachment
    • Discriminate Healthy Vegetation
  33. Degrees and Radians
    Degree - a measure for arcs and angles; "360° in a circle“

    Radian - a unit of plane angle, equal to 180/π (or 360/(2π)) degrees, or about 57.2958°, or ~ 57°17′45″.
  34. Remote Sensing
  35. Reasons why photo/image interpretation are powerful scientific tools:
    • http://Aerial/regional perspective
    • Ability to obtain knowledge beyond our human visual perception
    • Ability to obtain a historical image record to document change.

    works by electromagnetic energy
  36. Geocaching
    • High-tech treasure hunting game
    • Played throughout world by adventure seekers equipped with GPS devices
    • Locate hidden containers, called geocachesShare your experiences online.
    • Enjoyed by people from all age groups, with a strong sense of community and support for the environment.
  37. Terrain Mapping and Analysis

    Chapter 13
  38. Terrain analysis is used to
    • Determine stream networks
    • Develop contour lines
    • Delineate floodplains
    • Estimate soil erosion
    • Determine species composition based on aspect
  39. Types of data used
    DEM (digital elevation model)

    TIN (triangulated irregular network)

    Either model can be converted to the other to be used together
  40. Adding Breaklines
    • Breakline: line features that represent land surface changes such as streams, shorlines, ridges and roads.
    • A breakline, shown as a dashed line in (b), subdivides triangles in (a) into a series of smaller triangles in (c).
  41. Terrain Mapping
    Terrain mapping techniques include contouring, vertical profiling, hill shading, hypsometric tinting, and perspective view.
  42. Contour Line Map
    • Contour lines connect points of equal elevation
    • Contour interval represents the vertical distance between contour lines
    • Base contour is the contour from which contouring starts

    Ex: elevation from 743 – 1986, base contour = 800, 900, … 1900

    • Contour lines closely spaced represent greater changes in elevation with steeper gradients
    • Wider spaced contour lines represent gentle changes in topography
    • Streams are indicated by a “V” shape which points upstream

    • Contour lines never intersect one another or stop in the middle of the map
    • Contour lines are always enclosed, even though it may not occur within the boundary of the current map
  43. Vertical profile
    shows changes in elevation along a line, such as a hiking trail, road or stream

    shows changes in elevation along a line, such as a hiking trail, road stream or cross section
  44. hillshading
    Hillshading: simulates how the terrain looks with the interaction between sunlight and surface features.

    • Helps to distinguish landform features
    • Often used as the background for terrain mapping
  45. Four factors that control visual effects of hill shading
    • Sun’s azimuth: direction of incoming sunlight ranging from 0˚ – 360˚. Default is 315˚ (NW)
    • Sun’s altitude: angle of incoming light, measured between 0 and 90˚.
    • Slope: measures rate of change at a surface location ranging from 0 – 90˚.
    • Aspect: directional measure of slope from 0˚ – 360˚.
  46. hypsometric map
    Different elevation zones are shown in different gray symbols.
  47. Topo draped Shaded relief
    Shaded relief created from DEM effectively portrays land surface, shown here draped over USGS digital raster graphic of Juneau, Alaska
  48. Slope and Aspect
    Slope measures the rate of change of elevation at a surface location. May be expressed as percentage or degree.

    Aspect is the directional measure of slope. Aspect starts with 0° at the north, moves clockwise, and ends with 360° also at the north. Because it is a circular measure, We often have to manipulate aspect measures before using them in data analysis. 
  49. Viewsheds and Watersheds

