Why should you learn Spatial SQL?
Accessibility
SQL is much more accessible for the wider analytics community. Python and R may be widely used in Data Science communities, but for other less technical departments SQL is more commonly used - whether that's operations, marketing, GIS or business intelligence.
Efficiency
Spatial SQL allows you to run a wide range of both non-spatial and spatial analyses. This means that you don't become dependent on a single tool or data management solutions. If you host your data in a database with spatially enabled SQL, the rest is simple.
Versatility
With new data types (such as GEOMETRY and GEOGRAPHY) come a set of functions, commonly predicated with ST (such as ST_Intersects) which stands for spatial type. Many databases (PostgreSQL with PostGIS, Microsoft SQL Server, MySQL, Oracle Spatial) and data warehouses (Google BigQuery, Snowflake, Databricks, Amazon Redshift).
Who should learn Spatial SQL?
Teams working with data in SQL based databases or spatially enabled data warehouses
Spatial data scientists or data scientists using spatial data
GIS users looking for more flexibility in their analysis and workflows
Developers creating map-centric apps using spatial data
What are the advantages of Spatial SQL?
Work with larger scale Big Data in SQL enabled data warehouses
Ensure more efficiency in your analytics workflows
Enable more cross-functional collaboration, avoiding silos due to different languages being used
Find more repeatability inside and outside of your organization
Which types of data are typically analyzed with Spatial SQL?
Spatial data can be anything from addresses & latitude/longitude coordinates, to points, lines & polygons. You can also create spatial data with place names & administrative units such as countries and states.
However, as well as using internal data from their organizations (such as CRM, loyalty card, ecommerce), organizations also regularly gather publically available Open Data to enrich their analysis.
More & more premium spatial data streams such as Financial, Human Mobility, Road Traffic, Points of Interest, Weather, Climate & Housing are also being used.
How can you get started using Spatial SQL?
With more organizations looking to carry out spatial analysis, the number of resources available for those looking to get started with Spatial SQL is growing rapidly. There are a wide range of podcasts, webinars, tutorials, events and communities to get involved in - some of which can help you get started and some of which are more advanced.
Curious about the state of Spatial SQL? We surveyed hundreds of geospatial professionals to better understand the growing adoption of this language.
Keen to meet other users of Spatial Data Science & Spatial SQL? Join our upcoming SDSC conferences to network with like-minded professionals.
