07-08, 08:00–12:00 (US/Pacific), Room 317
The rapid expansion of the geospatial industry and accompanying increase in availability of geospatial data, presents unique opportunities and challenges in data science. As the need for skilled data scientists increases, the ability to manipulate and interpret this data becomes crucial. This workshop introduces the essentials of geospatial data manipulation and data visualisation, emphasizing hands-on techniques to transform, analyze and visualise diverse datasets effectively.
Throughout the workshop, attendees will explore the extensive ecosystem of geospatial Python libraries. Key tools include GeoPandas, Shapely and Cartopy for vector data, GDAL, Rasterio and rioxarray for raster data and participants will also learn to integrate these with popular plotting libraries such as Matplotlib, Bokeh, and Plotly for visualizations.
This tutorial will cover three primary topics: visualizing geospatial shapes, managing raster datasets, and synthesizing multiple data types into unified visual representations. Each section will incorporate data manipulation exercises to ensure attendees not only visualize but also deeply understand geospatial data.
Targeting both beginners and advanced practitioners, the workshop will employ real-world examples to guide participants through the necessary steps to produce striking and informative geospatial visualizations. By the end, attendees will be equipped with the knowledge to leverage advanced data science techniques in their geospatial projects, making them proficient in both the analysis and communication of spatial information.
This tutorial will give a broad overview of many of the core concepts in geospatial data science. Attendees will learn the skills needed to manipulate, analyse and plot geospatial data as well as combine geospatial datasets, generate new geospatial data from existing sources, generate insightful geospatial data visualisations and come away from the tutorial with the confidence to seek out new datasets to apply their skills to. When it comes to data visualisation, attendees will be encouraged to express themselves, material will be provided to get them to the point where they can generate their own visualisations without help but styling the plots will be up to them.
This tutorial will provide a high-level overview of geospatial data analysis and visualisation and provide a list of open-source datasets that can be used to practice newly learned skills. Furthermore, the packages used are not all specific to geospatial data visualisation and are applicable to a wide range of scientific and data science problems. As such, it is open to everyone and hopefully beginners, intermediates and experts will all come away with a new skill or two.
Installation instructions and script will be provided in the git repository. A draft version is outlined in the readme but will be updated when the materials are finalised - https://github.com/symmy596/Scipy2025
Prerequisites –The course is designed for all levels of experience. Each exercise starts with the basics and progresses into advanced topics. Beginners can spend time working on the basics and more advanced practioners can progress and focus on the more advanced topics.
The course and course materials will be hosted on Github use Jupyter notebooks, python packages will be installed through Conda hence a prerequisite knowledge of git, Jupyter and anaconda is helpful. Additionally, a familiarity with pandas and matplotlib is also helpful to enable progress through the exercises.
Dr. Adam Symington is a geospatial data scientist, currently working for Synmax. Adam has published a book on geospatial data visualisation, “PythonMaps, Geospatial Visualization with Python” and runs the PythonMaps project, a project designed to spread the love of geospatial data through eye-catching visualisation. Prior to his career in data science Adam was a computational materials scientist at the University of Bath in England where he used machine learning and other statistical techniques to predict the properties of materials. During Adams time in academia, he taught Python programming, developed a Python programming course and published open source software.