Think Spatial: Don't Ignore Location in your Models!
Giulia Carella

Data Scientist

Steve Isaac

Content Marketing Manager


  • Data with a spatial component is increasingly used in data science modelling practices but the spatial nature of the data is rarely taken into account. Spatial data science involves a skillset ranging from the visualization of data on a map to modelling (at feasible computational cost) spatially correlated errors.


In this technical webinar, Giulia Carella (Data Scientist) and Steve Isaac (Content Marketing Manager) share how thinking spatially can help you to build powerful models that outperform the typical data science tools.  The webinar covers two areas:


  1. - Introduction to spatial modelling, including tools to build such statistical models, estimate their parameters, and perform predictions.


  • - Demos of practical use cases where spatial models are useful and how to implement them taking advantage of CARTO’s tech stack (including CARTO Data Observatory for data enrichment, and CARTOframes for visualizations and returning spatial SQL queries).
  • A pdf of the slides from the presentation is also available for download here