Working with BigQuery GIS: 5 Useful Functions & Tools
BigQuery is a Google Data Warehouse with built-in Geographic Data Intake, Storage, and Analysis tools. To resolve complex data and examine massive datasets, it uses ordinary SQL queries. Examine some of BigQuery’s simple GIS tools and basic Geographic functionality to see whether they meet your needs. These technologies are frequently used for Geographical Data Processing and Visualization.
Table of Contents
In this in-depth article, you will learn everything about BigQuery GIS (Geographic Information System) along with tools required to carry out Geospatial Analysis using Bigquery GIS.
Table of Contents
- What is Google BigQuery?
- What is Geospatial Analytics?
- Tools Supported by BigQuery for Geospatial Analytics
- A Quick Tutorial on BigQuery GIS
- 5 Key BigQuery GIS Functions
- Conclusion
What is Google BigQuery?
BigQuery is Google’s Cloud-based enterprise Data Warehouse that uses several in-built features, such as Geospatial Analysis, Machine Learning, and Business Intelligence, to help Ingest, Store, Analyze, and Visualize your data. You can add data to the BigQuery in batches by uploading or directly streaming to gain real-time insights.
With BigQuery, Google manages the infrastructure, which helps you extend your analysis up to petabytes. The query engine runs SQL syntax on terabytes in seconds and petabytes in minutes. Thus, if you are good at using SQL, it is much easier to use BigQuery.
Key Features of Google BigQuery
The cost-effective, serverless Data Warehouse comprises several in-built features that aid in analyzing data and getting detailed insights. Check a few key features of BigQuery.
- Fully managed by Google: Google manages the Data Warehouse infrastructure. It maintains, updates, monitors, and deploys all your Data or Information. If your task fails, Google will know it.
- Easy to Implement: You do not require any additional Software, Cluster Deployment, Virtual Machines, or tools with BigQuery. BigQuery is one of the cost-effective, serverless Data Warehouses. All you need is to upload or directly stream your Data and run SQL to analyze and solve queries.
- Speed: BigQuery can process N number of rows in seconds. Also, it can run queries on terabytes in seconds and petabytes in minutes.
For further information on Google BigQuery, check out the official website here.
What is Geospatial Analytics?
Geospatial Analytics highlights historical changes and current shifts by collecting, displaying, and manipulating Imagery and Geographic Information System (GIS) Data related to a specific location. To be more precise, these are Geographic positions of an entity referred to as Geospatial Data that require Monitoring and Optimization at all times. These data depend on the Geographic Coordinates that further help create Data Visualizations to get more accuracy and predict unforeseen events.
Be it GPS, Mobile Devices, Location Sensors, Social Media, or Satellite Imagery often data from these sources for Geospatial Analytics is used. The gathered information helps create Data Visualizations, such as Graphs, Maps, Stats, and Cartograms. These reports help the human brain to understand the distance, proximity, and contiguity not visible in large datasets.
Tools Supported by BigQuery for Geospatial Analytics
BigQuery comprises various products for different tasks. BigQuery GIS is one of its products that support geospatial analysis. With the help of the BigQuery GIS tool, you can perform faster analysis with location intelligence. Explore new ways to view the spatial data, unlock support for arbitrary points, latitudes, polygons, and other geospatial data formats using BigQuery. This serverless architecture simplifies the analysis process. Thus, it works best for geospatial analysis. Check out some of the best tools in support from BigQuery for Geospatial Analytics.
1) BigQuery GeoViz
BigQuery Geo Viz is a web tool that uses Google Maps APIs for visualizing Geospatial Data. Like in BigQuery, you can run an SQL query and visualize the Geospatial Analytics query results on a map. However, you have access to display limited results by browser memory. Also, remember, you can run one SQL query at a time.
Its flexible features further aid in the analysis and exploration of data. Under BigQuery Geo Viz, the only authorized user who has access to execute queries can share visualizations, and downloading is not supported for offline editing. Thus, BigQuery Geo Viz is not a fully-featured tool.
2) Google Data Studio
It is a free tool from Google Marketing Platform that helps connect BigQuery with various other Data Sources. It is a Data Visualization service that supports the BigQuery GEOGRAPHY polygon’s geographic field type and choropleth map. Google Data Studio helps convert collected data into customizable informative reports for data visualization. The tool is best suitable for individuals and small teams. One can easily zoom in, pan around, or pop into Street View with Geographic Data. Also, you can share the generated tool reports via the context menu.
