Hi,
I am currently working on a process that would reshape vector features, to
add a classification field.
The resulting feature will be a copy of the source, with the additional
attribute calculated classifying the source on a specified attribute using
given intervals (by the user as input parameters).
This will allow further processing, for example aggregating on the class
attribute with the Aggregate process (using WPS nested processes).
For the purpose I would reuse:
- CategorizeFunction to calculate the class attribute
- TransformProcess to reshape the source, adding the new attribute
The new process will accept the following inputs:
- features (the source feature collection)
- classifyOnAttribute (the attribute to use as an input for
classification, e.g. PERCENTAGE)
- thresholds (comma delimited list of thresholds for the required
intervals, e.g 10,30,60,100)
- outputAttribute (name of calculated attribute with class values, e.g.
CLASS)
- outputValues (comma delimited list of values to be assigned for each
interval, A,B,C,D,E)
- include (true or false, to define if thresholds are included (compared
with greaterOrEqual) or excluded (compared with greater) in an interval
instead of the following one.
Any doubt / opinion / advice?
Thanks.
--
Regards,
Mauro Bartolomeoli
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