Google Maps Business Scraper — Country-Scale

What it does

Google caps any single Maps query at about 200 results, so "all the restaurants in Miami" is not one query — it is a tiling problem. This Actor splits the map into tiles and splits again wherever a tile comes back full, until every tile returns under the cap. Then it tells you honestly whether it finished.

{
  "name": "Panadería La Mérida",
  "address": "Av. 4 Bolívar, Mérida 5101, Venezuela",
  "latitude": 8.5897,
  "longitude": -71.1561,
  "place_id": "ChIJXxxxxxxxxxxxxxxxxxxxxxx",
  "feature_id": "0x8e7xxxxxxxxxxx:0x9axxxxxxxxxxxxx",
  "phone_e164": "+582742525252",
  "website": null,
  "rating": 4.4,
  "reviews_count": null,
  "categories": ["Panadería", "Cafetería"],
  "opening_hours": { "mon": "06:00-19:00", "tue": "06:00-19:00" },
  "language": "es",
  "from_cache": false,
  "data_age_hours": 0
}

Why this one

Use cases

• Build a complete business census of a city or a country for a vertical, rather than the first page of results.
• Price an area before extracting it, using the census pass alone.
• Generate lead lists with phone and website, knowing the fill rate for that market before you buy.
• Join Google Maps data to an existing Places API dataset on `place_id` without a matching step.
• Measure how a category is distributed geographically, using coordinates present on 100% of rows.

Input

Every field has a default, and the defaults are deliberately small so a first run is cheap enough to inspect before you commit to a sweep. This table mirrors the Actor's own input schema field for field.

Output and fill rates

A field being in the schema is not the same as it having a value. The percentages below were counted on real runs; the sample sizes are in Measurements. Anything not listed here is not promised.

Every key is always present. A field that exists but is empty comes back as explicit null, so a parser never has to guess.

Datasets

Different record types go to different datasets, so the main table never carries columns that are blank on most rows.

Pricing

Pay per delivered result. Charges are applied as each row is produced rather than in a lump at the end, so an aborted run bills only for what it actually gave you.

Measurements

Each figure is shown with the method that produced it. A benchmark without a method is a marketing claim wearing a number's clothes.

What it will not do

Stated plainly so you can judge fit before spending anything.

• `reviews_count` is present on 8.5% of rows. If you need it on every row, this is not the tool.
• No review text, ever. It is writing by identifiable people and it is out of scope.
• No email addresses. Google Maps does not publish them — zero email strings in 498 records across three verticals, and none on the place page either.
• No postal code field. It is embedded in `address` and its format varies by region, so parsing it out would be guesswork.
• Individual place pages are not fetched: phone, hours and website already arrive in the search response, and fetching each place would multiply the cost roughly 200-fold for nothing.
• It refuses to run on Spain, Portugal, France, Germany, Italy, the United Kingdom, Brazil and Cuba.
• A whole country does not fit in one run because of the run time limit. Split it into batches with a `requestBudget` and a `bbox` each; batches are independent and resumable.
• The language comes from the country and sets the language of `categories` and `opening_hours`, so two runs of the same area in different languages produce different rows.

Privacy

• Aggregating name, phone and coordinates for hundreds of thousands of small businesses produces a personal-data file, which is why eight countries are refused in code rather than merely flagged.
• Whoever runs the Actor is the data controller for the output.
• No review text and no reviewer identities: that is writing by identifiable people and it is out of scope.
• No email addresses are returned, because Google Maps does not publish them.
• Place names are kept exactly as Google publishes them, in their own alphabet and with their own accents. Mérida is not Merida.
• Removal requests: privacy@actorstack.dev

See also the data removal process.

Guides for this Actor

• Scrape Google MapsA walkthrough of the two-pass approach: classify the area first so you know what it costs, then extract only the tiles that need it.
• The 200-result capThe cap is per query, not per page, so the answer is geographic rather than paginated. What saturation means, why the threshold is 190, and how deep the splitting goes.
• The census passThe first pass extracts no businesses at all. It tells you which parts of an area are dense, which belong to a neighbouring country and which are empty — before you spend anything.
• Coverage is the marketPhone, hours, rating and website vary enormously by city and by vertical. Two independent samples, 16,725 businesses, and why an average would be the wrong thing to publish.
• The email questionTools advertising emails with Google Maps data are getting them somewhere else. Zero email strings across three verticals, in the search response and on the place page.
• Joining on place_idTwo identifiers ship on every row and they do different jobs: one deduplicates within and across runs, the other joins to anything you already have from Google.
• API versus scrapingGoogle's own API is the right tool for looking up a place. It is a different proposition when the question is every business in a region, and the difference is structural rather than about price alone.
• Reading the run summaryFour fields in the run summary decide whether your data covers the area you asked for. What each one means and which combinations should stop a pipeline.
• Field referenceEvery field the Actor returns, which are always present, which depend entirely on the market, and the four that are deliberately absent.
• The refused countriesAggregating name, phone and coordinates for hundreds of thousands of small businesses produces a personal-data file. Where that leads under the GDPR, UK GDPR and the LGPD.