The Geospatial Data Abstraction Library (GDAL)/OGR Simple Features Library combines two separate libraries that are generally downloaded together as a GDAL. This means that installing the GDAL package also gives access to OGR functionality, which is why they're covered together here. The reason GDAL is covered first is that other packages were written after GDAL, so chronologically, it comes first. As you will notice, some of the packages covered in this chapter extend GDAL's functionality or use it under the hood.

GDAL was created in the 1990s by Frank Warmerdam and saw its first release in June 2000. Later, the development of GDAL was transferred to the Open Source Geospatial Foundation (OSGeo). Technically, GDAL is a little different than your average Python package as the GDAL package itself was written in C and C++, meaning that in order to be able to use it in Python, you need to compile GDAL and its associated Python bindings. However, using conda and Anaconda makes it relatively easy to get started quickly. Because it was written in C and C++, the online GDAL documentation is written in the C++ version of the libraries. For Python developers, this can be challenging, but many functions are documented and can be consulted with the built-in pydoc utility, or by using the help function within Python.

Because of its history, working with GDAL in Python also feels a lot like working in C++ rather than pure Python. For example, a naming convention in OGR is different than Python's since you use uppercase for functions instead of lowercase. These differences explain the choice for some of the other Python libraries such as Rasterio and Shapely, which are also covered in this chapter, that has been written from a Python developer's perspective but offer the same GDAL functionality.

GDAL is a massive and widely used data library for raster data. It supports the reading and writing of many raster file formats, with the latest version counting up to 200 different file formats that are supported. Because of this, it is indispensable for geospatial data management and analysis. Used together with other Python libraries, GDAL enables some powerful remote sensing functionalities. It's also an industry standard and is present in commercial and open source GIS software.

The OGR library is used to read and write vector-format geospatial data, supporting reading and writing data in many different formats. OGR uses a consistent model to be able to manage many different vector data formats. We'll discuss this model when working with vector data in Chapter 5, Vector Data Analysis. You can use OGR to do vector reprojection, vector data format conversion, vector attribute data filtering, and more.

GDAL/OGR libraries are not only useful for Python programmers but are also used by many GIS vendors and open source projects. The latest GDAL version at the time of writing is 2.2.4, which was released in March 2018.