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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
  • Context
  • Creating a Style JSON using Maputnik
  • Hosting a Style JSON in Github
  • Adding custom basemaps to your organization
  • Creating a map using your custom basemap

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  1. Building interactive maps
  2. Data visualization

Customize your visualization with tailored-made basemaps

Last updated 11 months ago

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Context

The basemap is the foundational component of a map. It provides context, geographic features, and brand identity for your creations. Every organization is unique, and CARTO allows you to bring your own basemaps to fit your specific needs.

In this tutorial, you'll learn to customize your visualizations in Builder by using tailor-made basemaps. Don't have a custom basemap already? We'll start with the creation of a custom basemap using Maputnik, a free and open-source visual editor.

Prerequisites: You need to be an Admin user to add custom basemaps to your CARTO organization.

In this guide, we'll walk you through:


Creating a Style JSON using Maputnik

You might get overwhelmed by all the options available in the UI, but using it is simpler than it seems. To make it easier to recognize the different items you can update in the style, simply click on the map, and Maputnik will display the layers you can customize.

Now that you're more familiar with this tool, let's start customizing the look and feel of this map.

  1. Set the "buildings" layer to blue using this hex color code #4887BD.

  1. For the green spaces, set the "greenspaces" layer to #09927A and "woodland" to #145C42.

  1. To highlight the visualization of both "greenspace names" and "woodland names" labels, increase the size using the below JSON code and set the fill color to white.


Hosting a Style JSON in Github

In this section, we'll showcase how you can host Style JSON files using GitHub to consume them in your CARTO organization. We'll be using a feature called gist, which allows you to host files. Here’s how to do it:

  1. Ensure you have access to GitHub and your own repository and create a new gist. To do so:

    • Go to GitHub and create a new gist.

    • Drag your exported Style JSON into the gist.

    • Make sure the gist is public.

    • Create the public gist.

  1. Now we'll get the raw URL of the hosted Style JSON, to do so:

    • Access the raw version of the gist.

    • Copy the URL of the raw file. This URL will be used to consume the custom basemap in CARTO.


Adding custom basemaps to your organization

Note: You need to be the Admin of your organization to have the rights to add custom basemaps to your CARTO organization.

  1. Go to Organization > Settings > Customizations > Basemaps

  1. Click on "New basemap" to add your custom basemap, completing the following parameters:

    • URL: Enter the gist raw URL of the hosted Style JSON.

    • Name: The name you'd like to provide to your basemap

    • Attribution: Automatically filled but you can edit this if required.

  2. Once the basemap URL has been validated, you can use the interactive map to navigate to the desired basemap extent.

  1. Activate the custom basemap type in the "Enabled basemaps in Builder" section. Doing so, you'll enable all Editors of the organization to access all added custom basemaps.


Creating a map using your custom basemap

  1. Navigate to the Maps section and click on "New map".

  1. Provide the map with a title "Using custom basemaps" and load Bristol traffic accidents source. To do so:

    • Click on "Add sources from..."

    • Navigate to CARTO Data Warehouse > demo data > demo_tables.

    • Select "bristol_traffic_accidents" table.

    • Click "Add source".

The source and related layer is added to the map.

  1. Rename the newly added layer "Traffic Accidents".

  2. Go to the Basemap tab and choose your recently uploaded custom basemap.

  1. Style the "Traffic Accidents" layer:

    • In the Fill Color section, set the color to light yellow.

    • Configure the Size to 4.

  1. Now, you're done with your map creation and ready to share it with others!

Access the online version of Maputnik at . Then, click "Open" and select "Zoomstack Night." Zoomstack Night is an open vector basemap provided by Ordnance Survey's OS Open Zoomstack, showcasing coverage of Great Britain.

Once you're done, export the Style JSON and save it. You'll need this for the next section. Note depending on which style you have used as a template, you may need to include an access token at this point, such as from .

https://maplibre.org/maputnik/
MapTiler
Creating a Style JSON in Maputnik
Hosting a Style JSON using Github Gist
Adding your custom basemaps to CARTO
Creating a map using your custom basemap
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