How to Perform Geospatial Queries in BigQuery Using Geo Functions
- Anvita Shrivastava
- 2 days ago
- 3 min read
BigQuery, the serverless data warehouse offered by Google Cloud, has progressed to be much more than just a SQL analytics engine. One of its key capabilities is geospatial analysis, which enables organizations to quickly and intuitively analyze location-based data. With BigQuery Geo Functions, users can run complex spatial queries against spatial data directly within their datasets, eliminating the need to export data into specialized GIS tools.
In this blog, we will thoroughly examine how to run geospatial queries in BigQuery as well as discuss which functions perform the process, possible use cases, and some optimization techniques.
What Are Geospatial Queries in BigQuery?
Geospatial queries are those that involve data having a spatial or geographic component, such as latitude, longitude, polygons, or more complex geometries. A GEOGRAPHY data type is supported in BigQuery to conveniently store and manipulate spatial data.
Example scenarios for which geospatial queries might be usefully applied could include:
Calculating distances between locations
Finding points that fit within polygons (such as cities in a zip code)
Analyzing spatial relationships (intersects, contains)
Aggregating the data based on defined geographic areas
BigQuery handles this with its built-in Geo Functions, which are compliant with the OGC Simple Features standard.
BigQuery GEOGRAPHY Data Type
It is critical to be aware of the GEOGRAPHY data type before executing geospatial queries. The GEOGRAPHY data type supports spherical coordinates (latitude and longitude), and is geo-optimized for global-scale queries.
Example of creating a GEOGRAPHY column:
CREATE TABLE my_dataset.locations (
id STRING,
name STRING,
location GEOGRAPHY
);
You can insert a point using ST_GEOGPOINT:
INSERT INTO my_dataset.locations (id, name, location)
VALUES ('1', 'Central Park', ST_GEOGPOINT(-73.968285, 40.785091));
Core BigQuery Geo Functions
BigQuery offers a variety of geospatial functions. Here’s a breakdown of the most critical ones for technical users:
ST_GEOGPOINT
Creates a point from longitude and latitude.
SELECT ST_GEOGPOINT(-73.968285, 40.785091) AS central_park_point;
ST_DISTANCE
Calculates the minimum distance between two geographies in meters.
SELECT
ST_DISTANCE(
ST_GEOGPOINT(-73.968285, 40.785091),
ST_GEOGPOINT(-74.0060, 40.7128)
) AS distance_meters;
ST_CONTAINS
Checks if one geography contains another (useful for point-in-polygon queries).
SELECT *
FROM my_dataset.locations
WHERE ST_CONTAINS(
ST_GEOGFROMTEXT('POLYGON((-74 40, -74 41, -73 41, -73 40, -74 40))'),
location
);
ST_INTERSECTS
Determines if two geographies intersect, often used for overlapping areas.
SELECT *
FROM my_dataset.parks AS p
JOIN my_dataset.districts AS d
ON ST_INTERSECTS(p.location, d.boundary);
ST_BUFFER
Creates a buffer zone around a point or line, useful for proximity analysis.
SELECT ST_BUFFER(ST_GEOGPOINT(-73.968285, 40.785091), 500) AS buffer_zone;
Practical Use Cases
Use Case 1: Finding Nearby Locations
Suppose you want to find all restaurants within 1 km of a user’s location:
SELECT name, location
FROM my_dataset.restaurants
WHERE ST_DWITHIN(
location,
ST_GEOGPOINT(-73.985428, 40.748817),
1000
);
Use Case 2: Mapping Sales Regions
You can visualize sales performance by region using ST_CONTAINS and polygon data:
SELECT r.region_name, SUM(s.sales) AS total_sales
FROM my_dataset.sales AS s
JOIN my_dataset.regions AS r
ON ST_CONTAINS(r.boundary, s.store_location)
GROUP BY r.region_name;
Use Case 3: Geospatial Aggregations
BigQuery allows aggregating data spatially to find densities, counts, or clusters:
SELECT ST_CLUSTERDBSCAN(location, eps => 500, min_points => 3) AS cluster_id,
COUNT(*) AS num_locations
FROM my_dataset.locations
GROUP BY cluster_id;
Performance Tips for BigQuery Geospatial Queries
Partition and Cluster by Geography
When you do clusters on GEOGRAPHY columns or partition by geography, it greatly improves query performance.
ST_SIMPLIFY for Complicated Polygons
Reducing the complexities of a large polygon generally saves calculation time and does not have a noticeable impact on meaningful accuracy:
SELECT ST_SIMPLIFY(boundary, 0.001) AS simplified_boundary
FROM my_dataset.regions;
Minimize ST_DISTANCE Calls
Pre-filter with bounding boxes using ST_WITHIN or ST_INTERSECTS before precise distance calculations.
BigQuery's geospatial functionalities provide immense analytical capability directly in your data warehouse. From calculating distances to determining points within polygons, clustering, and buffering, geospatial functions enable engineers, analysts, and data scientists to perform sophisticated geospatial analysis at scale.
By understanding the GEOGRAPHY data types, using the right functions, and optimizing your queries with partitioning and simplification, you'll be able to take full advantage of BigQuery for geospatial analytical capabilities.
Whether your use case is building applications based on location, mapping sales territories, or analyzing transportation data, BigQuery is a powerful computing platform for fast, powerful, geospatial analytics.
For more information or any questions regarding Geo Functions, please don't hesitate to contact us at
Email: info@geowgs84.com
USA (HQ): (720) 702–4849
(A GeoWGS84 Corp Company)
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