Geospatial Computation and Visualization Cooperative Lab
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Experienced / Tutorial
March 9th 1:20 p.m. – 4:40 p.m.
Gain an in-depth understanding of Python's geospatial libraries by working in small groups on an extensive set of guided exercises with provided solutions. Groups are paired by common interest and answers to common questions are shared with the class every half-hour. Each category also has a challenge question and an attendee who successfully solves a challenge question receives a cash prize.
Abstract
Student should be well versed in Python and should have all of the standard geospatial packages installed before class (listed below).
The work-at-your-own pace format lets the tutorial cover a much larger set of topics than a standard presentation. Exercises are divided into five categories:
- core
- creating and comparing geometries with shapely/geos
- reading and writing kml/shapefiles with gdal
- transforming between spatial references with proj
- mapping
- reading and writing to spatial databases such as Spatialite and PostGIS with geoalchemy
- displaying vector and raster data using geojson and openlayers/polymaps
- creating static base layers with mapnik
- visualization
- displaying parts of 16-bit satellite images with matplotlib
- creating 3D maps with mayavis2
- extensions
- creating a qgis plugin
- creating an ArcGIS plugin
- creating a plugin for GeoProcessor.org
- parallelization
- writing simple algorithms to run on GPUs using PyCUDA
- scaling your geoprocessing framework using AMQP