Google Maps scraping is the process of extracting information such as business names, locations, and reviews from Google Maps using automated tools or scripts.
In this blog post, we will discuss the benefits of scraping Google Maps and walk through how to accomplish this using Python and automated tools.
What Are the Benefits of Scraping Google Maps?
Scraping Google Maps provides valuable business intelligence and location-based insights. Here are the key benefits:
✅ Extract Business Information – Gather essential details such as business name, address, phone number, website, ratings, reviews, and opening hours. This data is useful for lead generation, directory listings, and customer outreach.
✅ Conduct Market Analysis and Competitor Research – Analyze market trends, identify key competitors, and assess business density in specific locations. This helps businesses make informed decisions about expansion, pricing strategies, and customer engagement.
✅ Enhance Geographic Data Visualization and Optimization – Improve service coverage, optimize delivery routes, and plan store locations based on real-world business distributions. Companies can use this data to enhance operational efficiency and customer accessibility.
Challenges of Scraping Google Maps
Scraping Google Maps comes with significant challenges due to its strict anti-scraping mechanisms. Google imposes rate limits, CAPTCHAs, and API restrictions to prevent automated data extraction, making large-scale scraping difficult without encountering blocks.
Another challenge is the dynamic nature of Google Maps, which relies on JavaScript to load business information. Traditional scraping methods that rely on static HTML parsing are ineffective, requiring the use of headless browsers or automation frameworks to interact with the page and extract data.
To bypass these restrictions, scrapers must implement techniques such as proxy rotation and User-Agent spoofing. These methods help distribute requests across multiple IPs and simulate real user behavior, reducing the likelihood of detection and ensuring a more stable scraping process.
2 Special Methods to Scrape Google Maps
1. Google Maps Scraping API
Does Google provide Scraping API?
Yes.
Many popular websites, such as Twitter and Amazon, offer their own APIs, and Google is no exception. When Google Maps API was first launched, it quickly gained widespread attention and adoption. However, before using the official API, ask yourself: Do you really need to use Google Maps API?
Why does using the official Google Maps API not match your needs？
Let's start with pricing. Each user receives a $200 monthly free quota for Google Maps API, which includes:
- Up to 40,000 geocoding requests
- Up to 100,000 static map loads
- Up to 28,000 dynamic map loads
- Up to 40,000 route planning requests
At first glance, this quota seems sufficient, but in practice, it may not be enough. Like many other APIs, Google Maps API charges per request once the free quota is exceeded, and its pricing is relatively high. Consider this scenario: when using the Embed API, a single map load may trigger multiple API calls, such as address searches, route planning, or distance calculations, quickly consuming your quota. As your business scales, API calls increase, and Google Maps API can become a costly expense.
Beyond pricing, Google Maps API also has strict request limitations. Currently, Google enforces a rate limit of 100 requests per second, which means that in high-concurrency scenarios, access may be restricted, affecting data retrieval efficiency.
Can I still use an API to scrape Google Maps?
Absolutely! You just need an API service that is affordable, stable, and secure. However, finding one that meets all these criteria is incredibly challenging! Fortunately, Scrapeless Google Maps API stands out among many API products:
🔴 Cost-saving: Each API call costs as little as $0.80, and with a $49 subscription, you get a 10% discount!
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🔴 Stable and High Success Rate: Scrapeless guarantees a 99% success rate and reliability. The stability and accuracy of Google Trends scraping have reached nearly 100%! Currently, the average response time is around 3 seconds, significantly faster than most API providers. Moreover, data is returned in a standardized JSON format, making it ready for immediate use.
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2. Web Scraping
Web Scraper is one of the most common methods for website scraping. You can build your own Google Maps scraping tool to extract specific data. In this article, we will use Python Google Maps Scraper to scrape specific locations and routes from the map.
Keep scrolling still!
Method 1. Scrape with Google Maps API
Step 1. Obtain Your API Key
To get started, you’ll need to obtain your API Key from the Scrapeless Dashboard:
- Log in to the Scrapeless Dashboard.
- Navigate to API Key Management.
- Click Create to generate your unique API Key.
- Once created, simply click on the API Key to copy it.
