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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.
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.
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.
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.
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.
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.
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.
Measuring merchant attractiveness and performance in CPG with spatial scores
In the CGP industry, consolidating diverse data sources into a unified score becomes crucial for businesses to gain a comprehensive understanding of their product's potential in different locations. In this example, you will learn how to create spatial scores to both understand how attractive each merchant is and to identify how well they are performing when it comes to selling a product.