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  • Raster Analysis

    Raster cells store data (nominal, ordinal, interval/ratio)

    •Complex constructs built from raster data

    Connected cells can be formed in to networks

    Related cells can be grouped into neighborhoods or regions


    Predict fate of pollutants in the atmosphere

    The spread of disease

    Animal migrations

    Crop yields

    EPA - hazard analysis of urban superfund sites

    Local to global scale forest growth analysis

  • Raster


    require a

    special set

    of tools

  • Raster Analysis

    Map algebra Concept introduced and developed by by Dana Tomlin and

    Joseph Berry (1970’s)

    Cell by Cell combination of raster data layers

    Each number represents a value at a raster cell


    Simple operations can be applied to each number

    Raster layers may be combined through operations

    Addition, subtraction and multiplication

  • Scope: Local operations

  • Scope: Neighborhood operations

  • Scope: Global operation

  • Many Local


    (page412 of book)

  • Logical Operations

    AND Non-zero values are “true”, zero values are “false”

    N = null values

    Pg 412 of book

  • Logical Operations

    OR Non-zero values are “true”, zero values are “false”

    N = null values

    Pg 412 of book

  • Logical Operations


  • More Local Functions – logical comparisons

    (pg 414 of book)

  • Conditional


  • Nested


    no yes

    Output= Con(ISNULL(LayerA), LayerC, LayerB)

  • Neighborhood


    Moving Windows (Windows can be any size;

    often odd to provide a center)

  • Neighborhood


  • Neighborhood Operations: Mean Function

    What about the edges?

  • Neighborhood Operations: Separate edge kernals can be used

  • Example:Identifying

    spatial differences in

    a raster layer

  • Raster Analysis

    Moving windows and kernals can be used with a mean

    kernal to reduce the difference between a cell and

    surrounding cells. (done by average across a group of cells)

    Raster data may also contain “noise”; values that are large

    or small relative to their spatial context. (Noise often requiring correction or smooth(ing))

    Know as “high-pass” filters

    The identified spikes or pits can then be corrected or

    removed by editing

  • Raster Analysis

    High pass filters


    •Small values when smoothly changing values.

    •Large positive values when centered on a spike

    •Large negative values when centered on a pit

  • 35.7

  • Mean filter


    Note edge erosion

  • Moving windows: Consider the overlap in cell calculations

    Neighborhood operations often

    Increase spatial covariance

  • Overlay in Raster

    Union and Clip

    Cell by Cell Addition or Multiplication

    Attribute combinations corresponding to

    unique cell combinations

  • Raster Clip or “Mask” (used in Lab 10)

    What if you

    only want

    certain cells?

  • Raster Clip or “Mask” (used in Lab 10)

    Note: removed cell output values could be

    0 or N depending of the GIS software


  • Raster zonal function

  • Issues in Raster Addition

  • A Problem with Raster


    • Too many cells

    • Typically, one-to-one relationship between

    spatial object and attribute table

    • Rasters have multiple cells per feature

    • Attribute tables grow to be unwieldy

  • Vector Raster

  • Raster overlay as addition

  • Output layer DOES NOT

    have unique records Raster Overlay

  • What to do? First multiply Layer A by 10

  • Cost Surface

    The minimum cost of reaching cells in a layer from

    one or more sources cells

    “travel costs” Time to school; hospital;

    Chance of noxious foreign weed spreading out from an introduction point

    •Units can be money, time, etc.

    •Distance measure is combined with a fixed cost per unit

    distance to calculate travel cost

    •If multiple source cells, the lowest cost is typically placed in

    the output cell

  • Friction Surface (version of a Cost Surface)

    The cell values of a friction surface represent the cost per unit

    travel distance for crossing each cell – varies from cell to cell

    Used to represent areas with variable travel cost.


    •Barriers can be added.

    •Multiple paths are often not allowed

    •Cost and Friction Surfaces are always related to a source

    cell(s); “from something”

    •The center of a cell is always used the distance calculations

  • Digital Elevation Models &

    Terrain Analysis

  • Terrain determines or influences:

    - natural availability and location of surface water,

    and hence soil moisture and drainage.

    - water quality through control of sediment

    entrainment/transport, slope steepness.

    - direction which defines flood zones, watershed

    boundaries and hydrologic networks.

    - location and nature of transportation networks or

    the cost(methods) of house(road) construction.

  • Digital Elevation Models

    •Used for: hydrology, conservation, site planning, other

    infrastructure development.

    •Watershed boundaries, flowpaths and direction, erosion

    modeling, and viewshed determination all use slope and/or

    aspect data as input.

    •Slope is defined as the change is elevation (a rise) with a

    change in horizontal position (a run).

    •Slope is often reported in degrees (0° is flat, 90° is vertical)

  • Formats - Contour Elevation Data

    • Source Independent

    • USGS topo maps

    • Contour shows a

    line of constant


    • Generally used

    more as a



    From Sean Vaughn, MNDNR

  • DEM’s consist of an array representing

    elevation values at regularly spaced intervals

    commonly known as cells.


    VALUES (ft)

    Formats - Digital Elevation Models




    From Sean Vaughn, MNDNR

  • DEM = Raster = Grid

    Digital Elevation Models

    Raster (Format)

    DEM = Grid vs. Vector data format

    From Sean Vaughn, MNDNR

  • DEM Structure • Each cell usually

    stores the average

    elevation of grid cell.

    • Typically they store

    the value at the

    center of the grid cell.

    • Elevations are

    presented graphically

    in shades or colors.

    67 56 49

    53 44 37

    58 55 22

    D ig

    it a l

    G ra

    p h ic

    a l

    Digital Elevation Models

    From Sean Vaughn, MNDNR

  • DEMs are a common way of representing elevation where every

    grid cell is given an elevation value. This allows for very rapid

    processing and supports a wide-array of analyses.

    Digital Elevation Models

    From Sean Vaughn, MNDNR

  •  Resolution

     30 Meter

     USGS produced from Quad Hypsography.

     DNR published format in MN.

     Course resolution

     10 Meter

     Interpolated

     Resampled


    Previously Published National DEMs

    From Sean Vaughn, MNDNR

  •  Resolution

     1 Meter

     3 Meter

     Most common published format

    in MN.

     Storage requirements & faster

    drawing speeds. 53

    Previously Published National DEMs

  • Resolution Tradeoff

    • Lower resolution = Faster processing

    • Higher resolution = Maintain small features

    1-meter DEM claims 9-

    times more process

    resources and storage

    than a 3-meter DEM

    From Sean Vaughn, MNDNR

  • Viewshed

    The viewshed for a point is the collection

    of areas visible from that point.

    Views from any non-flat location are blocked by


    Elevations will hide a point if they are higher than the

    viewing point, or higher than the line of site between

    the viewing point and target point

  • not

  • Shaded Relief Surfaces

  •  The azimuth is the

    angular direction of the


     Measured from north in

    clockwise degrees from 0

    to 360.

     The altitude is the slope or


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