LogoLogo
HomeDocumentationLoginTry for free
  • CARTO Academy
  • Working with geospatial data
    • Geospatial data: the basics
      • What is location data?
      • Types of location data
      • Changing between types of geographical support
    • Optimizing your data for spatial analysis
    • Introduction to Spatial Indexes
      • Spatial Index support in CARTO
      • Create or enrich an index
      • Work with unique Spatial Index properties
      • Scaling common geoprocessing tasks with Spatial Indexes
      • Using Spatial Indexes for analysis
        • Calculating traffic accident rates
        • Which cell phone towers serve the most people?
    • The modern geospatial analysis stack
      • Spatial data management and analytics with CARTO QGIS Plugin
      • Using data from a REST API for real-time updates
  • Building interactive maps
    • Introduction to CARTO Builder
    • Data sources & map layers
    • Widgets & SQL Parameters
    • AI Agents
    • Data visualization
      • Build a dashboard with styled point locations
      • Style qualitative data using hex color codes
      • Create an animated visualization with time series
      • Visualize administrative regions by defined zoom levels
      • Build a dashboard to understand historic weather events
      • Customize your visualization with tailored-made basemaps
      • Visualize static geometries with attributes varying over time
      • Mapping the precipitation impact of Hurricane Milton with raster data
    • Data analysis
      • Filtering multiple data sources simultaneously with SQL Parameters
      • Generate a dynamic index based on user-defined weighted variables
      • Create a dashboard with user-defined analysis using SQL Parameters
      • Analyzing multiple drive-time catchment areas dynamically
      • Extract insights from your maps with AI Agents
    • Sharing and collaborating
      • Dynamically control your maps using URL parameters
      • Embedding maps in BI platforms
    • Solving geospatial use-cases
      • Build a store performance monitoring dashboard for retail stores in the USA
      • Analyzing Airbnb ratings in Los Angeles
      • Assessing the damages of La Palma Volcano
    • CARTO Map Gallery
  • Creating workflows
    • Introduction to CARTO Workflows
    • Step-by-step tutorials
      • Creating a composite score for fire risk
      • Spatial Scoring: Measuring merchant attractiveness and performance
      • Using crime data & spatial analysis to assess home insurance risk
      • Identify the best billboards and stores for a multi-channel product launch campaign
      • Estimate the population covered by LTE cells
      • A no-code approach to optimizing OOH advertising locations
      • Optimizing site selection for EV charging stations
      • How to optimize location planning for wind turbines
      • Calculate population living around top retail locations
      • Identifying customers potentially affected by an active fire in California
      • Finding stores in areas with weather risks
      • How to run scalable routing analysis the easy way
      • Geomarketing techniques for targeting sportswear consumers
      • How to use GenAI to optimize your spatial analysis
      • Analyzing origin and destination patterns
      • Understanding accident hotspots
      • Real-Time Flood Claims Analysis
      • Train a classification model to estimate customer churn
      • Space-time anomaly detection for real-time portfolio management
      • Identify buildings in areas with a deficit of cell network antennas
    • Workflow templates
      • Data Preparation
      • Data Enrichment
      • Spatial Indexes
      • Spatial Analysis
      • Generating new spatial data
      • Statistics
      • Retail and CPG
      • Telco
      • Insurance
      • Out Of Home Advertising
      • BigQuery ML
      • Snowflake ML
  • Advanced spatial analytics
    • Introduction to the Analytics Toolbox
    • Spatial Analytics for BigQuery
      • Step-by-step tutorials
        • How to create a composite score with your spatial data
        • Space-time hotspot analysis: Identifying traffic accident hotspots
        • Spacetime hotspot classification: Understanding collision patterns
        • Time series clustering: Identifying areas with similar traffic accident patterns
        • Detecting space-time anomalous regions to improve real estate portfolio management (quick start)
        • Detecting space-time anomalous regions to improve real estate portfolio management
        • Computing the spatial autocorrelation of POIs locations in Berlin
        • Identifying amenity hotspots in Stockholm
        • Applying GWR to understand Airbnb listings prices
        • Analyzing signal coverage with line-of-sight calculation and path loss estimation
        • Generating trade areas based on drive/walk-time isolines
        • Geocoding your address data
        • Find similar locations based on their trade areas
        • Calculating market penetration in CPG with merchant universe matching
        • Measuring merchant attractiveness and performance in CPG with spatial scores
        • Segmenting CPG merchants using trade areas characteristics
        • Store cannibalization: quantifying the effect of opening new stores on your existing network
        • Find Twin Areas of top-performing stores
        • Opening a new Pizza Hut location in Honolulu
        • An H3 grid of Starbucks locations and simple cannibalization analysis
        • Data enrichment using the Data Observatory
        • New police stations based on Chicago crime location clusters
        • Interpolating elevation along a road using kriging
        • Analyzing weather stations coverage using a Voronoi diagram
        • A NYC subway connection graph using Delaunay triangulation
        • Computing US airport connections and route interpolations
        • Identifying earthquake-prone areas in the state of California
        • Bikeshare stations within a San Francisco buffer
        • Census areas in the UK within tiles of multiple resolutions
        • Creating simple tilesets
        • Creating spatial index tilesets
        • Creating aggregation tilesets
        • Using raster and vector data to calculate total rooftop PV potential in the US
        • Using the routing module
      • About Analytics Toolbox regions
    • Spatial Analytics for Snowflake
      • Step-by-step tutorials
        • How to create a composite score with your spatial data
        • Space-time hotspot analysis: Identifying traffic accident hotspots
        • Computing the spatial autocorrelation of POIs locations in Berlin
        • Identifying amenity hotspots in Stockholm
        • Applying GWR to understand Airbnb listings prices
        • Opening a new Pizza Hut location in Honolulu
        • Generating trade areas based on drive/walk-time isolines
        • Geocoding your address data
        • Creating spatial index tilesets
        • A Quadkey grid of stores locations and simple cannibalization analysis
        • Minkowski distance to perform cannibalization analysis
        • Computing US airport connections and route interpolations
        • New supplier offices based on store locations clusters
        • Analyzing store location coverage using a Voronoi diagram
        • Enrichment of catchment areas for store characterization
        • Data enrichment using the Data Observatory
    • Spatial Analytics for Redshift
      • Step-by-step tutorials
        • Generating trade areas based on drive/walk-time isolines
        • Geocoding your address data
        • Creating spatial index tilesets
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. Advanced spatial analytics
  2. Spatial Analytics for BigQuery

