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GIS in Marine and Coastal Environments I-IV
AAG Centennial Meeting, PhiladelphiaMarch 17, 2004
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A New Object-Oriented Data Model for Oceans, Coasts,
Seas, and Lakes
AAG Centennial Meeting, PhiladelphiaMarch 17, 2004 dusk.geo.orst.edu/djl/arcgis
Dawn Wright, Oregon State UniversityPat Halpin, Duke University
Michael Blongewicz, DHIJoe Breman and Steve Grisé, ESRI
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ArcGIS “Custom” Data Models
• Basemap • Administrative
Boundaries• Utilities• Parcels• Transportation• Imageryetc ...
• Conservation/Biodiv• Hydro• Groundwater Hydro • Forestry• Geology• Petroleum• Marine• IHO-S57• Atmosphericetc ...
4Image courtesy of PISCO, OrSt
Marine Data Collection
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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"
• 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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• 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
Purpose of Marine Data Model
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Design Strategy
“Generic”
Marine Data Model
User Group
Data Model
User Group
Data Model
User Group
Data Model
Project
Data Model
Project
Data Model
Project
Data Model
Inh
erit
ance
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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
Marine boundaries (e.g., MPAs)
Geophysical time series
Sub-bottom profiling
Magnetics
Gravity
Seismics
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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Users’s View of Data
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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MDeviceIDEastNorthSpeedDirection112.110.88.6121111.312.57.922019.3-3.57.5130114.015.13.923417.312.09.1115
MeasuredData
InstantaneousPoint (ex: CTD)InstantaneousPoint (ex: CTD)
Measurement
XX
YY
TimeStampTimeStamp
MeasuringDevice
MDeviceIDNameTypeMeasurementID1Bob12Poncho13Juanita14Mia25Anita2
MeasuringDevice
MTypeIDVarNameVarDesc VarUnitsMDeviceID1Oranges12Bananas13Cubic cm24Rocks25Limes3MeasuredType
ZZ
MarineIDMarineCodeSeriesIDIPointTypeRecordedTime1AAA1105/04/58 12:00 002BBB1105/04/58 12:30 003CCC1105/04/58 13:00 00
InstantaneousPoints
MeasurementMeasureIDMarineIDZLocXlocYlocServiceTripSeviceDesc11-0.821-1.531-3.542-0.852-1.5
Michael Blongewicz
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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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MeasurementMeasureIDMarineIDZLocXlocYlocServiceTripSeviceDesc11-0.821-1.531-3.542-0.852-1.5
Measurement
TimeDurationPoint TimeDurationPoint (ex: moored ADCP)(ex: moored ADCP)
XX
YY
ZZMarineIDMarineCode1AAA2BBB3CCC
TimeDurationPoints
FeatureIDTSTypeID1112232425
TimeSeriesTurnTable TSTypeTSTypeIDVariableUnits1CurrentSpeed2Salinity3CurrentSpeed4Temperature5Salinity
TimeSeries3FeatureIDTSTypeIDTSDateTimeTSValue 112:00:0016.7112:20:0014.0112:40:0021.9113:00:0011.2113:20:0012.4
TimeSeries2FeatureIDTSTypeIDTSDateTimeTSValue 112:00:0016.7112:20:0014.0112:40:0021.9113:00:0011.2113:20:0012.4
TimeSeries1FeatureIDTSTypeIDTSDateTimeTSValue 112:00:0016.7112:20:0014.0112:40:0021.9113:00:0011.2113:20:0012.4
Michael Blongewicz
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TimeSeries3FeatureIDTSTypeIDTSDateTimeTSValue112:00:0016.7112:20:0014.0112:40:0021.9113:00:0011.2113:20:0012.4
TimeSeries2FeatureIDTSTypeIDTSDateTimeTSValue112:00:0016.7112:20:0014.0112:40:0021.9113:00:0011.2113:20:0012.4
TimeSeriesPoints TimeSeriesPoints (ex: ADCP in series)(ex: ADCP in series)
XX
YY
ZZ
MarineIDMarineCodeZlocation1AAA02BBB03CCC0
TimeSeriesPoints
TSTypeTSTypeIDVariable Units1CurrentSpeed2Wind3CurrentSpeed4Temperature5Wave Heights
TimeSeries1FeatureIDTSTypeIDTSDateTimeTSValue112:00:0016.7112:20:0014.0112:40:0021.9113:00:0011.2113:20:0012.4
FeatureIDTSTypeID1112232425
TimeSeriesTurnTable
Michael Blongewicz
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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
Build Better Models / Analysis
Geographic Space
Data Space
Geographic Space
Sample DataModel Habitat
Redefine Model
GIS Applications GIS ApplicationsStatistical Applications
1. Sampling
2. Statistical methods
3. GIS models
4. Model validation
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Implications (3)
Data Sharing
Within / Between Projects Internet Map Services (Geography Network, NSDI, OBIS…)
Internet Map Services: data conflation tools
DODS WMS
Z39.50FGDC
Tools/Protocols:
Data Type:vector data metadata mapraster data
XML
Distributed Generic Information Retrieval
Distributed OceanographicData 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/arcgisinc. downloads, join MDM
listserv
Next talk and…5236. Thursday, 10 a.m., Alyssa Aaby, Salon D