I am relatively new to FME, it would be greatly appreciated if someone can shed some light on a couple of problems I can't seem to figure out.
1.
Is there a transformer similar to the attribute filter transformer but would enable me to route unique value to the output port dynamically? I understand that I can using the import value function to bring in the unique attributes values, but sometimes I would only like to only filter a portion (which could be about 50 unique values) of the total number of unique values. Inputting the required attribute value manually is laborious and prone to mistakes.
What I would like to achieve is a 2 step dynamic filter of features, each feature has a Local Government Area (LGA), and a Suburb (suburb is a subset of LGA) attribute. Depending of the first LGA filter (ideally a check box list), the second transformer would route filtered features with unique suburb attributes to an output port. Is this possible?
2.
I am currently trying to incorporate a randomised probability sampling component to my workspace based on an attribute. In essence, it tries to simplisticly simulate urban development for x number of years at a parcel level.
The parcel features in my dataset has a "development_likelihood" attribute that is calculated based on summary statistics of similar types of features from a different dataset. The "development_likelihood" attribute determines the percentage of features that gets randomly sampled. The sampled features will receive a new attribute with the year when it was selected/developed. The un-sampled features will be passed on to the next sampling process. The intent is the loop this process for x number of years and visualise which lots get developed year by year.
I have so far found that the randomizedSampler transformer on the FME store is close to what I want to achieve in terms of sampling features randomly, however the Percentage to Sample parameter must be set by a published parameter, hence not able to read the likelihood attribute from the passing features.
How would I be able to get the transformer to use the "likelihood" attribute as an input to determine the sampling amount for a group of features that have the same value? I think I will end up with roughly 100 unique values, which would be another problem if I need to create 100 of these transformers all with different likelihood values. Any suggestion to improve on my simplistic approach would be much appreciated.
Regards,
MC
