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Introduction to geospatial analytics | BigQuery

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In a data warehouse like BigQuery, location information is common and can influence critical business decisions. You can use geospatial analytics to analyze and visualize geospatial data in BigQuery by using the GEOGRAPHY data type and GoogleSQL geography functions.

For example, you might record the latitude and longitude of your delivery vehicles or packages over time. You might also record customer transactions and join the data to another table with store location data. You can use this type of location data to do the following:

• Estimate when a package is likely to arrive.
• Determine which customers should receive a mailer for a particular store location.
• Combine your data with percent tree cover from satellite imagery to decide if delivery by aerial drone is feasible.

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 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:

• Loading data (load jobs)
• Exporting data (export jobs)
• Querying data (query jobs)
• Copying tables (copy jobs)

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

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.

• To get started with geospatial analytics, see Get started with geospatial analytics.
• To learn more about visualization options for geospatial analytics, see Visualize geospatial data.
• To learn more about working with geospatial data, see Work with geospatial data.
• To learn more about working with raster data, see Work with raster data.
• To learn more about incorporating Google Earth Engine geospatial data into BigQuery, see Load Google Earth Engine geospatial data.
• For documentation on GoogleSQL functions in geospatial analytics, see Geography functions in GoogleSQL.
• To learn about different grid systems, see Grid systems for spatial analysis.
• To learn more about geospatial datasets and geospatial analytics and AI, see Geospatial Analytics.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-07 UTC.

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