Julia

Compared to Python and R, Julia is a relatively young language that is slowly seeing increased usage among various research fields. In relation to geospatial computing, the language lacks the same breadth of packages that can be found in Python or R, but it’s increased performance and support for parallelization may make it useful when analyzing extraordinarily large datasets, especially in HPC environments.

Geospatial Libraries/Packages

A relatively comprehensive list of packages can be found in the Julia section of Awesome Geospatial.

Interactive Visualizations and Dashboards

Dashboard libraries enable you to create interactive visualizations from either Julia code or Jupyter Notebooks, which can be hosted on sparsely-resourced servers and shared as web applications.

Code Quality Tools

Formatting

Sometimes writing code can get a bit messy. Formatters can automatically reformat your code to make it cleaner and easier to read while following a set of standards and best practices.

Testing

Unit testing can be a helpful tool when writing large and complex scripts or programs. Julia enables you to define tests that can run over your functions and ensure they are following expected behavior in a range of practical scenarios via its standard library.