As the world's most widely used map service platform, Google Maps not only provides navigation services to users, but also contains a huge amount of commercial data. This article will dive into the core tech behind scraping data from Google Maps, weigh the pros and cons of different methods, and give you a solid and dependable solution.
Why scrape Google Maps data?
Google Maps is like a treasure trove of info. It’s got everything from business details and locations to reviews and photos. For businesses, this stuff is super useful for things like making operations smoother, diving into market research, and boosting customer experience.
Google Maps data can be widely used in the following scenarios:
Data Types | Common use cases |
Business Listings | Use for local business directories or competitor analysis. |
Reviews and Ratings | Conduct sentiment analysis, reputation management, or market research. |
Geolocation data | Used for map services or logistics planning. |
Photos and images | Enhance your business profile or perform image recognition analysis. |
Location Details | Provide data support for travel applications or business intelligence. |
Street View Imagery | Use for simulating tourism, real estate visualization or city planning. |
Traffic and route data | For use with navigation apps or ride-sharing services. |
By scraping Google Maps data, companies can analyze customer reviews, confirm target markets, discover trends, and develop strategic plans.
For example, restaurant chains can optimize their service strategies by scraping ratings and reviews of competing stores; logistics companies can dynamically adjust delivery routes to reduce fuel costs by combining real-time traffic data.
Why is it not recommended to use the official Google Maps API ?
When many companies are scraping data from Google Maps, the first thing they think of is to use the official Google Maps API . Just like many websites provide their official APIs, it provides legal and compliant data access channels with stable and reliable service quality.
Although the official Google Maps API, but its commercialization strategy has obvious limitations:
High costs : Only $200 per month is provided for free, and after that, there is a per-request charge, and costs can quickly increase.
Strict request limit : a maximum of 100 requests per second, which is difficult to meet the needs of large-scale data scraping.
Frequent rule changes : Google may adjust API rules at any time, increasing development and maintenance costs.
Therefore, for businesses that need large-scale, flexible scraping of Google Maps data, using a third-party solution may be a better choice.
LunaProxy's Google Maps scraping based on Python
1. Environment Preparation
Visit LunaProxy official website and choose the "Residential Proxy" or "Data Center Proxy" package
Get proxy authentication information: username:[email protected]:port
It is recommended to enable the "IP automatic rotation" function and set it to change the IP every 50 requests.
Selenium is a commonly used tool that automates browser operations and is suitable for scraping dynamically loaded web pages, such as Google Maps.
Install Python 3.8 or higher.
Install Selenium: pip install selenium
Download ChromeDriver (matching your Chrome browser version)
Installing the Library
pip install selenium undetected-chromedriver pandas webdriver-manager
2. Setting up Selenium
import undetected_chromedriver as uc
from selenium.webdriver.common.by import By
import pandas as pd
import time
3. Configure LunaProxy
proxy_options = {
'proxy': {
'http': f'http://username:[email protected]:port',
'ssl': f'http://username:[email protected]:port'
}
}
4. Browser Configuration
options = uc.ChromeOptions()
options.add_argument('--disable-blink-features=AutomationControlled')
driver = uc.Chrome(
options=options,
seleniumwire_options=proxy_options
)
5. Simulate human operation
driver.get("https://www.google.com/maps/search/Coffee + Taipei")
time.sleep(8) # Wait for anti-bot detection
6. Scroll to load full results
for _ in range(5):
driver.execute_script("window.scrollTo(0, document.body.scrollHeight);")
time.sleep(3)
7. Extracting data
stores = driver.find_elements(By.CSS_SELECTOR, '[role="article"]')
data = []
for store in stores:
try:
name = store.find_element(By.CSS_SELECTOR, 'div.fontHeadlineSmall').text
rating = store.find_element(By.CSS_SELECTOR, 'span.MW4Iwc').get_attribute('aria-label')
reviews = store.find_element(By.CSS_SELECTOR, 'span.UY7F9').text.strip('()')
data.append({
'Store name': name,
'Rating': rating.split()[0],
'Number of reviews': reviews
})
except:
continue
pd.DataFrame(data).to_csv('google_maps_data.csv', index=False)
driver.quit()
4. Using proxy services to meet challenges
when scraping large amounts of data . LunaProxy provides efficient proxy services to help you:
Avoid IP blocking
Achieve global geo-location
Provide stable and reliable request management
Run multiple scraping tasks at the same time to improve efficiency
Comparison of methods for scraping Google Maps data
Method | Features | Applicable scenarios |
Manual scraping (no proxy) | High flexibility, but easy to be blocked by IP | Small scale scrape or test |
Use proxy scraping | Improve success rate,reduce IP blocking, support geolocation | Medium to large scale scraping |
Conclusion
Scraping Google Maps data can help companies gain valuable market insights, but it requires overcoming technical challenges and IP blocking issues. LunaProxy is super fast and reliable for proxy services, so it's a great pick if you need to do a lot of data scraping. Visit LunaProxy's official website now to learn more and start your data scraping journey.
$0
