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 generate trade areas based on drive/walk-time isolines from Snowflake console and from CARTO Builder.
We provide an example that showcase how to easily geocode your address data using the Analytics Toolbox LDS module from the Snowflake console and from the CARTO Workspace.
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 Snowflake. You should use this procedure if you have a dataset that...
We are going to demonstrate how fast and easy it is to make a visualization of a Quadkey grid to identify the concentration of Starbucks locations in the US.
In this example we are going to showcase how to use the Minkowski distance to evaluate cannibalization across Starbucks stores in Los Ángeles, assuming that the ratio of cannibalization depends on the nearby store
In this example we will showcase how easily we can compute all the paths that interconnect the main four US airports using the Analytics Toolbox.
In this example we are going to use points clustering to analyze where to locate 10 new supplier offices in US so they can best serve all Starbucks locations.
Voronoi diagrams are a very useful tool to build influence regions from a set of points and the Analytics Toolbox provides a convenient function to build them. An example application of these diagrams is the calculation of the coverage area...
In this example we are going to characterize all Starbucks locations in the US by the total population covered by their catchment areas. We are going to define these catchment areas as a 3km buffer around each store.
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.