Kernel Map
The Kernel Map is a simple operation to analyze the
behavior of point pattern and it is an easy to use and understand
indicator. Essentially the Kernel map provides the punctual density of
the process in the study region through interpolation. As a result, a
general view of the process intensity in the study region is obtained.
About the Algorithm
In the algorithm fields, the user selects the function to be
used in the calculation, what will be calculated, and the radius to be
used. Typical options in Kernel maps, and TerraView defaults, are
Quartic Function and Compute Density.
The three Compute options are:
- Density:
calculates density of crimes per area unit.
- Spatial
Moving Average: calculates the expected number of crimes per
area unit.
- Probability:
calculates the probability of an already executed crime to be located
in a given area.
About Radius
Radius is an important parameter to create a Kernel map and
requires the user to understand the study region.
TerraView facilitates the definition task by allowing the creation of
Adaptive Kernel map. Adaptive option calculates a radius automatically
based on the number of events and the area. Results of maps created
with adaptive radius are reasonably good.
However, Adaptive option can be unselected and the user can set the radius based on the width of the study
area. This value is set using percentage compared to the total width.
It is
accessible through:
Plugins > Spatial Analysis >
Kernel Map...
This
interface consists of the
following steps:
1.
Input Information:
- Layer Name: Select the
desired Layer.
2. Kernel
Parameters:
- Attr Name: Attribute
name that identifies the desired information.
- Function: Quartic,
Normal, Triangular, Uniform and Negative Exponential.
- Estimation: Density, Spatial
Moving Average and Probability.
- Use
adaptative radius: Uses adaptative radius or defines the
percentage value of total width.
3. Output
Information:
- Support Region: Defines
how the output data will be created.
- Grid: The
output is a grid (image) where the kernel map information is a pixel
value.
- Data Set: The output
is the input data plus a attribute with kernel map information.
- Repository: Defines
where the output data will be saved.
- Layer Name: Defines the
name to create the output layer.
Click OK and then the kernel
map will be calculated.