Summary

Along with globalization of our societies comes the need to analyze the geospatial and geotemporal data that is needed to manage the growth in commerce, communications, and other activities. In order to make geospatial analytics more maintainable and scalable there has been an increase in the number of database engines that provide extensions to their SQL syntax that supports manipulation of spatial data. In this episode Matthew Forrest shares his experiences of working in the domain of geospatial analytics and the application of SQL dialects to his analysis.

Announcements

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Your host is Tobias Macey and today I’m interviewing Matthew Forrest about doing spatial analysis in SQL

Interview

Introduction
How did you get involved in the area of data management?
Can you describe what spatial SQL is and some of the use cases that it is relevant for?
compatibility with/comparison to syntax from PostGIS
What is involved in implementation of spatial logic in database engines
mapping geospatial concepts into declarative syntax
foundational data types
data modeling
workflow for analyzing spatial data sets outside of database engines
translating from e.g. geopandas to SQL
level of support in database engines for spatial data types
What are the most interesting, innovative, or unexpected ways that you have seen spatial SQL used?
What are the most interesting, unexpected, or challenging lessons that you have learned while working with spatial SQL?
When is SQL the wrong choice for spatial analysis?
What do you have planned for the future of spatial analytics support in SQL for the Carto platform?

Contact Info

LinkedIn
Website
@mbforr on Twitter

Parting Question

From your perspective, what is the biggest gap in the tooling or technology for data management today?

Closing Announcements

Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links

The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA

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