Step 2: Input Your API Key
You can now use your API Key to integrate Scrapeless into your project. Follow these steps to test and implement the API:
- Visit the API Documentation.
- Click "Try it out" for the desired endpoint.
- Enter your API Key in the "Auth" field.
- Click "Send" to get the scraping response.
Below is a sample code snippet that you can directly integrate into your Google Maps Scraper.
Python
import http.client
import json
conn = http.client.HTTPSConnection("api.scrapeless.com")
payload = json.dumps({
"actor": "scraper.google.maps",
"input": {
"engine": "google_maps",
"q": "coffee",
"type": "search",
"ll": "@40.7455096,-74.0083012,14z",
"hl": "en",
"gl": "us"
}
})
headers = {
'Content-Type': 'application/json'
}
conn.request("POST", "/api/v1/scraper/request", payload, headers)
res = conn.getresponse()
data = res.read()
print(data.decode("utf-8"))
JavaScript
var myHeaders = new Headers();
myHeaders.append("Content-Type", "application/json");
var raw = JSON.stringify({
"actor": "scraper.google.maps",
"input": {
"engine": "google_maps",
"q": "coffee",
"type": "search",
"ll": "@40.7455096,-74.0083012,14z",
"hl": "en",
"gl": "us"
}
});
var requestOptions = {
method: 'POST',
headers: myHeaders,
body: raw,
redirect: 'follow'
};
fetch("https://api.scrapeless.com/api/v1/scraper/request", requestOptions)
.then(response => response.text())
.then(result => console.log(result))
.catch(error => console.log('error', error));
Method 2. Build Your First Google Maps Scraper with Python
Environment setup
First, we need to set up a web scraping environment by preparing the following tools:
-
Python: Download it from Python's official website. It is recommended to install a version one or two releases behind the latest, rather than the newest version.
-
Python IDE: Any Python-supported IDE will work, but we recommend PyCharm, a specialized Python development tool. The free PyCharm Community Edition is sufficient.
Note for Windows users:
During installation, ensure that the "Add python.exe to PATH" option is selected. This allows you to use Python commands in the terminal. Since Python 3.4 and later include this by default, no manual setup is required.
To check if Python is installed, open the terminal or command prompt and run:
python --version
- A virtual environment:Creating a virtual environment is recommended to manage dependencies and avoid conflicts with other Python projects. Navigate to your project directory in the terminal and execute the following command to create a virtual environment called "google_flights_env":
python -m venv google_flights_env
Activate the virtual environment using the appropriate command for your OS:
- Windows:
google_flights_env\Scripts\activate
- macOS/Linux:
source google_flights_env/bin/activate
-
Installing required libraries: After activating the virtual environment, please install the necessary Python libraries for web scraping:
requestsandBeautifulSoup4. Install them follow our command:
pip install requests
pip install beautifulsoup4
-
Installing automation tools: Since Google Maps does not return a direct HTML page for parsing, automation tools are needed to retrieve raw HTML.
- Install Selenium, a Python automation package:
pip install selenium
- You also need to download the browser driver. Taking the most commonly used Google Chrome as an example, find the ChromeDriver that matches the browser version on the official website, download and install it locally, and then place it in the code directory in PyCharm.
Using Python to scrape location or navigation route data
Scraping locations
Step 1: Open Google Maps and search for "coffee".
Step 2. Scrape specific fields from the Google Maps search results.
