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How to Scrape Data from Google Maps: The Unofficial API Guide

With over 200 million businesses listed and over 1 billion monthly users, Google Maps has become an indispensable source of location data. This guide will teach you how to extract value from Google Maps at scale using web scraping.


Google Maps provides a wealth of data – business names, addresses, phone numbers, opening hours, customer reviews, images, live popularity metrics and more. This data can provide key competitive insights for market research, lead generation, targeted advertising and location-based services.

While Google provides a Maps API, it is limited to 100,000 free requests per month with additional requests costing $7 per 1000. For larger scraping projects, this can become prohibitively expensive. Web scraping offers a flexible, low cost alternative to access Google Maps data at scale.

Web scraping public online data is generally deemed legal under US law such as the Computer Fraud and Abuse Act. However, be sure to comply with Google‘s Terms of Service which prohibits scraping that is excessive, disruptive or circumvents their systems. Scraping too aggressively can get your IP address blocked.

Only collect data relevant to your needs, and do not republish scraped content verbatim. Google Maps contains personal information like emails and phone numbers which should be anonymized or pseudonymized before storage and analysis.

Adhering to ethical principles helps ensure your web scraping brings value to society. The ACM Code of Ethics is an excellent guide for responsible computing practices.

The Value of Google Maps Data

Here are some key stats that showcase the vast amounts of data available on Google Maps:

  • Over 200 million businesses listed on Google Maps globally as of 2021
  • Over 1 billion monthly active Google Maps users worldwide
  • Millions of reviews, images, opening hours and other data points on businesses
  • Live popularity metrics for over 10 million places worldwide
  • Historical archives of Street View images dating back over 15 years

Unlocking this data at scale can provide powerful competitive intelligence for data-driven decisions.

Overcoming Scraping Challenges

Google employs advanced bot detection systems to prevent abuse of their services. Here are some techniques to avoid getting blocked while scraping:

  • Selenium Browser Automation – Mimics real user actions like scrolling and clicking by automating a real browser like Chrome. More resistant to bot detection than requests.
  • Proxies – Rotate different IP addresses to distribute requests and mask scraping activity. Proxy services like BrightData offer thousands of IPs.
  • Captcha Solving – Google uses reCAPTCHA v2 on Maps which requires solving visual challenges manually. Tools like Anti-Captcha can help automate this.
  • Clean Data – Scraped data contains inconsistencies and duplicates that need cleaning before analysis. Budget time for data wrangling.

With the right tools and techniques, these challenges can be overcome to access Google Maps data at scale.

Scraping Google Maps with Selenium

Here is a step-by-step guide to scraping Google Maps using Selenium and Python:

Install Required Packages

pip install selenium pandas numpy regex pymongo

Setup Driver

from selenium import webdriver

driver = webdriver.Chrome()

Configure proxies and options as needed.

Search for Places


Extract Data

Use element selectors to extract key fields:

places = driver.find_elements(By.CLASS_NAME, "section-result")

names = [place.find_element(By.CLASS_NAME, "section-result-title").text for place in places]

addresses = [place.find_element(By.CLASS_NAME, "section-result-location").text for place in places]

place_urls = [place.find_element(By.CSS_SELECTOR, "a.section-result-action-icon").get_attribute("href") for place in places]

Click into each place to scrape additional data like reviews:

for url in place_urls:

    reviews = driver.find_elements(By.CLASS_NAME, "section-review-text")

Continuously Rotate Proxies

To scrape at scale, proxies must be cycled to avoid detection:

from brightdata.brightdata_service import BrightDataService

resolver = BrightDataService.create_proxy_resolver() 

while True:
    driver = webdriver.Chrome(resolver=resolver)

This allows scraping thousands of locations reliably.

Google provides live popularity data for places via an API endpoint. A sample response:

"popularTimes": [
   "day": 0,
   "data": [  
     {"hour": 8, "percent": 24},
     {"hour": 9, "percent": 100},  
     {"hour": 10, "percent": 88},

The percent field contains the live busyness metric ranging from 0-100. Here‘s how to extract it in Python:

import requests
import pandas as pd

api_url = place_url + "/data/details/json" 

times_data = requests.get(api_url).json()["popularTimes"] 

df = pd.DataFrame(times_data)[["day","hour","percent"]]

Visualizing this data can reveal weekly patterns.

Scraping Images

Place pages contain image galleries that can be scraped:

images = driver.find_elements(By.CLASS_NAME, "section-image")

image_urls = [img.get_attribute("src") for img in images]

Location data like latitude and longitude is encoded in the URLs.

Storing Data at Scale

For large scrapers, MongoDB is a great choice for storage compared to CSVs or Excel sheets. Some best practices:

  • Use NoSQL document schema to allow flexibility as fields change
  • Create indexes on fields you query on like business names or locations
  • Encode geodata like points instead of addresses for geospatial search
  • Schedule regular backups as scraping builds up data over time

Here is sample insertion code:

from pymongo import MongoClient

client = MongoClient()
db = client["google_maps"]
places = db["places"] 

post = {
    "name": name,
    "url" : url,
    "address": address,
    "location": {
        "type": "Point",  
        "coordinates": [lat, lng]
    "images" : image_urls


Analysis and Visualization

Once the data is scraped, the real value comes from analysis and visualization. Here are some examples:

Analysis TypeDescriptionLibraries
Sentiment AnalysisIdentify positive and negative themes in reviewsNLTK, TextBlob
Topic ModelingDiscover trending topics from reviews using LDAGensim, pyLDAvis
Image RecognitionExtract text from menus and other images with OCROpenCV, pytesseract
Geospatial AnalysisVisualize data layered on maps for analysisFolium, Plotly Express

Advanced analysis provides competitive intelligence to guide business decisions.

Use Cases

Scraped Google Maps data enables powerful location-based services:

  • Market Research – Compare competitor popularity and sentiment across locations
  • Lead Generation – Build targeted email and phone lists for outreach
  • Site Selection – Optimize new locations based on demographics and foot traffic
  • Advertising – Create hyperlocal ad campaigns based on customer movements
  • Demand Forecasting – Predict store traffic to optimize staffing for weekends
  • Logistics – Plot optimal routes for deliveries based on real-time traffic data

These are just some examples of how web scraped Google Maps data can drive innovation and growth.


While Google Maps provides a limited free API, web scraping offers complete access to map data at scale. With responsible use, these techniques allow individuals and organizations to harness the power of location-based data for competitive advantage.

The world‘s information mapped – it‘s out there. Now go grab it!

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