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Creating an Online Local Business Directory
The popularity of directory websites has increased recently. They make it easy to locate any kind of business or tourist destination with no effort, whether we’re looking for a bite to eat, a cinema, a mechanic, a plumber, or anything else.
In its most basic form, a directory website is only an organized set of links that may be browsed according to various criteria. They may include information on a wide range of topics, such as the best restaurants in town, or they may focus on a narrower subject, such as the best dinner restaurants in town.
Creating a directory on any such niche opens up many different ways to earn. Like ads and affiliates. This has increased the demand for directory creation. It is easy to design and interface with No-Code builders like WordPress. The most important thing is to create a database of businesses to be listed in this directory.
The Database for the Directory
It is possible to find information on local businesses from many different sources. Directory pages of local governments, local yellow pages, online yellow pages, and more. But the biggest and most detailed one is Google Maps. Google Maps is a service that has information about local businesses all over the world.
With Google Maps, we can find all the wellness centers in London, Paris, Berlin, New York, and Sydney in a few minutes. The results here are more than enough for us to set up a wellness directory site.
Although Google Maps provides such an opportunity, it is very difficult to record the data it provides one by one. At this point, the scraping service that extracts Google Maps data comes into play. This service is called Outscraper Google Maps Data Scraper. This scraping service from Outscraper allows you to perfectly extract Google Maps data without any Google limits.
Outscraper Google Maps Data Scraper
Outscraper Google Maps Data Scraper is an online service that allows you to extract data from this huge database of Google Maps according to the market you target. Whatever search queries are used in Google Maps, the same queries and parameters are used in the Google Maps Scraper service. There is no difference between the results obtained from the browser and those obtained from the scraping service.
Outscraper Google Maps Data Scraper service executes all extraction operations on its servers. No proxy is needed. It creates search queries according to the parameters and lists all the results given by Google Maps.
The first step and priority are to determine the category correctly. Google Maps has general business categories. For example, “Wellness Center” is included in this list. With Outscraper, all businesses in a target niche in these categories can be easily scanned and listed. To browse and check out these categories:
After the category is determined, other parameters are set. And a scraping task is created. Google Maps scraping tasks can be created with either the Outscraper UI or API services. Outscraper allows for both. To be able to upload to databases, Outscraper UI offers the opportunity to export in CSV file format. Let’s look at an example through “Wellness Centers”
Export CSV From Outscraper UI
Outscraper App Dashboard has a clean and simple interface. It is very simple to use it. It is enough to specify the category and location. As in Google Maps. With a simple example, these parameters are enough to search for the all “Wellness Centers” in New York we just mentioned.
Now we create an example task with the above parameters. The result file will be in CSV format as we have chosen.
Outscraper has listed all the wellness centers. Creating a database for a directory website about wellness is that simple with Outscraper Google Maps Data Scraper. Outscraper quickly extracts and lists all results on Google Maps. Here is the sample result file: Wellness Centers New York
The Wellness category is not a market with many businesses like bars, restaurants, and coffee shops. That’s why it’s possible to scrape all the wellness centers in a city in a single Google Maps query.
But what if we wanted to scrape categories like categories restaurants? Where many businesses are registered. Even in densely populated areas? This is a very important question and challenge. Because when we search for a category on Google Maps, it only shows a certain number of results. It also lists different results as you scroll through the pages. But ultimately it shows a maximum of 500 results for a single query. This query result limit also prevents us from finding all the operations in the category we are looking for.
Outscraper helps us overcome this hurdle as well. It allows us to scan and scrape each neighborhood of a densely populated area one by one using zip codes. In this way, we can create our own directory database without missing a single business. A detailed blog post on how it’s done can be read here:
There are also some more tricks to creating a task with Google Maps Scraper. You can read how to create a task using this UI in detail step by step in its tutorial: “How to Scrape Google Maps?”
Even though Outscraper gives its users access to filtering options with advanced parameters. So, we have the chance to get rid of useless results from the lists we made for the database. A detailed article on filtering can also be read here: “Google Maps Data Scraper Filters“
Outscraper Google Places API
Alternatively, to scraping all the data at once, you can just connect to Outscraper API and return information to your clients on demand. For example, when your visitors search for wellness centers in Berlin, your app can make a request to Outscraper API and show the results in real-time.
Here it’s an example of how you can scrape places from Google Maps by using search queries in Python:
# Search for businesses in specific locations: results = client.google_maps_search_v2(['restaurants brooklyn usa'], limit=20, language='en', region='us') # Scrap Places by Two Queries results = client.google_maps_search_v2( ['restaurants brooklyn usa', 'bars brooklyn usa'], limit=50, # limit of palces per each query language='en', region='US', ) # Iterate over the results for query_places in results: for place in query_places: print('query:', place['query']) print('name:', place['name']) print('phone:', place['phone']) print('website:', place['site'])