Marine Geospatial Ecology Lab

ArcGIS Tutorial

In ArcGIS, MGET appears as a geoprocessing toolbox. The toolbox contains about 300 tools. The tools may be executed individually, wired together into graphical workflows using the ArcGIS ModelBuilder, executed programmatically from geoprocessing scripts, or executed from the ArcGIS Command Line window.

Accessing the MGET toolbox in the ArcToolbox window

After you have installed MGET (instructions here), you can access it from the ArcToolbox window in ArcMap or ArcCatalog. If the ArcToolbox window is not visible, click the ArcToolbox button to bring it up:

If the toolbox is not there but you know MGET is installed, you simply need to add it to the ArcToolbox window.

The MGET toolbox

The MGET toolbox groups its tools into a hierarchy according to their function.


The MGET toolbox with some of the tool groups open

The top-level toolsets are:

  • Connectivity Analysis – Tools for modeling connectivity between patches of marine habitat by simulating the hydrodynamic dispersal of larvae. This toolset may be expanded in the future to consider other approaches to modeling connectivity.
  • Conversion – Tools for batch-converting between data formats. These are similar to tools provided by ArcGIS but fill important gaps not addressed by ArcGIS, such as the ability to convert gridded data in HDF or netCDF format to ArcGIS-compatible rasters.
  • Data Management – Miscellaneous tools useful for certain data management tasks. Many of these were written in the early years of ArcGIS 9.x to provide basic batch processing capabilities not present in ArcGIS itself. Now that modern versions of ArcGIS include batch processing, many of these tools may be obsolete. Some remain useful in certain scenarios.
  • Data Products – Tools for acquiring and working with various data products that are popular in the marine spatial ecology community. Use these to quickly download oceanographic products in ArcGIS-compatible formats, obtain values of oceanographic products at points in space and time, identify fronts and eddies in satellite images, and so on. These tools are individually tailored to specific products and provide access to them with a minimum of fuss.
  • Fishery Analysis – Tools designed specifically for analyzing fisheries data. While the rest of MGET includes many tools that may be applied to fisheries data, this toolset contains those that are uniquely applicable it.
  • Oceanographic Analysis – Tools for performing oceanographic analysis on generic input data. At present, there is only one toolset within here, containing tools for identifying fronts in ArcGIS rasters or binary files. Most of MGET’s oceanographic analysis tools actually appear in the Data Products toolset because they are tailored to work with specific products (e.g. a tool that detects fronts in NOAA NODC’s 4km AVHRR Pathfinder SST data).
  • Spatial and Temporal Analysis – General-purpose tools for spatial and temporal analysis.
  • Statistics – Tools that perform statistical analysis or modeling. Some of MGET’s most popular tools appear here, such as those for predictive modeling, often used in modeling and mapping the distribution of species. Many of these tools interface ArcGIS with the R statistics program.

Getting started with geoprocessing in ArcGIS

If you have not worked with geoprocessing tools in ArcGIS before, we strongly suggest you get some basic experience running Arc’s built-in tools and using the ModelBuilder workflow system before trying MGET. To get started, we suggest browsing the materials at ESRI’s Geoprocessing Resource Center.

Once you are able to execute tools and build models with the ModelBuilder, proceed to the cookbook below for examples of how to apply MGET’s tools in common scenarios for which MGET was designed.

Please see our downloadable habitat model example for a step-by-step example of one of the most popular applications of MGET. This example covers a number of the topics we plan to cover in the cookbook, although there are many topics that are not covered. If you have any questions please contact us.

Capabilities in MGET

  • Obtaining data in GIS-compatible formats:
    • Downloading popular products supported by MGET in raster format
    • Converting HDF, netCDF, and other formats to rasters
    • Interpolating (a.k.a. sampling) values of rasters and gridded products
    • Querying OBIS and other DiGIR servers for point observations of species
    • Obtaining climate index values for a table of dated records
  • Analyzing and visualizing oceanographic data:
    • Animating time-series data in ArcMap
    • Creating climatologies
    • Creating line features representing current or wind vectors
    • Finding fronts in SST images
    • Finding eddies in SSH images
  • Building predictive species distribution models:
    • Preparing input data for analysis
    • Exploring the data and detecting possible problems
    • Fitting (a.k.a. training) a predictive model
    • Evaluating the model’s performance
    • Predicting rasters for the response variable
  • Analyzing fisheries data:
    • Detecting temporal periodicity in fishing events
    • Detecting spatiotemporal autocorrelation in fishing events
    • Modeling fishing effort using the Fishing Effort Envelope Tool (FEET)
  • Simulating marine hydrodynamic connectivity:
    • Building habitat rasters and initializing a simulation
    • Downloading ocean currents into the simulation
    • Running the simulation
    • Animating the output density rasters
    • Analyzing the output connectivity network
  • Performing exploratory statistical analysis and accessing R from ArcGIS:
    • Creating scatterplots
    • Creating Cleveland dot plots
    • Creating density histograms
    • Executing arbitrary R scripts from ArcGIS
  • Conducting other spatial and temporal analyses:
    • Mapping species diversity from point observations of species
    • Detecting temporal periodicity in a table of dated records
    • Calculating the moon phase for a table of dated records
    • Creating fishnets (polygon grids) that spatially summarize point data
    • Finding the feature nearest to each point
    • Interpolating values for no data cells of a raster