Scraping data from Google Places API

Google Maps Platform contains large amount of POI (Point Of Interest) data that one might want to scrape for purposes such as lead generation, real estate OSINT, academic research and so on. However Google Maps web/mobile apps are highly complex pieces of software and may prove difficult to reverse engineer for data extraction. Once could be doing some browser automation for scraping, but the very same POI data can be extracted through scraping the official Google Places API that is free to use at small scale (Google provides some free monthly credits) and is billed at pay-as-you-go model at non-trivial volumes. We will go through some basic examples of how this data can be scraped with Python. If you retain the data for your own purposes this still violates the platform terms, but it’s pretty much the least shady and most convenient way to extract some of the data from Google Maps Platform as it relies on the official API.

Let us review what we have here to work with:

  • Place Search functionality is implemented via three endpoints:
    • Find Place (/api/place/findplacefromtext/) to search by place name, address, POI category or phone number. Fuzzy matching is applied when searching.
    • Nearby Search (/api/place/nearbysearch) to search for places around given coordinates with POI filtering criteria being applied. Can be used to autocomplete search queries in mobile or web apps.
    • Text Search (/api/place/textsearch) to search for places by arbitrary search queries. Can be given location and radius to bias (but not filter) search results.
  • Place Details endpoint (/api/place/details/) takes place_id value from Place Search result and provides more details about the POI including reviews, opening hours, photos and so on. One can apply field masks to skip on some fields to improve cost and performance.
  • Place Photos endpoint (/api/place/photo) takes a photo_reference from Place Search or Place Details response and gives you a photo rendered to given size preference.

In addition to that, there’s two helper endpoints to assist searches via autocompletion.

At the time of writing Google has launched a set of next generation APIs that you may need to enable via Google Cloud console and switch to if the old API gets deprecated.

For a complete list of data fields one can get, see Place Data Fields page. Note that business emails are not provided via Places API, but phone numbers and website URLs are. If API scraping is done for lead generation purposes, the gap could be bridged by doing opportunistic scraping of company websites or by performing data enrichment via services like Hunter or PDL.

Place Search endpoints have one limitation that must be noted and addressed. One search query can return at most 60 results (3 pages, 20 results each). That may pose a problem if we want to collect non-trivial amounts of data. The solution to this is to compute a grid of locations and run many search queries to cover the territory.

But first we need to get the API key from Google. To proceed, you need a Google Cloud account with billing setup completed. Follow the instructions on these two Google Developer portal pages:

  1. Set up your Google Cloud project
  2. Use API Keys with Places API

Now we have the API key. We can verify that everything works by doing a single request with curl:

$ curl -s ",name,international_phone_number&key=[REDACTED]" | jq
  "html_attributions": [],
  "result": {
    "international_phone_number": "+1 650-253-0000",
    "name": "Googleplex"
  "status": "OK"

We will use the official Google Maps Python module to benefit from the Pythonic API wrapper it provides. Furthermore, we will use the haversine module to help us compute a grid (since Earth is not flat the geospatial software community uses Haversine formula that this module implements for computing distances between points on the globe).

The Python script is as follows:


import csv
import configparser
import math
import os
from pprint import pprint

from haversine import haversine
import googlemaps

FIELDNAMES = ["name", "phone", "address", "website", "latitude", "longitude"]

def make_grid(gmaps_client, grid_element_side, location):
    # Search for location and get bounding box.

    resp = gmaps_client.find_place(location, "textquery", language="en")
    if resp.get("candidates") is None or len(resp.get("candidates")) == 0:
        print("Error: Location not found!")

    place_id = resp["candidates"][0]["place_id"]

    resp2 =, language="en")

    viewport = resp2.get("result", dict()).get("geometry", dict()).get("viewport")
    if viewport is None:
        print("Error: viewport not found!")

