• Features
• Advantages over Google Maps API
• Input configuration
• Results
• Usage on Apify platform and locally
• How the search works
• Using country, state, county, city and postal code parameters
• Personal data
• Changelog
• Contributions

This Google Maps crawler will enable you to get more data from Google Places than the official Google Maps Places API. The scraper supports extraction of this data for each place:

• Title, subtitle, category, place ID and URL
• Address, location, plus code and exact coordinates
• Phone and website if available
• Temporarily or permanently closed status
• Popular times - histogram & live occupancy
• Average rating (totalScore), reviews count and reviews distribution
• List of images (optional)
• List of detailed characteristics (additionalInfo, optional)
• Opening hours (optional)
• People also search (optional)

The scraper also supports scraping of all reviews with detailed information:

• Review text
• Published date
• Stars
• Review ID & URL
• Response from owner - text and published date

Personal data extraction about reviewers has to be explicitly enabled in input (see Personal data section):

• Reviewer name
• Reviewer ID & URL
• Reviewer number of reviews
• Is local guide

The Google Maps Scraper also provides other very useful features:

• Geolocation - Enables scraping whole country, state, county, city or postal code (integration with Nomatim Maps API)
• Language & translation settings
• Reviews sorting
• Proxy configuration
• Browser & scraping configuration

The official Google Maps Places API is an adequate option for many use cases, but this scraper can provide:

• Unlimited results
• Popular place times histogram (no data for that in official API)
• All place reviews (only up to 5 reviews from official API)
• All place photos (only up to 10 photos from official API)

When running the Google Maps Scraper, you need to configure what you want to scrape and how it should be scraped. This input is provided either as a JSON file or in the editor on the Apify platform. Most input fields have reasonable default values.

Example input:

{
  "searchStringsArray": ["pubs near prague"],
  "lat": "50.0860729",
  "lng": "14.4135326",
  "zoom": 10
}

With this input, the actor searches places at this start URL: https://www.google.com/maps/search/pubs+near+prague/@50.0860729,14.4135326,10z

For detailed description and examples of all input fields please visit the dedicated Input page.

You can force the scraper to access places only from a specific country. We recommend this to ensure that you receive the correct language in the results. This only works reliably for the US (most of our proxies are from the US). Currently, this option is not available in the Editor input - you have switch to JSON input. After you switch, your configuration will remain the same, so just update the proxyconfig field with apifyProxyCountry property to specify the country, e.g.

"proxyConfig": {
    "useApifyProxy": true,
    "apifyProxyCountry": "US"
  }

The easiest way to use Google Maps scraper is to provide country, state, county, city or postalCode input parameters. But in rare cases your location might not be found or you want to customize it. In that case you can use manual polygon for the creation of start URLs. It should have the following GeoJSON structure from the Nominatim Api (here for the example of Cambridge in Great Britain)

The scraped data is stored in the dataset of each run. The data can be viewed or downloaded in many popular formats such as JSON, CSV, Excel, XML, RSS and HTML.

A single place result looks like this:

{
  "title": "The PUB Praha 2",
  "totalScore": 4,
  "categoryName": "Restaurant",
  "address": "Hálkova 6, 120 00 Nové Město, Czechia",
  "locatedIn": "Azalea Square",
  "plusCode": "3CGH+F8 New Town, Prague, Czechia",
  "website": "thepub.cz",
  "phone": "+420222940414",
  "temporarilyClosed": false,
  "permanentlyClosed": false,
  "rank": 1,
  "placeId": "ChIJXRQlXoyUC0cRq5R4OBRKKxU",
  "url": "https://www.google.com/maps/place/The+PUB+Praha+2/@50.0761791,14.4261789,17z/data=!3m1!4b1!4m5!3m4!1s0x470b948c5e25145d:0x152b4a14387894ab!8m2!3d50.0761791!4d14.4283676",
  "location": {
    "lat": 50.0761791,
    "lng": 14.4283676
  },
  "searchString": "pubs near prague 2",
  "popularTimesLiveText": "25% busy at .; Not too busy",
  "popularTimesLivePercent": 25,
  "popularTimesHistogram": {
    "Su": [],
    "Mo": [
      {
        "hour": 6,
        "occupancyPercent": 0
      },
      {
        "hour": 7,
        "occupancyPercent": 0
      },
      {
        "hour": 8,
        "occupancyPercent": 0
      },
      {
        "hour": 9,
        "occupancyPercent": 0
      }
      // ... (shortened)
    ],
    // ... (shortened)
  },
  "openingHours": [
    {
      "day": "Monday",
      "hours": "11AM–2AM"
    },
    {
      "day": "Tuesday",
      "hours": "11AM–2AM"
    },
    {
      "day": "Wednesday",
      "hours": "11AM–2AM"
    },
    // ... (shortened)
  ],
  "peopleAlsoSearch": [],
  "reviewsCount": 698,
  "reviews": [
    {
      "name": "Robert Nalepa",
      "text": null,
      "publishAt": "a day ago",
      "likesCount": null,
      "stars": 4
    },
    {
      "name": "Martin Mudra",
      "text": null,
      "publishAt": "6 days ago",
      "likesCount": null,
      "stars": 4
    },
    // ... (shortened)
  ],
  "imageUrls": [
    "https://lh5.googleusercontent.com/p/AF1QipMQKrnbWNFed4bhBaMn_E1hf83ro3af1JT6BuPe=s508-k-no",
    "https://lh5.googleusercontent.com/p/AF1QipNVV1EkzaddM7UsE9bh0KgT5BFIRfvAwsRPVo0a=s516-k-no",
    "https://lh5.googleusercontent.com/p/AF1QipPDAjMIuulyFvHqTWCz_xeQhiDgretyMsHO6Rq_=s677-k-no",
    "https://lh5.googleusercontent.com/p/AF1QipOEsLwms2XreZ7_kzgH_As5SeTfS7jz32ctw5iY=s516-k-no",
    // ... (shortened)
  ],
  "additionalInfo": {
    "Service options": [
      {
        "Takeaway": true
      },
      {
        "Delivery": false
      }
    ],
    "Highlights": [
      {
        "Bar games": true
      },
      {
        "Karaoke": true
      },
      {
        "Live music": true
      },
      {
        "Outdoor seating": true
      }
    ],
    "Offerings": [
      {
        "Beer": true
      },
      {
        "Food": true
      },
      {
        "Vegetarian options": true
      },
      {
        "Wine": true
      }
    ],
    "Dining options": [
      {
        "Breakfast": true
      },
      {
        "Lunch": true
      },
      {
        "Dinner": true
      },
      {
        "Dessert": true
      },
      {
        "Seating": true
      }
    ],
    "Amenities": [
      {
        "Toilets": true
      }
    ],
    "Atmosphere": [
      {
        "Casual": true
      },
      {
        "Cosy": true
      }
    ],
    "Crowd": [
      {
        "Groups": true
      }
    ],
    "Planning": [
      {
        "LGBTQ-friendly": true
      }
    ]
  }
}

