Spatial Analysis Using Grids
description
Transcript of Spatial Analysis Using Grids
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Spatial Analysis Using Grids
• Continuous surfaces or spatial fields representation of geographical information
• Grid data structure for representing numerical and categorical data
• Map algebra raster calculations • Interpolation • Calculate slope on a raster using
– ArcGIS method based in finite differences
– D8 steepest single flow direction– D steepest outward slope on grid
centered triangular facets
Learning Objectives
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• http://resources.arcgis.com/en/help/main/10.1/index.html#/What_is_raster_data/009t00000002000000/ Raster and Images, starting from "Introduction/What is raster data" to end of " Fundamentals of raster data/Raster dataset attribute tables"
Readings – at http://resources.arcgis.com/en/help/
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Readings – at http://resources.arcgis.com/ • What is the ArcGIS Spatial Analyst extension
http://resources.arcgis.com/en/help/main/10.1/index.html#/What_is_the_ArcGIS_Spatial_Analyst_extension/005900000001000000/
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x
dx)y,x(f)y(f
x
y
f(x,y)
Two fundamental ways of representing geography are discrete objects and fields.
The discrete object view represents the real world as objects with well defined boundaries in empty space.
The field view represents the real world as a finite number of variables, each one defined at each possible position.
(x1,y1)
Points Lines Polygons
Continuous surface
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Numerical representation of a spatial surface (field)
Grid or
Raster
TIN Contour and flowline
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Six approximate representations of a field used in GIS
Regularly spaced sample points Irregularly spaced sample points Rectangular Cells
Irregularly shaped polygons Triangulated Irregular Network (TIN) Polylines/Contours
from Longley, P. A., M. F. Goodchild, D. J. Maguire and D. W. Rind, (2001), Geographic Information Systems and Science, Wiley, 454 p.
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Discrete (vector) and continuous (raster) data
Images from http://resources.arcgis.com/en/help/main/10.1/index.html#/Discrete_and_continuous_data/009t00000007000000/
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Raster and Vector Data
Point
Line
Polygon
Vector Raster
Raster data are described by a cell grid, one value per cell
Zone of cells
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Raster and Vector are two methods of representing geographic data in GIS
• Both represent different ways to encode and generalize geographic phenomena
• Both can be used to code both fields and discrete objects
• In practice a strong association between raster and fields and vector and discrete objects
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A grid defines geographic space as a mesh of identically-sized square cells. Each cell holds a numeric value that measures a geographic attribute (like elevation) for that unit of space.
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The grid data structure• Grid size is defined by
extent, spacing and no data value information– Number of rows, number of
column– Cell sizes (X and Y) – Top, left , bottom and right
coordinates• Grid values
– Real (floating decimal point)– Integer (may have
associated attribute table)
Numberof
rows
Number of Columns
(X,Y) Cell sizeNODATA cell
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Points as Cells
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Line as a Sequence of Cells
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Polygon as a Zone of Cells
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NODATA Cells
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Cell Networks
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Grid Zones
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Floating Point Grids
Continuous data surfaces using floating point or decimal numbers
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Value attribute table for categorical (integer) grid data
Attributes of grid zones
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Raster Sampling
from Michael F. Goodchild. (1997) Rasters, NCGIA Core Curriculum in GIScience, http://www.ncgia.ucsb.edu/giscc/units/u055/u055.html, posted October 23, 1997
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Cell size of raster data
From http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html#/Cell_size_of_raster_data/009t00000004000000/
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Raster Generalization
Central point ruleLargest share rule
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Map Algebra/Raster Calculation
Precipitation-
Losses (Evaporation,
Infiltration)=
Runoff5 22 3
2 43 3
7 65 6
-
=
Cell by cell evaluation of mathematical functions
Example
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Runoff generation processes
Infiltration excess overland flowaka Horton overland flow
Partial area infiltration excess overland flow
Saturation excess overland flow
P P
P
qrqs
qo
P P
P
qo
f
P P
P
qo
f
f
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Runoff generation at a point depends on
• Rainfall intensity or amount• Antecedent conditions• Soils and vegetation• Depth to water table (topography)• Time scale of interest
These vary spatially which suggests a spatial geographic approach to runoff estimation
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Cell based discharge mapping flow accumulation of generated runoff
Radar Precipitation grid
Soil and land use grid
Runoff grid from raster calculator operations implementing runoff generation formula’s
Accumulation of runoff within watersheds
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Raster calculation – some subtleties
Analysis extent
+
=
Analysis cell size
Analysis mask
Resampling or interpolation (and reprojection) of inputs to target extent, cell size, and projection within region defined by analysis mask
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Spatial Snowmelt Raster Calculation Example
The grids below depict initial snow depth and average temperature over a day for an area.
