Advanced Google Maps Scraping: Extracting Local Business Data at Scale

Scraping local business listings from Google Maps can be incredibly valuable for lead generation and market research. However, Google’s complex response structure presents unique challenges. This guide provides a complete walkthrough for extracting business data from Google Maps at scale.

Understanding Google Maps API Responses

Google Maps API responses are notoriously difficult to work with. Unlike standard JSON responses, Google’s data is structured in a proprietary format that requires special handling to transform into usable information.

When inspecting network requests while browsing Google Maps listings, look for XHR requests with curly bracket icons. These contain the valuable data you need, though both the payload parameters and response format appear messy at first glance.

Transforming the Response into Usable JSON

The key to working with Google Maps data is transforming their proprietary response format into clean JSON. This requires several processing steps:

1. Remove trailing characters from the response string
2. Extract the relevant substring
3. Parse the resulting text into JSON
4. Navigate the complex nested array structure

Google stores data in deeply nested arrays where each piece of information (business name, address, website, etc.) consistently appears in the same position across different results. A lookup function can help navigate this structure to extract specific data points.

Extracting Key Business Information

Once properly transformed, you can extract valuable business information including:

• Business name
• Street address
• City, state, and zip code
• Website URL
• Business hours
• Category tags
• Place ID (unique Google identifier)

The website URL is particularly valuable as it allows you to potentially extract email addresses. Facebook pages linked from business listings are also excellent sources for finding contact information.

Optimizing Search Parameters

To maximize the number of results you can extract, several query parameters can be modified:

Result Count

The default page size is around 20 results, but this can be increased by modifying the ‘7i’ parameter to request up to 200 results per query.

Pagination

The ‘8i’ parameter controls the starting point for results, allowing you to implement pagination to retrieve more than the maximum 200 results per query.

Zoom Levels

The ‘1D’ parameter controls the zoom level, which affects which businesses appear in results. Some businesses only appear at certain zoom levels, so varying this parameter can yield additional unique results.

Grid-Based Approach for Comprehensive Coverage

For complete coverage of a geographic area (like all coffee shops in New York City), implement a grid-based approach:

1. Get the viewport coordinates (northeast and southwest bounds) for the target area
2. Divide the area into a grid of smaller coordinate pairs
3. Query each grid coordinate with multiple zoom levels
4. Filter out duplicate results using Place IDs

This approach allows you to extract hundreds or even thousands of business listings within minutes.

Handling Request Volume

When making numerous requests, use Promise.all() to execute them concurrently rather than sequentially. This dramatically improves performance. Additionally, implementing proxies and retry logic helps prevent blocking and ensures reliable data collection.

With this approach, it’s possible to collect nearly 1,000 unique business listings in just a few seconds – far more efficient than manual scrolling or browser automation techniques.

Conclusion

Google Maps scraping offers tremendous value for businesses seeking lead generation opportunities. By understanding the intricacies of Google’s response format and implementing grid-based searching with optimized parameters, you can efficiently extract comprehensive business data at scale.