    Chapter 14
  50. Viewshed
    Viewshed: portion of land surface visible from one or more viewpoints. 
  51. Output from Viewshed Analysis
    • Output of a viewshed analysis is a binary map showing visible and not visible areas.
    • Given one viewpoint, a viewshed map has value of 1 for visible and 0 for not visible.
    • Given two or more viewpoints, use of 0, 1, and 2.
    • A viewshed map based on two or more viewpoints is often called a cumulative viewshed map. 
  52. Two options for presenting a cumulative viewshed map
    the counting option

    the Boolean option
  53. Parameters of Viewshed Analysis
    • Viewshed results can be influenced by:
    • viewpoint
    • height of observer
    • viewing azimuth
    • viewing radius
    • vertical viewing angle limits
    • Earth’s curvature
    • tree height
  54. Watershed Analysis
    • Defined by topographic divides
    • Watershed is an area that drains surface water to a common outlet.
    • Watershed analysis refers to the process of using DEMs and raster data operations to delineate watersheds and to derive topographic features such as stream networks.
  55. Watershed Delineation
    • Mark outlet
    • Mark streams and/or wetlands
    • Mark highest elevations
    • Connect with lines

    sort of what we did with the last lab but not really...
  56. Delineation of Watersheds
    • Delineation of watersheds can take place at different spatial scales.
    • Delineation of watersheds can also be area-based or point-based.
    • An area-based method divides a study area into a series of watersheds, one for each stream section.
    • A point-based method  derives a watershed for each select point. 
  57. Stream links
    each section of stream network is assigned a unique value and a flow direction. 
  58. Point-Based Watersheds
    Point-based watersheds have one watershed associated with each point, which may be a stream gage station, a dam, or a surface drinking water system intake location. In watershed analysis, these points of interest are called pour points or outlets. 
  59. Factors Influencing Watershed Analysis
    • Resolution and quality of DEM  
    • Algorithm for deriving flow direction network.
  60. Geocoding and Dynamic Segmentation 

    Chapter 16
  61. Geocoding
    • Creating map features from addresses lacking x-, y- coordinates
    • Based on attributes associated with a referenced geographic database, typically a street network
    • Geocoding typically uses Interpolation to find location information about an address.

    • Similar to an attribute join of a standalone table to an attribute table of a spatial data set, except:
    • It can use multiple fields to match records
    • It can match records that are similar but not exactly the same

    Spatial data set is called reference layer
  62. Dynamic Segmentation
    Works in linear distances, like mileposts, to report accidents

    • Popular with government agencies, such as DOTs to coordinate addresses and mileposts for:
    • Rest spots
    • Exits
    • Speed Limit Zones
    • Bridges

    Used by Natural Resource Agencies for stream reach data
  63. Applications for Geocoding
    • Emergency response agencies
    • Driving Directions
    • Crime Analysis
    • Marketing
    • Cadastral Data
    • Postal

    Cadastral used in surveying and public administration; refers to division of land into units for surveying, taxation or administrative purposes. 
  64. “Fuzzy” Matching
    • Joins and queries are based on exact matches
    • Geocoding is able to match records when values may be close but not identical
  65. Requirements for Geocoding
    • Reference layer:  a shapefile or feature class with attributes for matching
    • TIGER-style street centerline data (Topologically Integrated Geographic Encoding and Referencing system with address ranges)
    • Geocoding service set up for reference layer
    • Attribute table with records to be matched
  66. Address Matching
    An alternative to linear interpolation is the use of a “location of address” database in which the location of an address is denoted by a pair of x- and y-coordinates, corresponding to the centroid of a building base or footprint. 
  67. Address Matching Process
    Consists of 3 phases: preprocessing, matching, and plotting.

    • Preprocessing: involves parsing and address standardization.
    • Matching: geocoding engine matches address against a reference database.
    • Plotting point features by interpolating where address falls within address range (linear interpolation).
  68. Address Geocoding
    • Addresses to be matched are parsed into separate components (number, street name, direction, etc.)
    • Each component is compared to same fields in reference layer
    • Candidates are scored based on closeness of matches (0-100)
    • 80-100 is a good match
  69. Quality of Geocoding
    • The result of geocoding is expressed as the percentage of addresses matched or ‘hit rate.’
    • For crime mapping and analysis, one researcher has stated that a 60% hit rate is unacceptable and another has derived statistically a minimum acceptable hit rate of 85%. 
    • To bring the rate to 95% or better, as required for competitive location-based services, a current and accurate reference database and additional effort in validating street addresses would be required.
    • Besides hit rates, positional accuracy has also been proposed to assess the quality of geocoding. 
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