3) Google Earth Engine
Google Earth Engine is another tool that can help you visualize geospatial data. To use Earth Engine, all you require is to export BigQuery data to Cloud Storage in the first place. Later, import this information to the Earth Engine for Data Visualization.
4) Jupyter Notebooks
Use the GeoJSON extension to perform data visualizations in Jupyter Notebooks. In order to use the extension, one must make sure all the geospatial data is in GeoJSON format.
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Sign up here for a 14-Day Free Trial!A Quick Tutorial on BigQuery GIS
If you wish to analyze big or small data, go for BigQuery. The powerful tool integrates with other Business Intelligence tools to visualize and resolve complex data. You can get real-time analysis of massive datasets, predict business outcomes, and easily share insights with team members using BigQuery. In this tutorial, we will give you a quick overview of Google BigQuery and the key function of BigQuery’s Geography function. You can use BigQuery Sandbox to experiment with functionalities with a few limitations.
Further, we will discuss how these functions help convert latitude and longitude values into geographical points and run GIS queries into BigQuery. The tutorial is for each person who wishes to visualize GEOGRAPHY data via BigQuery. To join data by location, administrators can use spatial processes.
Each geography function begins with a signature ST_. These functions help create BigQuery GEOGRAPHY values. We have penned down some of the key functions that will be beneficial in analyzing the geographical data, constructing or manipulating GEOGRAPHYs, and deciding the spatial relationships between their features.
Remember, the return value will always be NULL if any input argument is NULL.
5 Key BigQuery GIS Functions
1) BigQuery GIS Function: ST_GeoPoint
Syntax: ST_GEOGPOINT(longitude, latitude)
ST_GEOGPOINT function requires a single point to create GEOGRAPHY. This function uses a specific FLOAT64 longitude and latitude parameter to create the point and later returns it as a GEOGRAPHY value.
Constraints:
- Any latitude exceeding the range [-90, 90] will result in an error.
- It allows any longitude value ranging outside [-180, 180]. To obtain value within this range, the function uses the input longitude modulo 360.
Return type: GEOGRAPHY
2) BigQuery GIS Function: ST_Distance
Syntax: ST_DISTANCE(geography_1, geography_2[, use_spheroid])
Returns the minimum distance covered in meters by the two non-empty GEOGRAPHYs. ST_DISTANCE returns NULL if any of the two input GEOGRAPHYs are empty. To analyze how the function measures the distance, you must use the use_spheroid parameter. Its default value is FALSE, which implies the distance is measured on a perfect sphere surface.
Return type: FLOAT64
3) BigQuery GIS Function: ST_MaxDistance
Syntax: ST_MAXDISTANCE(geography_1, geography_2[, use_spheroid])
ST_MaxDistance function returns the maximum distance covered in meters by the two non-empty GEOGRAPHYs.
ST_MaxDistance function returns the distance covered between the two most distant vertices if both the GEOGRAPHY values (geography_1 and geography_2) are the same. However, the function returns NULL if any of the two input GEOGRAPHYs are empty.
To analyze how the function measures the distance, you must use the use_spheroid parameter. Its default value is FALSE, which implies the distance is measured on a perfect sphere surface.
Return type: FLOAT64
4) BigQuery GIS Function: ST_Intersection
Syntax: ST_INTERSECTION(geography_1, geography_2)
Each point in the intersection appears in both input GEOGRAPHYs, as a result, it returns a GEOGRAPHY. If no point appears in both input GEOGRAPHYs (geometry_1 and geometry_2), then the function returns an empty GEOGRAPHY.
Return type: GEOGRAPHY
5) BigQuery GIS Function: ST_Equals
Syntax: ST_EQUALS(geography_1, geography_2)
If both the input GEOGRAPHYs (geometry_1 and geometry_2) show the same value, the ST_Equals function returns TRUE.
To be clear, it can create two conditions:
- ST_COVERS(geography_1, geography_2) = TRUE
- ST_COVERS(geography_2, geography_1) = TRUE
Thus, the return value will always be equal as long as they show the same geometric structure or order of the vertices differ.
Constraints:
- There is no guarantee of ST_EQUALS being a transitive function.
Return type: BOOL
Conclusion
BigQuery is Google’s Data Warehouse with in-built features that helps ingest, store, and analyze Geospatial Data. It runs standard SQL queries to resolve complex data and explore large datasets. Have a look at some of the easy-to-use BigQuery GIS Tools and basic Geographic functionality of BigQuery as per your requirement. These tools are commonly used for the Analysis and Visualization of Geospatial Data. In case you want to export data from a source of your choice into your desired Database/destination like BigQuery then Hevo Data is the right choice for you!
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