Geospatial data analysis
You can use the geospatio-temporal library to expand your data science analysis in Python notebooks to include location analytics by gathering, manipulating and displaying imagery, GPS, satellite photography and historical data.
The gespatio-temporal library is available in all IBM watsonx.ai Studio Spark with Python runtime environments.
Key functions
The geospatio-temporal library includes functions to read and write data, topological functions, geohashing, indexing, ellipsoidal and routing functions.
Key aspects of the library include:
- All calculated geometries are accurate without the need for projections.
- The geospatial functions take advantage of the distributed processing capabilities provided by Spark.
- The library includes native geohashing support for geometries used in simple aggregations and in indexing, thereby improving storage retrieval considerably.
- The library supports extensions of Spark distributed joins.
- The library supports the SQL/MM extensions to Spark SQL.
Getting started with the library
Before you can start using the library in a notebook, you must register STContext in your notebook to access the st functions.
To register STContext:
from pyst import STContext
stc = STContext(spark.sparkContext._gateway)
Next steps
After you have registered STContext in your notebook, you can begin exploring the spatio-temporal library for:
- Functions to read and write data
- Topological functions
- Geohashing functions
- Geospatial indexing functions
- Ellipsoidal functions
- Routing functions
Check out the following sample Python notebooks to learn how to use these different functions in Python notebooks:
- Use the spatio-temporal library for location analytics
- Use spatial indexing to query spatial data
- Spatial queries in PySpark
Parent topic: Notebooks and scripts