Google Maps Reviewer Geo Profile API

Infer a Google Maps reviewer's home region from their public review history. Give the API a contributor ID and it fetches that reviewer's reviews, reverse-geocodes the coordinates attached to every review, and clusters them into a single derived profile: a standardized home-region guess (city, state, country, plus ISO codes), a confidence score, how many reviews sit in the home cluster versus travel outliers, a compact footprint of the regions they review in, and a centroid and bounding box. One row per reviewer.

Built for aggregate reviewer vetting, reputation research, and review-fraud detection. It answers "where is this reviewer based, and how far do they roam?" at region level. It is not a tool for locating a specific individual, and it deliberately stops at city/region granularity.

What you get

One derived row per reviewer:

• Home region: home_region_guess plus the standardized breakdown home_city, home_admin (state/province), home_admin_code (ISO 3166-2), home_country, home_country_code (ISO 3166-1)
• Signal quality: confidence, home_cluster_size, travel_outliers, located_reviews, total_reviews, distinct_regions
• Footprint: footprint, the top regions the reviewer reviews in, each with a count and a share
• Geometry: centroid (mean lat/lon of the home cluster) and bounding_box (the full geographic spread)
• Reviewer profile for context: contributor_name, contributor_level, contributor_local_guide, contributor_points, contributor_contributions

How it works

Every Google Maps review carries the coordinates of the place reviewed. This API reverse-geocodes those coordinates offline against a bundled GeoNames dataset, so the region output is standardized and language-independent (it does not depend on how the address happens to be written). The densest cluster of a reviewer's reviews is a strong proxy for where they are based; the scattered tail is travel. Deeper history sharpens the signal, so raise maxResultsPerContributor when a reviewer's recent activity skews toward travel.

Use cases

• Vet a reviewer before trusting their reviews: does their footprint look like a real local, or a scattered pattern typical of paid reviews?
• Reputation research: profile an expert witness, public figure, or vendor by the region their review history clusters in
• Review-fraud detection: flag accounts whose reviews are spread implausibly wide or concentrated on a single distant target
• Compare a batch of reviewers on one place to see whether they share an improbable geographic pattern
• Feed an AI agent a reviewer's geo profile to summarize whether their reviews are locally coherent

Input

Field	Type	Description
contributorId	string	A single Google Maps contributor ID (the long numeric ID from a reviewer's profile). Provide this, contributorIds, or both.
contributorIds	array of strings	A batch of contributor IDs to profile in one run. Merged with contributorId and de-duplicated.
regionGranularity	string	Level the home-region guess is computed at: city (default), admin1 (state/province), or country.
hl	string	Optional two-letter language code for the source reviews. Default en.
maxResultsPerContributor	integer	Reviews to analyze per contributor, most recent first. Default 100, maximum 200.

You can read a contributor ID from any place review's reviewer.

Choosing a granularity

• city gives the sharpest answer (e.g. "Chicago, IL") and is the default.
• admin1 rolls up to state/province, which is steadier for reviewers who move around a metro area.
• country is best for spotting cross-border patterns.

The row always includes the full standardized breakdown (city, admin, country, ISO codes) no matter which granularity drives the headline home_region_guess.

Example input

{
  "contributorId": "107022004965696773221",
  "regionGranularity": "city",
  "maxResultsPerContributor": 100
}

Sample output

{
  "result_type": "reviewer_geo_profile",
  "contributor_id": "107022004965696773221",
  "contributor_name": "Matt Moeini",
  "contributor_level": 5,
  "contributor_local_guide": true,
  "home_region_guess": "Chicago, IL",
  "home_city": "Chicago",
  "home_admin": "Illinois",
  "home_admin_code": "US-IL",
  "home_country": "United States",
  "home_country_code": "US",
  "confidence": 0.72,
  "home_cluster_size": 23,
  "travel_outliers": 9,
  "located_reviews": 32,
  "total_reviews": 32,
  "distinct_regions": 6,
  "region_granularity": "city",
  "footprint": [
    { "region": "Chicago, IL", "count": 23, "share": 0.7188 },
    { "region": "Miami, FL", "count": 3, "share": 0.0938 }
  ],
  "centroid": { "latitude": 41.9354, "longitude": -87.6443 },
  "bounding_box": { "min_latitude": 25.7617, "min_longitude": -87.6847, "max_latitude": 41.9784, "max_longitude": -80.1918 }
}

Pricing

Pay-per-event: a small actor_start fee plus one charge per contributor analyzed. You pay once per reviewer, regardless of how deep the history goes, so pulling a full 200-review history to sharpen the signal costs the same as a shallow run.

