openModeller Desktop
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Static spatial distribution modelling tool.   Free
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openModeller Desktop is a generic framework for carrying out fundamental niche modelling experiments - typically used to predict species distribution given a set of environmental raster layers. The openModeller Desktop application builds on the openModeller library by providing a user friendly graphical user interface for carrying out niche modelling experiments.
  • A layerset manager. This is for creating named collections of layers for use in modelling experiments. Having layers organised in named sets removes the tedium of repeatedly having to select layers from the file
What's New
Version 1.4.0:
  • New CONSENSUS algorithm that can be used to generate models with different algorithms by converting each individual model into a binary model given a threshold and then "merging" them into a final combined model according to a specified level of agreement.
  • Upgraded gSOAP version being used by OMWS to 2.8.15.
  • Included library API versioning, starting with 0 from this release.
  • Moved some of the compiler definitions from CMake to internal #defines, so that client programs can include openModeller headers without worrying about adding specific definitions if they want to compile directly without using CMake.
  • New command-line tool om_evaluate to return raw model values given a set of points and an environmental scenario.
  • Fixed segfault when needNormalization was called on a deserialized SVM algorithm.
  • Modified om_pseudo command-line tool to also accept XML input (in this case also producing XML output), optionally storing it into a file. When a model is specified as a parameter, presences are generated inside the model, while absences are generated outside.
  • New implementation of the SOAP client/server programs compatible with OMWS 2.0.
  • New driver to fetch remote rasters available through http, https or ftp. Layers can now be identified by URLs.
  • New class to read openModeller settings from a configuration file (key = value). Possible configurations include: CACHE_DIRECTORY, ALGS_DIRECTORY and DATA_DIRECTORY.
  • Changed initialization priority order for paths: 1) programatic setting, 2) configuration file, 3) environment variable, 4) default value through compiler constant.
  • Renamed compiler constant PKGDATAPATH and environment variable OM_DATA_PATH to OM_DATA_DIR.
  • Renamed compiler constant PLUGINPATH and environment variable OM_ALG_PATH to OM_ALGS_DIR.
  • New optional parameter --config-file in most command line tools to indicate a configuration file for openModeller: om_model, om_test, om_project, om_points, om_pseudo, om_evaluate, om_sampler and om_algorithm. om_console also accepts an optional third argument to specify the configuration file location.
  • New command-line tool om_layer to either check if a specified layer id can be read from openModeller or to recursively scan a specified directory looking for openModeller-compatible layers.
  • New configuration FETCH_WCS (true or false) allowing WCS rasters to be fetched and stored as local Erdas Imagine copies before being used.
Version 1.4.0:
  • New CONSENSUS algorithm that can be used to generate models with different algorithms by converting each individual model into a binary model given a threshold and then "merging" them into a final combined model according to a specified level of agreement.
  • Upgraded gSOAP version being used by OMWS to 2.8.15.
  • Included library API versioning, starting with more...
Requirements
Intel/PPC, OS X 10.4 or later






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Downloads:2,535
Version Downloads:145
Type:Education : Science
License:Free
Date:05 Apr 2014
Platform:PPC 32 / Intel 32 / OS X
Price:Free0.00
Overall (Version 1.x):
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openModeller Desktop is a generic framework for carrying out fundamental niche modelling experiments - typically used to predict species distribution given a set of environmental raster layers. The openModeller Desktop application builds on the openModeller library by providing a user friendly graphical user interface for carrying out niche modelling experiments.
  • A layerset manager. This is for creating named collections of layers for use in modelling experiments. Having layers organised in named sets removes the tedium of repeatedly having to select layers from the file system for different model runs.
  • An algorithm profile manager. Algorithm profiles let you store the custom parameters you use for running models in a reusable way.
  • An experiment designer. The experiment designer allows you to set up an experiment consisting of multiple species and algorithm profiles.
  • Many more features.


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