Empowering Geospatial Workflows with Python by Eleni Tokmaktsi | PyConSe | 2025

Geospatial research projects often involve large, complex datasets, and the analysis frequently needs to be repeated, which can be time-consuming and error-prone. This makes automation, reproducibility, and scalability essential. Traditional Geographic Information System (GIS) software provides tools to support automation, but they can be difficult to maintain, hard to share, challenging to learn, not always free, and difficult to integrate with other data systems. While many GIS platforms (e.g., ArcGIS and QGIS) include Python scripting, using Python standalone allows combining different libraries and methodological approaches, developing web-based GIS applications, and creating workflows that are efficient, flexible, and capable of handling hundreds of spatial data formats. Packages such as GeoPandas, Shapely, Rasterio, and GDAL replace manual GIS steps with clear, scriptable workflows. These workflows can be version-controlled, parameterized, and shared across projects or organizations. Python allows geospatial specialists not only to automate tasks like data cleaning and map-making, but also to integrate spatial analysis with areas such as machine learning, web applications, and cloud platforms. This work includes examples from real projects, such as cleaning shapefiles, fixing projections, and comparing multiple layers of data. While these tasks can also be performed using traditional GIS software, using the appropriate Python libraries provides faster, more reliable, and more reproducible solutions. #pyconse #pyconse2025