Transcript Geoinformatics - the vision

Teaching Geoinformatics: A Geoscience
Perspective
Randy Keller
Professor and Edward Lamb McCollough Chair in Geophysics
School of Geology and Geophysics
University of Oklahoma
Geoinformatics - the vision
It is too hard to find and
work with data that already
exist.
It is too hard to acquire
software and make it work.
We have too little access to
modern IT tools that would
accelerate progress.
The result is too little time
for science!
The EarthScope Scientific Vision
To understand
the structure
(evolution) and
deformation
of the North
American
continent
in four
dimensions
(x,y,z,t)
EarthScope
Cyberinfrastructure for the Geosciences
Why do we need it?
Future research opportunities in the geosciences will be
significantly affected both by the availability and utilization of
Information Technology. Understanding the rock record that
preserves ~4.5 billion years of history, Earth structure, and
the processes at work is the key to answering scientific
questions associated with studies of biodiversity, climate
change, planetary processes, natural resources and
hazards, and the 4-D architecture and evolution of
continents. It has become evident that we can only answer
these complex questions through the integration of all the
data we have at hand and that this will require the
application of modern IT tools.
What is Geoinformatics?
Geoinformatics is a science which develops and uses information science
infrastructure to address the problems of geosciences and related branches
of engineering.
The three main tasks of geoinformatics are:
･development and management of databases of geodata
･analysis and modeling of geodata
･development and integration of computer tools and software for the first
two tasks.
Geoinformatics is related to geocomputation and to the development and
use of geographic information systems or Spatial Decision Support
Systems
Applications･An object-relational database (ORD) or object-relational
database management system (ORDBMS) Object-relational mapping (or
O/RM) Geostatistics
Geoinformatics Research & Education Geoinformatics Research Group,
School of Civil Engineering & Geosciences, Newcastle University, UK
Geoinformatics - Some key elements
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A strong partnership between domain experts
(geoscientists) and computer scientists
A shared goal of doing better (and more) science
A desire to create products that the scientific community
actually needs and will use (not what you think they need or
should want)
Always give credit to original sources of data, software, etc.
A desire to preserve data, make it easily used and
discovered, and create living databases
A desire to create user friendly and platform independent
software
A desire to facilitate data integration
A desire to create cyberinfrastructure breakthroughs (e.g.,
visualization, 3-D model building editing, etc.)
A desire to democratize the use of cutting edge technology
in geoscience research and education
www.Geoinformatics.info
A Scientific Effort Vector
Background
Research
Background
Research
Data Collection and
Compilation
Software Issues
Data
Collection
and
Compilation
Software Issues
Science
Science
Science - Analysis, Modeling, Interpretation, Discovery
Some Definitions about Data
Data Set: A relatively raw compilation of data
(standards, formats, completeness may be questionable)
Data Base: A mature data compilation that has been
“cleaned”, standardized with input from the scientific
community, formatted for use by others (independent of
proprietary software, e.g., ORACLE)
Data System: A linked and organized set of data bases
including public domain software (not platform dependent),
tutorials, workflows, and procedures to analyze the data
Data systems needed
Property
X
Y
Z
(depth)
T
Seismicity
Earthquake location
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Gravity
Density
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inferred
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Aeromagnetic
Magnetic
Susceptibility
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inferred
Seismic Reflection
Arrival times
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inferred
Seismic Refraction
Arrival times
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inferred
Electromagnetic
Electrical
conductivity
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inferred
Heat Flow
Thermal
conductivity
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Drill Hole Data
Depth,
Lithology, Physical
properties
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Z
(elevation)
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Data systems needed (continued)
Property
X
Y
Z
(elevation)
Geologic Maps
Distribution
of units
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Faults
(mapping and imaging)
Geometry
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Geochemistry/Petrology
Composition
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Geochronology
Age
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Global Positioning System
Position
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Digital Elevation Model
Elevation grid
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Remote Sensing (SAR)
Image of
reflectivity
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Remote Sensing
(multispectral)
Image of
reflectivity
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Paleontology
Ancient life
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Sedimentology
Ancient
environments
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Z
(depth)
T
inferred
inferred
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inferred
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inferred
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Data is only the beginning
Decision
Support
Knowledge
Information
Data
Some considerations in setting up a class
The audience (obviously) - what do they know coming in?
