Introduction to geospatial analytics
In a data warehouse like BigQuery, location information is very common. Many critical business decisions revolve around location data. For example, you may record the latitude and longitude of your delivery vehicles or packages over time. You may also record customer transactions and join the data to another table with store location data.
You can use this type of location data to determine when a package is likely to arrive or to determine which customers should receive a mailer for a particular store location. Geospatial analytics let you analyze and visualize geospatial data in BigQuery by using geography data types and GoogleSQL geography functions.
Geospatial analytics is subject to the following limitations:
- Geography functions are available only in GoogleSQL.
- Only the BigQuery client library for Python currently supports
GEOGRAPHYdata type. For other client libraries, convert
GEOGRAPHYvalues to strings by using the
ST_ASGEOJSONfunction. Converting to text using
ST_AsTextstores only one value, and converting to WKT means that the data is annotated as a
STRINGtype instead of a
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.
- For documentation on GoogleSQL functions in geospatial analytics, see Geography functions in GoogleSQL.