Data Management Plan (Deprecated)

Geospatial data representing the Aquaculture Opportunity Area (AOA) study developed during 2024 for the State of Alaska. Included in this dataset are:

(1) Study areas in State of Alaska waters developed based on maximum sea ice coverage, distance from coastal populated places, existing and active aquaculture sites, and the Alaska state water boundary. Ten distinct study areas were identified (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell).

(2) Suitability modeling results for the ten study areas. Results between each study area should not be compared relative to outputs of another study area, as each study area was run as a discrete model and thus the outputs of the analysis cannot be compared between study areas.

(3) Precision siting outputs from the Aquaculture Opportunity Atlas for the State of Alaska. Clusters were identified within each of the ten study areas (Cordova, Craig, Juneau, Ketchikan, Kodiak, Petersburg, Seward, Sitka, Valdez, and Wrangell) for two scenarios: intertidal and subtidal mariculture.

(4) Final AOA Options for the state of Alaska based on selection of highest ranking polygons from the precision siting outputs for each of the ten study areas for both scenarios.

Process Steps:

• Study areas within Alaskan state waters were identified using maximum sea ice coverage, distance from coastal populated places, active aquatic farming sites, and the Alaska state water boundary to narrow down the spatial coverage of the study region. The results were ten distinct study areas encompassing southeast, southcentral, and southwest Alaska. Travel distance from major towns and harbors, as well as a maximum depth were used to further narrow down the final study areas.
• Study area polygons with a 5 acre hexagonal grid were created from the ten distinct study areas identified.
• 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 continuous data and a constant downscore value for categorical data. Any grid that contained a data layer considered a constraint was given a 0 score (i.e., constraints data layer) and was deemed unsuitable for aquaculture and therefore not considered further in the analysis. Next, an overall suitability score was calculated for each submodel (i.e., national security, industry, natural or cultural resources, fisheries) by taking the geometric mean of all scores within each grid cell that was not considered a constraint. The geometric mean of the five submodels was used to calculate an overall suitability score on the same 0 to 1 scale.
• The resulting suitability scores were used as inputs for the cluster analysis, which uses the Local Index of Spatial Association (LISA) analysis (further referred to as a cluster analysis) to identify statistically significant clusters and outliers of the final relative suitability modeling results. Two scenarios were applied to the suitability model outputs in order to filter the data to attain distinct outputs for both intertidal and subtidal mariculture. The intertidal scenario only retains grid cells in areas which are classified as marine or estuarine wetlands according to the NWI dataset, whereas the subtidal scenario only retains model outputs in areas which are in depths of 15ft to 200ft.
• A precision siting model was developed using custom rules and an adapted version of Techniques for Order of Preference by Similarity to Ideal Solutions (TOPSIS) to identify the most suitable potential AOA Options within each study area scenario. The TOPSIS analysis used a number of characterization parameters to score the clusters defined by the cluster analysis that were larger than 50 acres (i.e. minimum AOA size requirement). In order to both refine individual clusters and select from all potential options, the precision siting model approach applied both a within-cluster and among-cluster approach. The within-cluster precision siting approach refined the clusters in size by running a TOPSIS analysis on each grid cell within a cluster in order to find the highest ranking cells for each cluster independent of the others in a given study area. The among-cluster precision siting approach refined the total number of clusters considered a final AOA option by comparing the entire clusters within a study area against each other and getting scores for each of the clusters after they had been refined using the within-cluster approach.
• Final AOA options were selected from the precision siting process using a combination of the among-cluster TOPSIS scores and manual inspection. A subset of clusters were chosen for each study area to try and meet the goal of maximum 4,000 acres of subtidal, and 100 acres of intertidal area per study area.

These data are accessible via REST web map services for visualization within the NOAA Geoplatform via ArcGIS Online and associated web applications, including a StoryMap. Direct data downloads may not be available at this time.