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Geospatial Analytics for Government Agencies and the General Public: The CyberGIS Toolkit as an Enabler U.S. Department of the Interior U.S. Geological Survey Michael P. Finn High Performance Computing and Geospatial Analytics Workshop Argonne National Laboratory 29 30 Apr 2014

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Collaborators Shaowen Wang, Anand Padmanabhan, Yan Liu University of Illinois at Urbana-Champaign (UIUC), CyberInfrastructure and Geospatial Information Laboratory David M. Mattli, Jeff Wendel, E. Lynn Usery, Michael Stramel USGS, Center of Excellence for Geospatial Information Science (CEGIS) Kristina H. Yamamoto USGS, National Geospatial Technical Operations Center Babak Behzad UIUC, Department of Computer Science Eric Shook Kent State University, Department of Geography Qingfeng (Gene) Guan China University of Geosciences

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Where Do We Want to Go? Geospatial Analytics Spatial Modeling Geovisualization (GeoViz/ Visual Analytics) For Decision Makers (agencies/ citizens) Protect natural resources Empower cultures Provide for our future

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Geospatial Analytics Spatial Modeling/ Geovisualization

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Data / Software Geospatial Methods, Technologies, and Applications GIScience and Cyberinfrastructure Geospatial Toolkits Geospatial Analytics (Spatial Modeling / GeoViz) So: -Where have we been? -Where are we now? -Where do we want to go?

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Data Analog Digital Big Data Spatial Data (geometric structure) Data: Open? mostly Findable, Accessible, Exploitable (standard format) Example: USGS Data holdings 8 Layers of the National Map Soon: Hyperspectral cubes and LiDAR point cloud s

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Quality Level Horizontal Point Spacing (meters) Vertical Accuracy (centimeters) Description 10.359.25 High accuracy and resolution lidar example: lidar data collected in the Pacific Northwest 20.79.25 Medium-high accuracy and resolution lidar 31-2