The Top Geospatial Python Packages: What’s Driving Their Growth?
Geospatial Python has seen an explosion in adoption, with several key libraries surpassing 10 million downloads this year. As geospatial analysis becomes increasingly critical in data science, urban planning, environmental monitoring, and AI applications, understanding why these libraries are growing can provide insight into where the industry is heading.
Here’s a breakdown of the most downloaded geospatial Python packages in 2024, categorized by those exceeding 10 million downloads and those still growing under that threshold.
Packages with Over 10M Downloads
Rasterio (36.4M, ↗️ 96%)
Rasterio has cemented its place as the go-to tool for raster data manipulation. Its popularity stems from its ease of use, alignment with GDAL, and the shift toward cloud-native geospatial workflows. The rapid growth is likely driven by the increasing use of Cloud-Optimized GeoTIFFs (COGs) and the demand for scalable raster processing in the cloud.
xarray (68.6M, ↗️ 95%)
Xarray’s meteoric rise highlights the growing importance of multidimensional data analysis, particularly in climate science and remote sensing. With strong integrations into machine learning and scalable computing frameworks like Dask, it’s no surprise that it’s one of the most downloaded geospatial libraries.
H3 (55.5M, ↗️ 77%)
Hexagonal indexing has never been more relevant. With H3’s spatial indexing capabilities, organizations can perform efficient geospatial aggregation and analysis at scale, making it a preferred choice for mobility, environmental, and urban analytics.
Folium (15.8M, ↗️ 72%)
Folium continues to thrive due to its simplicity in creating interactive maps with Leaflet.js. As more analysts and developers prioritize web-based visualization, its growth reflects the broader trend of bringing spatial analytics into dashboards and storytelling applications.
Apache Sedona (17.3M, ↗️ 52%)
Apache Sedona’s steady rise signifies the increasing need for scalable spatial analytics. With support for Spark, it has become a cornerstone for big geospatial data processing, aligning well with cloud-based geospatial ETL pipelines.
Geopandas (80.0M, ↗️ 37%)
Geopandas remains the king of vector geospatial processing. Its slower relative growth suggests market saturation, but its sheer volume of downloads speaks to its entrenchment as a fundamental geospatial tool for Python users.
Packages Under 10M Downloads (But Growing Fast!)
rio-cogeo (1.3M, ↗️ 254%)
A stunning 254% growth rate? This signals a rapid industry-wide transition to Cloud-Optimized GeoTIFFs (COGs). As more geospatial workflows shift to cloud-native formats, rio-cogeo is becoming an indispensable tool.
Leafmap (623K, ↗️ 119%)
Leafmap’s ability to integrate with Jupyter notebooks and its support for Google Earth Engine has made it a rising star for spatial data visualization. It’s the next logical step for those outgrowing Folium.
OSMnx (2.1M, ↗️ 91%)
Urban analytics and routing are gaining momentum, and OSMnx continues to dominate in extracting and analyzing OpenStreetMap data. The rise of smart cities and mobility research likely fuels this growth.
PySAL (1.2M, ↗️ 77%)
Spatial statistics are essential for geospatial data science, and PySAL remains at the forefront. With growing interest in spatial econometrics and machine learning, its sustained adoption makes perfect sense.
Dask-Geopandas (460K, ↗️ 72%)
The scalability of geospatial workflows is a pressing issue, and Dask-Geopandas is stepping in to bridge the gap. The geospatial community is increasingly recognizing the need to parallelize their Pandas-based workflows.
Placekey (1.3M, ↗️ 48%)
Placekey’s unique approach to spatial indexing and entity resolution has gained traction, particularly in retail analytics and logistics. Its integration into various location intelligence platforms is driving adoption.
ArcGIS (1.1M, ↗️ 15%) (PyPI Only)
While ArcGIS remains a staple in the geospatial world, its relatively slow growth in PyPI downloads suggests that users may still prefer direct installations through Conda or Esri’s ecosystem.
Why Are These Packages Growing?
- Cloud-Native and Big Data Integration – Packages like rio-cogeo, Sedona, and Dask-Geopandas are benefiting from the shift to scalable, cloud-based geospatial processing.
- Visualization and Accessibility – Libraries like Leafmap and Folium cater to the increasing demand for intuitive geospatial visualization in Jupyter notebooks and web applications.
- Urban and Environmental Analytics – Tools like H3 and OSMnx are well-positioned in a world increasingly focused on urban planning, mobility, and climate analysis.
- Scalability and Performance – Libraries that improve efficiency in handling large geospatial datasets (Dask-Geopandas, PySAL, and Rasterio) are seeing increased adoption as more organizations scale their geospatial workflows.
Final Thoughts
The rise of geospatial Python packages in 2024 showcases an industry-wide shift towards scalable, cloud-native, and data-intensive workflows. While traditional tools like Geopandas and Rasterio remain dominant, emerging libraries like rio-cogeo and Leafmap hint at where the next wave of innovation is headed.
