Proposal Summary:

The Python geospatial ecosystem is experiencing rapid growth and accelerating adoption across numerous disciplines. From large-scale Earth system analysis with Pangeo, to the rise of spatial data science in the social and life sciences powered by PySAL and GeoPandas, and the development of scalable, interactive environments via GeoJupyter, Python is becoming the platform of choice for geospatial research and applications.

This Birds of a Feather (BoF) session will bring together developers, users, and stakeholders to foster dialogue across projects and domains. By strengthening connections within the community, we aim to identify shared needs, reduce fragmentation, and collaboratively advance the capabilities and sustainability of the Python geospatial stack.

Rationale and Motivation:

Python’s geospatial stack is a vibrant yet decentralized ecosystem built on modular,
interoperable libraries. Key components include:

Pangeo, enabling cloud-native, parallel analysis of massive geoscience datasets;
GeoPandas, Shapely, and Fiona, offering intuitive, high-level tools for working with vector data;
PySAL, a comprehensive library for spatial data science, supporting spatial econometrics, clustering, accessibility modeling, and more;
xarray, rasterio, and rio-tiler for multi-dimensional and raster data handling;
Cartopy, Datashader, and Bokeh for advanced geospatial visualization;
GeoJupyter, integrating these tools within scalable, Jupyter-based platforms for reproducible research and education.

Despite this progress, challenges remain—ranging from fragmented documentation and
inconsistent APIs to a lack of coordinated governance and limited visibility across domains.

Session Goals:

Facilitate connections between developers and domain experts in Earth, social, and life sciences.
Share success stories (e.g., GeoJupyter deployments, Pangeo use cases, PySAL in education).
Identify interoperability needs and opportunities for tighter integration across libraries.
Discuss education, outreach, and documentation strategies to support new users.
Explore community governance and sustainable funding models.
Initiate collaborative efforts to close gaps and develop shared roadmaps.

Target Audience:

Developers of Python geospatial and spatial data science libraries
Applied researchers and analysts in academic, public, and private sectors
Educators, data scientists, and students interested in spatial methods
Open-source contributors and infrastructure providers
Anyone curious about the evolving landscape of geospatial Python

Format and Structure:

The session will begin with brief overviews by representatives from key projects (e.g., PySAL, GeoJupyter, Pangeo), followed by thematic breakout discussions (e.g., vector/raster integration, cloud-native tools, education). We will close with a group synthesis of key takeaways and actionable next steps.

Conclusion:

This BoF session is a unique opportunity to unify a diverse yet overlapping community of practice. By sharing insights, aligning efforts, and cultivating relationships, we can strengthen the foundation of Python’s geospatial ecosystem and accelerate the next generation of spatial data science.