How to Scrape Data from Google Maps: A Step-by-Step Guide

Parsing Google Maps Data
Exporting Google Maps Data to CSV

What is Google Maps scraping?

Google Maps scraping is the process of extracting data from Google Maps, a widely-used mapping platform provided by Google. This data includes a wide range of information, including business listings, addresses, reviews, ratings, and geographic coordinates. Scraping allows users to automate the retrieval of this data, which can be valuable for various purposes. For instance, businesses may use it to gather information about their competitors, identify potential leads, or optimize their marketing strategies.

When engaging in web scraping, including Google Maps scraping, it's important to maintain legal and ethical boundaries. While web scraping itself is not exactly illegal, it's crucial to respect the terms of service of the platform being scraped.

How to scrape data from Google Maps

First and foremost a project environment needs to be set up using proper tools and libraries that will help extract data from Google Maps. Some popular choices include Python, Selenium and Beautiful Soup. Let us dive deeper into the details to analyze which could be your ideal choice.

1. Python: Python is a versatile and widely-used programming language known for its simplicity and readability. It's a popular choice for web scraping due to its wide range of libraries and packages. For Google Maps scraping, Python provides libraries like Beautiful Soup and Requests that simplify the process of fetching and parsing HTML content.

2. Beautiful Soup: Beautiful Soup is a Python library that provides the parsing and extraction of data from HTML and XML documents. It provides convenient methods for navigating and searching through the structure of a webpage, making it an excellent choice for extracting information from Google Maps.

3. Selenium: Selenium is a powerful automation tool primarily used for web browser automation. It allows you to interact with web pages in a dynamic manner, simulating human-like behavior. This is particularly useful for scenarios where data retrieval from Google Maps involves complex interactions or requires JavaScript execution.

Using Python to establish data scraping algorithm

Say you’re the owner of a restaurant looking to establish and expand your business further by researching competitors. Here are the steps you can follow to collect necessary data.

Step1: Choosing the Right Language:

When it comes to web scraping, Python is your best friend. It's a versatile and user-friendly programming language as Python's simplicity makes it accessible for beginners too.

Step 2: Installing the Essentials:

Before we start, let's make sure you have the right tools. Install two Python libraries titled `requests` and `Beautiful Soup`. These will help us communicate with Google Maps and extract the data we need.

Step 3. Getting Your API Key:

First things first, we'll need an API key from Google. Like Twitter and Amazon, Google has its own API module that you can access and develop necessary parameters

Step 4. Define Your Data Goals:

What information do you want to scrape? Business names, addresses, reviews, ratings? It's important to know what you're after before diving in.

Step 5. Sending Requests:

We'll use the `requests` library to ask Google Maps for the data we want.

query = {"q": "donuts in portland"}

# build to url to make request
url = f"https://api.serply.io/v1/maps/" + urllib.parse.urlencode(query)
print(url)

resp = requests.get(url, headers=headers)
results = resp.json()
print(results)

Step 6. Parsing HTML Content:

Google Maps will respond with a web page. Now, we'll use `Beautiful Soup` to read and extract the juicy details. It's like having a magic wand that sifts through the webpage and picks out what we need.

Step 7. Handling Pagination (if needed):

If you're scraping a lot of data, it might be spread across multiple pages. We'll show you how to navigate through them smoothly.

Step 8. Storing Your Collected Data:

Once you've collected your data, where do you keep it safe? We'll guide you on storing it in a format that suits your needs – whether it's a spreadsheet or a database.

Step 9. Dealing with CAPTCHAs and Anti-Scraping Measures:

Sometimes, Google might throw a puzzle your way. Don't worry, we've got tips on how to solve it without breaking a sweat.

Step 10. Optional: Using Proxies for Large Scraping Jobs:

If you're going big, consider using proxies to prevent any hiccups. It's like having a team of helpers to assist you in your data-gathering journey.

Parsing Google Maps Data

Once we have the HTML content of the search results page, we can use the BeautifulSoup library to parse the data. In this example, we'll extract the following data points from each place listed in the search results—Name, Place Type, Address, Rating, Price Level, Rating Count, Latitude, Longitude, Hours, and other details.

First, open the browser and open the same URL that you used in the code. Right-click on any of the listings and select Inspect.
Try to create a selector that selects exactly one listing at a time.
One possible selector is [role='heading]. The other is [data-id]. We will use the [data-id] in this example.
We can loop over all the matches and look for specific data points.
The next step is to create a CSS selector for each data point you want to scrape. For example, you can select the name of the restaurant with the following CSS selector:
Afterwards you can save this data as CSV.

Exporting Google Maps Data to CSV

With the data parsed, the final step is to export it to a CSV file. We'll use the Pandas library to create a DataFrame and save it as a CSV file:

import csv

f = csv.writer(open("places.csv", "w", newline=''))

# Write CSV Header, If you dont need that, remove this line
f.writerow(["place", "description", "address", "website", "reviews"])

for entry in results["places"]:
    f.writerow([
        entry['place'], 
        entry['description'], 
        entry['address_string'],
        entry['website']["link"] if 'website' in entry else None,
        entry['reviews']["link"],
    ])

When you run this code, it will save the data to a CSV file named data.csv.

Conclusion

Remember, while web scraping can be a powerful tool, it's important to do so responsibly and ethically. Always respect the terms of service of the platform you're scraping from, and ensure you're not violating any legal or ethical guidelines.

By following these steps, you'll be well on your way to scraping valuable data from Google Maps for your specific needs.