Google Maps Business Scraper Extract business data from Google Maps at scale. Get names, addresses, phone numbers, websites, ratings, reviews, opening hours, popular times, photos, and 40+ data points per listing. ## What Data Can You Extract? ### Business Information - Business name, category, subcategories - Full address with street, city, state, postal code, country - Phone number and website URL - Google Place ID and Maps URL ### Ratings & Reviews - Average rating (1-5 stars) - Total review count - Individual reviews with text, rating, date, author - Owner responses to reviews ### Location Data - Latitude and longitude coordinates - Plus Code (Google's location code) - "Located in" parent location (e.g., mall, airport) ### Operational Details - Opening hours (daily schedule) - Popular times (hourly occupancy by day) - Temporarily/permanently closed status - Price level ($, $$, $$$, $$$$) ### Rich Content - Photo URLs (configurable limit) - Menu URL - Order/delivery links (DoorDash, UberEats, etc.) - "People also search" related businesses ### Search Context - Search query that found the business - Rank in search results - Sponsored/advertisement indicator ## Quick Start ### Basic Search json { "searchStringsArray": ["restaurants"], "location": "New York, NY", "maxResultsPerSearch": 100 } ### Search by Coordinates json { "searchStringsArray": ["coffee shops"], "lat": 40.7128, "lng": -74.0060, "zoom": 15, "maxResultsPerSearch": 50 } ### Large Area with Grid Search json { "searchStringsArray": ["hotels"], "customGeolocation": { "type": "Polygon", "coordinates": [[[-122.5, 37.7], [-122.3, 37.7], [-122.3, 37.85], [-122.5, 37.85], [-122.5, 37.7]]] }, "zoom": 16, "maxResultsPerSearch": 200 } ### With Reviews json { "searchStringsArray": ["dentists"], "location": "Chicago, IL", "maxResultsPerSearch": 50, "maxReviews": 20, "reviewsSort": "newest" } ### Direct Place ID Lookup json { "searchStringsArray": ["place_id:ChIJN1t_tDeuEmsRUsoyG83frY4"] } ## Input Parameters ### Search Options | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | searchStringsArray | string[] | ["restaurants"] | Search queries or Place IDs (place_id:ChIJ...) | | startUrls | object[] | [] | Direct Google Maps URLs to scrape | | location | string | "New York, NY" | Location text appended to search | ### Geographic Targeting | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | lat | number | - | Latitude for coordinate search (-90 to 90) | | lng | number | - | Longitude for coordinate search (-180 to 180) | | customGeolocation | object | - | GeoJSON Polygon/MultiPolygon for grid search | | zoom | integer | 14 | Map zoom level (1-21). Higher = smaller grid cells | | country | string | - | Country filter | | state | string | - | State/region filter | | city | string | - | City filter | | postalCode | string | - | Postal/ZIP code filter | ### Extraction Limits | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | maxResultsPerSearch | integer | 5 | Max results per search query (up to 10,000) | | maxReviews | integer | 0 | Max reviews per business (0 = skip reviews) | | maxImages | integer | 10 | Max photo URLs per business | ### Output Options | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | reviewsSort | string | "relevant" | Sort reviews: relevant, newest, highest, lowest | | skipClosedPlaces | boolean | false | Skip closed businesses | | oneReviewPerRow | boolean | false | Output one row per review (for CSV) | | language | string | "en" | Language code for results | ### Performance | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | maxConcurrency | integer | 5 | Parallel browser pages (1-20) | | proxyConfiguration | object | - | Apify proxy settings | ### Resume Options | Parameter | Type | Description | |-----------|------|-------------| | resumeRunId | string | Run ID to resume from (loads previously scraped Place IDs) | ## Output Schema Each business is saved with the following structure: json { "placeId": "ChIJ49sQuK-AhYARM6U646SA12Y", "title": "Blue Bottle Coffee", "url": "https://www.google.com/maps/place/...", "categoryName": "Coffee