This workshop will provide a fast paced introduction to open source GIS tools, especially QGIS (but also R). We will explore QGIS’s power and functionalities for manipulating and analyzing vector GIS data. The workshop will be especially useful for students and researchers who use ArcGIS, but would like to learn about open source GIS tools. Participant should have at least one semester or equivalent exposure to GIS.
This workshop will cover introductory GIS concepts, tools, and techniques. We will use ArcGIS to learn basics of GIS by solving 2-3 specific problems. We will use the graphical user interface of ArcGIS and no programming experience is required for this workshop. The workshop will also cover the basics of projections and spatial data.
The workshop is meant for students and researchers who want to have a quick and simple exposure to GIS concepts and tools.
Scope: Location analysis plays a central role in a variety of situations such as identifying potential breeding areas for mosquitoes, finding the best path for emergency evacuation, demarcating suitable area for a national park, and identifying the hot spots of air-pollution and green-house gas emissions. This workshop will help you develop a solid foundation in location analytics with ArcGIS and Python. We will primarily focus on vector data (points, lines, and polygons) analyses, but will also touch upon situations where raster (remotely sensed observations) and vector data can be combined to get a better handle on a problem.
Pedagogy: The workshop will follow a problem-based learning approach where real life examples play a central role. We will use appropriate moments and opportunities provided by examples to discuss and learn about fundamental concepts in GIS, computational geometry, and spatial statistics. The workshop will also emphasize and help you appreciate systematic trial and error as a central tenet of algorithmic problem solving.
Prerequisites: Interests in location analysis and exposure to GIS. If you do not have any previous experience with GIS, consider taking Geospatial Analysis with Python, a free workshop offered by CSCAR on November 2, 2016.
This is an introductory workshop. Future workshops will build on this and include intermediary and advanced tools required to solve location related problems in a variety of policy, scientific, and business contexts.
This workshop will cover basic geospatial analysis in Python. Topics covered will include reading and writing various GIS file formats (shapefile, KML, geojson, csv), geocoding, common geometric operations like finding closest line to a point, point in polygon, spatial indexing and spatial joins etc. The workshop will focus solely on vector data (points, lines, polygons). The will be mostly accomplished using the Python modules: fiona, shapely, rtree (but not arcpy).
CSCAR (Consulting for Statistics, Computing and Analytics Research) is offering expanded support for geospatial analysis and geographic information systems (GIS), effective immediately.
Researchers seeking guidance in this area are encouraged to schedule an appointment by calling 764-7828.
Several members of the CSCAR staff have expertise in modeling and analysis of geospatial data, and can provide consultations on basic and advanced methods. A variety of tools including R, Matlab, Python, and Arc-GIS are supported for work in this area. The CSCAR team was recently joined by a consultant holding a PhD in Earth/Environment Sciences, specializing in GIS and remote sensing.
As a result, CSCAR is now able to support a broad range of geospatial analysis activities including GIS, geostatistics, mechanistic modeling, geospatial visualization, and large-scale geospatial data processing on Flux and other advanced infrastructure systems. New workshops in Arc-GIS and other geospatial tools will begin in November (details will appear on this website).