Use the new geospatial datasets from Google to enhance your BigQuery analysis.
Google Bigquery Geospatial
It’s revealing new geospatial analytics datasets and features from Earth Engine and Google Maps Platform today at Google Cloud Next 25. These are seamlessly integrated into BigQuery, Google’s unified data to AI platform. You already understand the value of data-driven insights as BigQuery users, and these new features will allow you to analyze data from a wider range of sources and use extensive geospatial data to make decisions more quickly and effectively.
Geospatial analytics trends and challenges
Geospatial analytics is growing rapidly due to generative AI, hyper-localization, and powerful analytical tools. Despite these advances, many industries still fail to adequately employ geospatial analytics. First, finding new, accurate, and complete data in an analysis-ready format can take time and resources. Second, companies struggle with integration and analysis because many data sources introduce variability, necessitating a great deal of planning and transformation.
Finally, without specialised knowledge and consistent methods, growing geospatial analytics applications can be challenging.
How the new geospatial capabilities address these challenges
With more than 10 million websites and apps, Google Maps Platform is a reliable geospatial tool that enhances the lives of more than 2 billion consumers. Additionally, Earth Engine has given data scientists access to more than 90 petabytes of satellite imagery and geospatial data during the past 15 years.
In order to make better business and sustainability decisions, customers seek to gain more insights from the current, extensive geographical data. For this reason, will are integrating a few datasets from the Google Maps Platform, as well as the datasets and analysis tools from Earth Engine, straight into BigQuery for the first time. This implies that new, extensive, and worldwide geospatial data may now be readily accessed and analyzed by data analysts and decision-makers using the well-known BigQuery platform.
These additional datasets and capabilities enable the following:
- New perspectives, well-known instruments: Use Google’s new, worldwide geospatial data without requiring sophisticated remote sensing or GIS knowledge.
- Geospatial data integration: Combine your current data with rich geospatial datasets to gain previously unattainable new insights.
- Streamlined data access and discovery: Bid farewell to laborious data wrangling. Geospatial data may be accessed and analysed just like any other BigQuery dataset.
Customers may now use data clean rooms to extract insights without disclosing raw data by integrating analysis-ready images and datasets from Earth Engine, Places, and Street View into their current BigQuery processes for the first time.
Imagery Insights
By combining the global size of Street View data, Vertex AI-powered analysis, and the capacity of BigQuery, the first Imagery Insights dataset available in Experimental for the US, Canada, UK, and Japan helps you expedite your infrastructure asset management.
With this combination, you can use Street View images to rapidly detect and automatically evaluate the present state of your infrastructure assets, such as road signs and utility poles, with the possibility of adding many more attribute types in the future.
For instance, if you are a municipal planner who has to figure out how much money you should spend each year on road sign repairs, photos Insights may use Street View photos to pinpoint the precise number and locations of signs that need maintenance. Better planning and operational efficiency are made possible by this integration, which also optimizes workflows and makes it possible to make more informed decisions based on data.
Places Insights
To help you make better business decisions, Places Insights gives you access to aggregate insights from Google Maps data for over 250 million businesses and locations, which are updated every month. You can go beyond simple POI information like wheelchair accessibility and price range with the help of this Places dataset’s extensive insights. A more detailed understanding of millions of businesses and points of interest, such as the whereabouts of the majority of coffee shops within a zip code, will be available to you.
These insights from its Places data can be combined with proprietary data using BigQuery’s data clean room environment to reveal more in-depth information about specific areas. Typical use cases include a better grasp of local market dynamics and determining the best shop sites based on the locations of complementing firms.
Roads Management Insights
Through data-driven traffic management, Roads Management Insights assists the public sector and road authorities in enhancing the effectiveness and safety of the road network. These insights are the result of analysing past data to find trends in traffic on your road networks, discover possible reasons for slowdowns, and take appropriate action. Within seconds of changes occurring on the roadways, authorities can identify and react to abrupt speed dips, identify the source, and maybe reroute traffic when they have access to real-time monitoring.
Earth Engine in BigQuery
The greatest features of Earth Engine’s geospatial raster data analytics are brought straight into BigQuery with Earth Engine in BigQuery. Even if you are not a specialist in remote sensing, this capability enables the SQL community to perform sophisticated geospatial analysis of datasets produced from satellite photography.
A new BigQuery geography function called ST_REGIONSTATS() uses Earth Engine to effectively read and examine geospatial raster data inside a specified geographic area. Additionally, you can now access an expanding selection of Earth Engine datasets from within BigQuery to Analytics Hub’s Earth Engine datasets, which makes data access and discovery easier.
Key business and environmental decisions, including how to optimize infrastructure operations and maintenance, enable sustainable sourcing with global supply chain transparency, enhance road safety and ease traffic, and much more, can be made possible by utilising Google’s geospatial analytics datasets within BigQuery.
