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.
Limitations
Geospatial analytics is subject to the following limitations:
- Geography functions
are available only in GoogleSQL.
- Only the BigQuery client library for Python currently 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.
What's next