Because:
- Exactly follows curves and doesn't require segmentizing input geometry
- Also interpolates z/m values if they are present in input geometry
- Is faster
This algorithm returns the portion of a line (or curve) which falls
between the specified start and end distances (measured from the
beginning of the line).
Z and M values are linearly interpolated from existing values.
Adds a native k-means clustering algorithm.
Based on a port of PostGIS' ST_ClusterKMeans function, this
new algorithm adds a new cluster ID field to a set of input
features identify the feature's cluster based on the k-means
clustering approach. If non-point geometries are used as input,
the clustering is based off the centroid of the input geometries.
Allows the full range of formatting options exposed through
text renderer - e.g. scalebar text with buffers, shadows,
background shapes, letter spacing, etc.
Say goodbye to unreadable scale bar text!
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.
This algorithm swaps the X and Y coordinate values in input
geometries. It can be used to repair geometries which have
accidentally had their latitude and longitude values reversed.
Adds two new algorithms which expose QgsGeometry's methods
for segmentizing curved geometries.
"Segmentize by maximum distance":
The segmentization is performed by specifying the maximum
allowed offset distance between the original curve and the
segmentized representation.
"Segmentize by maximum angle":
The segmentization is performed by specifying the maximum
allowed radius angle between vertices on the straightened
geometry (e.g the angle of the arc created from the
original arc center to consective output vertices on the
linearized geometry).