(Geospatial skills, computer programming skills, general
computer skills, mathematical background, geological
background)
How formal will the structure be? (mix of lecture, lab, seminar
style)
How mathematical do you want to be?
What is mix of computer science and geoscience?
Relation to “Computer Applications in the Geoscience” class?
I strongly recommend that a computer science colleague be
involved to some degree and that there be some computer
science students in the class.
Learning Environments
Time
Different
Same
Same
Face
To
Face
Tele / Video
conference
Collaboratory
Different
Library
Drop-in
Lab
Cyberinfrastructure
Email
DATA
The independent scientist is not a thing of the past, but more and more big
advances are made through collaboration.
A class
schedule
A class
schedule
(cont.)
Uncertainty, reliability, provenance. Etc.
Class assignments
Read papers from the recent literature (<2004 is old )
Set up a modest personal website
Laboratory exercise on EXCEL
Laboratory exercise on GIS
Laboratory exercise on MATLAB
Laboratory exercise on using Google Earth quantitatively
Find an interesting piece of software on-line and demo it to
the class
Create a modest web service
Term project to create a modest web portal
The class project
The class project - some topics
Geoinformatics:
Data to
Knowledge
GSA Special
Paper
Table of Contents I
Table of Contents II
Geoinformatics - Cambridge University Press
Geoinformatics: Cyberinfrastructure for the Solid Earth Sciences
Co-editors: G. Randy Keller, University of Oklahoma, USA
Chaitanya Baru, San Diego Supercomputer Center, University of California
I. INTRODUCTION
1. Introduction to Science Needs and Challenges
G. Randy Keller, University of Oklahoma
2. Introduction to IT Concepts and Challenges
Chaitanya Baru, University of California, San Diego
II. DATA COLLECTION AND MANAGEMENT
3. Framework for Managing LiDAR/Remote Sensing Data, Ramon
Arrowsmith, and Christopher Crosby, Arizona State University
4. Algorithms for Gridding and Analysis of Remote Sensing Data,
S. B. Baden, Christopher Crosby, Ramon Arrowsmith, Arizona State University
5. Digital Field Data Collection,
John Oldow and Douglas Walker, University of Idaho and University of Kansas
6. Sensor Networks and Embedded Cyberinfrastructure for Sensor Networks,
Tony Fountain, Frank Vernon, Scripps Institute of Oceanography
Geoinformatics - Cambridge University Press
III. MODELING SOFTWARE AND COMMUNITY CODES
7. Community Codes for Geodynamics,
Mike Gurnis and Walter Landry, CalTech
8. Community Codes for Earthquake Wave Propagation Research: The
TeraShake Platform
Philip Maechling, Yifeng Cui, Kim Olsen, David Okaya, Ewa Deelman, Amit
Chourasia, Gaurang Mehta, Reagan Moore, and Thomas H. Jordan,
Southern California Earthquake Center, University of Southern California
9. Parallelizing Finite Element Codes for Geodynamics
Mian Liu, University of Missouri
10.Designing and Building a Grid-enabled Synthetic Seismogram
Computational Resource
Dogan Seber, Choonhan Youn, Tim Kaiser, Cindy Santini, University of
California at San Diego
11. The PaleoAtlas for ArcGIS
Chris Scotese, University of Texas at Arlington
Geoinformatics - Cambridge University Press
IV. VISUALIZATION AND DATA REPRESENTATION
12. Visualization of Seismic Model Data
Steve Cutchin and Amit Chourasia, UCSD
13. Integrated Visualization of 4D Data
Charles Meertens, UNAVCO
14. Visualization and Fusion of Remote Sensing Data
Eric Frost, San Diego State University
15. Database Development and Visualization for the
Yellowstone National Park Region
Robert B. Smith, Jaime Farrell, and Charles Meertens,
University of Utah, UNAVCO
Geoinformatics - Cambridge University Press
V. KNOWLEDGE MANAGEMENT AND DATA INTEGRATION
16. Data Integration for Paleo Studies: Why and How?
Allister Rees, Chris Scotese, Ashraf Memon, John Alroy, Univeristy of Arizona, UCSD,
University of California at Santa Barbara, University of Texas at Arlington,
