Published on 01 January 2025
Coastal oceanographic connectivity at global scale: A dataset of pairwise probabilities and travel times derived from biophysical modeling
View DatasetDescription
The dataset encompasses two components: (1) a GeoPackage file (referencePolygons.gpkg) designed for geographic information systems (GIS), containing the spatial distribution of the hexagon-shaped coastal sites, each with a unique identifier (id), and (2) a comma-separated values compressed file (oceanographicConnectivity.csv.zip) containing a matrix detailing the realized connections between pairs of sites. This matrix includes information on the source site (id), sink site (id), date of particle release (day, month, year), and the corresponding travel time expressed in days.Users have two primary options for utilizing the dataset: they can either work directly with the geospatial vector for GIS, paired with the corresponding matrix of realized connections, to retrieve pairwise connectivity estimates between coastal sites globally, or they can use the coastalNet R package for streamlined access and analysis.For users opting to work within a GIS environment, the geospatial vector file contains the spatial distribution of the coastal hexagons, each identified by a unique ID. By identifying the source and sink sites of interest within the spatial data, users can retrieve the corresponding hexagon IDs. These IDs can then be cross-referenced with the matrix of realized connectivity events, which includes information on particle release date, source ID, sink ID, and travel time. This allows users to easily extract the specific connectivity events between selected sites, providing a flexible and detailed approach to analyzing connectivity patterns directly within GIS.For users opting to work within R, the coastalNet package can facilitate the use of the provided oceanographic connectivity estimates. It offers a comprehensive suite of functions for accessing, analyzing, and visualizing connectivity data. Check https://github.com/jorgeassis/coastalNet for additional information.
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Publication Details
Subfield
Geography, Planning and Development
Field
Social Sciences
Domain
Social Sciences
Confidence Score
50%
Source
Scholar Data Model