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Examples of Gridded Maps |
Contents
| IDW
Nearest Neighbours Interpolation
IDW Fixed Radius Interpolation Nearest Neighbour Gridding Voronoi Polygon Interpolation Triangulations Dominance Grids |
Grids are probably the most useful form of output for the end-user. They are succinct, take almost no effort to understand, are easily manipulated in a GIS and perform well as inputs to numerical models. They are very well suited for use in offshore planning and feasibility studies. They can be sent as grids, gridded points or polygon coverages.
Grids resolve the difficult issue of multiple data items per sample site (such as dual core + photo investigations; description + analysis of same sample) ; in point plots which of these data items shows on top depends on the internal machinations of the chosen GIS; but only one item can be seen at a time. Gridding allows a consensus such as the average, median, maximum, minimum, etc.
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This is probably the best depiction of physical parameters where abundant
data is available and complete coverage of the map area is required.
After loading the point dataset (ALL, EXT, PRS or CLC, see the points) and selecting by query the points with data, the grid is created in ArcView/Spatial Analyst using <Surface/InterpolateGrid/ OutPutGridExtent=[BoxFile]/OutputGridCellSize=[0.01deg]/OK /IDW/ZValueField=[Grainsize]/NearestNeighbours/NoOfNeighbours=[3]/OK>. Before doing the gridding be sure to set the View properties to degrees
coordinates and km distance.
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This is a more scientific depiction of physical parameters where data
is not densely or evenly distributed: it shows the areas where further
data collection is needed. But it does not suit some applications where
complete coverage of the map area is required.
After loading the point dataset (ALL, EXT, PRS or CLC, see the points) and selecting by query the points with data, the grid is created in ArcView/Spatial Analyst using <Surface/InterpolateGrid/ OutPutGridExtent=[BoxFile]/OutputGridCellSize=[0.01deg]/OK /IDW/ZValueField=[Grainsize]/FixedRadius/Radius=[10km]/OK>. Before doing the gridding be sure to set the View properties to degrees coordinates and km distance. |
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This method does not interpolate; it renders with values equal to those
of nearest data point. It leaves areas without data showing that data needs
to be collected.
The method is very suitable to parameters that do not form a continuum of values on maps: for example grainsizes may suddenly change from gravel to clay at a geological boundary, and should not be interpolated. After loading the point dataset (ALL, EXT, PRS or CLC, see the points) and selecting by query the points with data, the grid is created in ArcView/Spatial Analyst using <Analysis/NeighbouhoodStatistics/ OutPutGridExtent=[BoxFile]/OutputGridCellSize=[0.01deg]/OK /Field=[Grainsize]/Statistic=[Median]/ Neighbourhood=[Rectangle]/Width=[20km]/Height=[20km] /Units=[Distance]/OK>. Other settings may include: mean and circle; also cellsize and width, height to suit map scales and data densities. Before doing the gridding be sure to set the View properties to degrees coordinates and km distance. |
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This method is poor at rendering the property distributions from unevenly
distributed data. It covers the whole map, but wide extrapolations from
widely spaced points often pass across bathymetric and geological boundaries,
cutting at the map's credibility. No interpolation is done so: (i) sharp
boundaries (such as between gravel and clay) are likely to be preserved,
and (ii) individual patches of seabed variation are not diluted and show
as polygons.
After loading the point dataset (ALL, EXT, PRS or CLC, see the points) and selecting by query those points with data, the grid is created in ArcView/Spatial Analyst using <Surface/InterpolateGrid/ OutPutGridExtent=[BoxFile]/OutputGridCellSize=[0.01deg]/OK /IDW/ZValueField=[Grainsize]/NearestNeighbours/NoOfNeighbours=[1]/Power=[0]/OK>. |
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This method also produces polygons, but tempered by calculation based
on each 3 points. Extrapolations are less extreme. As a disadvantage, some
of the patchiness of the seafloor is dissolved.
After loading the point dataset (ALL, EXT, PRS or CLC, see the points) and selecting by query those points with data, the grid is created in ArcView/Spatial Analyst using <Surface/InterpolateGrid/ OutPutGridExtent=[BoxFile]/OutputGridCellSize=[0.01deg]/OK /IDW/ZValueField=[Grainsize]/NearestNeighbours/NoOfNeighbours=[3]/Power=[0]/OK>. |
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This type of visualization is a very effective method of outlining
the principal textural zones across a region.
Griddings of Rock, Gravel, Sand, Mud are generated from the ALL file and imported into ArcView as separated themes with (*.avl) legends "rck_gd", "gvl_gd", "snd_gd" and "mud_gd" applied. In dbSEABED there is a convention to display rock, gravel, sand and mud as purple, red, yellow and green hues respectively. To generate grids, use the Query tool of Arcview to select against -99"
(the dbSEABED "No Data Value") for the parameter concerned. Then use:
Slight areas of masking may occur between themes, so different orderings
of the themes may result in slightly different maps.
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In this variant of a Dominance Grid, grid cell sizes happen to be set
to less than the typical spacing of the data points - here 1km with points
spaced at about 4km. One virtue of this display is that it does show to
users where more data needs to be collected - where the blindspots are
on a map.
The display here is a dominance display such as in a map above. purple - rock, red - gravel, yellow - sand, green - mud. Thus, for gravel: dark red represents >80% gravel, intense red represents >60% gravel, pink >40%; values; <40% are not shown. Some polygons of rock areas have also been added, as has Landsat imagery of the land nearby. |
Chris Jenkins (Email)
INSTAAR, University of Colorado
5-Feb-2002
