The 5 Best Geospatial Packages to Use in Julia

Key Takeaways

  • Julia is a powerful language for geospatial data science.
  • There are a number of great geospatial packages available for Julia.
  • The 5 packages discussed in this article are a great place to start for geospatial data handling.

Introduction
Julia is a high-level, high-performance programming language that is becoming increasingly popular for geospatial data science. There are a number of great geospatial packages available for Julia, each with its own strengths and weaknesses. In this article, we will discuss the 5 best geospatial packages to use in Julia, and how to best use each of them.

1. GDAL.jl

What it does: GDAL.jl is a thin Julia wrapper for the GDAL library, which is a powerful open-source library for reading, writing, and manipulating geospatial data. GDAL.jl allows you to read and write a wide variety of geospatial data formats, including Shapefiles, GeoTIFFs, and KML files.

How to use it: GDAL.jl is easy to use. To read a Shapefile, for example, you would use the following code:

using GDAL

shp = GDAL.open("my_shapefile.shp")

Once you have opened a geospatial dataset, you can use GDAL.jl to access its features, attributes, and metadata.

2. GeoDataFrames.jl

What it does: GeoDataFrames.jl is a Julia package that provides a geospatial extension to the popular DataFrames.jl package. GeoDataFrames.jl allows you to store geospatial data in a DataFrame, which makes it easy to manipulate and analyze this data.

How to use it: GeoDataFrames.jl is easy to use. To create a GeoDataFrame from a Shapefile, for example, you would use the following code:

using GeoDataFrames

shp = read_shapefile("my_shapefile.shp")

df = GeoDataFrame(shp)

Once you have created a GeoDataFrame, you can use it like any other DataFrame. You can access its features, attributes, and metadata, and you can use it to perform a variety of geospatial analysis tasks.

3. GeoJSON.jl

What it does: GeoJSON.jl is a Julia package that provides a parser and serializer for the GeoJSON format. GeoJSON is a popular format for representing geospatial data in JSON.

How to use it: GeoJSON.jl is easy to use. To parse a GeoJSON file, for example, you would use the following code:

using GeoJSON

geojson = parse_geojson("my_geojson.json")

Once you have parsed a GeoJSON file, you can access its features, attributes, and metadata. You can also serialize a GeoDataFrame to GeoJSON format using the to_geojson function.

4. GMT.jl

What it does: GMT.jl is a Julia package that provides a wrapper for the GMT library, which is a powerful command-line tool for creating maps and other geospatial visualizations.

How to use it: GMT.jl is easy to use. To create a map, for example, you would use the following code:

using GMT

map = GMT.plotm(1:10, 1:10, "my_map.png")

The plotm function takes a variety of arguments that allow you to customize the appearance of your map.

5. GeoMakie.jl

What it does: GeoMakie.jl is a Julia package that provides a high-level API for creating interactive geospatial visualizations. GeoMakie.jl is based on the Makie package, which is a powerful visualization library for Julia.

How to use it: GeoMakie.jl is easy to use. To create a simple map, for example, you would use the following code:

using GeoMakie

map = GeoMakie.plot(1:10, 1:10)

GeoMakie.jl provides a variety of functions for creating different types of geospatial visualizations.

Conclusion

The 5 geospatial packages discussed in this article are just a few of the many great options available for Julia. These packages provide a wide range of functionality, and they can be used to perform a variety of geospatial data science tasks. Julia is often a forgotten solution for data science challenges and problems, and that’s because most people are already using Python, R, SQL, and Excel for their data science tech stack, but Julia has a number of advantages that make its use compelling. While not as robust as other programming frameworks for geospatial solutions, Julia still has the tools to provide a legitimate analysis for users looking to add new tools and skills to their belts.

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