Key functions

The geospatial 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, when run in IBM Analytics Engine standalone or in a solution that uses IBM Analytics Engine take advantage of the distributed processing capabilities provided by Spark.
• The library includes native geohashing support for geometries used in simple aggregations and in IBM Cloud Object Storage indexing, whereby 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

Learn more

Check out the following Python notebooks to learn how to use the spacio-temporal library functions in Python notebooks. To access these notebooks, you need an IBM Watson® Studio instance. See Provisioning an IBM Watson® Studio instance.

• Use the spatio-temporal library for location analytics
• Use spatial indexing to query spatial data
• Spatial Queries in PySpark