Google Maps Scraper

Google Maps Scraper (Apify Actor)

Fast, reliable Google Maps scraping on Apify — collect business listings, place details, reviews, photos, addresses, and coordinates with a simple form. Ideal for lead generation, local SEO, data enrichment, and market research. 🚀

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Table of Contents

• Features
• How it works
• Input parameters
• Example inputs
• Output
• Best practices
• Troubleshooting
• FAQ
• Keywords

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Features

Capability	Description
🔎 Search maps	Keyword search with optional geo context
📍 Nearby search	Find places around coordinates
🏷️ Place details	Enrich by business_id or place_id
⭐ Reviews	Paginated reviews with sorting
🖼️ Photos	Photo URLs for a place
📫 Geocoding	Address ➜ coordinates
🧭 What is here	Reverse lookup by coordinates

How it works

1. Fill the input form in Apify.
2. Run the actor for your selected mode.
3. Results are saved to the run’s default dataset. Export as JSON/CSV/Excel. ✅

Input parameters

Field	Type	Required	Modes	Description	Default
mode	string	yes	all	Select: nearby, searchmaps, place, geocoding, reviews, whatishere, photos, review	searchmaps
query	string	when applicable	nearby, searchmaps, geocoding	Keyword or address	cafe
lat	string	nearby/whatishere (req), searchmaps (opt)	nearby, searchmaps, whatishere	Latitude	48.8577848
lng	string	nearby/whatishere (req), searchmaps (opt)	nearby, searchmaps, whatishere	Longitude	2.2888379
limit	integer	no	where supported	Max results	20
offset	integer	no	where supported	Pagination offset	0
zoom	integer	no	nearby, searchmaps	Map zoom level	12
country	string	no	all	ISO country code	us
lang	string	no	all	Language code	en
business_id	string	place/reviews/photos	place, reviews, photos	Business identifier
place_id	string	place	place	Google Place ID
cursor	string	reviews (opt)	reviews	Cursor for review pagination
sort	string	reviews (opt)	reviews	Relevant, Newest, HighestRating, LowestRating	Relevant
review_id	string	review	review	Specific review ID

Tips:

• 📌 Use geocoding to turn addresses into coordinates, then run nearby around them.
• 🧠 Store business_id/place_id to re-enrich later without duplicates.
• 🌐 Set country and lang for localized output.

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Example inputs

Nearby places

{
  "mode": "nearby",
  "query": "cafe",
  "lat": 48.8577848,
  "lng": 2.2888379,
  "limit": 3,
  "country": "us",
  "lang": "en",
  "offset": 0,
  "zoom": 12
}

Search maps

{
  "mode": "searchmaps",
  "query": "restaurant",
  "limit": 2,
  "country": "us",
  "lang": "en",
  "lat": 51.5072,
  "lng": 0.12,
  "offset": 0,
  "zoom": 13
}

Place details

{
  "mode": "place",
  "business_id": "0x47f4eb87e91f866d:0x9629fabb993eb66",
  "country": "fr",
  "lang": "en",
  "place_id": "ChIJk_grnPDq9EcRE7gOH9gAPZA"
}

Geocoding

{
  "mode": "geocoding",
  "query": "Navigation Square",
  "lang": "en",
  "country": "fr"
}

Reviews

{
  "mode": "reviews",
  "business_id": "0x47f4ea8ed352d3c5:0x7bb3bc00dfc039a9",
  "country": "us",
  "lang": "en",
  "limit": 20,
  "cursor": "id here",
  "sort": "Relevant"
}

What is here

{
  "mode": "whatishere",
  "lat": 51.8977449,
  "lng": -8.4620383,
  "lang": "en",
  "country": "us"
}

Photos by business

{
  "mode": "photos",
  "business_id": "0x47e66e2964e34e2d:0x8ddca9ee380ef7e0",
  "lang": "en",
  "country": "us"
}

Single review by ID

{
  "mode": "review",
  "review_id": "ChdDSUhNMG9nS0VJLVlfZFhBMzl2enVBRRAB"
}

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Output format

Each run saves a single dataset item with:

• success (boolean)
• mode (string)
• params (object)
• data (raw response)
• fetched_at (ISO datetime)

