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:jason.roberts@duke.edu, bbest@duke.edu

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!