Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

Explore APISCRAPY, your AI-powered Google Map Data Scraper. Easily extract Business Location Data from Google Maps and other platforms. Seamlessly access and utilize publicly available map data for your business needs. Scrape All Publicly Available Data From Google Maps & Other Platforms.

Outscraper's Location Intelligence Service is a powerful and innovative tool that harnesses the rich data available from Google Maps to provide valuable Point of Interest (POI) data for businesses. This service is an excellent solution for local intelligence needs, using advanced technology to efficiently gather and analyze data from Google Maps, creating precise and relevant POI datasets​.

This Location Intelligence Service is backed by reliable and up-to-date data, thanks to Outscraper's advanced web scraping technology. This ensures that the data extracted from Google Maps is both accurate and fresh, providing a dependable source of data for your business operations and strategic planning​.

A key feature of Outscraper's Location Intelligence Service is its advanced filtering capabilities, enabling you to retrieve only the POI data you require. This means you can target specific categories, locations, and other criteria to get the most relevant and valuable data for your business needs, eliminating the need to sift through irrelevant records​.

With Outscraper, you also get worldwide coverage for your POI data needs. The service's advanced data scraping technology allows you to collect data from any country and city without limitations, making it an invaluable tool for businesses with global operations or those seeking to expand internationally​.

Outscraper provides a vast amount of data, offering the largest number of fields available to compile and enrich your POI data. With more than 40 data fields, you can create comprehensive and detailed datasets that provide deep insights into your areas of interest​.

Outscraper's Location Intelligence Service is designed to be user-friendly, even for those without coding skills. Creating a Google Maps scraping task is quick and simple with the Outscraper App Dashboard, where you select a few parameters like category, location, limits, language, and file extension to scrape data from Google Maps​.

Outscraper also offers API support, providing a fast and easy way to fetch Google Maps results in real-time. This feature is ideal for businesses that need to access location data quickly and efficiently​.

Scrape business and place information from Google Maps in real time. Get addresses, phone numbers, websites, ratings, reviews, photos, business hours, and location coordinates. Useful for business directories, store locators, review analytics, and local search tools.

The data represent web-scraping of hyperlinks from a selection of environmental stewardship organizations that were identified in the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017). There are two data sets: 1) the original scrape containing all hyperlinks within the websites and associated attribute values (see "README" file); 2) a cleaned and reduced dataset formatted for network analysis. For dataset 1: Organizations were selected from from the 2017 NYC Stewardship Mapping and Assessment Project (STEW-MAP) (USDA 2017), a publicly available, spatial data set about environmental stewardship organizations working in New York City, USA (N = 719). To create a smaller and more manageable sample to analyze, all organizations that intersected (i.e., worked entirely within or overlapped) the NYC borough of Staten Island were selected for a geographically bounded sample. Only organizations with working websites and that the web scraper could access were retained for the study (n = 78). The websites were scraped between 09 and 17 June 2020 to a maximum search depth of ten using the snaWeb package (version 1.0.1, Stockton 2020) in the R computational language environment (R Core Team 2020). For dataset 2: The complete scrape results were cleaned, reduced, and formatted as a standard edge-array (node1, node2, edge attribute) for network analysis. See "READ ME" file for further details. References: R Core Team. (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Version 4.0.3. Stockton, T. (2020). snaWeb Package: An R package for finding and building social networks for a website, version 1.0.1. USDA Forest Service. (2017). Stewardship Mapping and Assessment Project (STEW-MAP). New York City Data Set. Available online at https://www.nrs.fs.fed.us/STEW-MAP/data/. This dataset is associated with the following publication: Sayles, J., R. Furey, and M. Ten Brink. How deep to dig: effects of web-scraping search depth on hyperlink network analysis of environmental stewardship organizations. Applied Network Science. Springer Nature, New York, NY, 7: 36, (2022).

Outscraper's Global Location Data service is an advanced solution for harnessing location-based data from Google Maps. Equipped with features such as worldwide coverage, precise filtering, and a plethora of data fields, Outscraper is your reliable source of fresh and accurate data.

Outscraper's Global Location Data Service leverages the extensive data accessible via Google Maps to deliver critical location data on a global scale. This service offers a robust solution for your global intelligence needs, utilizing cutting-edge technology to collect and analyze data from Google Maps and create accurate and relevant location datasets. The service is supported by a constant stream of reliable and current data, powered by Outscraper's advanced web scraping technology, guaranteeing that the data pulled from Google Maps is both fresh and accurate.

One of the key features of Outscraper's Global Location Data Service is its advanced filtering capabilities, allowing you to extract only the location data you need. This means you can specify particular categories, locations, and other criteria to obtain the most pertinent and valuable data for your business requirements, eliminating the need to sort through irrelevant records.

