For the CoML Modeling and Visualization Workshop Jason Roberts and Ben Best 3-Feb-2009, Long Beach,...
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Transcript of For the CoML Modeling and Visualization Workshop Jason Roberts and Ben Best 3-Feb-2009, Long Beach,...
![Page 1: For the CoML Modeling and Visualization Workshop Jason Roberts and Ben Best 3-Feb-2009, Long Beach, CA.](https://reader035.fdocuments.us/reader035/viewer/2022070410/56649edc5503460f94bec368/html5/thumbnails/1.jpg)
for the CoML Modeling and Visualization Workshop
Jason Roberts and Ben Best3-Feb-2009, Long Beach, CA
![Page 2: For the CoML Modeling and Visualization Workshop Jason Roberts and Ben Best 3-Feb-2009, Long Beach, CA.](https://reader035.fdocuments.us/reader035/viewer/2022070410/56649edc5503460f94bec368/html5/thumbnails/2.jpg)
What is MGET?A collection of geoprocessing tools for marine ecology
Oceanographic data management and analysisHabitat modeling, connectivity modeling, statisticsHighly modular; designed to be used in many scenariosEmphasis on batch processing and interoperability
Free, open source softwareWritten in Python, R, MATLAB, and C++Minimum requirements: Win XP, Python 2.4 ArcGIS 9.1 or later needed for some toolsArcGIS and Windows are only non-free requirements
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Talk outlineOverview of MGET’s software architectureQuick tour of the toolsLive demonstration
QuestionsAsk questions when neededShort discussions encouragedLong discussions may need to be deferred
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MGET’s software architectureMGET “tools” are really just Python functions, e.g.:
pythoncom2x.dll
IDispatchIMyTool
MyToolCOM class
MyTool.py
MGET ArcGIS Toolbox
Python programs
ArcGIS 9.xEarly-bound COM clients (e.g. C++)
Late-bound COM clients
(e.g. VBScript)
MGET
(a.k.a. the GeoEco Python Package)
External callers
win32com module
MGET COM module
MGET exposes them to several types of external callers:
def MyTool(input1, input2, input3)
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Integration The Python functions can invoke C++, MATLAB, R, ArcGIS, and COM classes.
R interpreter
MyTool.m MyTool.r
MyTool.py
Python extension DLL
MyTool.cpp
C++
MyTool.pyd
Python extension DLL
MyToolMatlab.pyd
MATLAB Component Runtime (MCR)
rpy module
MGET COM module
win32com module
R packagesMATLAB toolboxes
IDispatch
COM Automation
classes
MGET ArcGIS module
arcgisscripting or win32com
module
ArcGIS geoprocessor
C libraries
ArcGIS toolboxes
Python packages
MGET R module
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MGET interface in ArcGISThe MGET toolbox appears in the ArcToolbox window
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MGET interface in ArcGISDrill into the toolbox to find the toolsDouble-click tools to execute directly, or drag
to geoprocessing models to create a workflow
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Quick tour of the tools
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Analyzing coral reef connectivityCoral reef ID and % cover maps Ocean currents data
Tool downloads data for the region and dates you specify
Larval density time series rasters
Edge list feature class representing dispersal network
Original research by Eric A. Treml
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Converting data
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Batch processingCopy one raster at a time
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Batch processingCopy rasters that you list in a table
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Batch processingCopy rasters from a directory tree
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Tools for specific products
Downloads sea surface height data from http://opendap.aviso.oceanobs.com/thredds
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Identifying SST fronts
~120 km
AVHRR Daytime SST 03-Jan-2005
28.0 °C
25.8 °C
Mexico
Front
Cayula and Cornillion (1992) edge detection algorithm
Freq
uenc
y
Temperature
Optimal break 27.0 °C
Strong cohesion front present
Step 1: Histogram analysis
Step 2: Spatial cohesion test
Weak cohesion no front
Bimodal
Example output
Mexico
ArcGIS model
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Identifying geostrophic eddies
Aviso DT-MSLA 27-Jan-1993 Red: Anticyclonic Blue: Cyclonic
Negative W at eddy core
SS
H a
nom
aly
Available in MGET 0.8
Example output
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Sampling raster dataSampling is the procedure of overlaying points over a map and storing the map’s value as an attribute of each point.
Chlorophyll-a DensityChl attribute of the points filled with values from the map
MGET has sampling tools for various scenarios
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Modeling habitat (demo)
Chlorophyll
SST
Bathymetry
Presence/absence observations
Sampled environmental data
Multivariate statistical model
Probability of occurrence predicted from environmental covariates
Binary classification
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AcknowledgementsThanks to OBIS SEAMAP and its data providers for sharing the data used here.
Thanks to our funders:
http://seamap.env.duke.edu
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For more informationDownload MGET:
http://code.env.duke.edu/projects/mget
Contact us:[email protected], [email protected]
Learn more about habitat modeling:Guisan, A., Zimmermann, N.E., 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135, 147–186.
Thanks for attending!