17. Creating a dynamic, calibrated geologic time-line using databases, Web applications, and
services,
Cinzia Cervato and Peter Sadler, Iowa State University
18. Data Models and Tools for Geochemistry Databases, Kerstin Lehnert, Doug Walker,
Richard Carlson, Columbia University, University of Kansas, Carnegie Institution of
Washington
19. Spatial and Process Ontologies of Subduction Zones,
Hassan Babaie, Georgia State University
20. GeoSciML - A GML application for geoscience information interchange
Stephen M. Richard and CGI Interoperability working group, Arizona Geological Survey
21. Bottom-Up Ontologies and Recommendation Systems for Geoscience Applications
Mark Gahegan, Pennsylvania State University
22. Knowledge Representation in Geology,
Krishna Sinha and Kai Lin, Virginia Tech University, University of California at San Diego
Geoinformatics - Cambridge University Press
V. KNOWLEDGE MANAGEMENT AND DATA INTEGRATION
23. Web Services and Observation Data Catalogs for Uniform Hydrologic Data Access and
Analysis
I. Zaslavsky, D. Valentine, T. Whitenack, D. Maidment
University of California at San Diego, University of Texas at Austin
24. Web Services for Seismic Data Archives
Tim Ahern and Linus Kamb, IRIS
25. Creating CI resources for gravity and magnetic data: Algorithms, Tools, and Web Services
Leo Salayandia, Raed Aldouri, Ann Gates, Vladik Kreinovich, and G. Randy Keller,
University of Texas at El Paso and University of Oklahoma
26. Use of Scientific Workflows in Geoscience
Ilkay Altintas, Efrat Jaeger-Frank, Bertram Ludaescher
University of California at Davis, University of California at San Diego
27. Workflow-Driven Ontologies: A methodology to create scientific workflows from domain
knowledge
Leonardo Salayandia, Paulo Pinheiro da Silva, and Ann Q. Gates, UTEP
28. Science Portal for Research and Education in Geosciences
Ashraf Memon, Sandeep Chandra, Choonhan Youn, UCSD
Geoinformatics - Cambridge University Press
VII. Emerging International Efforts
29. The evolution of Earth Science data integration in the Federal
Government of the US: Policy, Practice, and Informatics
Linda Gunderson, U. S. Geological Survey
30. Geosciences Data in India
K. V. Subbarao, Indian Institute of Technology. Department, Department of
Earth Sciences
31. Global Earth Observations Grid
Satoshi Sekiguchi, Satoshi Tsuchida, and Ryosuke Nakamura, National
Institute of Advanced Industrial Science and Technology (AIST), Japan
32. GEO-GRID – eScience for the Earth- and Environmental Science
Jens Klump, GeoForschungsZentrum, Potsdam, Germany
Some thoughts about a Geoinformatics
curriculum
(B.S. in Geoscience with Computer Science Minor)
Mathematics background (Calculus, statistics, numerical
analysis)
Computer Programming [which language(s)?]
GIS
Geophysics/Remote Sensing (Introductory classes)
Geology (at least a minor)
Database - Data Structures
Software Engineering (informal participation)
Computer Applications in the Geosciences
Skills needed: Data manipulation, web presence, uncertainty
analysis, visualization/graphics, basic hardware handling
Some Thoughts About the Need for Cyberinfrastructure
•The Geosciences are a discipline that is strongly data driven,
and large data sets are often developed by researchers and
government agencies.
•The complexity of the fundamental scientific questions being
addressed require a variety of data with highly integrative and
innovative approaches if we are to find solutions.
•Geoscientists have a tradition of sharing of data, but being
willing to share data if asked or even maintaining an obscure
website accomplishes little. Also as a community, we have
no mechanisms to share the work that has been done when a
third party cleans up, reorganizes or embellishes an existing
database.
•We waste a large amount of human capital in duplicative
efforts and fall further behind by having no mechanism for
existing databases to grow and evolve via community input.