shop", "address": "315 Linden St, San Francisco, CA 94102", "street": "315 Linden St", "city": "San Francisco", "state": "CA", "postalCode": "94102", "countryCode": "US", "phone": "+1 510-653-3394", "website": "https://bluebottlecoffee.com", "totalScore": 4.5, "reviewsCount": 1842, "priceLevel": "$$", "location": { "lat": 37.7763, "lng": -122.4158 }, "plusCode": "QHFJ+8R Hayes Valley, San Francisco, CA", "openingHours": [ { "day": "Monday", "hours": "7 AM - 6 PM" }, { "day": "Tuesday", "hours": "7 AM - 6 PM" } ], "popularTimes": [ { "day": "Monday", "hours": [ { "hour": 7, "occupancyPercent": 20 }, { "hour": 8, "occupancyPercent": 45 }, { "hour": 9, "occupancyPercent": 65 } ] } ], "imageUrls": ["https://lh3.googleusercontent.com/..."], "peopleAlsoSearch": [ { "title": "Sightglass Coffee", "placeId": "ChIJ...", "url": "..." } ], "menuUrl": "https://bluebottlecoffee.com/menu", "orderLinks": [ { "platform": "DoorDash", "url": "https://..." } ], "temporarilyClosed": false, "permanentlyClosed": false, "searchString": "coffee shops", "rank": 3, "isAdvertisement": false, "scrapedAt": "2025-12-06T10:30:00.000Z", "reviews": [ { "name": "John D.", "text": "Great coffee and atmosphere!", "rating": 5, "date": "2 weeks ago", "responseFromOwner": "Thanks for visiting!" } ] } ## Scaling to Millions of Records Google Maps limits search results to ~120-400 per query. To scrape millions of businesses: ### Strategy 1: Grid Search (Recommended) Use customGeolocation to define a polygon. The scraper auto-generates grid points based on zoom level: | Zoom | Grid Cell Size | Example: California | |------|----------------|---------------------| | 14 | ~6 km | ~2,500 cells | | 15 | ~3 km | ~10,000 cells | | 16 | ~1.5 km | ~40,000 cells | Each cell runs a separate search, so: - 10,000 cells × 200 results/cell = 2 million potential results json { "searchStringsArray": ["restaurants"], "customGeolocation": { "type": "Polygon", "coordinates": [[[...california boundary...]]] }, "zoom": 15, "maxResultsPerSearch": 200 } ### Strategy 2: Multiple Search Terms Different queries return different businesses: json { "searchStringsArray": [ "restaurants", "cafes", "bars", "hotels", "gyms", "dentists", "plumbers", "lawyers" ], "location": "Los Angeles, CA" } ### Strategy 3: Neighborhood Batching Run separate Actor calls per area: bash # Run 1: Downtown {"searchStringsArray": ["restaurants"], "location": "Downtown, Los Angeles"} # Run 2: Hollywood {"searchStringsArray": ["restaurants"], "location": "Hollywood, Los Angeles"} ## Resume & Recovery ### Automatic Protection - Deduplication: Place IDs saved to persistent storage - Incremental saves: Results pushed immediately (never lost) - Migration handling: State saved when Apify migrates servers ### Manual Resume If a run crashes, start a new run with resumeRunId: json { "searchStringsArray": ["restaurants"], "location": "San Francisco, CA", "resumeRunId": "abc123xyz" } The scraper loads Place IDs from the previous run and skips already-scraped businesses. ## Anti-Detection Features Built-in protections for reliable scraping: - Browser fingerprint rotation - Webdriver detection masking - Human-like scrolling with random delays - Rate limiting (30 requests/minute) - CAPTCHA detection with exponential backoff - Automatic retry with session rotation Tip: For high-volume scraping, use residential proxies via proxyConfiguration. ## Performance | Metric | Typical Value | |--------|---------------| | Speed | 800-1,200 listings/hour | | Memory | < 1 GB | | Concurrency | 1-20 parallel pages | | Retry attempts | 3 per failed request | ## Export Formats Download from Apify Dataset: - JSON - Full nested structure - CSV - Flattened for spreadsheets - Excel - .xlsx format - XML - For integrations ## Use Cases - Lead Generation: Build prospect lists with contact info - Market Research: Analyze competitors, pricing, ratings - Local SEO: Track rankings and reviews - Real Estate: Map amenities near properties - Investment: Assess retail density and competition - Data Enrichment: Add location data to existing records