Some excellent tools and platforms for visualizing your data include:
Observable
This is a data visualization platform that enables users to create sharable Javascript notebooks using a simplified version of the D3 library.
We recommend that users who have data privacy concerns avoid using online platforms.
Integrated Development Environments (IDEs)
Lots of IDEs, such as RStudio and VSCode, have built-in plot viewers or data exploration tools that display visualizations directly inside the interface, allowing users to iteratively tweak code and see the effect.
IDEs are often integrated well with reporting tools, so they can be used to combine your code and visuals and share these effectively.
Programming languages such as R or Python are powerful tools to use in combination with an IDE to import, prepare and visualize data.
Various packages or libraries can be used to help with spatial visualizations, temporal and statistical visualization, and remote sensing data visualization. Examples in R are: ggplot, sf and raster. For Python, users could consider using plotly, matplotlib, or ipyleaflet.
Geographic Information System (GIS) platforms
Both ArcGIS and QGIS can be used to visualize, edit and manage spatial data, such as orthomosaics or kelp canopy extent polygons. These platforms can also be used to combine multiple data layers to support spatial decision-making. Both support vector files (e.g. Shapefiles, Geopackage, or GeoJSON) and raster files (e.g., GeoTIFF, JPEG and NetCDF).
ArcGIS is proprietary software developed by ESRI, and requires a license to use. QGIS is open-source and free, developed and supported by the community.