Uses Kernel Density Estimation to interpolate a surface
from the input Point or Polyline features. The
function fits a symmetrical surface over each input point using
Gaussian kernel.
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The surface has maximum value in the input point, decreases
as the distance from the input point increases and has 0 values at distance
equal to the search radius. The volume of the created surface is equal to
the value of the input point. The density of each cell of the
output raster is calculated by adding the values of all individual
surfaces for that cell. |
The search radius does not influence the volume
of the surface, but has a major impact on the generalization of the
data. The larger the search tolerance, the smoother and more
generalized surface will be interpolated. As you can see from the
image below the selection of the search radius is very important and
influences greatly the surface. You should evaluate your data and
have always in mind what is the goal of the task when deciding on
what search tolerance to use.
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Inputs:
- A Point or Polyline feature layer or feature
class.
- Output raster name and format
- Cell Size of the output raster
- Search radius.
- Value field - a field from the
attribute table to be used as value for each point. The value might be the
population at this point, the number of incidents at the location etc. If
you do not have such a field in the attribute table, just create a new field
and calculate the values of all records equal to 1.
- Area Units - The
value of each cell of the output raster will actually have value measured in
Value per square unit. The default unit is the unit of the spatial reference
of the input dataset, but you might want to change this to a different area
unit (for example incidents per square kilometer)
Output:
Example - the surface created from the 6
points in the profile above:
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Search Radius = 2000 meters - smoother more
generalized appearance |
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Search Radius = 1000 meters - more pronounced
influence of the individual points |
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Both surfaces superimposed - two very different
surfaces. The volume of both however is the same and is equal to the
sum of the values of the input points. |
Notes:
- Initially the name of the output raster
defines the raster format
- no extension specified - ESRI binary GRID
- .img extension (for example raster1.img) -
ERDAS IMAGINE image.
- .tif extension (for example raster1.tif -
Tagged Image File Format (TIFF) image.
- The initial output raster format can be
changed by selecting the desired output in the dialog.
- Currently only file based rasters are
supported. Rasters cannot be stored in a GeoDatabase. After you get the
desired result, you can export the raster to a GeoDatabase using the
standard ArcGIS tools.
- The input feature class must be in a projected
coordinate system
ToolBox
implementation
Command line syntax
ETS_GPDensity <Input Dataset> <Out Raster> <Elevation Field> <
Cell Size>
<Interpolate Radius> <Area Units>
Parameters
Expression |
Explanation |
<Input Dataset> |
A
Point, Polyline or Polygon layer or feature class |
<Out
Raster> |
A String
- the full name of the output raster (A raster with the same full
name should not exist). The output raster type depends on the extension
of the output file(see Notes above) |
<Value Field> |
A String representing the name
of the field which values are going to be used for interpolation. |
<Cell Size> |
A Double representing the cell
size of the output raster. |
<Interpolate Radius> |
A Number - see main
description above |
<Area Units> |
A String - see description
above |
Scripting syntax
ETS_GPDensity (Input Dataset, Out
Raster, Value Field, Cell Size, Interpolate Radius, Area Units)
See the explanations above:
<> - required parameter
{} - optional parameter
All ESRI
products mentioned are trademarks of Environmental Systems Research
Institute, Inc.
Copyright: Ianko Tchoukanski |