Definition

What is Spatial SQL?

Spatial SQL uses the same elements & structure of normal SQL, but allows you to work with geospatial data types such as geometries & geographies.

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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).

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Who should learn Spatial SQL?

1

Teams working with data in SQL based databases or spatially enabled data warehouses
2

Spatial data scientists or data scientists using spatial data
3

GIS users looking for more flexibility in their analysis and workflows
4

Developers creating map-centric apps using spatial data
5

Analysts looking for more flexibility from traditional model building tools or software

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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.

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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.

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