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Geospatial data analysis

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 Watson 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:

Parent topic: Notebooks and scripts

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