Why should you learn Spatial SQL?

Accessibility

SQL is much more accessible for the wider analytics community. It is 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. 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.

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.
  • Analysts looking for more flexibility from traditional model building tools or software.
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Which types of data are used in Location Intelligence?

Location 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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