iGlobe iWhat?

[Patrick Hogan]

NASA, in concert with the Geographic Information Science and Technology (GIST) Group at the Oak Ridge National Laboratory (ORNL), www.ornl.gov/sci/gist (http://www.ornl.gov/sci/gist/), and Australia's CSIRO Marine and Atmospheric Research group, www.cmar.csiro.au (http://www.cmar.csiro.au/), the National Center for Atmospheric Research (NCAR), www.ncar.ucar.edu (http://www.ncar.ucar.edu), along with other government and international agencies, are collaborating to advance iGlobe -- an open source weather science and climate research technology based on NASA World Wind technology, www.goworldwind.org (http://goworldwind.org/).

The iGlobe analysis-technology facilitates understanding for scientists, researchers, decision-makers and the public by providing a flexible interface for sophisticated analysis and interactive visualization of NetCDF/HDF data.

If you would like to participate in this effort, direct your favorite SVN software to this location for 'read access': https://worldwind23.arc.nasa.gov/iglobe. 'Write access' is available to developers who demonstrate interest and value.

[Patrick Hogan]

iGlobe Purpose:
Provide the world's most advanced weather science and climate research tool that integrates analysis of climate model outputs with planetary scale remote sensing, demographic and environmental data, to better understand global and regional phenomenon, allowing for impact analysis of climate extremes on population and environment.

iGlobe faces these problems:
~ Data in different geographical projections, resolutions, and formats (i.e., geoTIFF, NetCDF, HDF, etc.);
~ Data in different remote access protocols (GeoServer, MapServer, FTP);
~ Lack support for geographic data in existing scalable visualization solutions (i.e., VTK, VisIt);
~ Lack of integrated analysis tools in existing GIS solutions (i.e., Google Earth).

iGlobe integrates visualization and analysis in one open source app that the world community can continually advance:
~ Allows seamless access to remote data repositories;
~ Allows users to run sophisticated data analysis algorithms at the server via a high-end server analytic engine;
~ Allows quick statistical analysis on client side via a thin client analytic engine.

Server-side Analysis:
~ Provides support for different data analysis algorithms for identifying patterns in spatial-temporal data, i.e., change detection, anomaly detection, clustering and frequent pattern analysis.

Client-side Analysis:
~ Provides support for simple statistical operations on entire or selected regions using all or selected layers, i.e., spatial mean, median, variance, correlation;
~ Provides time series correlation for spatial-temporal data layers;
~ Provides simple time-series functions using the time-series panel, i.e., auto-correlation, signal processing functions, etc.