In this section we provide a set of examples that showcase how to leverage the functions of our Analytics Toolbox to unlock advanced spatial analyses in your data warehouse platform. They cover a broad range of use cases with methods for data transformations, enrichment, spatial indexing in Quadkey and H3, statistics, clustering, spatial data science methods and more.
The Analytics Toolbox can be leveraged from the SQL Query editor in Builder, your data warehouse console, or in SQL and Python Notebooks using the SQL clients provided by the different cloud platforms.
We provide a set of examples that showcase how to easily create tilesets based on spatial indexes allowing you to process and visualize very large spatial datasets stored in BigQuery. You should use this procedure if you have a dataset that contains a column with a spatial index identifier instead of a geometry and you want to visualize it at an appropriate zoom level.
The pains of working with data in different spatial aggregations can be greatly eased by using spatial indexes. In this example we showcase how, in a single query, we can create a quadkey grid of resolution 15 of all supermarket POIs in the US and enrich it with population data.
In this example we are going to showcase the extent of quadkey tiles at different resolutions. For this purpose we are using the United Kingdom census areas dataset from CARTO's Data Observatory.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.