Geospatial data analysis

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

Create an application that uses the spatio-temporal library and runs as a Spark job on your Cloud Pak for Data cluster by using the Spark jobs REST APIs of Analytics Engine powered by Apache Spark:

• Sample code showing how to use spatio-temporal functions in applications

Parent topic: Notebooks and scripts