With Outscraper, you gain worldwide coverage for your location data needs. The service's advanced data scraping technology lets you collect data from any country and city without restrictions, making it an indispensable tool for businesses operating on a global scale or those looking to expand internationally. Outscraper provides a wealth of data, offering an unmatched number of fields to compile and enrich your location data. With over 40 data fields, you can generate comprehensive and detailed datasets that offer deep insights into your areas of interest.

The global reach of this service spans across Africa, Asia, and Europe, covering over 150 countries, including but not limited to Zimbabwe in Africa, Yemen in Asia, and Slovenia in Europe. This broad coverage ensures that no matter where your business operations or interests lie, you will have access to the location data you need.

Experience the Outscraper difference today and elevate your location data analysis to the next level.

Dataset is taken from the scraping results using the Instant Data Scraper extension on the Google Maps website for the largest printing company in Central Sulawesi, Palu City. This dataset still needs thorough ETL processing to obtain clean and informative data.

Overall, scraping a restaurant list of Dhaka can provide valuable insights into the food and beverage industry in the city and help individuals and businesses make informed decisions about where to eat and what to order.

This dataset contains over 140,000 unique Geoguessr user IDs, which were collected through web scraping of map HTML files. These user IDs serve as the primary key to retrieve player data from the Geoguessr API. Each user ID can be used to obtain detailed player statistics.

Dataset

This dataset was created by Yohana Sri Rejeki

Contents

Table of Contents - Inspiration - About the Dataset - Notes - Understanding the Data - Questions - Source

Inspiration

I took an interest in the Valorant Champion Tour (VCT) when I watched their 2023 final in LA. I wanted to develop a project where I would do data analysis or display the data from VCT. However, there was no API for VCT data, and the only way to get it was through data scraping. I took the initiative to scrape the data so I could improve my skills and make the data accessible to everyone.

About the Dataset

The dataset includes matches, agents, and player data from VCT 2021–2025. This was obtained via data scraping at vlr.gg. Each year contains three folders: agents, matches, player stats, and IDs.

The agents folder contains agent pick rates, map pick rates, attacker and defender side win/loss percentage, team pick rates on an agent, and win/loss rate.

The matches folder contains team picks and bans, their economy on each round of a match, their economy stats on a match, players kills performance on other players, players kill stats, maps that were played on a match, the scores from the map, players overview stats, a player kills performance on players and their agent on a specific round, matches scores and their results, a list of abbreviated team names with their full names, the count of the method that occurred for a team for a match they played and its round number.

The player stats folder only contains player stats.

The ids folder contains the ids for the teams, players, tournaments, stages, match types, matches, and games.

The all_ids folder contains all the IDs, and the abbreviated team name with their full name.

Notes

Starting Masters Toronto (VCT 2025), the loadout value in eco_rounds.csv will be missing.

A lot of stats from matches played in China are missing. From what I read online, the Chinese hosted events do not have APIs available for post game stats.

There is a player named “nan”. If you are interested in their data, please make sure to not have the code read their name as null.

There is a team named "TBD". This is not a real team. Either the players were not on a team or their team name was missing at the time the match was played.

There are two teams with the same name but different IDs: Exotic (4964, year 2021) and Exotic (1301, year 2022).

The ID for match types can be null because an ID was not found for it during the scraping. The ID for stages and match types are null if it referring to all the stages and all the match types for a tournament.

The column "Initiated" can contain null values because it is not possible to initialize a pistol round.

The columns "Player", "Enemy", "Player Kills", "Enemy Kills", and "Difference" can contain null values because the name was missing or the player never got first kills and/or never got kills using the Operator and vice versa.

The columns "2k", "3k", "4k", "5k", "1v1", "1v2", "1v3", "1v4", and "1v5" can contain null values because the player never got a multi-kill elimination or never won in a multi-players match up.

The columns "Team A Overtime Score", "Team B Overtime Score" and "Duration" can contain null values because the match never reached to overtime or the duration was not recorded on the website.

All the columns related to the stats can contain null values because the stats were missing or the player did not encounter situations in a match conductive to recording those specific stats.

The columns "Eliminator", "Eliminated", and "Eliminated Team" can contain null values because the name were missing.

There are two teams with the same abbreviation of TP: Typhoon and Typhone.

Understanding the Data

Tournament ID - The ID for the tournament

Stage ID - The ID for the stage

Match Type ID - The ID for the match type

Match ID - The ID for the match

Game ID - The ID for the map that was played from the match

Pick Rate - How many times an agent has been picked

Total Maps Played - How many times a map has been played

Attacker Side Win Percentage - The percentage of rounds won by the attacking side on that map

Defender Side Win Percentage - The percentage of rounds won by the defending side of that map

Total Wins By Map - How many times the team won with the given agent on that map

Total Loss By Map - How many times the team lost with the given agent on that map

Total Maps Played - The total amount the team played on that map

Loadout Value - The total value of all the weapons, abilities, and shields purchased by all players on the team in a round

Remaining Credits - How much credits a team has after spending

Type - The economy round type (eco, semi-eco, s...