We can use BeautifulSoup to parse the HTML and locate the elements containing the desired information. Below are some examples:
- Scrape the coffee shop name:
Extract the element and use Python to scrape the text data:
def scrape_title(listing):
maps_element = listing.select_one('div.NrDZNb div.qBF1Pd.fonHeadlineSmall')
return maps_element.text.strip()
- Scrape the ratings:
Extract the element and use Python to scrape the text data. Since the rating might be empty, we’ve added a null check:
def scrape_rating(listing):
maps_element = listing.select_one('span.MW4etd[aria-hidden="true"]')
if maps_element is None:
return None
else:
return maps_element.text.strip()
- Scrape the image link:
Extract the href attribute of the element and use Python to scrape the image link:
def scrape_image_links(listing):
maps_image_link = listing.select_one('div.SpFAAb').select_one("img")
return maps_image_link['src']
The above is just a partial demonstration. You can refer to our final code below:
# Import necessary libraries
import time
from bs4 import BeautifulSoup
import json
from selenium import webdriver
driver = webdriver.Chrome()
# Function to scrape listing elements from Google Maps
def scrape_listings(soup):
return soup.select('div.Nv2PK.THOPZb.CpccDe')
# Function to scrape title from google_maps
def scrape_title(listing):
maps_element = listing.select_one('div.NrDZNb').select_one('div.qBF1Pd.fontHeadlineSmall')
return maps_element.text.strip()
# Function to scrape ratings from google_maps
def scrape_rating(listing):
maps_element = listing.select_one('span.MW4etd[aria-hidden="true"]')
if maps_element is None:
return None
else:
return maps_element.text.strip()
def scrape_image_links(listing):
maps_image_link = listing.select_one('div.SpFAAb').select_one("img")
return maps_image_link['src']
# Main function
def main():
# Make a request to Google Maps URL and parse HTML
url = 'https://www.google.com/maps/search/coffee/@47.4641284,-122.3855982,11z?entry=ttu&g_ep=EgoyMDI1MDIwOS4wIKXMDSoASAFQAw=='
driver.get(url)
time.sleep(2)
page_source = driver.page_source
soup = BeautifulSoup(page_source, 'html.parser')
# Scrape map listings
listings = scrape_listings(soup)
# Iterate through each listing and extract map information
maps_data = []
for listing in listings:
title = scrape_title(listing)
rating = scrape_rating(listing)
image_link = scrape_image_links(listing)
# Store map information in a dictionary
maps_info = {
'title': title,
'rating': rating,
'image_link': image_link
}
maps_data.append(maps_info)
# Save results to a JSON file
with open('google_maps_data.json', 'w') as json_file:
json.dump(maps_data, json_file, indent=4)
if __name__ == "__main__":
main()
Step 3. Scrape results and store data.
A JSON file named google_maps_data.json will be generated in your PyCharm directory, containing the scraped data. Below is an example of the scraped results:
[
{
"title": "Lucky Mugs",
"rating": null,
"image_link": "https://streetviewpixels-pa.googleapis.com/v1/thumbnail?panoid=36eAZcAEe5VpSUaFnPcOVA&cb_client=search.gws-prod.gps&w=80&h=92&yaw=139.65103&pitch=0&thumbfov=100"
},
{
"title": "Retro Coffee",
"rating": "4.4",
"image_link": "https://lh5.googleusercontent.com/p/AF1QipPxJNNq_ddJeY4metdP0-Yv1gfAb8hwqtdjEk3R=w80-h106-k-no"
},
{
"title": "Caffe Migliore.",
"rating": "4.4",
"image_link": "https://lh5.googleusercontent.com/p/AF1QipN5LhTxOs7TEicnyDcaeIwp5iOVD46aQ-vMpco=w80-h106-k-no"
},
{
"title": "Local Coffee Spot",
"rating": "4.2",
"image_link": "https://lh5.googleusercontent.com/p/AF1QipO9UVq4zJ3G-xw62mFyQEj8jTdlehq7eLlZ4RyL=w163-h92-k-no"
},
{
"title": "BigFoot Java",
"rating": "4.1",
"image_link": "https://lh3.googleusercontent.com/gps-proxy/ALd4DhGHbsVNZu27z_GxIxq3jTyZbJI5MUY_rpPTL7yZQX0Mbx4BEbKTwMTz6vJ16y1u1qVA97qxlMtjejRYMjEk7Riqx2dLl1CeDEcGfIWKWmIF5E3qcna_9faDEBRwLvhoiBcJywVx_TZ7jtM1tKUmynpAZdn-vvYju1pEGKDA34jUT38S8qip3Drk=w92-h92-k-no"
},
{
"title": "Caffe Ladro - Downtown on Pine Street",
"rating": "4.3",
"image_link": "https://lh5.googleusercontent.com/p/AF1QipO84r3OrCAb6IjtMYbki4pAMpuWf8KoK5_ods89=w92-h92-k-no"
},
{
"title": "Coffee Tree",
"rating": "4.4",
"image_link": "https://lh5.googleusercontent.com/p/AF1QipPYbEDGEskTGDCcpBHxF-nQqrFhR2yFi5cBt6Lu=w122-h92-k-no"
}
]
Scraping navigation routes
Step 1. Enter the origin and destination in Google Maps.
