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GIS – the future of scientific visualization?

GIS – the future of scientific visualization?. Jon Blower, Reading e-Science Centre, University of Reading j.d.blower@reading.ac.uk. The importance of visualization. Detecting features in data (e.g. storms) Diagnosing problems Previewing data before downloading

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GIS – the future of scientific visualization?

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  1. GIS – the future of scientific visualization? Jon Blower, Reading e-Science Centre, University of Reading j.d.blower@reading.ac.uk

  2. The importance of visualization • Detecting features in data (e.g. storms) • Diagnosing problems • Previewing data before downloading • Making sense of large datasets • Putting data into context • Communicating complex concepts

  3. Existing scientific visualization software • Problem-solving environments • Matlab, IDL • 3-D desktop visualization • MayaVi • 3-D remote visualization • Silicon Graphics • Web-based • Live Access Server • All require technical knowledge • Bias towards gridded data • Limited interoperability between systems

  4. (Microsoft case study with BP: Hurricane Management System) • "This solution is changing the way we do business. When the data is presented through a map-based interface, it’s amazing. It gives you a richer, bigger, more intelligent picture of what’s going on. " – Steve Fortune, BP

  5. Can a new approach tovisualization change the waywe do science?

  6. What is missing from existingvisualization tools? • Ease of use • User is required to understand too many details of file formats • Interactivity • E.g. point-and-click to reveal data values • Ability to quickly overlay and compare data sets • Compare datasets with each other (intercomparison) • Provide context (e.g. with a topographic map) • These are characteristics of Geographic Information Systems

  7. “A geographic information system (GIS)captures, stores, analyzes, manages, andpresents data that refers to or is linked tolocation.”–Wikipedia

  8. Typical historical GIS characteristics • Map-oriented • Users usually visualize data on the horizontal plane • Vector and raster data • 2.5 dimensional data model • Data usually locked to the land surface • Where is more important than what • Commercial solutions mutually incompatible • A new era of “open GIS” is changing this…

  9. Web Feature Service Web Coverage Service Web Map Service Features (GML) Gridded data (NetCDF, GeoTIFF) Map imagery (PNG, JPG, GIF) Open Geospatial Web Services • Open Geospatial Consortium publishes open standards for geospatial Web Services: (plus many more!)

  10. NASA World Wind Google Earth Microsoft Virtual Earth OpenLayers Open GIS • Open standards • Data visualization: Web Map Service, KML • Data transfer: • Web Feature Service (vector) • Web Coverage Service (raster) • Data encoding: GML, GeoRSS, GeoJSON • These technologies are driving a new generation of tools and components • Virtual Globes: Google Earth, NASA World Wind • Web-GIS: OpenLayers, Google Maps • It is now much easier to build custom GIS-style data visualization systems

  11. The Gallery

  12. Web Map Service in action http://www.reading.ac.uk/godiva2

  13. Combining data on a Virtual Globe:Hurricane Katrina, August 2005 • Storm track positions (analysed from ECMWF vorticity data) by Lizzie Froude, ESSC • Sea surface temperature data from UK Met Office FOAM model • Combination shows cooling of surface waters on right-hand side of cyclonic storm track • High winds cause upwelling of cool, deep water

  14. Real-time data visualization • British Antarctic Survey (BAS) used KML data feeds to support 2 scientific cruises in 2006 • Multiple data streams (ship location, sea temp and salinity, air temp and pressure) streamed to Google Earth in near real time • Enabled real-time decision-making (e.g. tracking of predators, left) • Saved ship time, hence £££ King penguin track overlain with concurrent chlorophyll and satellite imagery

  15. Comparing model and satelliteozone data • Simple Web-GIS • Data: • Data-assimilated model • Satellite swaths • Context: • Topography • Coastlines • Reveals errors in satellite data at high latitudes • Mysteriously low ozone over high topography

  16. NERC Environmental Data Portal

  17. Comparing ocean model and in situobservations (“OceanDIVA”) • Scientists need to compare ocean models and observations • A Web Service performs comparison, then outputs results in KML • Red dots show bad model-obs fits, green dots are good fits • Clicking on an observation brings up more information • Driving real improvements in the model http://www.nerc-essc.ac.uk/~alg/OceanDIVA.html

  18. Doh! Ecosystem Health portal:diagnosis of data problems 5 degree misfit??

  19. Interrisk portal (Plymouth Marine Labs) Global chl-aNASA, USA Modelled chl-a, concentration Met Office, UK Current speed met.no, NO Bathymetry and coast data PML, UK Ferrybox data, POL, UK Wind fields (red) met.no, NO

  20. Gallery Summary • These applications have a variety of uses: • Quick comparisons + hypothesis generation • Diagnosis of problems in models and observations • Illustrating relationships between processes • Key technologies: • Presentation layer: OpenLayers/Google Maps + Javascript, Google Earth • Web Services: WMS, ad-hoc KML, WFS • Data processing: ad-hoc server-side code • No one-size-fits-all solution • You probably wouldn’t want one anyway • Reusable components are the key Fitz Hall, Wigan Athletic

  21. How do these systems fit in tothe scientific process? Have an idea Do the work Disseminate Discuss/explore Formally publish Open GIS Open GIS Matlab, IDL etc

  22. But… let’s not get too carried away • “Great examples of reuse of data sets” • No: we saw reuse of data products that were produced using controlled processes and designed for reuse • “Great examples of OGC-driven interoperability” • The only true interop was the overlay of data onto 3rd-party topo maps • In most cases the same people developed the client and server • There is very little meaningful OGC-driven interoperability currently in the real scientific world • “Let’s build a mashup portal for all NERC data” • Careful – intelligent data services scale very badly (<10 simultaneous users per Web Map Server) • Developing a single application for large numbers of unknown users is very very hard • Only a small number of data combinations is actually scientifically useful

  23. Conclusions 1:Things that I’m happy about • Web-GIS and Virtual Globes are driving a new approach to scientific visualization • Controlled overlaying is the key • Scientists are demanding this! • Can act as an incentive for scientists to format data correctly • Great for gathering requirements for more complex applications • NERC is in a good position to deliver truly innovative services to the scientific community

  24. Conclusions 2:Things that worry me • We need to understand better the difference between R&D and engineering • We have no idea of the resource required to achieve the level of interoperability we think we need • We probably overestimate the reuse value of most data • We don’t have enough input from software engineers • We need constant reality-checks from scientists • Involves taking smaller, simpler steps and developing iteratively • INSPIRE mandates us to build general-purpose infrastructures based upon bleeding edge technologies, with no well-defined use cases http://scienceblogs.com/zooillogix/

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