Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.
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Transcript of Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.
![Page 1: Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.](https://reader035.fdocuments.us/reader035/viewer/2022062516/56649da05503460f94a8b72b/html5/thumbnails/1.jpg)
Map Algebra and Beyond:1. Map Algebra for Scalar
Fields
Xingong LiUniversity of Kansas
3 Nov 2009
![Page 2: Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.](https://reader035.fdocuments.us/reader035/viewer/2022062516/56649da05503460f94a8b72b/html5/thumbnails/2.jpg)
Topics
• The conventional map algebra– Local operations– Focal operations– Zonal operations– Global operations
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Map Algebra
Precipitation-
Losses (Evaporation,
Infiltration)=
Runoff
5 22 3
2 43 3
7 65 6
-
=
• Raster layers are manipulated by math-like expression to create new raster layers
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Map Algebra Operations
• Tomlin (1990) defines and organizes operations as local, focal, zonal, and global according to the spatial scope of the operations– Geographic Information System and Cartographic
Modeling, Englewood Cliffs: Prentice Hall, 1990.
Local ZonalFocal Global
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Local Operations
• Compute a new raster layer.
• The value for each cell on the output layer is a function of one or more cell values at the same location on the input layer(s).
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Local Operations
• Arithmetic operations
+, -, *, /, Abs, …
• Relational operators
>, <, …
• Statistic operations
Min, Max, Mean, Majority, …
• Trigonometric operations
Sine, Cosine, Tan, Arcsine, Arccosine, …
• Exponential and logarithmic operations
Sqr, sqrt, exp, exp2, …
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Local Operation--Examples
9 9 7
9 8 5
6 3 0
0 0 2
0 0 1
0 0 0
9 9 9
9 8 6
6 3 0+ =
9 9 7
9 8 5
6 3 0
0 0 2
0 0 1
0 0 0
N N 3.5
N N 5
N N N/ =
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Removing Clouds Using a Local Operation
• Two consecutive ocean surface temperature raster layers for the same area (measured at a slightly different time).
Images are from: http://rs.gso.uri.edu/amy/avhrr.html
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30-Year (1971-2000) Monthly PRISM Precipitation
Dec.
Oct.
Aug.
Jun.
Apr.
Feb.
How do we define seasonality of precipitation at a single
location?
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Seasonality at San Francisco
[4.01 3.48 2.69 1.30 0.48 0.11 0.01 0.02 0.19 0.74 1.57 4.09]
Total = 18.69”
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Monthly Precipitation as a Vector Quantity
1
2
3
4
530
210
60
240
90270
120
300
150
330
180
0
x=p*sin(monthAngle)y=p*cos(monthAngle)
Each month’s duration is equivalent to a 30° angleMonthly data are plotted at midpoint: 15°, 45°, 75°, ……
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Seasonality Analysis
5
10
1530
210
60
240
90270
120
300
150
330
180
0
Monthly precipitation (in inches): [4.01 3.48 2.69 1.30 0.48 0.11 0.01 0.02 0.19 0.74 1.57 4.09]
1
2
3
4
530
210
60
240
90270
120
300
150
330
180
0
x=p*sin(monthAngle)y=p*cos(monthAngle) Add all vectors = Resultant vector
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Seasonality at San Francisco
• Average monthly precipitation at San Francisco in inches– [4.01 3.48 2.69 1.30 0.48 0.11 0.01 0.02 0.19 0.74 1.57 4.09]
• Precipitation vectors– x=1.09, 2.46, 2.59, 1.26, 0.34, 0.03, 0, -0.01, -0.18, -0.71, -1.11, -
1.04– y=3.86, 2.47, 0.74, -0.32, -0.33, -0.11, -0.01, -0.01, -0.05, 0.19, 1.11,
3.95• Resultant vector
– sx=4.7– sy=11.5– Magnitude=12.41– Direction=22.25
• Time of occurrence – Direction in month (= January)
• Seasonality index– 1-magnitude of resultant vector/total precip.– = 1- (12.41/18.69)– = 0.34 (larger number means more uniform)
