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abstract: This data collection contains geospatial data from models predicting the suitability of Wind Energy Areas (WEA) for the Gulf of Mexico Call Area. It includes a constraints shapefile created using 18 data layers that represent conflicts for offshore wind development. Also included is a final suitability shapefile, created using 53 data layers. The geometric mean of the six submodels (i.e., national security, industry, and operation, natural and cultural resources, fisheries, logistics, and economics) was used to calculate an overall suitability score. The geometric mean was chosen because it grants equal importance to each variable (Bovee 1986; Longdill et al. 2008; Silva et al. 2011; Muñoz-Mas et al. 2012). Furthermore, all submodels had equal weight within the suitability model. The resulting suitability score is for general offshore wind spatial planning, with scores approaching 0 representing low suitability and 1 representing high suitability relative to the other grid cells. Any grid that contained a data layer with a 0 score (i.e., constraints data layer) was deemed unsuitable for offshore wind development.
purpose: Planning and siting for Wind Energy Areas (WEAs) requires thorough synthesis and spatial analyses of critical environmental data and ocean space use conflicts (Kapetsky et al. 2013). This dataset is to aid with visualization of Wind Energy Areas (WEAs) suitability model in the Gulf of Mexico. A gridded relative suitability analysis, commonly used in a Multi-Criteria Decision Analysis (MCDA), was performed to identify the grid cells with the highest suitability for offshore wind development in the Call Area (Longdill et al. 2008; Radiarta et al. 2008; Gimpel et al. 2015; Bwadi et al. 2019). Spatial data layers included in the suitability analysis identify space-use conflicts and environmental constraints such as active national security areas, maritime navigation, ocean industries, and natural resource management. We utilized a submodel structure to capture ocean use and conservation concerns including national security, natural and cultural resources, industry and operations, fisheries, logistics, and economics. Data layers with no compatibility with offshore wind development (e.g., shipping fairways) were captured in the list of incompatible constraints and removed from further analysis due to known incompatibility with offshore wind. This model structure ensures that each submodel is given equal representation in the overall suitability model regardless of how many data layers are present in each submodel.
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supplementalInformation: Submission Package ID: 4XKJXC Methods: 1. Study area polygons with a 4.05 ha (10 ac) hexagonal grid was created using the extent of BOEM’s Gulf of Mexico Call Area. 2. Each data layer was scored on a 0 to 1 scale, with scores approaching 0 representing low suitability and 1 representing high suitability relative to the other grid cells for offshore wind. Any grid that contained a data layer with a 0 score (i.e., constraints data layer) was deemed unsuitable for offshore wind, and not considered further in the analysis. Next, an overall suitability score was calculated for each submodel (i.e., national security, industry and operations, natural and cultural resources, fisheries, logistics, and economics) by taking the geometric mean of all scores within each grid cell. The geometric mean of the six submodels was used to calculate an overall suitability score. 3. Suitability scores are presented as categories (“Unsuitable,” “Low,” “Moderately Low”, “Moderate,” “Moderately High”, “High”) grouped by quantiles of the calculated scores, with all scores of 0 being in the “Unsuitable” category and represented by the color red. Within the suitability submodel and overall model maps, standardized colors were used to depict categories, with orange representing “Low,” yellow “Moderately Low,” light green “Moderate”, medium green “Moderately High”, and forest green “High” suitability and coinciding with each proportion of quantile values. With all suitability maps, relative categories still represent values ranging from 0 to 1, with the “Low” category representing the lowest quantile of the data, “Moderate” the middle quantile, and “High” the upper quantile. Presenting categories rather than actual suitability scores simplified interpretation of results and provided optimal contrast among categories. Further, distribution of scores varies among the suitability submodels (e.g. number of data layers, score range of data distribution depicted); for example, in one submodel a score of 0.5 could be classified as “High,” while in another submodel or region a score of 0.5 could be “Low” because the scores are relative. Thus, suitability scores among the different submodels should not be compared, as the score is unique to each submodel. File Information: Total File Size: 18 files in 1 folder, 30.2 MB unzipped, 8.02 MB zipped
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