40 50 55
42 47 43
42 44 41
100 m
100
m
(a) Initial snow depth (cm)
4 6
2 4
150 m
150
m
(b) Temperature (oC)
One way to calculate decrease in snow depth due to melt is to use a temperature index model that uses the formula
TmDD oldnew Here Dold and Dnew give the snow depth at the beginning and end of a time step, T gives the temperature and m is a melt factor. Assume melt factor m = 0.5 cm/OC/day. Calculate the snow depth at the end of the day.
40 50 55
4347
414442
42
100 m
100
m
4
2 4
6
150 m
150
m
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Lets Experiment with this in ArcGIS
temp.asc
ncols 2nrows 2xllcorner 0yllcorner 0cellsize 150NODATA_value -99994 62 4
snow.asc
ncols 3nrows 3xllcorner 0yllcorner 0cellsize 100NODATA_value -999940 50 5542 47 4342 44 41
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New depth calculation using Raster Calculator
“snow100” - 0.5 * “temp150”
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Example and Pixel Inspector
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The Result
38 52
41 39
• Outputs are on 150 m grid.
• How were values obtained ?
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Nearest Neighbor Resampling with Cellsize Maximum of Inputs
40 50 55
4347
414442
42
100
m
4
2 4
6150
m
40-0.5*4 = 38
55-0.5*6 = 5238 52
41 39
42-0.5*2 = 41
41-0.5*4 = 39
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Scale issues in interpretation of measurements and modeling results
The scale triplet
From: Blöschl, G., (1996), Scale and Scaling in Hydrology, Habilitationsschrift, Weiner Mitteilungen Wasser Abwasser Gewasser, Wien, 346 p.
a) Extent b) Spacing c) Support
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From: Blöschl, G., (1996), Scale and Scaling in Hydrology, Habilitationsschrift, Weiner Mitteilungen Wasser Abwasser Gewasser, Wien, 346 p.
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Use Environment Settings to control the scale of the output
Extent
Spacing & Support
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Raster Calculator “Evaluation” of “temp150”
4 6
2 4
6
2
4
44
4 6
Nearest neighbor to the E and S has been resampled to obtain a 100 m temperature grid.
2 4
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Resample to get consistent cell size
4 6
2 4
5
3
3
54
4 6
2 4
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Calculation with consistent 100 m cell size grid
• Outputs are on 100 m grid as desired.
• How were these values obtained ?
38 52
41 39
47.5
40.5
42.5
40.545
“snow100” - 0.5 * “temp100”
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100 m cell size raster calculation
40 50 55
4347
414442
42
100
m15
0 m
40-0.5*4 = 38
42-0.5*3 = 40.538 52
41 39
43-0.5*5 = 40.5
41-0.5*4 = 39
47.5
40.5 45 40.5
42.5
50-0.5*5 = 47.5
55-0.5*6 = 52
47-0.5*4 = 45
42-0.5*2 = 41
44-0.5*3 = 42.546
2 4
5
3
3
54
46
2 4
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What did we learn? • Raster calculator automatically uses nearest
neighbor resampling• The scale (extent and cell size) can be set
under options
• What if we want to use some other form of interpolation? From Point
Natural Neighbor, IDW, Kriging, Spline, …
From Raster Resample (Nearest, Bilinear, Cubic)
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InterpolationEstimate values between known values.
A set of spatial analyst functions that predict values for a surface from a limited number of sample points creating a continuous raster.