22.25°
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Seasonality Analysis: Local functions at each cell over the whole domain
• sy– Cos(15) * [p01] + Cos(45) * [p02] + Cos(75) * [p03] + Cos(105)
* [p04] + Cos(135) * [p05] + Cos(165) * [p06] + Cos(195) * [p07] + Cos(225) * [p08] + Cos(255) * [p09] + Cos(285) * [p10] + Cos(315) * [p11] + Cos(345) * [p12]
• sx– Sin(15) * [p01] + Sin(45) * [p02] + Sin(75) * [p03] + Sin(105) *
[p04] + Sin(135) * [p05] + Sin(165) * [p06] + Sin(195) * [p07] + Sin(225) * [p08] + Sin(255) * [p09] + Sin(285) * [p10] + Sin(315) * [p11] + Sin(345) * [p12]
• Magnitude of resultant vector– Sqrt(Sqr([sx]) + Sqr([sy]))
• Total precipitation– [p01] + [p02] + [p03] + [p04] + [p05] + [p06] + [p07] + [p08] +
[p09] + [p10] + [p11] + [p12]• Seasonality
– 1 - ([Mag. Of resultant vector] / [Total Precip])
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Time of Occurrence
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Seasonality Index
Large number means more uniformSmall number means more seasonal
0.34
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Map Algebra Operations
• Operations are grouped as local, focal, zonal, and global according to the spatial scope of the operations.
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Focal Operations• Compute an output value for each cell as a function of
the cells that are within its neighborhood
• Widely used in image processing with different names– Convolution, filtering, kernel or moving window
• Focal operations are spatial in nature
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Neighborhoods
• The simplest and most common neighborhood is a 3 by 3 rectangle window
• Other possible neighborhoods – a rectangle, a circle, an annulus (a donut) or a wedge
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Finding Appropriate Wind Farm Sites
• Wind speed– Higher elevation higher speed– Elevation (>= 1000m)
• Aspect– facing prevailing wind direction
• Wind exposure– Not blocked by nearby hills in the
prevailing wind direction
• Data – Prevailing wind direction
• 225 to 315– DEM
• Wedge neighborhood– 0 degree is East, counterclockwise
(135—225)
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Wind Exposure Analysis
• Find max elevation in the prevailing wind direction– FocalMax with a wedge neighborhood
• Find cells not blocked by hills in the neighborhood– DEM > FocalMax
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Map Algebra Operations
• Operations are grouped as local, focal, zonal, and global according to the spatial scope of the operations.
![Page 23: Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.](https://reader035.fdocuments.us/reader035/viewer/2022062516/56649da05503460f94a8b72b/html5/thumbnails/23.jpg)
Zonal Operations
• Compute a new value for each cell as a function of the cell values within a zone containing the cell
• Zone layer– defines zones
• Value layer– contains input cell values
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Zonal Statistical Operations
• Calculate statistics for each cell by using all the cell values within a zone
• Zonal statistical operations:– ZonalMean, ZonalMedian, ZonalSum,
ZonalMinimum, ZonalMaximum, ZonalRange, ZonalMajority, ZonalVariety, ….
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Zonal Statistical Operation Example
1 1 4 3 3
1 1 4 3 3
2 2 2 3 4
2 1 2 3 4
1 1 4 4 4
1 2 3 4 5
6 7 8 9 1
2 3 4 5 6
7 8 9 1 2
3 4 5 6 7
Zone Layer Value Layer
ZonalMax
Output Layer
8 8 8 9 9
8 8 8 9 9
9 9 9 9 8
9 8 9 9 8
8 8 8 8 8
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Outputs of Zonal Operations
• Raster layer– All the cells within a zone have
the same value on the output raster layer
• Table– Each row in the table contains
the statistics for a zone.
– The first column is the value (or ID) of each zone.
– The table can be joined back to the zone layer.
