Changes between Version 173 and Version 174 of WikiStart

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09/15/09 08:58:57 (4 years ago)
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jjr8
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    v173 v174  
    11= Marine Geospatial Ecology Tools = 
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    3 Marine Geospatial Ecology Tools (MGET), also known as the {{{GeoEco}}} Python package, is an open source geoprocessing toolbox designed for coastal and marine researchers and GIS analysts who work with spatially-explicit ecological and oceanographic data in scientific or management workflows. MGET includes over 150 tools useful for a variety of tasks, such as converting oceanographic data to ArcGIS formats, identifying fronts in sea surface temperature images, fitting and evaluating statistical models such as GAMs and GLMs by integrating ArcGIS with the R statistics program, analyzing coral reef connectivity by simulating hydrodynamic larval dispersal, and building grids that summarize fishing effort, CPUE and other statistics. Currently under development are tools for identifying rings and eddy cores in sea surface height images, for analyzing connectivity networks, for estimating fishing effort when no effort data are available, for predicting hard bottom habitat from coarse grain bathymetry, and much more. 
     3Marine Geospatial Ecology Tools (MGET), also known as the {{{GeoEco}}} Python package, is an open source geoprocessing toolbox designed for coastal and marine researchers and GIS analysts who work with spatially-explicit ecological and oceanographic data in scientific or management workflows. MGET includes over 180 tools useful for a variety of tasks, such as converting oceanographic data to ArcGIS formats, identifying fronts in sea surface temperature images, fitting and evaluating statistical models such as GAMs and GLMs by integrating ArcGIS with the R statistics program, analyzing coral reef connectivity by simulating hydrodynamic larval dispersal, and building grids that summarize fishing effort, CPUE and other statistics. Currently under development are tools for identifying eddy cores in sea surface height images, for analyzing connectivity networks, for estimating fishing effort when no effort data are available, for predicting hard bottom habitat from coarse grain bathymetry, and much more. 
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    55MGET may be accessed from ArcGIS as a toolbox in the !ArcToolbox window and from programming languages as a set of Python modules and COM components.