Step-by-step tutorials

Last updated 8 months ago

Was this helpful?

In this section we provide a set of examples that showcase how to leverage the functions of our 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 Quadbin and H3, statistics, clustering, spatial data science methods and more.

Analytics Toolbox for BigQuery
Cover

How to create a composite score with your spatial data

In this tutorial we show how to combine (spatial) variables into a meaningful composite indicator using CARTO Analytics Toolbox for BigQuery.

STATISTICS

Space-time hotspot analysis: Identifying traffic accident hotspots

Spatiotemporal analysis plays a crucial role in extracting meaningful insights from data that possess both spatial and temporal components. This example shows how to identify space-time hotspots using the Analytics Toolbox.

STATISTICS

Cover

Spacetime hotspot classification: Understanding collision patterns

Spatiotemporal analysis is crucial in extracting meaningful insights from data that possess both spatial and temporal components. This example shows how to identify and classify space-time hot and coldspots using the Analytics Toolbox.

STATISTICS

Cover

Time series clustering: Identifying areas with similar traffic accident patterns

Spatiotemporal analysis plays a crucial role in extracting meaningful insights from data that possess both spatial and temporal components. This example shows how to cluster geolocated time series using the Analytics Toolbox.

STATISTICS

Cover

Detecting space-time anomalous regions to improve real estate portfolio management (quick start)

In this tutorial we show how to detect space-time anomalous regions using CARTO Analytics Toolbox for BigQuery.

STATISTICS

Cover

Detecting space-time anomalous regions to improve real estate portfolio management

In this tutorial we show how to detect space-time anomalous regions using CARTO Analytics Toolbox for BigQuery.

STATISTICS

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.

STATISTICS

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.

STATISTICS

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.

STATISTICS

Cover

Analyzing signal coverage with line-of-sight calculation and path loss estimation

Coverage analysis is fundamental for assessing the geographical areas where a network's signal is available and determining its quality. This guide shows how to use CARTO telco functionality in the Analytics Toolbox for signal coverage analysis.

TELCO

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.

LDS

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.

LDS

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.

CPG

Calculating market penetration in CPG with merchant universe matching

In this example, you will learn how to run universe matching analysis in CPG to match a company's current distributors to a more extensive set of all potential distributors in order to derive market penetration insights.

CPG

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.

CPG

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.

CPG

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.

RETAIL

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.

RETAIL

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.

RETAIL STATISTICS H3 DATA

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.

H3

Cover

Data Enrichment using the Data Observatory

In this guide you will learn how to perform data enrichment using data from your Data Observatory subscriptions and the different data enrichment methods available in the Analytics Toolbox.

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.

CLUSTERING

Interpolating elevation along a road using krigin

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.

STATISTICS

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.

PROCESSING

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.

PROCESSING

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.

TRANSFORMATIONS

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.

CONSTRUCTORS

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

TRANSFORMATIONS

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.

CONSTRUCTORS

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.

TILER

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.

TILER

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.

TILER

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.

RASTER

Cover

Using the routing module

In this tutorial you will learn how to use the routing module of our Analytics Toolbox for BigQuery to generate routes and calculate isolines, with all needed data and computation happening natively in BigQuery.

ROUTING

Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover
Cover