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NEXRAD Cell Precipitation
Measurement spatial resolution
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Subwatershed Precipitation from NEXRAD Cells Precipitation
model/application resolution
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NEXRAD Subwatershed Precipitation
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Calculating Subwatershed Precipitation Depth
i
ii
a
pa
aaaa
papapapa ***** depth precip. edsubwatersh
4321
44332211
p1 p2
p3 p4
a1a2
a3 a4
mean zonal*
* depth precip. edsubwatersh
So, . size cell is layer, edsubwatershraster aWith
n
p
an
pa
a
pa
aa
ii
i
ii
i
pi—precipitation depth in an hour
ai—portion of the watershed that falls in the ith cell
![Page 31: Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.](https://reader035.fdocuments.us/reader035/viewer/2022062516/56649da05503460f94a8b72b/html5/thumbnails/31.jpg)
Map Algebra Operations
• Operations are grouped as local, focal, zonal, and global according to the spatial scope of the operations.
![Page 32: Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.](https://reader035.fdocuments.us/reader035/viewer/2022062516/56649da05503460f94a8b72b/html5/thumbnails/32.jpg)
Global Operations
• Operations that compute an output raster where the value of each output cell is a function of all the cells in the input raster
• Global statistical operations
• Distance operations.
– Euclidean distance
– Cost distance
![Page 33: Map Algebra and Beyond: 1. Map Algebra for Scalar Fields Xingong Li University of Kansas 3 Nov 2009.](https://reader035.fdocuments.us/reader035/viewer/2022062516/56649da05503460f94a8b72b/html5/thumbnails/33.jpg)
Distance Operations
• Characterize the relationships between each cell and source cells (usually representing features)– Distance to nearest source cell
– Direction to nearest source cell
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Euclidean Distance Operation
1 1
1
2
• Calculates the shortest straight distance from each cell to its nearest source cell (EucDistance)
• Assigns each cell the value of its nearest source cell (EucAllocation)
• Calculates the direction from each cell to its nearest source cell (EucDirection)
1 1 1 1 1 1
1 1 1 1 1 1
1 1 1 1 1 1
2 1 1 1 1 1
2 2 2 1 1 1
2 2 2 2 2 1
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EucDistance Example
Buffers can be delineated from the distance raster
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EucAllocation Example
Thesisen’s polygon
Voronoi diagram
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Non-Euclidean Distance (Cost Distance)
• Straight line distance (between A and B) is a type of cost
• Cost could also be measured as time or money spent• Friction may vary space• Least cost and least-cost-path
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CostDistance Operation
• Compute the least accumulative cost from each cell to its least-cost source cell
• Source raster– Representing features (points,
lines, and polygons) – No-source cells are set to
NODATA value
• Friction raster– Cost encountered while moving in
a cell (distance, time, dollars and efforts)
– Unit is: cost per unit distance– Can have barriers (NODATA cells)
friction
source
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Fungus Invasion
• Fungus spreading depends on the availability of precipitation
• A fungus is introduced at a seaport in January 1
• Questions
– Which area would be affected by July?
– Will the fungus reach Austin by the end of July?
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Fungus Spreading Speed
• Fungus travel speed depends on precipitation.
– < 100 mm/month, 0 m/day
– 100 – 200 mm/month, 4000 m/day
– > 200 mm/month, 7000 m/day
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The Friction Raster
Friction = 1 / speed
Unit of the friction raster:days per unit distance
Travel speed
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The Least Cost Raster
What do the values mean on the cost raster layer?
The days that the fungus will take to reach a cell
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Friction Varies in Space & Time
• Precipitation varies both in space and time.
• How could we model the spreading of the fungus now?
AprilFebruary
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Fungus Invasion
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Fungus Invasion by Month
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Sum of Two Cost Surfaces
The least cost between A and B and passes through a cell.
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Corridor Analysis
Corridor = accumulative cost < a threshold value
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Summary Concepts
• What you have just seen is the basis for the map algebra language in ArcGIS Grid and Spatial Analyst– Local functions
– Focal functions
– Zonal functions
– Global functions