Ben Best, Jason Roberts, Pat Halpin. Nov 2011 Draft Report (550 p)
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,...
for the CoML Modeling and Visualization Workshop
Jason Roberts and Ben Best3-Feb-2009, Long Beach, CA
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
Talk outlineOverview of MGET’s software architectureQuick tour of the toolsLive demonstration
QuestionsAsk questions when neededShort discussions encouragedLong discussions may need to be deferred
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)
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
MGET interface in ArcGISThe MGET toolbox appears in the ArcToolbox window
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
Quick tour of the tools
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
Converting data
Batch processingCopy one raster at a time
Batch processingCopy rasters that you list in a table
Batch processingCopy rasters from a directory tree
Tools for specific products
Downloads sea surface height data from http://opendap.aviso.oceanobs.com/thredds
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
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
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
Modeling habitat (demo)
Chlorophyll
SST
Bathymetry
Presence/absence observations
Sampled environmental data
Multivariate statistical model
Probability of occurrence predicted from environmental covariates
Binary classification
AcknowledgementsThanks to OBIS SEAMAP and its data providers for sharing the data used here.
Thanks to our funders:
http://seamap.env.duke.edu
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!