Paul Shapley's Open Source Geospatial Blog

Until now there has not been a book that covers QGIS and Python. There was 'Rapid GUI Programming with Python and Qt' by Mark Summerfield and 'Python Geo-spatial Development' by Eric Westra but for people like me who never learned programming in thier youth this prospective new book may be the incentive I need to get started. There are plenty of Python Books available but if I'm going to learn a scripting language I want it to be relevant to the geo-spatial field I work in. Gary E. Sherman author of 'The Geospatial Desktop' and 'Desktop GIS, Mapping the Planet with Open Source Tools' is promising to fill the gap again with 'The PyQGIS Programmers Guide, Extending Quantum GIS with Python '. Although not available yet it is due in the second quarter of 2013. Click on the link for the proposed content. http://pythongisbook.com/

Don't forget the dates...17th-21st September 2013 East Midlands Conference Centre, Nottingham. This event is directly following on from the AGI Conference. http://2013.foss4g.org/

Now that OSGIS is an acceptable alternative to proprietary offerings we need to be aware of 'the other' tools and libraries that rarely get a second look against the likes of QGIS, Geoserver and PostGIS. I'm referring to Cameron Shorter's excellent OSGEO Live DVD, a ready compiled .iso image which can be copied and run from a usb drive or DVD and used without installation on the host computer. It is worth checking this presentation for a comprehensive selection of Open Source Mapping Tools. http://live.osgeo.org/

Hope someone was lucky enough to get along to this weeks fully booked AGI Cymru Open Source event in Wrexham sponsored by Astun Tech and Dotted Eyes. Hope i wasn't the only AGI Cymru member to miss this one along with a 'no show' for the GeoDATA 2012 Seminar in Wales. At least we won the Grand Slam!

Some examples using the r.mapcalc module in Grass GIS - reposting soon

A Simple Tutorial On GUI Programming Using Qt Designer With PyQt4

There are advantages for using raster data over vector, any 'continuous' varying coverage is far better displayed as a raster or (pixel grid). The height data I'm using is low resolution lidar. One advantage of converting from raster to vector is that you can add columns to your data attribute table for further analysis at a later date. You can do some of this analysis with the GRASS Module (r.mapcalc) if you wish to stay with raster data. Step 1.      Open the GRASS Mapset which contains your lidar (raster) grid data (as above). Step 2.      Load your small area of data onto the QGIS canvas (above). Step 3.      In the GRASS Module List search for 'r.to.vect.area' , 'INPUT' file = 'testascii' , 'OUTPUT' file = 'testascii_vectorised' . I've always found it useful to name the output files with some indicator to which module or description was used to produce it. Step 4.      When ...