LogoLogo
HomeDocumentationLoginTry for free
  • 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
Powered by GitBook
On this page
  • Requirements
  • Example
  • Geocoding from the Snowflake console
  • Geocoding from CARTO Workspace

Was this helpful?

Export as PDF
  1. Advanced spatial analytics
  2. Spatial Analytics for Snowflake
  3. Step-by-step tutorials

Geocoding your address data

Last updated 1 year ago

Was this helpful?

Requirements

To run this example you'll need:

  • An active CARTO organization

  • The latest version of the Analytics Toolbox Advanced installed in your Snowflake database

Example

In this example, we will geocode a table with some Starbucks address data that we have available in Snowflake. The geocoding process will add a new column to your input table called “geom” (or the name that you choose) with a Point geometry based on the geographic coordinates of the location; which are derived from the location information in your table (e.g. street address, postal code, country, etc.).

WARNING

This function consumes isolines quota. Each call consumes as many units of quota as the number of rows your input table or query has. Before running, we recommend checking the size of the data to be geocoded and your available quota using the LDS_QUOTA_INFO() function.

Geocoding from the Snowflake console

As a module within CARTO’s Analytics Toolbox, the location data services (lds) capabilities are available as SQL procedures that can be executed directly from your Snowflake console or client of choice after connecting your Snowflake project with your CARTO account. To check whether your Google account or Service Account has access to the LDS module, please execute this query:

SELECT CARTO.CARTO.VERSION_ADVANCED()

The lds module is generally available in the Analytics Toolbox since the “July 26, 2022” version. Please check the Getting Access section if you run into any errors when running the query above.

For this example we will use a table with the Starbucks addresses that can be found in the publicly available MYDB.MYSCHEMA.STARBUCKS_NY_GEOCODE . The table contains a column called “full_address” that we will use as input for the geocoding process.

Once you are all set getting access to the lds module, geocoding your data is as easy as opening your Snowflake console or SQL client and running the GEOCODE_TABLE() procedure as detailed in the following query:

CALL CARTO.CARTO.GEOCODE_TABLE(
    'MYDB.MYSCHEMA.STARBUCKS_NY_GEOCODE',
    'FULL_ADDRESS','GEOM', 'US');
-- The table 'CARTO_DEV_DATA.DEMO_TABLES.STARBUCKS_NY_GEOCODE' will be updated
-- adding the columns: geom , carto_geocode_metadata.

As a result of the query, we obtain the input table modified with a new column called “GEOM” with the geographic coordinates (latitude and longitude) and the “CARTO_GEOCODE_METADATA” column with additional information of the geocoding result in JSON format.

Geocoding from CARTO Workspace

You will find the option Geocode table available from the Data Explorer in tables that do not contain any geometry column. To find your table please select the corresponding connection, pick the right dataset/folder and find the table you want to geocode from the collapsible tree.

Clicking on the “Geocode table” button will trigger a wizard that you can follow along to configure the different parameters to geocode your data.

In this case, to reproduce the geocoding example that we have done before from a SQL console, we will select geocode by address and we will choose the “full_address” column as input parameter. You can also provide extra location information choosing “United States of America” in the country selector.

Click on “Continue” to proceed to the next step where you can review the summary of the operation that will be performed on your data and confirm it by clicking on “Geocode”.

The geocoding process could take some minutes, remember that you may be geocoding a big amount of data and that the operation is calling an external geocoding service. You can minimize the process window and continue working with CARTO in the meantime and follow the progress of the geocoding process at any time you want.

Once the process finishes, you will be able to access your geocoded table, which will have a new column called “GEOM” including the geographic coordinates of your input data.

In this case, we select MYDB.MYSCHEMA.STARBUCKS_NY_GEOCODE as input table and “full_address” as address column. We choose the “GEOM_TOMTOM” as the column name for the geometry column, and we also specify the name of the country based on its ISO 3166-1 alpha-2 code . You can refer to the SQL reference if you need more details about this procedure and its parameters.

The Data Explorer offers you a graphical interface that you can use to . Let’s use it here to reproduce the same use case that we have done from the Snowflake console but from the CARTO Workspace.

This project has received funding from the research and innovation programme under grant agreement No 960401.

ISO 3166-1 alpha-2 code
geocode your data
European Union’s Horizon 2020
Intermediate difficulty banner
EU flag