Allows users to select a list of fields to keep, and all other fields
will be dropped from the layer. Helps with making flexible models where
input datasets may have a range of different fields and you need to drop
all but a certain subset of these
This algorithm calculates the rotation required to align point features
with their nearest feature from another reference layer. A new field is
added to the output layer which is filled with the angle (in degrees,
clockwise) to the nearest reference feature.
Optionally, the output layer's symbology can be set to automatically
use the calculated rotation field to rotate marker symbols.
If desired, a maximum distance to use when aligning points can be set,
to avoid aligning isolated points to distant features.
Designed for use cases like aligning building point symbols to follow
the nearest road direction!
Offers the same functionality as the Random points inside polygons
algorithm, and in addition it includes (possible to opt-out) the attributes
from the containing polygon and the ability to specify a seed for the random
number generator.
Could replace the Python algorithm Random points inside polygons, but
this C++ implementation only supports "Points count" ("number of points
for each feature") directly, while "Points density" is supported by allowing the
point count to be data defined (using the polygon area to calculate the
number of points).
The Random points on lines algorithm supplements the existing "Random points along line" algorithm, and will prove to be more useful to the majority of users than the "original".
Features:
The points are distributed randomly over the lines based on "along the line" distance, meaning that the distribution of the points will be flat over the length of the line (each place on the feature has the same probability of being "hit").
The Random points along line, on the other hand, uses a line segment based approach, meaning that the density will depend on the segment length (short segments will have a higher point density than longer ones).
This algorithm filters features by their geometry type. Incoming
features will be directed to different outputs based on whether
they have a point, line or polygon geometry.
Allows for model creation which responds to different input
layer geometry types by applying different logic depending on the
input geometry type.
Takes an input layer, existing field and a new name for the field, and
outputs a new layer with the selected field renamed.
While this result could also be achieved with the Refactor Fields
algorithm, Refactor Fields isn't particularly model friendly. It
relies on a constant, fixed table structure, and can't adapt to
input tables with different field structures.
In constrast, this simple Rename Field algorithm adapts nicely for
model use, because it operates on a single field only and leaves
all the other fields untouched.
to take attributes from matching feature with largest area of overlap only
This allows for easy polygon->polygon joins, where you expect there to be
only a single matching feature and don't want to include features which
are just touching or have just tiny sliver polygon overlaps.
Sponsored by SMEC/SJ