On errors, success: false plus status and error are recorded. 💡

By mode at-a-glance

Mode	data shape	Key fields
nearby, searchmaps	Array of places	business_id, name, full_address, latitude, longitude, rating, review_count, types, price_level, photos[], working_hours
place, photos	Single place object	Same as above + optional website_full, is_claimed, verified, photos[]
geocoding	Object	address, lat, lng, timezone
whatishere	Object	address, place_id, coordinates{lat,lng}, timezone, town, country_name, places[]
reviews	Object	Business metadata + photos[], and optionally reviews[] (when included)
review	Object with reviews[]	Array items include review_id, user_name, review_text, review_rate, timestamps, links

Example: Nearby/SearchMaps result (trimmed)

{
  "success": true,
  "mode": "nearby",
  "params": {
    "query": "cafe",
    "lat": 48.8577848,
    "lng": 2.2888379,
    "limit": 3,
    "country": "us",
    "lang": "en",
    "offset": 0,
    "zoom": 12
  },
  "data": [
    {
      "business_id": "0x47e66f0f16582607:0xd264c78df0c9ba38",
      "name": "Mood Coffee Shop",
      "full_address": "Mood Coffee Shop, 66 Av. Kléber, 75116 Paris, France",
      "latitude": 48.8681057,
      "longitude": 2.2905479,
      "review_count": 70,
      "rating": 4.9,
      "timezone": "Europe/Paris",
      "website": null,
      "place_id": "ChIJByZYFg9v5kcROLrJ8I3HZNI",
      "types": ["Coffee shop"],
      "price_level": null,
      "working_hours": {"Friday": ["8:30 AM–5:30 PM"]},
      "city": "Paris, France",
      "photos": [
        {"src": "https://lh3.googleusercontent.com/p/AF1Qip...=w86-h114-k-no", "max_size": [3024,4032], "min_size": [86,86] }
      ],
      "state": "Open ⋅ Closes 5:30 PM",
      "description": []
    }
  ],
  "fetched_at": "2025-10-10T00:00:00.000Z"
}

Example: Geocoding result

{
  "success": true,
  "mode": "geocoding",
  "params": {"query": "Navigation Square", "lang": "en", "country": "fr"},
  "data": {
    "address": "Navigation Square, Ballintemple, Cork, Ireland",
    "lat": 51.8977449,
    "lng": -8.4620383,
    "timezone": "Europe/Dublin"
  },
  "fetched_at": "2025-10-10T00:00:00.000Z"
}

Example: Single review by ID

{
  "success": true,
  "mode": "review",
  "params": {"review_id": "ChdDSUhNMG9nS0VJLVlfZFhBMzl2enVBRRAB"},
  "data": {
    "reviews": [
      {
        "review_id": "ChdDSUhNMG9nS0VJLVlfZFhBMzl2enVBRRAB",
        "user_name": "Andrea Page",
        "review_rate": 5,
        "review_text": "I had a fantastic experience...",
        "user_avatar": "https://lh3.googleusercontent.com/...",
        "user_link": "https://www.google.com/maps/contrib/102773189...",
        "iso_date": "2025-05-29 00:25:37",
        "review_link": "https://www.google.com/maps/reviews/data=!4m8!..."
      }
    ]
  },
  "fetched_at": "2025-10-10T00:00:00.000Z"
}

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Best practices

• 🎯 Targeting: Combine lat, lng, zoom for precise, local results.
• 🔁 Pagination: Use limit + offset (where supported) to page results.
• 🌐 Localization: Set lang + country for consistent language/region.
• 🧩 Idempotence: Save business_id/place_id to re-enrich later.
• ⏱️ Scheduling: Automate recurring runs and export datasets for your pipeline.

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Troubleshooting

• ❌ No results: Check query, lat, lng, zoom. Try broader keywords.
• ⚠️ Validation errors: Ensure required fields for the chosen mode are set.
• 🕳️ Missing fields: Some places do not expose phone/website; that’s expected.

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FAQ

• What data can I get? Names, categories, ratings, addresses, coordinates, plus reviews and photos when available. 📦
• Do I need to code? No. Configure the input form and run the actor. 🧰
• How do I export results? Open the run’s default dataset in Apify and export JSON/CSV/Excel. 📤
• Which identifiers should I keep? Store business_id and/or place_id for future enrichment. 🏷️
• Can I fetch a single review? Yes — use mode: "review" with review_id. 🎯

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Find Me better

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