google-maps-reviewer-geo-profile-api

Infer a Google Maps reviewer home region and footprint from their review history. Apify-backed n8n community node, pay-per-result, and usable as an AI agent tool.

Package Information

Released: 3 mo ago
Downloads: 285 weekly / 285 monthly
Latest Version: 0.1.1
Author: John Cole

Available Nodes

Google Maps Reviewer Geo Profile icon
Google Maps Reviewer Geo Profile
Infer a Google Maps reviewer home region and footprint from their public review history

Documentation

n8n-nodes-google-maps-reviewer-geo-profile-api

An n8n community node that infers a Google Maps reviewer's home region and geographic footprint from their public review history. It is backed by the Google Maps Reviewer Geo Profile API on Apify.

Give it a Google Maps contributor ID and it returns one derived profile per reviewer: a standardized home-region guess (city, state, country, plus ISO 3166 codes), a confidence score, how many reviews sit in the home cluster versus travel outliers, a footprint of the regions they review in, and a centroid and bounding box. It is built for aggregate reviewer vetting, reputation research, and review-fraud detection, at region level. It is not a tool for locating a specific individual.

It also works as an AI Agent tool: an agent can call it to profile a reviewer's home region and judge whether their footprint looks locally coherent.

Installation

  1. In n8n, go to Settings, Community Nodes.
  2. Select Install and enter n8n-nodes-google-maps-reviewer-geo-profile-api.
  3. Agree to the risks of using community nodes and install.

This is a self-hosted community node. See the n8n community nodes docs.

Credentials

You need an Apify API key. Create a free account at Apify, then copy your token from Settings, Integrations in the Apify Console.

In n8n, add an Apify API credential and paste the token (OAuth2 is also supported).

Operation

Resource: Reviewer, Operation: Geo Profile.

Parameter Type Default Description
Contributor ID string - A single Google Maps contributor ID (the long numeric ID from a reviewer profile).
Contributor IDs string - Profile several reviewers at once. Separate IDs by commas, spaces, or new lines.
Region Granularity options City Level the home-region guess is computed at: City, State or Province, or Country.
Minimum City Population number 100000 Snap each review to the nearest city of at least this size, so metro neighborhoods group under the principal city.
Maximum Reviews Per Contributor number 100 Reviews to analyze per contributor, most recent first (maximum 200).
Language Code string en Two-letter language code for the source reviews.

Provide Contributor ID, Contributor IDs, or both. One row is returned per reviewer.

Output

Choose how much data to return with the Output parameter:

  • Simplified (default): a compact, LLM-friendly subset (home region, confidence, cluster vs travel, footprint). Forced automatically when used as an AI Agent tool.
  • Raw: every field the API returns.
  • Selected Fields: pick exactly which fields you want.

Key output fields:

Field Description
home_region_guess The reviewer's most likely home region at the chosen granularity.
home_city, home_admin, home_country Standardized city, state/province, and country of the home cluster.
home_admin_code, home_country_code ISO 3166-2 subdivision code and ISO 3166-1 country code.
confidence Share of located reviews in the home region (0 to 1).
home_cluster_size, travel_outliers Located reviews in the home region vs outside it.
located_reviews, total_reviews, distinct_regions Coverage and spread of the analysis.
footprint Top regions by review count, each with count and share.
centroid, bounding_box Mean position of the home cluster and the full geographic extent.

Example workflows

Vet one reviewer. Manual Trigger, then this node with Contributor ID set and Region Granularity = City. Read home_region_guess and confidence to judge whether the reviewer looks like a genuine local.

Batch reputation research. A data source that emits contributor IDs, then this node with those IDs in Contributor IDs and Output = Selected Fields. Route reviewers with many distinct_regions and high travel_outliers to a review queue.

Pricing

The backing Actor is pay-per-result: you pay per contributor analyzed, regardless of how deep the history goes. See current pricing on the Actor page.

Links

License

MIT

Discussion