Data Observatory

Augment your internal data & broaden your analysis with the latest & greatest in third party location data, reducing the time spent gathering, evaluating & cleaning data.

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The Data Observatory in numbers

CARTO's Data Observatory brings together thousands of open and premium datasets enabling a wide range of organizations to take the pain out of sourcing spatial data.

Explore catalog
10
categories
> 12,000
datasets
44
sources

Working with spatial data

  • Finding the location data I need can take weeks as there are too many providers offering similar data and it’s difficult to assess quality
  • Licensing can also be a long and tedious process: I’ll have to negotiate terms with every single data provider
  • Enriching my data will be hard as I’ll need to deal with different formats and support geographies

How the Data Observatory can help

  • Simple access to public and premium data from vetted sources
  • Faster licensing process thanks to existing agreements with leading data providers
  • Easy enrichment with data already presented in standardized formats
How CARTO Helps

How does it work?

You can access open and premium datasets from Data Observatory in your own work environment.

For Data Scientists

Discover the most suitable datasets for your analysis, access data, select variables you want to use and enrich your dataframes - all from your Jupyter notebook using CARTOframes.

1from cartoframes.data.observatory import * 
2dataset = Dataset.get('ags_sociodemogr_a7e14220') 
3variables = dataset.variables 
4variables
1[<Variable.get('BLOCKGROUP_30e525a6')> #'Geographic Identifier',
2<Variable.get('POPCY_4534fac4')> #'Population (2019A)',
3<Variable.get('POPCYGRP_3033ef2e')> #'Population in Group Quarters (2019A)',
4<Variable.get('POPCYGRPI_1e42899')> #'Institutional Group Quarters Population (2019A)',
5<Variable.get('AGECY0004_aaae373a')> #'Population age 0-4 (2019A)',
6<Variable.get('AGECY0509_d2d4896c')> #'Population age 5-9 (2019A)',
7...]
Discover CARTOframes

For Analysts

Easily bring spatial datasets to CARTO Builder, our drag and drop mapping tool, extract key insights and create lightweight, intuitive dashboards to share across your organization.

Learn more about Builder

For Developers

Generate custom tilesets from your Data Observatory datasets and integrate them in your spatial analytics apps using our developer toolkit.

Learn more about CARTO for development

Integrate with other tools

Want to access datasets from other platforms via the Data Observatory? Get access to data from BigQuery or contact us to discuss your needs in other platforms, such as Snowflake or AWS.

Contact us
Other tools

DATA AVAILABLE

Thousands of datasets at your fingertips

The Data Observatory allows you to use the highest quality location data out there from across the globe, such
 as human mobility, financial, and POI data. We partner with market-leaders to bring together accurate, ready-to-use datasets to reduce your time
 to insight, whatever your use case.
Explore Spatial Data Catalog
Financial
Financial
Human Mobility
Human Mobility
Environmental
Environmental
Demographics
Demographics
Geographies
Geographies
Road Traffic
Road Traffic
Points of Interest
Points of Interest
Behavioral
Behavioral ... and more
Expand

Data Analysis Examples

CPG Firms

CPG firms select pop up sites by using CARTO’s POI data to identify where certain target segments may also visit, allowing them to reduce the risk of a poor site decision. By looking at age, gender, rental price data, and transport accessibility, CPG firms can identify ideal locations based on the target audience of the product potentially being sold at the pop-up site.

Category

Data Set

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Data Analysis Examples

Credit Card Providers

In the Financial Services world, Mastercard’s Retail Location Insights applications use boundary data through CARTO’s Data Observatory in the US, Canada, Australia and the UK, mapping their credit card insight data products to a wide range of granularities across the globe.

Category

Data Set

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Data Analysis Examples

Local Governments

Local governments (such as New York City) rely on GPS data to gain insights that measure the risk of overcrowding by station. In this example, NYC was able to blend MTA turnstile data with Safegraph’s human mobility data to identify which subway stations were at higher risk during the COVID-19 outbreak.

Category

Data Set

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Data Analysis Examples

Retail Big Box Stores

Retail Big Box Stores in the US use CARTO’s platform to bring together their internal data on sales and performance with external credit card insights from Mastercard. This allows them to identify potential targets for expansion or consolidation, seeing how economic trends will affect their brick and mortar revenues.

Category

Data Set

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EU flag

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 960401.

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