17.29. Other programs

Module contributed by Paolo Cavallini - Faunalia

Note

This chapter shows how to use additional programs from inside Processing. To complete it, you must have installed, with the tools of your operating system, the relevant packages.

17.29.1. GRASS

GRASS is a free and open source GIS software suite for geospatial data management and analysis, image processing, graphics and maps production, spatial modeling, and visualization.

It is installed by default on Windows through the OSGeo4W standalone installer (32 and 64 bit), and it is packaged for all major Linux distributions.

17.29.2. R

R is a free and open source software environment for statistical computing and graphics.

It has to be installed separately, together with a few necessary libraries (LIST). To enable the use of R in QGIS, the Processing R Provider plugin must also be installed.

The beauty of Processing implementation is that you can add your own scripts, simple or complex ones, and they may then be used as any other module, piped into more complex workflows, etc.

Test some of the preinstalled examples, if you have R already installed (remember to activate R modules from the General configuration of Processing).

17.29.3. Others

LASTools is a set of mixed, free and proprietary commands to process and analyze LiDAR data. Availability in various operating system varies.

More tools are available through additional plugins, e.g.:

  • LecoS: a suite for land cover statistics and landscape ecology

  • lwgeom: formerly part of PostGIS, this library brings a few useful tools for geometry cleanup

  • Animove: tools to analyse the home range of animals.

More will come.

17.29.4. Comparison among backends

Buffers and distances

Let’s load points.shp and type buf in the filter of the Toolbox, then double click on:

  • Fixed distance buffer: Distance 10000

  • Variable distance buffer: Distance field SIZE

  • v.buffer.distance: distance 10000

  • v.buffer.column: bufcolumn SIZE

  • Shapes Buffer: fixed value 10000 (dissolve and not), attribute field (with scaling)

See how speed is quite different, and different options are available.

Exercise for the reader: find the differences in geometry output between different methods.

Now, raster buffers and distances:

  • first, load and rasterize the vector rivers.shp with GRASS ► v.to.rast.value; beware: cell size must be set to 100 m, otherwise the computation time will be enormous; resulting map will have 1 and NULLs

  • same, with SAGA ► Shapes to Grid ► COUNT (resulting map: 6 to 60)

  • then, proximity (value= 1 for GRASS, a list of rivers ID for SAGA), r.buffer with parameters 1000,2000,3000, r.grow.distance (the first of the two maps; the second will show the areas pertaining to each river, if done on the SAGA raster).

Dissolve

Dissolve features based on a common attribute:

  • GRASS ► v.dissolve municipalities.shp on PROVINCIA

  • QGIS ► Dissolve municipalities.shp on PROVINCIA

  • OGR ► Dissolve municipalities.shp on PROVINCIA

  • SAGA ► Polygon Dissolve municipalities.shp on PROVINCIA (NB: Keep inner boundaries must be unselected)

Note

The last one is broken in SAGA <=2.10

Exercise for the reader: find the differences (geometry and attributes) between different methods.