Step 2. Scrape specific fields from the Google Maps navigation route search results.
Continue using BeautifulSoup to parse the HTML and locate the elements containing the desired information:
- Scrape the "via" field:
Extract the element and use Python to scrape the text data:
def scrape_via(listing):
maps_element = listing.select_one('h1.VuCHmb.fontHeadlineSmall')
return maps_element.text.strip()
- Scrape the time:
Extract the element and use Python to scrape the text data:
def scrape_time(listing):
maps_element = listing.select_one('div.Fk3sm.fontHeadlineSmall.delay-light')
return maps_element.text.strip()
- Scrape the distance:
def scrape_distance(listing):
maps_element = listing.select_one('div.ivN21e.tUEI83.fontBodyMedium').select_one('div')
return maps_element.text.strip()
- Here's the final code:
# Import necessary libraries
import time
from bs4 import BeautifulSoup
import json
from selenium import webdriver
driver = webdriver.Chrome()
# Function to scrape listing elements from Google Maps
def scrape_listings(soup):
return soup.select('div.UgZKXd')
# Function to scrape via from google_maps
def scrape_via(listing):
maps_element = listing.select_one('h1.VuCHmb.fontHeadlineSmall')
return maps_element.text.strip()
# Function to scrape time from google_maps
def scrape_time(listing):
maps_element = listing.select_one('div.Fk3sm.fontHeadlineSmall.delay-light')
return maps_element.text.strip()
# Function to scrape distance from google_maps
def scrape_distance(listing):
maps_element = listing.select_one('div.ivN21e.tUEI8e.fontBodyMedium').select_one('div')
return maps_element.text.strip()
# Main function
def main():
# Make a request to google maps URL and parse HTML
url = 'https://www.google.com/maps/dir/Austin-Bergstrom+International+Airport/5540+N+Lamar+Blvd,+Austin,+TX+78756/@30.2603068,-97.7871692,12z/data=!3m1!4b1!4m13!4m12!1m5!1m1!1s0x8644b13b8b4aff45:0x1ca7fca8c9dc2768!2m2!1d-97.6710889!2d30.194085!1m5!1m1!1s0x8644cba140fad1fb:0x2db903443245739c!2m2!1d-97.7286733!2d30.3247493?hl=en&entry=ttu&g_ep=EgoyMDI1MDIwOS4wIKXMDSoASAFQAw%3D%3D'
driver.get(url)
time.sleep(2)
page_source = driver.page_source
soup = BeautifulSoup(page_source, 'html.parser')
# Scrape maps listings
listings = scrape_listings(soup)
# Iterate through each listing and extract maps information
maps_data = []
for listing in listings:
via = scrape_via(listing)
time_consuming = scrape_time(listing)
distance = scrape_distance(listing)
# Store maps information in a dictionary
maps_info = {
'via': via,
'time': time_consuming,
'distance': distance
}
maps_data.append(maps_info)
# Save results to a JSON file
with open('google_maps_dir_data.json', 'w') as json_file:
json.dump(maps_data, json_file, indent=4)
if __name__ == "__main__":
main()
Step 3. Scrape results and store data.
A JSON file named google_maps_dir_data.json will be generated in your PyCharm directory, containing the scraped data. Below is an example of the scraped results:
[
{
"via": "via 183 Toll",
"time": "17 min",
"distance": "13.4 miles"
},
{
"via": "via State Hwy 71 W and I-35 N",
"time": "19 min",
"distance": "15.5 miles"
},
{
"via": "via S Hwy 183 and 183 Toll",
"time": "17 min",
"distance": "13.6 miles"
}
]
The Bottom Lines
Now you've got the skills to scrape Google Maps like a pro! Two methods in this blog can definitely save your time and rescue your wallet! Don't have that much time? Confused by the complicated coding process? Please use our API Key to call our Google Maps API for free to easily complete data scraping!
Are you an API enthusiast or a hardcore web scraper? Share your experience in our community!
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