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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