Apparent improvement in resolution may not be justified
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Interpolation methods
• Nearest neighbor• Inverse distance
weight• Bilinear
interpolation• Kriging (best linear
unbiased estimator)• Spline
ii
zr1z
)dyc)(bxa(z
iizwz
ii eei yxcz
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Nearest Neighbor “Thiessen” Polygon Interpolation Spline Interpolation
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Grayson, R. and G. Blöschl, ed. (2000)
Interpolation Comparison
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Further Reading
Grayson, R. and G. Blöschl, ed. (2000), Spatial Patterns in Catchment Hydrology: Observations and Modelling, Cambridge University Press, Cambridge, 432 p.
Chapter 2. Spatial Observations and Interpolation
http://www.catchment.crc.org.au/special_publications1.html
Full text online at:
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Spatial Surfaces used in Hydrology
Elevation Surface — the ground surface elevation at each point
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3-D detail of the Tongue river at the WY/Mont border from LIDAR.
Roberto GutierrezUniversity of Texas at Austin
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Topographic Slope
• Defined or represented by one of the following– Surface derivative z (dz/dx, dz/dy)– Vector with x and y components (Sx, Sy)– Vector with magnitude (slope) and direction (aspect) (S, )
See http://www.neng.usu.edu/cee/faculty/dtarb/giswr/2012/Slope.pdf
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ArcGIS “Slope” tool
a b c
d e f
g h i
cingx_mesh_spa * 8i) 2f (c - g) 2d (a
dxdz
acing y_mesh_sp* 8 c) 2b (a -i) 2h (g
dydz
22
dydz
dxdz
runrise
runriseatandeg
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ArcGIS Aspect – the steepest downslope direction
dxdz
dydz
dy/dzdx/dzatan
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Example30
80 74 63
69 67 56
60 52 48
a b c
d e f
g h i229.0
30*8)4856*263()6069*280(
dxdz
cingx_mesh_spa * 8i) 2f (c - g) 2d (a
329.030*8
)6374*280()4852*260(acing y_mesh_sp* 8
c) 2b (a -i) 2h (gdydz
o8.21)401.0(atan
o8.34329.0
229.0atanAspect
o
o
2.145
180
145.2o
401.0329.0229.0Slope 22
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80 74 63
69 67 56
60 52 48
80 74 63
69 67 56
60 52 48
30
45.02304867
50.030
5267
Slope:
Hydrologic Slope (Flow Direction Tool)- Direction of Steepest Descent
30
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32
16
8
64
4
128
1
2
Eight Direction Pour Point Model
ESRI Direction encoding
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?
Limitation due to 8 grid directions.
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Flowdirection.
Steepest directiondownslope
1
2
1
234
5
67
8
Proportion flowing toneighboring grid cell 3is 2/(1+
2)
Proportionflowing toneighboringgrid cell 4 is
1/(1+2)
The D Algorithm
Tarboton, D. G., (1997), "A New Method for the Determination of Flow Directions and Contributing Areas in Grid Digital Elevation Models," Water Resources Research, 33(2): 309-319.) (http://www.engineering.usu.edu/cee/faculty/dtarb/dinf.pdf)
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Steepest direction downslope
1
2
1
2 3
4
5
6 7
8
0
The D Algorithm
If 1 does not fit within the triangle the angle is chosen along the steepest edge or diagonal resulting in a slope and direction equivalent to D8
10
211 ee
eeatan
210
221 eeeeS
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D∞ Example30
eo
e7 e8
o
70
871
9.1452674852atan
eeeeatan
14.9o284.9o
517.030
526730
4852S22
80 74 63
69 67 56
60 52 48
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Summary Concepts
• Grid (raster) data structures represent surfaces as an array of grid cells
• Raster calculation involves algebraic like operations on grids
• Interpolation and Generalization is an inherent part of the raster data representation
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Summary Concepts (2) • The elevation surface represented by a grid digital
elevation model is used to derive surfaces representing other hydrologic variables of interest such as– Slope– Drainage area (more details in later classes)– Watersheds and channel networks (more details
in later classes)
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Summary Concepts (3)
• The eight direction pour point model approximates the surface flow using eight discrete grid directions.
• The D vector surface flow model approximates the surface flow as a flow vector from each grid cell apportioned between down slope grid cells.