Introduction to geospatial analytics

In a data warehouse like BigQuery, location information iscommon and can influence critical business decisions. You can use geospatialanalytics to analyze and visualize geospatial data in BigQueryby using theGEOGRAPHY data typeandGoogleSQL geography functions.

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

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

Limitations

Geospatial analytics is subject to the following limitations:

• Geography functionsare available only in GoogleSQL.
• Only the BigQuery client library for Python supportstheGEOGRAPHY data type. For other client libraries, convertGEOGRAPHYvalues to strings by using theST_ASTEXT orST_ASGEOJSON function.Converting to text usingST_ASTEXT stores only one value, and convertingto WKT means that the data is annotated as aSTRING type instead of aGEOGRAPHY type.

Quotas

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

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

For more information on all quotas and limits, seeQuotas and limits.

Pricing

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

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

For information on storage pricing, seeStorage pricing.

For information on query pricing, seeAnalysis pricing models.

Many table operations are free, including loading data, copying tables, andexporting data. Though free, these operations are subject toBigQuery'sQuotas and limits. For informationon all free operations, seeFree operations on thepricing page.

What's next

• To get started with geospatial analytics, seeGet started with geospatial analytics.
• To learn more about visualization options for geospatial analytics,seeVisualize geospatial data.
• To learn more about working with geospatial data, seeWork with geospatial data.
• To learn more about working with raster data, seeWork with raster data.
• To learn more about incorporating Google Earth Engine geospatial data intoBigQuery, seeLoad Google Earth Engine geospatial data.
• For documentation on GoogleSQL functions in geospatial analytics,seeGeography functions in GoogleSQL.
• To learn about different grid systems, seeGrid systems for spatial analysis.
• To learn more about geospatial datasets and geospatial analytics and AI, seeGeospatial Analytics.