I just finished the #30DayMapChallenge, which was the perfect task to overview the tools I use the most as a geospatial data scientist.
To clarify, I come from the data science world and have no experience with point-and-click GIS software, I have been doing the vast majority of all my data manipulation and map visualisation in Python. Also, this stack heavily revolves around geospatial data visualisation - and the data processing and manipulation needed to support that. Therefore, this collection lacks tools for things like modelling and machine learning. However, it aims to be a hands-on, ready-to-use guide on the basics of spatial analytics with a heavy boost on visuals using Python.
I split these tools I use into three categories, as you will find them below: i) data processing and manipulation, ii) data visualisation, and iii) unique libraries. While these categories slightly overlap, as some unique tools visualise things in particular ways, and some essential tools also have visualisation capabilities, this collection intends to be a guide on the must-have and nice-to-have libraries for geospatial data analytics.
Disclaimer: this is a subjective collection of libraries, and I am always exploring new ones. To let me know in the comments what your favourites are!
I split these tools I use into three categories as you will find them below: i) data processing and manipulation, ii) data visualization, and iii) unique libraries. While these categories slightly overlap, as there are some unique tools that are actually visualizing things — just in very specific ways, and some basic tools also have visualization capabilities, this collection intends to be a guide on the must-have and nice-to-have libraries for geospatial data analytics.
All images were created by the author.
Data processing and manipulation
First, you must acquire solid skills to parse, clean, process, transform and analyse your data. While Pandas and Numpy are the basics in literally every Python-based analytics project, GeoPandas will be your best friend handling geometric data. While most likely will use GeoPandas directly…
![Milan Janosov](data:image/svg+xml,<svg xmlns="http://www.w3.org/2000/svg" width="20" height="20"><rect fill-opacity="0"/></svg>){kind=small}
