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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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  • Data sources
  • Adding sources to Builder
  • Map layers

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  1. Building interactive maps

Data sources & map layers

Last updated 2 months ago

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When you begin a new map in CARTO Builder, the left panel is your starting point, providing the tools to add data sources that will be visualized as layers on your map. In Builder, each data source creates a direct connection to your data warehouse, allowing you to access your data without the need to move or copy it. This cloud-native approach ensures efficient and seamless integration of large datasets.

Once a data source is added, CARTO's advanced technology renders a map layer that visually represents your data, offering smooth and scalable visualization, even with extensive datasets.

In this section, we'll take you through the various data source formats that CARTO Builder supports. We'll also explore the different types of map layers that can be rendered in Builder, enhancing your understanding of how to effectively visualize and interact with your geospatial data.

Data sources

Builder data sources can be differentiated in the following geospatial data types:

  • Simple features: These are unaggregated features using standard geometry (point, line or polygon) and attributes, ready for use in Builder. These spatial and non-spatial attributes are ready to be used in Builder.

  • Aggregated features based on Spatial Indexes: These data sources are aggregated for improved performance or specific use cases. The properties of these features are aggregated according to the chosen aggregation type in Builder. CARTO currently supports two different types of utilize a spatial indexes, Quadbin and H3.

  • Pre-generated tilesets: These are tilesets that have been previously pre-generated using CARTO Analytics Toolbox procedure and stored directly in your data warehouse. Ideal for handling very large, static datasets, these tilesets ensure efficient and high-performance visualizations.

  • Raster: Raster sources uploaded to your data warehouse using CARTO raster-loader, allowing both analytics and visualization capabilities.

Adding sources to Builder

In Builder, you can add data sources either as table sources, by connecting to a materialized table in your data warehouse, or through custom SQL queries. These queries execute directly in your data warehouse, fetching the necessary properties for your map.

Table sources

You can directly connect to your data warehouse table by navigating through the mini data explorer. Once your connection is set, the data source is added as a map layer to your map.

SQL query sources

You can perform a custom SQL query source that will act as your input source. Here you can select the precise columns for better performance and customize your analyses according to your need.

Best practices for SQL Query sources

  • SQL Editor is not designed for conducting complex analysis or detailed step-by-step geospatial analytics directly, as Builder executes a separate query for each map tiles. For analysis requiring high computational power, we recommend two approaches:

    • Materialization: Consider materializing the output result of your analysis. This involves saving the query result as a table in your data warehouse and use that output table as the data source in Builder.

    • Workflows: Utilize for conducting step-by-step analysis. This allows you to process the data in stages and visualize the output results in Builder effectively.

Map layers

Once a data source is added to Builder, a layer is automatically added for that data source. The spatial definition of the source linked to a layer specifies the layer visualization type and additional visualization and styling options. The different layer visualization types supported in Buider are:

  • Point: Displays as point geometries. Point data can be dynamically aggregated to the following types: grid, h3, heatmap and cluster.

  • Polygon: Displays as polygon geometries.

  • Line: Displays as line geometries.

  • H3: Displays features as hexagon cells.

  • Grid: Displays features as grid cells.

  • Raster: Displays data as grid of pixels.

CARTO Workflows