Introduction to geospatial analytics

Geospatial analytics in BigQuery lets you analyze and visualize location information to influence critical business decisions. You can use the GEOGRAPHY data type and GoogleSQL geography functions to track delivery vehicles, map customer transactions, and extract actionable insights from your spatial data.

Limitations

Geospatial analytics is subject to the following limitations:

  • Geography functions are available only in GoogleSQL.
  • Only the BigQuery client library for Python supports the GEOGRAPHY data type. For other client libraries, convert GEOGRAPHY values to strings by using the ST_ASTEXT or ST_ASGEOJSON function. Converting to text using ST_ASTEXT stores only one value, and converting to WKT means that the data is annotated as a STRING type instead of a GEOGRAPHY type.

Quotas

Quotas and limits on geospatial analytics apply to the different types of jobs you can run against tables that contain geospatial data, including the following job types:

For more information on all quotas and limits, see Quotas and limits.

Pricing

When you use geospatial analytics, your charges are based on the following factors:

  • How much data is stored in the tables that contain geospatial data.
  • The queries you run against the data.

For information on storage pricing, see Storage pricing.

For information on query pricing, see Analysis pricing models.

Many table operations are free, including loading data, copying tables, and exporting data. Though free, these operations are subject to BigQuery's Quotas and limits. For information on all free operations, see Free operations on the pricing page.

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