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.
Find similar locations based on their trade areas
In this example, we demonstrate how easy it is to use the Analytics Toolbox functions to find how similar different locations are to a chosen one.
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 to find the most similar locations in Texas, US.
Opening a new Pizza Hut location in Honolulu
We find the best new location for a specific target demographics using spatial indexes and advanced statistical functions.
Interpolating elevation along a road using kriging
In this example, we will perform kriging interpolation of the elevation along the so-called roller coaster road on the island of Hokkaido, Japan, using as reference points a nearby elevation measurement.
Creating simple tilesets
We provide a set of examples that showcase how to easily create simple tilesets allowing you to process and visualize very large spatial datasets stored in BigQuery. You should use it if you have a dataset with any geography type (point, line, or polygon) and you want to visualize it at an appropriate zoom level.
Creating aggregation tilesets
We provide a set of examples that showcase how to easily create aggregation tilesets allowing you to process and visualize very large spatial datasets stored in BigQuery. You can use this procedure if you have a point dataset (or anything that can be converted to points, such as polygon centroids) and you want to see it aggregated.
An H3 grid of Starbucks locations and simple cannibalization analysis
We are going to demonstrate how fast and easy it is to make a visualization of an H3 grid to identify the concentration of Starbucks locations in the US.
Enriching a quadkey grid with population data from the Data Observatory
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.
New police stations based on Chicago crime location clusters
In this example we are going to use points clustering to analyze where to locate five new police stations in Chicago based on 5000 samples of crime locations.
Analyzing weather stations coverage using a Voronoi diagram
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 areas of a series of weather stations. In the following query we are going to calculate these influence areas in the state of New York.
A NYC subway connection graph using Delaunay triangulation
Providing a good network connection between subway stations is critical to ensure an efficient mobility system in big areas. Let's imagine we need to design a well-distributed subway network to connect the stations of a brand-new subway system. A simple and effective solution to this problem is to build a Delaunay triangulation of the predefined stations, which ensures a good connection distribution.
Computing US airport connections and route interpolations
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.
Identifying earthquake-prone areas in the state of California
In this example we are going to use some of the functions included in CARTO's Analytics Toolbox in order to highlight zones prone to earthquakes, using a BigQuery public dataset.
Bikeshare stations within a San Francisco buffer
In this example we are going to showcase how easily we can compute buffers around geometries using the Analytics Toolbox
Census areas in the UK within tiles of multiple resolutions
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.
Applying GWR to understand Airbnb listings prices
Geographically Weighted Regression (GWR) is a statistical regression method that models the local (e.g. regional or sub-regional) relationships between a set of predictor variables and an outcome of interest. Therefore, it should be used in lieu of a global model in those scenarios where these relationships vary spatially. In this example we are going to analyze the local relationships between Airbnb's listings in Berlin and the number of bedrooms and bathrooms available at these listings using the GWR_GRID procedure.
Computing the spatial autocorrelation of POIs locations in Berlin
In this example we analyze the spatial correlation of POIs locations in Berlin using OpenStreetMap data and the MORANS_I_H3 function available in the statistics module.
Identifying amenity hotspots in Stockholm
In this example we identify hotspots of amenity POIs in Stockholm using OpenStreetMap data and the GETIS_ORD_H3 function of the statistics module.
Using raster and vector data to calculate total rooftop PV potential in the US
In this example, you will learn how to easily load raster data into BigQuery, and then combine it with vector data using the raster module of the Analytics Toolbox. To illustrate this we will compute the total rooftop photovoltaic power (PV) potential across all buildings in the US.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.