Analytics Toolbox for BigQuery

Analytics Toolbox for BigQuery

Examples

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.

    Segmenting CPG merchants using trade areas characteristics

    A key analysis towards understanding your merchants’ potential is to identify the characteristics of their trade areas and to perform an appropriate profiling and segmentation of them.

    Generating trade areas based on drive/walk-time isolines

    We generate trade areas based on drive/walk-time isolines from BigQuery console and from CARTO Builder.

    Geocoding your address data

    We provide an example that showcase how to easily geocode your address data using the Analytics Toolbox LDS module from the BigQuery console and from the CARTO Workspace.

    Store cannibalization: quantifying the effect of opening new stores on your existing network

    Cannibalization is a very common analysis in retail that consists in quantifying the impact of new store openings on existing stores.

    Creating spatial index tilesets

    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.

    Find twin areas of your top performing stores

    The Twin Areas analysis can be used to build a similarity score with respect to an existing site (e.g. the location of your top performing store) for a set of target locations, which can prove an essential tool for Site Planners looking at opening, relocating, or consolidating their retail network. In this example we select as potential origin locations the locations of the top 10 performing liquor stores in 2019 in Iowa, US from the publicly available [Liquor sales dataset](https://data.iowa.gov/Sales-Distribution/Iowa-Liquor-Sales/m3tr-qhgy) to find the most similar locations in Texas, US.

EU flag

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.