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Marine Geospatial Ecology Tools: an integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++
Roberts, J.J.; Best, B.D.; Dunn, D.C.; Treml, E.A.; Halpin, P.N. (2010). Marine Geospatial Ecology Tools: an integrated framework for ecological geoprocessing with ArcGIS, Python, R, MATLAB, and C++. Environ. Model. Softw. 25(10): 1197-1207. dx.doi.org/10.1016/j.envsoft.2010.03.029
In: Environmental Modelling & Software. Elsevier: Oxford. ISSN 1364-8152; e-ISSN 1873-6726, more
Peer reviewed article  
Available in  Authors 
Keyword
    Marine/Coastal
Author keywords
    Marine ecology; Spatial ecology; Software integration; Interoperability;Informatics; Habitat modeling; Oceanography; GIS
Authors  Top 
  • Roberts, J.J.
  • Best, B.D.
  • Dunn, D.C., more
  • Treml, E.A.
  • Halpin, P.N.
Abstract
    With the arrival of GPS, satellite remote sensing, and personal computers, the last two decades have witnessed rapid advances in the field of spatially-explicit marine ecological modeling. But with this innovation has come complexity. To keep up, ecologists must master multiple specialized software packages, such as ArcGIS for display and manipulation of geospatial data, R for statistical analysis, and MATLAB for matrix processing. This requires a costly investment of time and energy learning computer programming, a high hurdle for many ecologists. To provide easier access to advanced analytic methods, we developed Marine Geospatial Ecology Tools (MGET), an extensible collection of powerful, easy-to-use, open-source geoprocessing tools that ecologists can invoke from ArcGIS without resorting to computer programming. Internally, MGET integrates Python, R, MATLAB, and C++, bringing the power of these specialized platforms to tool developers without requiring developers to orchestrate the interoperability between them. In this paper, we describe MGET’s software architecture and the tools in the collection. Next, we present an example application: a habitat model for Atlantic spotted dolphin (Stenella frontalis) that predicts dolphin presence using a statistical model fitted with oceanographic predictor variables. We conclude by discussing the lessons we learned engineering a highly integrated tool framework.
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