Plan	Per contributor analyzed	Start fee
Free	$0.02	$0.001
Bronze	$0.015	$0.001

How to get started

1. Open Google Maps Reviewer Geo Profile on the Apify Store.
2. Enter a contributorId (or a contributorIds list).
3. Run the Actor and read the derived home-region profile from the dataset.
4. Export as JSON, CSV, or Excel, or pull it from the API.

Prefer code? See johnvc's GitHub for setup guides and code examples.

Run from the API

curl -X POST "https://api.apify.com/v2/acts/johnvc~google-maps-reviewer-geo-profile-api/run-sync-get-dataset-items?token=YOUR_APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"contributorId":"107022004965696773221","regionGranularity":"city"}'

🔌 Use this API from Claude (MCP)

This Actor is compatible with the Model Context Protocol (MCP), so AI agents can call it as a tool. Add it through the hosted Apify MCP server using this Actor-specific URL:

https://mcp.apify.com/?tools=actors,docs,johnvc/google-maps-reviewer-geo-profile-api

If you run agents from Claude Code (free trial) or Claude Cowork (free trial), add the Apify MCP server and ask it to "profile this reviewer's home region and tell me whether their footprint looks locally coherent."

Apify MCP integration docs: https://docs.apify.com/platform/integrations/mcp

Data and attribution

Region names, ISO codes, and coordinates are resolved offline using data from GeoNames, licensed under CC BY 4.0.

FAQ

What is a contributor ID? It is the long numeric ID that identifies a Google Maps reviewer. You can read it from any place review's reviewer profile, then pass it here.

Can this locate a specific individual? No. It reports an aggregate, region-level home area derived from where a reviewer's public reviews cluster. It does not resolve a home address or track a person, and it stops at city/region granularity by design. Use it for reviewer vetting and reputation or fraud research, not to identify where someone lives.

How accurate is the home region? It reflects where the reviewer's reviews cluster, which is a strong proxy for a home base but not a guarantee. The confidence score tells you how concentrated the cluster is, and located_reviews tells you how much data it is based on. Deeper history (raise maxResultsPerContributor) improves it.

What if a reviewer travels a lot? The travel_outliers count and the footprint show the spread. A low confidence with many distinct regions is exactly the signal that a reviewer roams (or that the account is not a genuine local).

Can I profile many reviewers at once? Yes. Pass a contributorIds list; each reviewer is analyzed independently and returned as its own row.

Why standardized ISO codes? So you can group or filter reviewers by country (home_country_code) or state/province (home_admin_code) without parsing free-text addresses that vary by language.

Featured Tasks

Ready-to-run examples that show this API solving a specific problem. Each opens its own setup so you can run it on your account in one click.

• Detect Out-of-Town Google Reviewers
• Profile a Google Reviewer's Location via MCP
• Verify a Google Reviewer Is a Real Local
• Find a Google Reviewer's Home Country
• Spot Geographically Inconsistent Reviewers
• Bulk-Analyze Google Reviewers' Home Regions

Также на русском (Russian)

• Проверка иногородних авторов отзывов Google Maps: API
• Гео-профиль автора отзывов Google Maps через MCP
• Проверка автора отзывов Google: местный или нет
• Страна и город автора отзывов Google Maps по API
• Выявление фейковых отзывов: география авторов на картах
• API: массовый анализ регионов авторов отзывов Google Карт

中文版 (Chinese)

• 识别异地Google Maps评论者：常驻地区+置信度反刷评
• 让AI通过MCP推断谷歌地图评论者的家乡地区与出行足迹
• 谷歌地图评论者本地身份核验：置信度评分与足迹分析
• 谷歌地图评论者所在国家与城市识别｜ISO代码地区归属推断API
• 谷歌地图虚假评论检测API：识别地理位置异常的评论者
• 批量分析谷歌地图评论者常住地区与出行足迹｜信誉与虚假评论调查API

n8n integration

Available as an n8n community node, n8n-nodes-google-maps-reviewer-geo-profile-api. In n8n: Settings, Community Nodes, install n8n-nodes-google-maps-reviewer-geo-profile-api, then use it in any workflow (it also works as an AI Agent tool).

Last Updated: 2026.07.08