carto-geocoding

Original：🇺🇸 English

Translated

Builds geocoding workflows in CARTO that convert street addresses or place names into geographic coordinates. Triggers when the user mentions geocoding, address to coordinates, address resolution, geolocate addresses, "add geometry from addresses", lat/lon from address, place name to point, address matching, forward geocoding, converting addresses to points, or has tabular data with address columns but no spatial geometry column and needs to create one.

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Sourcecartodb/agent-skills

Added on2026-05-26

NPX Install

npx skill4agent add cartodb/agent-skills carto-geocoding

SKILL.md Content

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Geocoding Addresses in CARTO Workflows

Converts street addresses or place names into geographic coordinates (point geometries). This is an essential first step when working with tabular data that has an address column but no spatial column.

Prerequisites: Load

carto-create-workflow

for the development process, JSON structure, and validation commands.

Instructions

A geocoding workflow follows this pipeline:

Source Table (with address column) -> Geocode -> (Filter successful) -> Save

Step 1: Load Source Data

Use

native.gettablebyname

. The input table must contain a column with address strings (e.g.

address

,

full_address

,

location

).

Success: Node outputs a table with at least one text column containing address data.

Step 2: Geocode

Use

native.geocode

with:

Input	Description	Required
`source`	Table with address data	Yes
`address`	Column containing the address string	Yes
`country`	Country filter to improve accuracy (e.g. `"United States"` , `"United Kingdom"` )	No, but strongly recommended

The address column can contain full addresses (

"123 Main St, Springfield, IL 60001"

) or composite values built from multiple columns (concatenate street + city + postal code in a prior

native.selectexpression

step).

Two output handles: The geocode component produces two separate outputs:

match
: Rows where geocoding succeeded -- a
```
geom
```
column with point geometry is added, plus a
```
CARTO_GEOCODE_METADATA
```
JSON column with quality info (confidence score, match type).
unmatch
: Rows where geocoding failed --
```
geom
```
is NULL.

Connect downstream nodes to the correct handle based on your needs.

Success: The geocode node is configured with the address column and (ideally) a country filter. Edges connect to the

match

and/or

unmatch

output handles.

Step 3: Filter or Review Results (optional)

For the match output:

Optionally filter by confidence using the metadata column (e.g. extract confidence from
```
CARTO_GEOCODE_METADATA
```
via
```
native.selectexpression
```
).

For the unmatch output:

Save to a separate table for review and manual correction.
Common failure causes: typos, incomplete addresses, PO boxes, ambiguous place names.

Success: High-confidence geocoded rows are isolated; failed rows are captured for review.

Step 4: Save Results

Use

native.saveastable

to persist the geocoded output. The

geom

column contains WGS84 (EPSG:4326) point geometries, ready for visualization in CARTO Builder or further spatial analysis.

Success: Validated workflow that can be uploaded via

carto workflows create

.

Geocoding Under the Hood

The workflow component wraps the CARTO Analytics Toolbox function

GEOCODE_TABLE

, which:

Adds a
```
geom
```
column with point geometry to each row
Adds a
```
CARTO_GEOCODE_METADATA
```
JSON column with quality information (confidence, match type)
Uses CARTO Location Data Services (LDS) -- each geocoded row consumes LDS quota

Check available quota by querying the Analytics Toolbox

LDS_QUOTA_INFO()

function. The fully-qualified name is provider-specific (BigQuery:

`carto-un.carto`.LDS_QUOTA_INFO()

; Snowflake:

CARTO.CARTO.LDS_QUOTA_INFO()

; Databricks: stored procedure in the dedicated AT schema). Load

carto-create-workflow

and consult

references/providers/<provider>.md

for the AT path on your warehouse.

Gotchas

Geocoding consumes LDS quota. Each row geocoded counts against the account's Location Data Services quota. Check quota availability before bulk operations, especially on large tables.
Two output handles:
match
and
unmatch
. Don't connect to the wrong one --
```
match
```
has geometries,
```
unmatch
```
has NULLs. If you connect the
```
unmatch
```
handle to a spatial operation, it will fail.
Country filter is strongly recommended. Without it, ambiguous addresses may resolve to the wrong country (e.g. "Springfield" exists in 30+ US states and in other countries). The country parameter improves both accuracy and speed.
Address formatting matters. Well-formatted addresses produce better results:
```
"123 Main St, Springfield, IL 60001"
```
works better than
```
"123 main street springfield"
```
. Include city, state/region, and postal code when available.
Provider casing & SQL dialect. Examples in this skill use lowercase column names (BigQuery / Databricks / Postgres / Redshift convention); on Snowflake unquoted identifiers surface UPPERCASE (e.g.
```
CARTO_GEOCODE_METADATA
```
,
```
GEOM
```
). When writing dialect-specific SQL or referencing the AT path, see
```
carto-create-workflow/references/providers/<provider>.md
```
.
For large tables, consider batching. Geocoding hundreds of thousands of rows in a single run can exhaust quota or time out. Split into batches if needed.
Output geometry is always WGS84 points. The
```
geom
```
column contains EPSG:4326 point geometries regardless of the input address format or country.
Failed geocodes deserve review. The
```
unmatch
```
output is not just noise -- it often reveals data quality issues (missing postal codes, abbreviated city names, non-standard formatting) that can be fixed and re-geocoded.

Reference Templates

Academy tutorials and workflow templates covering geocoding:

Resource	Description	URL
Geocoding (BigQuery AT)	Step-by-step geocoding with Analytics Toolbox for BigQuery	Academy link
Geocoding (Snowflake AT)	Step-by-step geocoding with Analytics Toolbox for Snowflake	Academy link
Workflow template: Geocode street addresses	Generating new spatial data from addresses	Academy link

Common Variations

Variation	How
Composite address from multiple columns	Add a `native.selectexpression` step before geocoding to concatenate street, city, state, zip into one column
Geocode + spatial join	Chain: Geocode -> match -> Spatial Join (e.g. point-in-polygon to assign regions)
Geocode + enrichment	Chain: Geocode -> match -> Buffer/Isochrone -> Enrich (add demographics around each geocoded point)
Capture failures for re-processing	Connect both `match` and `unmatch` handles to separate `native.saveastable` nodes
Filter by confidence	After geocode, use `native.selectexpression` to extract confidence from the metadata JSON, then filter