Context

Valorant is still relatively new to the eSports scene, so the data analysis for pro or semi-pro games is still in its infancy stage. One of the biggest issues is sourcing the data. Vlr.gg is similar to CSGO's hltv.org that provides some great information on matches, but extracting its data isn't very accessible. Luckily, they (for now) allow scraping the website as much as you want.

I had a lot of issues because even though the HTML/CSS format is generally the same, there's a bunch of edge cases to account for and even times where the formatting completely breaks my parser. I didn't upload my code because it's honestly super messy, but I might in the future when I clean it up. The data set currently get most matches up to Jan 1, 2022, and I think there's like 400 out of ~11.5k that got errors and I couldn't add to the database. Probably about 200 are from the very first matches that got posted on vlr.gg.

Content

There is four tables. The top level is Matches that will tell you teams playing and match (map) score. Game is the next level that breaks down the specific maps played. Then Game_Rounds gives a round by round breakdown which shows who won, economy of each team, win type, and buy type, whenever the info is available. The game rounds are packaged in one string that you should be able to cast as a json. Lastly there is Game_Scoreboard which gives you the player performance, as well as things like number of first kills, first deaths, 2Ks, 3Ks, One v Ones, One v Twos, ect.

Inspiration

https://www.kaggle.com/hidious/valorant-vlrgg-results-and-stats

This dataset scrapes the results pages based on map score, but will only get match score, or map score if they only played 1 map. My data set tries to scrape everything available.

Creating a rental market database for data analysis and machine learning.

How does it work ?

You scrape the property ads (sale or rent) on internet and you get a dataset.

Then 3 fancy solutions are possible:

• Run your webcrawler everyday for a specific place, upload the data in your data warehouse, and monitor the trends in real estate market prices.

• Apply machine learning to your database and get a sense of the relative expensiveness of the properties.

• Localize every property ads on a Google map using color-coded points in order to visualize the most cheap and expensive neighborhoods.

Original Data Source

For the sake of example, and for proximity reasons, we fetched information from a mid-sized Swiss city, called Lausanne, based in the south of Switzerland. The country has the particularity that people get often puzzled by the level of prices swarming almost everywhere in the rental markets. This is mostly related to the very high living standards prevailing over here. So we used one of the public property ads available in this french-speaking part of the country : https://www.homegate.ch/

Because the booming Swiss housing market is mainly a rental market (foreign investments have been riding high for the sales of property, and mortgage loans are closed to record low), I focused on real estate for rent ads in the Homegate website.

Building a webcrawler

In the Kernels section, you will find out how the Python looks like. I used BeautifulSoup and Urllib Python libraries to grab data from the website. As you can figure out, the code is simple, but really efficient.

What you get

In this example, I extracted data as of 03/17/2017, and I named the DataFrame "Output", available in CSV format to make the data compatible with most commonly preferred tools for analysis. It allows you to get a DataFrame with 12 columns:

• the date

• is it a rent or a buy

• the location

• the address of the property

• the zip code

• the available description of the property

• the number of rooms

• the surface

• the floor

• the price

• the source

Machine learning

In the Kernels section, you will see a very simple ML algorithm applied to the dataset in order to the "theoretical" price of each asset, at the end of the code. For the sake of simplicity, I ran a very straightforward linear regression using only 3 features (the 3 only quantitative factors I have at hand) :

• the number of rooms

• the floor

• the surface

I know what you're thinking right at the moment : those 3 features can barely explain the price of a property. Other determinants, such as the location, the neighborhood, the fact that it is outdated, badly maintained by a students roommate partying every night, ... , are of interest when it comes to assessing an appartment. But straightaway, I reduced the model to this.

Google Map display of the property ads and their relative expensiveness

cf Capture.PNG file

Upcoming improvements

• Add new features to machine learning process, especially a dummy variable accounting for the neighborhood to which the property pertains.

• See to what extent a logistic regression could overcome a linear regressor.

• Test more complex machine learning algorithms.

• Display trends in rental property prices, for each neighborhood, after establishing a larger database (with a few weeks of scraped data).

Dataset ini berisi ulasan dari Google Maps yang terkait dengan kampus BUMN. Data dikumpulkan untuk keperluan penelitian dan edukasi, dengan fokus pada pengalaman dan umpan balik pengguna. Dataset ini dapat digunakan untuk analisis data eksploratif, analisis sentimen, dan penelitian lainnya.

Deskripsi Dataset
File: CyberU_reviews.csv
Format: CSV
Jumlah Baris: 78
Jumlah Kolom: 9