Teaching Geoinformatics: A Geoscience Perspective

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Teaching Geoinformatics A Geoscience Perspective
Randy Keller Professor and Edward Lamb McCollough
Chair in Geophysics School of Geology and
Geophysics University of Oklahoma
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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!
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The EarthScope Scientific Vision
To understand the structure (evolution) and
deformation of the North American continent in
four dimensions (x,y,z,t)
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Cyberinfrastructure for the GeosciencesWhy do we
need it?
EarthScope
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.
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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
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Geoinformatics - Some key elements

• 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

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www.Geoinformatics.info
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A Scientific Effort Vector

Background Research
Data Collection and
Compilation Software Issues
Science
Back- ground Research
Data Collection and Compilation Software
Issues

Science
Science - Analysis, Modeling, Interpretation,
Discovery
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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
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Data systems needed
Property X Y Z (elevation) Z (depth) T
Seismicity Earthquake location ? ? ? ?
Gravity Density ? ? ? inferred ?
Aeromagnetic Magnetic Susceptibility ? ? ? inferred
Seismic Reflection Arrival times ? ? ? inferred ?
Seismic Refraction Arrival times ? ? ? inferred
Electromagnetic Electrical conductivity ? ? ? inferred ?
Heat Flow Thermal conductivity ? ? ?
Drill Hole Data Depth, Lithology, Physical properties ? ? ? ?

• A GEOINFORMATICS DATA SCHEME

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Data systems needed (continued)
Property X Y Z (elevation) Z (depth) T
Geologic Maps Distribution of units ? ? ?
Faults (mapping and imaging) Geometry ? ? ? inferred
Geochemistry/Petrology Composition ? ? inferred
Geochronology Age ? ? ?
Global Positioning System Position ? ? ? ?
Digital Elevation Model Elevation grid ? ? ?
Remote Sensing (SAR) Image of reflectivity ? ? ? ?
Remote Sensing (multispectral) Image of reflectivity ? ? ?
Paleontology Ancient life ? ? ?
Sedimentology Ancient environments ? ? inferred inferred ?

• A GEOINFORMATICS DATA SCHEME

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Data is only the beginning
DecisionSupport
Value
Knowledge
Volume
Information
Data
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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.
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Learning Environments
Collaboratory
Time
Same
Different
Face To Face
Library Drop-in Lab
Same
Cyberinfrastructure
Place
Tele / Video conference
Email
Different
DATA
The independent scientist is not a thing of the
past, but more and more big advances are made
through collaboration.
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A class schedule
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A class schedule(cont.)
Uncertainty, reliability, provenance. Etc.
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Class assignments
Read papers from the recent literature (lt2004 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
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The class project
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The class project - some topics
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Geoinformatics Data to Knowledge GSA Special
Paper
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Table of Contents I
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Table of Contents II
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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
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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
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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
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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
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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
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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
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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
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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.