    from_longitude = viewport.get("southwest").get("lng")
    from_latitude = viewport.get("southwest").get("lat")
    to_longitude = viewport.get("northeast").get("lng")
    to_latitude = viewport.get("northeast").get("lat")

    print("Longitude range: {} to {}".format(from_longitude, to_longitude))
    print("Latitude range: {} to {}".format(from_latitude, to_latitude))

    horiz_len_km = haversine(
        (from_latitude, from_longitude), (from_latitude, to_longitude)
    vert_len_km = haversine(
        (from_latitude, from_longitude), (to_latitude, to_longitude)

    n_horiz = math.ceil(horiz_len_km / grid_element_side)
    n_vert = math.ceil(vert_len_km / grid_element_side)

    grid_elements = []

    latitude_step = (to_latitude - from_latitude) / n_vert
    longitude_step = (to_longitude - from_longitude) / n_horiz

    for i in range(n_horiz):
        for j in range(n_vert):
            south_lat = from_latitude + j * latitude_step

            north_lat = south_lat + latitude_step
            north_lat = min(north_lat, to_latitude)

            west_lng = from_longitude + i * longitude_step
            east_lng = west_lng + longitude_step
            east_lng = min(east_lng, to_longitude)

                "rectangle:{},{}|{},{}".format(south_lat, west_lng, north_lat, east_lng)


    return grid_elements, from_latitude, to_latitude, from_longitude, to_longitude

def main():

    config = configparser.ConfigParser()"gplaces.ini")

    google_api_key = config["Google"]["APIKey"]
    grid_element_side = config["Google"]["GridElementSideKM"]
    grid_element_side = float(grid_element_side)

    out_f = open("gplaces.csv", "w", encoding="utf-8")

    gmaps_client = googlemaps.Client(key=google_api_key)

    csv_writer = csv.DictWriter(out_f, fieldnames=FIELDNAMES, lineterminator="\n")

    location = input("Location: ")
    query = input("Search for: ")

    grid_elements, from_latitude, to_latitude, from_longitude, to_longitude = make_grid(
        gmaps_client, grid_element_side, location

    seen_place_ids = set()

    # Iterate across each subregion and perform search with given query in that region
    for location_bias in grid_elements:
        resp = gmaps_client.find_place(
            fields=["place_id", "name", "geometry", "formatted_address"],
        # Iterate across search results and get contact info for each of them.
        for result in resp.get("candidates", []):
            place_id = result.get("place_id")
            if place_id in seen_place_ids:

            latitude = result.get("geometry").get("location").get("lat")
            longitude = result.get("geometry").get("location").get("lng")

            if latitude < from_latitude or latitude > to_latitude:

            if longitude < from_longitude or longitude > to_longitude:


            resp2 =
                fields=["international_phone_number", "website"],


            row = {
                "name": result.get("name"),
                "phone": resp2.get("result", dict()).get("international_phone_number"),
                "address": result.get("formatted_address"),
                "website": resp2.get("result", dict()).get("website"),
                "latitude": latitude,
                "longitude": longitude,



if __name__ == "__main__":

When launched, this script asks to things to be entered into standard input: location name and search query to be used with Text Search API endpoint. The API key is found in the gplaces.ini file that is parsed with a configparser module. Location name is used to find the overall territory and retrieve it’s bounding box in make_grid() function, then split the bounding box into list of grid elements by doing some basic geometric computations. The make_grid() function returns not only list of grid elements in a form that can be used for Place Search locationBias parameter, but also the boundaries of the overall bounding box for some sanity checks to be done later.

Once we have that, we can retrieve the POI data - one grid element at a time. To remove duplicates, we keep place_id value in seen_place_ids set and skip duplicate POIs. We also skip POIs with coordinates outside the bounding box (remember that Text Search endpoint supports biasing, but not filtering search results towards a given location). For each result that passes the filtering, we call the Place Details API (via .place() method of API client) to get two additional fields: international_phone_number and website. The resulting scraped data is written to a CSV file.

Trickster Dev

Code level discussion of web scraping, gray hat automation, growth hacking and bounty hunting

By rl1987, 2023-11-05