Advanced Google Maps Scraping: Extracting Business Data Without APIs

Google Maps is a goldmine of business information for lead generation, especially for B2B companies looking to expand their client base. While many approaches rely on Google’s APIs, there are effective alternatives when working with the platform’s complex structure.

Understanding the Challenges of Google Maps Scraping

Google Maps presents unique challenges for data extraction. Unlike many websites, it uses Protocol Buffers (Protobuf) – a method for serializing structured data that’s not fully documented. Recent changes to Google’s data structure have made older scraping methods less reliable.

Additionally, Google Maps has several structural characteristics that complicate extraction:

The page uses infinite scroll rather than pagination
Fields appear in different sequences or locations depending on the business
Many fields are optional, changing the entire page structure
Class names are generic and likely change with each refresh

A More Effective Approach

Instead of relying on XPath selectors or class names, this approach focuses on targeting attributes from HTML tags. The strategy involves:

Capturing all business listing links from search results
Visiting each link to extract comprehensive business information
Using attribute-based selectors rather than relying on page structure

Data Points You Can Extract

With the right approach, you can extract comprehensive business information including:

Business name and complete address
Phone numbers and website URLs
Star ratings and review counts
Detailed review breakdowns (5-star, 4-star, etc.)
Price range indicators
Business categories
Accessibility information
Opening hours by day
Geographic coordinates (latitude/longitude)
Featured images
Service availability (reservations, delivery, etc.)

Implementation Strategy

The implementation involves several key components:

1. Managing Infinite Scroll

Since Google Maps uses infinite scroll rather than pagination, the script needs to programmatically scroll down to load additional results. This is achieved by identifying the feed container and incrementally scrolling in small steps (e.g., 500 pixels at a time).

2. Identifying Business Cards

Business cards can be identified by their title elements, which have more consistent formatting than other elements. Once identified, the parent containers are targeted to extract the links to detailed business pages.

3. Extracting Detailed Information

For each business page, specific attribute selectors are used to extract data. Some information (like ratings) may require scrolling to make elements visible before extraction.

4. Handling Latitude and Longitude

Geographic coordinates are extracted from the internal URL structure, which contains this data even when not explicitly displayed on the page.

Avoiding Data Loss

To prevent data loss in case of interruptions, the script saves data incrementally after processing each business. This ensures that even if the process is stopped midway, all previously scraped data remains available.

Applications and Use Cases

This scraping approach is particularly valuable for:

Lead generation for B2B sales teams
Market research and competitive analysis
Building location-based service directories
Real estate market analysis
Restaurant and retail analytics

The technique demonstrated can be adapted to other websites with similar structural characteristics, making it a versatile addition to any web scraping toolkit.