Spatial data science
You didn’t become a data scientist to export shapefiles
Spatial data is slow to source, awkward to join and hard to share. Enrich it, model it and share the results where your data already lives.
How it works
Spatial data science, minus the data wrangling
CARTO adds advanced spatial functions, no-code workflows and map building on top of your warehouse. Your data and your compute stay where they are.
Stop wrangling location data
Sourcing, cleaning and joining location data eats the sprint before modeling even starts. Subscribe to ready-made demographics, mobility and weather in the Data Observatory, then enrich your own tables from SQL, a notebook or a Workflow.
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Webinar
Pixels to Predictions: Geospatial AI with Google AlphaEarth Foundation Models and CARTO
Skip the boilerplate
Most spatial pipelines start with the same hand-written setup, and every new piece of analysis means more manual setup. Build your pipelines visually in Workflows instead, or describe the analysis to your coding agent and iterate on each step with natural language. The logic stays versioned and reusable, so the next analysis starts from yours.
Put your results in the right people's hands
A model stuck in a notebook doesn't change decisions, and every follow-up question lands in your queue. Publish it as a live map or an AI Agent, and let your team explore independently.
Frequently asked questions
Yes. With CARTO for Agents, you can direct coding agents such as Claude and Microsoft Copilot to build Workflows and maps in natural language. An extended CLI, 20+ open Agent Skills and a hosted MCP Server give them what they need, and every step stays auditable in your account.
Yes. Use CARTO to engineer the spatial features your models need: build location features, enrich your data with demographics, mobility and other third-party datasets, and add geospatial foundation model embeddings. The results are tables in your warehouse, ready for the ML frameworks and pipelines you already use. For quick prototyping, Workflows also includes BigQuery ML and Snowflake ML components.
Yes. The Analytics Toolbox adds spatial functions to your warehouse as SQL, so you can call them from any notebook through your warehouse's Python client, then carry on with pandas, scikit-learn or whatever you already use.
It applies statistics and machine learning to data that has a location, so your models capture where things happen as well as what happens. With CARTO you can run the core techniques, from geographic feature engineering and spatial clustering to hotspot detection and forecasting, in SQL or Workflows.
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