GIS in Marine and Coastal
Environments I-IV
AAG Centennial Meeting, Philadelphia
March 17, 2004
1
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 2
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 ...
3
Marine Data
Collection
Image courtesy of PISCO, OrSt
4
5
Figure courtesy of Anne Lucas, U. of Bergen, Norway
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"
• can’t 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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“Generic”
Marine Data Model
User Group
Data Model
Project
Data Model
User Group
Data Model
Project
Data Model
User Group
Data Model
Inheritance
Design Strategy
Project
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
Marine mammal movement
Sidescan sonar/Backscatter
Atmospheric influences
Shoreline
Sea state
Marine boundaries (e.g., MPAs)
Wave activity
Geophysical time series
Sea surface temperature
Sub-bottom profiling
Salinity
Magnetics
Sensor calibration data
Gravity
Current meters
Seismics
Density
Sediment transport
etc. ...
etc. ...
Image by Joe Breman, ESRI
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Users’s View
of Data
10
Steve Grisé, ESRI
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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
InstantaneousPoints
MarineID
1
2
3
MarineCode
AAA
BBB
CCC
SeriesID
1
1
1
IPointType
1
1
1
RecordedTime
05/04/58 12:00 00
05/04/58 12:30 00
05/04/58 13:00 00
TimeStamp
Y
Measurement
MeasureID
1
2
3
4
5
MarineID
1
1
1
2
2
ZLoc
-0.8
-1.5
-3.5
-0.8
-1.5
Xloc
Yloc
ServiceTrip
SeviceDesc
Measurement
MeasuringDevice
MeasuringDevice
MDeviceID
1
2
3
4
5
Name
Bob
Poncho
Juanita
Mia
Anita
MeasuredType
MTypeID
VarName
1
2
3
4
5
Type
VarDesc
MeasurementID
1
1
1
2
2
VarUnits
Oranges
Bananas
Cubic cm
Rocks
Limes
Z
MDeviceID
1
1
2
2
3
MeasuredData
MDeviceID
1
1
1
1
1
East
12.1
11.3
9.3
14.0
7.3
North
10.8
12.5
-3.5
15.1
12.0
Speed
8.6
7.9
7.5
3.9
9.1
Direction
121
220
130
234
115
13
14
Image courtesy of the Neptune Project, www.neptune.washington.edu, University of Washington Center for Environmental Visualization
TimeDurationPoint (ex: moored ADCP)
TimeDurationPoints
MarineID
1
2
3
Michael Blongewicz
Z
MarineCode
AAA
BBB
CCC
Measurement
X
Measurement
MeasureID
1
2
3
4
5
MarineID
1
1
1
2
2
ZLoc
-0.8
-1.5
-3.5
-0.8
-1.5
Xloc
Yloc
ServiceTrip
SeviceDesc
Y
TimeSeriesTurnTable
FeatureID
1
1
2
2
2
TSTypeID
1
2
3
4
5
TSType
TSTypeID
1
2
3
4
5
Variable
CurrentSpeed
Salinity
CurrentSpeed
Temperature
Salinity
Units
TimeSeries3
FeatureID
TSTypeID
TSDateTime
TSValue
1
12:00:00
16.7
TimeSeries2
1
12:20:00
14.0
FeatureID
TSTypeID 1 TSDateTime
TSValue 21.9
12:40:00
1
1 12:00:00 13:00:0016.7
11.2
TimeSeries1
1
1 12:20:00 13:20:0014.0
12.4
12:40:00
FeatureID
TSTypeID 1 TSDateTime
TSValue 21.9
1 12:00:00 13:00:00 16.7
11.2
1
1 12:20:00 13:20:00 14.0
12.4
1
1
12:40:00
21.9
1
13:00:00
11.2
1
13:20:00
12.4
15
TimeSeriesPoints (ex: ADCP in series)
Michael Blongewicz
TimeSeriesPoints
MarineID
1
2
3
MarineCode
AAA
BBB
CCC
Zlocation
0
0
0
X
Y
TimeSeriesTurnTable
FeatureID
1
1
2
2
2
TSTypeID
1
2
3
4
5
Z
TSType
TSTypeID
1
2
3
4
5
Variable
CurrentSpeed
Wind
CurrentSpeed
Temperature
Wave Heights
Units
TimeSeries3
FeatureID
TSTypeID
TSDateTime
TSValue
1
12:00:00
16.7
1
12:20:00
14.0
FeatureID
TSTypeID
TSDateTime
TSValue
1
12:40:00
1
12:00:00
16.7 21.9
TimeSeries1
1
13:00:00
1
12:20:00
14.0 11.2
1
13:20:00
FeatureID
TSTypeID
TSDateTime
TSValue
1
12:40:00
21.9 12.4
1 1
12:00:00
16.7
13:00:00
11.2
1 1
12:20:00
14.0 12.4
13:20:00
1
12:40:00
21.9
1
13:00:00
11.2
1
13:20:00
12.4
TimeSeries2
16
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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
GIS Applications
Data Space
Statistical Applications
GIS Applications
Geographic Space
Geographic Space
2. Statistical methods
Redefine Model
Sample Data
1. Sampling
Model Habitat
4. Model validation
3. GIS models
Implications (3)
Data Sharing
 Within / Between Projects
 Internet Map Services (Geography Network, NSDI, OBIS…)
 Internet Map Services: data conflation tools
Data Type:
Tools/Protocols:
vector data
XML
raster data
DODS
metadata
Z39.50
FGDC
Distributed Generic
Distributed Oceanographic
Information Retrieval Data System
map
WMS
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
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