# 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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/data-types#geography_type)
and
[GoogleSQL geography functions](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/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](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/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:

- [Loading data](https://docs.cloud.google.com/bigquery/quotas#load_jobs) (load jobs)
- [Exporting data](https://docs.cloud.google.com/bigquery/quotas#export_jobs) (extract jobs)
- [Querying data](https://docs.cloud.google.com/bigquery/quotas#query_jobs) (query jobs)
- [Copying tables](https://docs.cloud.google.com/bigquery/quotas#copy_jobs) (copy jobs)

For more information on all quotas and limits, see [Quotas and limits](https://docs.cloud.google.com/bigquery/quotas).

## 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](https://cloud.google.com/bigquery/pricing#storage).

For information on query pricing, see [Analysis pricing models](https://cloud.google.com/bigquery/pricing#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](https://docs.cloud.google.com/bigquery/quotas). For information
on all free operations, see [Free operations](https://cloud.google.com/bigquery/pricing#free) on the
pricing page.

## What's next

- To get started with geospatial analytics, see [Get started with geospatial analytics](https://docs.cloud.google.com/bigquery/docs/geospatial-get-started).
- To learn more about visualization options for geospatial analytics, see [Visualize geospatial data](https://docs.cloud.google.com/bigquery/docs/geospatial-visualize).
- To learn more about working with geospatial data, see [Work with geospatial data](https://docs.cloud.google.com/bigquery/docs/geospatial-data).
- To learn more about working with raster data, see [Work with raster data](https://docs.cloud.google.com/bigquery/docs/raster-data).
- To learn more about incorporating Google Earth Engine geospatial data into BigQuery, see [Load Google Earth Engine geospatial data](https://docs.cloud.google.com/bigquery/docs/geospatial-data#load-ee).
- For documentation on GoogleSQL functions in geospatial analytics, see [Geography functions in GoogleSQL](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions).
- To learn about different grid systems, see [Grid systems for spatial analysis](https://docs.cloud.google.com/bigquery/docs/grid-systems-spatial-analysis).
- To learn more about geospatial datasets and geospatial analytics and AI, see [Geospatial Analytics](https://docs.cloud.google.com/solutions/geospatial).