Aside from the performance benefits, the Python version of this
algorithm occasionally fails on Travis with odd errors. Hopefully
by porting to c++ it will fix these, or at least give useful
debug information in the event of a fail.
Also add support for curved input geometries.
Like the main Join Attributes by Location algorithm, this algorithm
takes two layers and combines the attributes based on a spatial
criteria.
However this algorithm calculates summaries for the attributes for
all matching features, e.g. calculating the mean/min/max/etc.
The list of fields to summaries, and the summaries to
calculate for those, can be selected.
This algorithm is no longer required - it's been replaced by
the 'Promote to multipart' and 'Collect geometries" algorithms.
Tagged as feature to remember to include in release notes
Tagged as feature to be included in release notes.
Because:
- The use case for this algorithm is very unclear for users - the name
does not describe what the algorithm does, and there's no help
documentation available for the algorithm either. Given this I suspect
that the algorithm is not being put into use.
- The algorithm needs enhancement to be more useful. There's no logic
in place which dictates how neighbouring features are chosen to
dissolve into the selected feature (it's effectively random - you're
just as likely to get a huge narrow polygon stretching across a map as
you are a nice compact cluster). To be more useful the algorithm would
need logic to either minimise the area of the dissolved feature, or
minimise the total number of dissolved features, or ... ?
Now that the extra features of the "polygon from vector layer extent"
algorithm are covered by the new "Minimum bounding geometry" algorithm,
we can replace the previous two "polygon from vector extent" and
"polygon from raster extent" algorithms by a single "polygon
from layer extent" algorithm.
This algorithm creates geometries which enclose the features
from an input layer.
Numerous enclosing geometry types are supported, including
bounding boxes (envelopes), oriented rectangles, circles and
convex hulls.
Optionally, the features can be grouped by a field. If set,
this causes the output layer to contain one feature per grouped
value with a minimal geometry covering just the features with
matching values.
Improvements:
- Maintain Z/M values
- Keep original data type for group/order fields
- Group field is optional
- Added unit tests
- Don't export text files for features by default
Improvements:
- transparent reprojection to match hub/spoke CRS
- keep all attributes from matched hub/spoke features
- don't break after matching one hub point to spoke - instead
join ALL hub/spoke points with matching id values
