GIS in Marine and Coastal Environments I-IV

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GIS in Marine and Coastal Environments I-IV
AAG Centennial Meeting, Philadelphia March 17,
2004
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A New Object-Oriented Data Model for Oceans,
Coasts, Seas, and Lakes

• Dawn Wright, Oregon State University
• Pat Halpin, Duke University
• Michael Blongewicz, DHI
• Joe Breman and Steve Grisé, ESRI

AAG Centennial Meeting, Philadelphia March 17,
2004
dusk.geo.orst.edu/djl/arcgis
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ArcGIS Custom Data Models

• Basemap
• Administrative Boundaries
• Utilities
• Parcels
• Transportation
• Imagery
• etc ...

• Conservation/Biodiv
• Hydro
• Groundwater Hydro
• Forestry
• Geology
• Petroleum
• Marine
• IHO-S57
• Atmospheric
• etc ...

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Marine Data Collection
Image courtesy of PISCO, OrSt
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Figure courtesy of Anne Lucas, U. of Bergen,
Norway
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A Georelational to a Geodatabase Model

• coverage and shapefile data structures
• homogenous collections of points, lines, and
  polygons with generic, 1- and 2-dimensional
  "behavior"
• cant distinguish behaviors
• Point for a marker buoy, same as point for OBS
• smart features in a geodatabase
• lighthouse must be on land, marine mammal siting
  must be in ocean

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Purpose of Marine Data Model

• basic template for implementing GIS projects
• input, formatting, geoprocessing, creating maps,
  performing analyses
• basic framework for writing program code and
  maintaining applications
• development of tools for the community
• promote networking and data sharing through
  established standards

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Design Strategy
Generic Marine Data Model
Inheritance
User Group Data Model
User Group Data Model
User Group Data Model
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Steps in Data Modeling

• (1) Model the user's view of data
• what are the basic features needed to solve the
  problem?
• (2) Select the geographic representation
• points, lines, areas, rasters, TINs

Bathymetry Sidescan sonar/Backscatter Shoreline M
arine boundaries (e.g., MPAs) Geophysical time
series Sub-bottom profiling Magnetics Gravity Seis
mics Sediment transport etc. ...
Marine mammal movement Atmospheric influences Sea
state Wave activity Sea surface
temperature Salinity Sensor calibration
data Current meters Density etc. ...
Image by Joe Breman, ESRI
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Userss View of Data
Steve Grisé, ESRI
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(No Transcript)
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Steps in Data Modeling (cont.)

• (3) Define objects and relationships
• draw a UML diagram
• (4) Match to geodatabase elements
• specify relationships, behaviors
• (5) Organize geodatabase structure

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InstantaneousPoint (ex CTD)
Michael Blongewicz
X
TimeStamp
Y
Measurement
MeasuringDevice
Z
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Image courtesy of the Neptune Project,
www.neptune.washington.edu, University of
Washington Center for Environmental Visualization
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TimeDurationPoint (ex moored ADCP)
Michael Blongewicz
Z
Measurement
X
Y
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TimeSeriesPoints (ex ADCP in series)
Michael Blongewicz
X
Y
Z
TSType
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(No Transcript)
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Implications (1)

• Inputting Formatting Data
• Provides common data structures
• Allows control of required data fields from
  collection through analysis phases

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Implications (2)

• Geoprocessing Analysis
• Allows explicit spatial temporal relationships
  to be used in geoprocessing and analysis

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Build Better Models / Analysis
Data Space
Geographic Space
Geographic Space
2. Statistical methods
Redefine Model
Model Habitat
4. Model validation
Sample Data
1. Sampling
3. GIS models
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Implications (3)

• Data Sharing
• Within / Between Projects
• Internet Map Services (Geography Network, NSDI,
  OBIS)
• Internet Map Services data conflation tools

Distributed Generic Information Retrieval
Distributed Oceanographic Data System
Web Mapping Services
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Project is Ongoing

• Case studies , tool development
• Interested participants via web site
• 275 people, 31 countries
• Refine UML - abstract and feature classes,
  descriptions, rules/behaviors
• 2004 ESRI UC sessions
• 2005 ESRI Press book
• Agency buy-in
• Publicizing and publishing
• Tie-in w/ other model efforts

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More information

• dusk.geo.orst.edu/djl/arcgis
• inc. downloads, join MDM listserv
• Next talk and
• 5236. Thursday, 10 a.m., Alyssa Aaby, Salon D