Open-Source has become one of the fastest means to grow software and have more users on any platform that exists on the internet.
A good example is the number of users who use QGIS compared to those who use ArcGIS. I will not dwell much into detail because the war between these two technologies never seems to end between the users.
Besides being able to acquire the technology for free, Open-Source also has its developmental advantages which include its rapid growth within a minimum period of time.
Take a look at how QGIS plugins and versions are evolving. Almost every month, at least three plugins are either upgraded or introduced into the system. I mean, we even have users creating their own plugins to suit their own personal needs.
Now compare that rapid development to any other proprietary software that you can think of. Try to measure the development speed and how long they usually take to make an upgrade or release a new version.
The coming of Open-Source has also provided some motivation to dedicated individuals who would like to see software and technology grow. In this article, we will be talking about some of the Geospatial Libraries for Python that you need to explore and consider for your projects and software development.
One thing worth mentioning is that these libraries help you as a developer to easily integrate certain processes and tasks into your software without having to re-invent the wheel.
How many people have developed software that requires user authentication? Instead of developers having to spend time re-designing and thinking of such a component of their system, the different libraries available for such a task can smoothen the process and reduce time spent developing the software too.
Same concept when it comes to Geospatial libraries. Imagine you have an application that takes data in GeoJSON format, reads the data, and displays the data on a map for the user. How much time, thought and energy is it going to take you to design the logic for the backend to produce the needed result?
Beginners could even take a whole year just to achieve such. 😢
Some of the libraries that are essential and would probably be useful as your begin to build software for geospatial purposes are;
The Geospatial Data Abstraction Library commonly known by its abbreviation as GDAL is a translator library for raster and vector geospatial data formats that is released under an X/MIT style Open Source License by the Open Source Geospatial Foundation.
GDAL is the most used library by every geospatial application and library. I certainly do not think there is any software out there that does not implement at least a component or element of GDAL. The library has so many drivers covering both raster and vector components.
Documentation - https://gdal.org/
The libraries and packages listed below also rely on GDAL in order to carry out their functions.
This library is the main python interface version to PROJ.
PROJ is a generic coordinate transformation software that transforms geospatial coordinates from one coordinate reference system (CRS) to another.
As long as you are dealing coordinates, PyProj always has your back.
Documentation - https://pyproj4.github.io/pyproj/stable/
Thanks to Rasterio, we can now deal with and handle raster data in a more efficient way. The library was mainly developed to extend GDAL through Python but provides a little abstraction for GDAL’s C API.
Without Rasterio, this meant that most programs using the GDAL C language API would tend to run like C programs where the bindings require users to watch out for dangling C pointers which are potential crashers of programs. This would be bad for progress' sake.
The library was introduced to provide integration means and access to modern Python language which also seems to be evolving rapidly.
Documentation - https://rasterio.readthedocs.io/en/latest/index.html
Fiona is GDAL’s neat and nimble vector API for Python programmers.
Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR.
In your software, if you are going to be dealing with, handling, or manipulating any data, you are definitely going to use this library.
Just like reading and writing text files in Python, Fiona works the same with Geospatial data.
Documentation - https://fiona.readthedocs.io/en/stable/
Have you used Pandas before? If that's a yes, then this is its geospatial extension.
GeoPandas is an open-source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types.
Geometric operations are performed by shapely. Geopandas further depends on fiona for file access and matplotlib for plotting.
When you install Geopandas, you are also probably going to need to install Fiona and Shapely too or the installer will automatically include those for you. As you can already see these are the dependencies. Without them, Geopandas will not work.
Documentation - https://geopandas.org/en/stable/
For the cartographers out there who love coding, this one was designed for you.
Cartopy is a Python package designed to make drawing maps for data analysis and visualization easy.
So after performing that analysis with GeoPandas you can always come up with the maps you love using this library.
Documentation - https://scitools.org.uk/cartopy/docs/latest/
PyCRS is a pure Python GIS package for reading, writing, and converting between various common coordinate reference system (CRS) string and data source formats.
As you can see PyCRS has combined the functionality of Fiona and PyProj into one and came up with their own library for that.
So basically much of the functionality will be the same from Fiona and Pyproj.
Documentation - http://github.com/karimbahgat/PyCRS
As you can see, the libraries are so many and can cater for almost anything that you need to do in a geospatial environment.
It is up to the developer to build and contribute to the geospatial community.Hey!😃
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