Improving Data Consumption
- OSM contains a wealth of information related to landuse and landcover. The vagaries of how data are created, stored and post-processed mean that creating consistent thematic data from OSM is often too large a challenge for the regular GIS user. This project aims to address this issue within the open source GIS package QGIS.
Python, QGIS, GDAL
The project envisages a python-coded plugin for QGIS which provides a point-and-click interface for the following: extract specific polygon data from OSM based on tags; normalise attributes according to coded rules; repair straightforward polygon error types; apply a sequence of one or more rule-based transformations to the input polygon datasets. The plugin should allow for the addition of transformations, but only two need to be created to validate the principle (and polygon repair might be suitable candidate as well). Example transformations include: merge (union) adjacent or overlapping polygons with shared attributes; gridding and ungridding set of polygons (useful performance enhancer in QGIS), applying the painter's algorithm to tesselate an area from differently attributed polygons.
I'm always available for Hangout sessions to explain how they work at the moment and where they could be improved. Then we'll need to have followup sessions where you can show your ability to work on the code. Candidates with good OpenStreetMap edits will get extra credit. Mapping on OSM is the only way you can understand the inherent complexities of post-processing polygon data. ).This means you will already be spending quite a lot of time before GSoC starts and there are no guarantees we'll be able to select you. Of course, we hope you'll also stay on board after GSoC ends, making edits and code improvements.