intro to raster gis
DESCRIPTION
Intro to Raster GIS. GTECH361 Lecture 11. CELL. COLUMN. ROW. CELL with VALUE. NO DATA. Raster Attribute Table. NODATA. Coordinate Space and the Raster Dataset. Discrete and Continuous. Resolution. Realm of Analysis. - PowerPoint PPT PresentationTRANSCRIPT
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Intro to Raster GIS
GTECH361Lecture 11
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CELL
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ROW
COLUMN
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CELL with VALUE
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NO DATA
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Raster Attribute Table
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NODATA
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Coordinate Space and the Raster Dataset
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Discrete and Continuous
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Resolution
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Realm of Analysis• Window
identifies spatial limits for future analysis and processing
• Mask further defines the cells for processing
• Cell size determines the size of the cells on the output grids; default is the coarsest input grid
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Window
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The Effect of a Mask
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Map Algebra
aka Cartographic Modeling
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Dana Tomlin 1990
Geographic Information Systems
and Cartographic Modeling
Prentice-Hall
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Map Algebra
Mathematical combinations of layers
Several types of functions: Local Focal Zonal Global
Functions can be applied to one or more layers
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Local Functions
Sometimes called layer functions
Work on every single cell in a raster layer
Cells are processed without reference to surrounding cells
Operations can be arithmetic, trigonometric, exponential, logical or logarithmic functions
As we are dealing with numbers, we can use a plethora of mathematical computations.
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Local Functions
new layer is a functionof two or more inputlayers
output value for eachcell is a function ofthe values of the corresponding cells in the input layers
neighboring or distant cells have no effect
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Local Function Examples
Multiply cells by a constant value
Use a multiplier grid
We can use a range of arithmetic functions
1 1 2
4 2 3
2 3 0 4
2 0 1 1
3 3 6
12 6 9
6 9 0 12
6 0 3 3
X 3 =
1 1 2
4 2 3
2 3 0 4
2 0 1 1
3 3 6
12 6 9
6 9 0 12
6 0 3 3
X
3 3 12
48 12 27
12 27 0 48
12 0 3 3
=
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Local Operations
Creating grids from nothing (0-ary operations)
Random Populate the cell values with independent identically distributed (iid) floating point values between 0 and 1
NormalPopulate the cell values with iid values from the standard Normal distribution (mean 0 and variance 1).
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Local Operations- unary operations -
Rescale: multiply all values by a constant
Compare a grid to a constant value. The result of a comparison is 1 for the cells where the comparison is true, 0 where the comparison is false
Apply a mathematical (or logical) function to a grid, cell by cell
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Mathematical operators
MOD * / + - ()exp
Logical operators AND OR XOR NOT
Local Operations- binary operations -
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Neighborhood Operations
(focal)
output cell value isa function of a groupof neighboring cellsin the input raster
operations could be- average (focalmean)- sum (focalsum)- variance (focalvar)- …..
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Focal Functions
Focal functions process cell data depending on the values of neighboring cells
We define a ‘kernel’ to use as the neighborhood for example, 2x2, 3x3, 4x4 cells
Sometimes in spatial analysis we use shapes to define the focal neighborhood
Around edges a reduced kernel size is used Types of focal functions might be:
focal sum, focal mean, focal max, focal min, focal range
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Focal Function Examples
Focal Sum (sums the value of a neighborhood)
Focal Mean (computes the moving average of a neighborhood)
1 1 3 2
4 2 2 3
2 3 0 4
2 0 1 1
8 13 13 10
13 18 20 14
13 16 16 11
7 8 9 6
=
1 1 3 2
4 2 2 3
2 3 0 4
2 0 1 1
2.02.22.22.5
2.22.02.22.3
2.22.01.81.8
1.81.31.51.5
=
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Another Focal Example
slope- steepness of slope in elevation layer- computed by comparing cell elevation with neighboring values- measured as the angle from horizontal
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Zonal Functions
Process and analyze cells on the basis of ‘zones’
Zones define cells that share a commoncharacteristic
Cells in the same zonedon’t have to be contiguous
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A typical zonal function requires two grids a zone grid which defines the size, shape and
location of each zone a value grid which is to be processed
Typical zonal functions include zonal mean, zonal max, zonal sum, zonal variety
also: sum of the values in different raster that fall into the same zone (e.g., mean district elevation)
results could be assigned to each cell in that zone, or written to a summary table
Zonal Functions
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Zonal Function Example
Zonal maximum - identify the maximum in each zone
Useful when we have some regions to classify with for example, different forest types
8 8 5 5
7 5
5 7 7 8
5 5 8 8
=
1 1 2 2
3 2
2 3 3 1
2 2 1 1
5 5 5 5
1 2 3 4
5 6 7 8
1 2 3 4
Zone Grid Value Grid
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Buffer as a Zonal Function
Can be thought of as spreading a feature by a given distance
Feature
Buffer
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Global Functions
The output value of each cell is a function of the entire grid
Typical global functions are distance measures, flow directions, or weighting measures.
Useful when we want to work out how cells ‘relate’ to each other
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Global Function Examples
Distance measures Euclidean distance computes distance
based on cell size
Use a ‘cost’ grid to weight functions
1.4 1 1.4 2
1 0 1 1
1.4 1 1 0
2 1 0 0
=2
1
1 1
2
1
1 1
Cost Grid
4.2 3 4.2 6
3 0 3 3
1.4 1 1 0
2 1 0 0
=
3 3 3 3
3 3 3 3
1 1 1 1
1 1 1 1
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Map Algebraon Multiple Layers
outgrid = zonalsum(zonegrid, valuegrid)
outgrid = focalsum(ingrid1, rectangle, 3, 3)
outgrid = (ingrid1 div ingrid2) * ingrid3
Map algebra can also be used for multivariate and regression analysis
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Application Functions
Surface Analysis
Hydrologic Analysis
Geometric Transformation
Generalization
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Surface Analysis Slope
Aspect
Hill shade
View shed
Curvature
Contour
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Hydrologic Analysis
Stream network
Watershed Discharge .. more under
modeling
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Generalization
Original satellite “Nibble” Majority filter
image
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GIS-based Spatial Analysis
Mapping distance
Mapping density
Interpolating to raster
Surface analysis
Neighborhood statistics
Cell statistics
Zonal statistics
Reclassifying data
Raster calculator
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Mapping Distance
Straight line distance (Thiessen/Voronoi)
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What Direction?
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Allocation Identifying the
customers served by a series of stores
Finding out which hospital is the closest
Finding areas with a shortage of fire hydrants
Locating areas that are not served by a chain of supermarkets
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Cost-weighted Distance
Cost can be money, time, or preference
Two input grids One regular distance grid One friction surface
Reclassifying your datasets to a common scale
Weighting datasets according to percent influence
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Cost Raster
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Shortest Path
Combination of (weighted) distance and direction
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Density Mapping
In a simple density calculation, points or lines that fall within the search area aresummed and then
divided by the search area size to get each cell’s density value.
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Interpolation
e.g. precipitation
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Surface Analysis
Contours Slope, aspect Viewshed
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Illumination at different times of the day
45° 315°
Viewshed analysis
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Cell Statistics
Majority Maximum Mean Median Minimum Minority Range Standard deviation Sum Variety
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Neighborhood Statistics
Neighborhood shapes
Application: ecosystem variety
rectangle circle annulus wedge
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Zonal Statistics