Big geospatial data include datasets that are too large to be processed using traditional GIS tools. The objective of GIS Data Science/ Geospatial Big Data workstream at CGA is to:
- Apply Data Science, Machine Learning, AI techniques for complex geospatial analysis
- Design solutions for geospatial big data problems which cannot be handled by traditional GIS technologies
- Scale geospatial applications on cluster (FASRC) and cloud (AWS/MOC) computing environments
- Use geospatial databases (PostGIS, OmniSci) to perform large scale complex analysis on big data
- Visualize large geospatial data at high speed using GPU based databases and other tools
More information on this workstream can be found in our detailed presentation here.
CGA's key resources in this area:
- Self- Service Tools
- Tutorials
- Workshops
- DataSets
- Github
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Key projects:
- Geospatial Infrastructure Enhancement
- Partisan Segregation Analysis
- Climate Data Analysis
- Use of Social Media Data to study Climate Change
- Weibo Mobility Index
- Detroit Zoning Analysis
- Network Analysis of Geospatial Big Data for Brazil
- Infogroup US Historical Business DataSet Analysis
- Billion Object Platform v1.0
- Billion Object Platform v2.0
- Evaluating OmniSci
- Travel Time Estimation on Big Data
Questions about GIS Data Science/Big Data? Contact us or email Devika Kakkar.
See also: Services