Apify platform allows you to choose from many dataset formats but also to restructure the output itself.

Normally, each result item contains data about a single place. Each item is displayed as one row in tabulated formats. There is a lot of data about each place so the tabulated formats gets very messy and hard to analyze. Fortunately, there is a remedy.

For example, if you need to analyze reviews, you can configure the download to only contain data you need and adjust the row/column format. This is how to get a list of reviews with a place title one review per row: Copy the download link in a format you need, paste it to a different tab and add &unwind=reviews&fields=reviews,title to the end of the link URL and then press Enter to download it. unwind=reviews means that each review will be on its own row. fields=reviews,title means that only reviews nad title will be downloaded, skipping the other data. Otherwise, the output would be very big but there is no problem if you don't use fields at all.

The whole download link for e.g. CSV would look like this (just with fullfilled dataset ID): https://api.apify.com/v2/datasets/DATASET_ID/items?clean=true&format=csv&attachment=true&unwind=reviews&fields=reviews,title

If you want to run the actor on the Apify platform, you need to have at least a few proxy IPs to avoid being blocked by Google. You can use your free Apify Proxy trial or you can subscribe to one of Apify's subscription plans.

We recommend that you run the actor with at least 8GB memory. On the Apify platform, 8GB memory gives you:

• 100 Google place details for 1-2 compute units
• 100 Google place details with images and reviews for 4-8 compute units - the usage really depends on the number of images and reviews for each place scraped.

You can easily run this scraper locally or on your favorite platform. It can run as a simple Node.js process or inside a Docker container.

It works exactly as though you were searching Google Maps on your computer. It opens https://www.google.com/maps/ and relocates to the specified location, then writes the search to the input. Then it presses the next page button until it reaches the final page or maxCrawledPlaces. It enqueues all the places as separate pages and then scrapes them. If you are unsure about anything, just try this process in your browser - the scraper does exactly the same.

There is one feature of Google Maps that is sometimes not desirable. As you progress to the next page, there might not be enough places of the type that you have searched for, e.g. restaurants in your city. Google will naturally zoom out and include places from a broader area. It will happily do this over a large area and might include places from far out that you are not interested in. There are three ways to solve this:

• Limit maxCrawledPlaces - This is the simplest option, but you usually don't know how many places there are, so it isn't that useful.
• Use the maxAutomaticZoomOut parameter to stop searching once Google zooms out too far. It counts how far it zoomed out from the first page. Keep in mind that zoom: 1 is the whole world and zoom: 21 is a tiny street. So you usually want maxAutomaticZoomOut to be between 0 and 5.
• Use country, state, county, city & postalCode parameters.

You can only use any combination of the geolocation parameters: country, state, county, city & postalCode. The scraper uses nominatim maps to find a location polygon and then splits that into multiple searches that cover the whole area. You should play around with the zoom number to find the ideal granularity for searches. Too small a zoom level will find only the most famous places over a large area, too big a zoom level will lead to overlapping places and will consume a huge number of CUs. We recommend a number between 10 and 15.

Reviews can contain personal data such as a name, profile image and even a review ID that could be used to track down the reviewer. Personal data is protected by GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers. This scraper allows you to granularly select which personal data fields you want to extract from reviews and which not.

This scraper is under active development. We are always implementing new features and fixing bugs. If you would like to see a new feature, please submit an issue on GitHub. Check CHANGELOG.md for a list of recent updates

We are very happy for any issues or pull requests created by community.

Special thanks to: mattiashtd zzbazza