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  • 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
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On this page
  • How Spatial Indexes are supported in CARTO
  • Spatial Indexes & our Analytics Toolbox
  • Visualizing Spatial Indexes in CARTO Builder
  • Visualizing point data as Quadbins
  • Zoom-based rendering
  • Controlling the resolution

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  1. Working with geospatial data
  2. Introduction to Spatial Indexes

Spatial Index support in CARTO

Leverage the power of Spatial Indexes in CARTO

Last updated 2 months ago

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How Spatial Indexes are supported in CARTO

As mentioned in the , Spatial Indexes like H3 and Quadbin have their location encoded with a short reference string or number. CARTO is able to "read" that string as a geographic identifier, allowing Spatial Index features to be plotted on a map and used for spatial analysis.


Spatial Indexes & our Analytics Toolbox

CARTO's Analytics Toolbox is where you can find all of the tools and functions you need to turn data into insights - and Spatial indexes are an important part of this. Whether you are using CARTO Workflows for low-code analytics, or working directly with SQL, some of the most relevant modules include:

  • H3 or Quadbin modules for creating Spatial Indexes and working with unique spatial properties (e.g. conversion to/from geometries, K-rings).

  • Data module for enriching Spatial Indexes with geometry-based data .

  • Statistics module for leveraging Spatial Indexes to employ Spatial Data Science techniques such as Local Moran's I, Getis Ord and Geographically Weighted Regression.

  • Tiler module for generating tilesets from Spatial Indexes, enabling massive-scale visualizations.

Support for Spatial Indexes may differ depending on which cloud data warehouse you use - please refer to our documentation (links below) for details.


Visualizing Spatial Indexes in CARTO Builder

CARTO Builder provides a lot of functionality to allow you to craft powerful visualizations with Spatial Indexes.

The most important thing to know is that Spatial Index layers are always loaded by aggregation. This means that if you want to use a Spatial Index variable to control the color or 3D extrusion of your layer, you must select an aggregation method such as sum or average. Similarly, the properties for widgets and pop-ups are also aggregated. Because of this, all property selectors will let you select an aggregation operation for each property.

Let's explore the other aspects of visualizing Spatial Indexes!

Visualizing point data as Quadbins

Zoom-based rendering

One of the most powerful features of visualizing Spatial Indexes with CARTO is zoom-based rendering. As the user zooms in further to a map, more detail is revealed. This is incredibly useful for visualizing data at a scale which is appropriate and easy to understand.

Try exploring this functionality on the map below!

Note the maximum, most detailed resolution that can be rendered is the "native" resolution of the Spatial Index table.

Controlling the resolution

With Spatial Index data layers, you can control the granularity of the aggregation by specifying what resolution the Spatial Index should be rendered at. The higher the resolution, the higher the granularity of your grid for each zoom level. This is helpful for controlling the amount of information the user sees.

Note the maximum, most detailed resolution you can visualize is the "native" resolution of the table.

If you add a small point geometry table (<30K rows or 30MB depending on your cloud data warehouse - more information ) to CARTO Builder, you can visualize it as a Quadbin Spatial Index without requiring any processing! By doing this, you can visualize aggregated properties, such as the point count or the sum of numeric variables.

Layer type selector for Point layers loaded as tiles

Learn more about styling your maps in our .

here
documentation
Introduction to Spatial Indexes
Google BigQuery Analytics Toolbox
Snowflake Analytics Toolbox
AWS Redshift Analytics Toolbox
Databricks Analytics Toolbox
How Spatial Indexes are supported in CARTO
Spatial Indexes & our Analytics Toolbox
Visualizing Spatial Indexes in CARTO Builder
USA Median income