The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

By leveraging this business listings data, you can enhance your marketing efforts, generate leads, and gain a competitive edge in your industry.

Why choose our Google Maps Business Dataset?

Comprehensive Coverage: Gain access to a vast array of businesses listed on Google Maps across all 50 states. Enriched Data: Our dataset includes essential details like company names, websites, categories, and enriched email addresses, enabling you to connect with potential customers more effectively. Boost Your Marketing Efforts: Leverage this data to supercharge your marketing campaigns, generate targeted leads, and expand your customer base. Competitive Pricing: We offer an unbeatable pricing structure, ensuring affordability without compromising on data quality.

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​.

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.

Discover the booming Google Maps Platform consulting market! Our analysis reveals a $2B market in 2025 projected to reach $6B by 2033, driven by location intelligence and online service adoption. Explore key trends, regional insights, and leading companies shaping this dynamic landscape.

The global digital map market is booming, projected to reach [estimated 2033 value based on CAGR] by 2033, driven by AI, 5G, and the rise of location-based services. Explore market trends, key players (Google, TomTom, ESRI), and growth opportunities in this comprehensive analysis. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

Data includes reviews of different restaurants on Google Maps. There are 1100 comments in total and pictures of each comment in the data set. The data is labeled according to 4 classes (Taste, Menu, Indoor atmosphere, Outdoor atmosphere) for the artificial intelligence to predict. The dataset has been prepared in a way that can be used in both text processing and image processing fields.

The dataset contains the following columns: business_name, author_name, text, photo, rating, rating_category

IMPORTANT: The rating_category column is related to the photo of the review. If you want to use this dataset for NLP, you need to label it yourself. I will label it for you when I am available.

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

Online questionnaire incorporating a Google Maps response ('painting' particular spatial areas). Developed by Nick Bearman, UEA (n.bearman@uea.ac.uk) in Sept-Nov 2010 using Google Maps API v3. More details (including academic paper and videos) can be found at http://www.nickbearman.me.uk/go/bearman_appleton_2012. Other. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-09-22 and migrated to Edinburgh DataShare on 2017-02-21.

The custom digital map service market is booming, projected to reach $24.7 billion by 2033 with a 15% CAGR. Driven by automotive, location services, and business analytics, this report analyzes market trends, segmentation (custom maps, real-time data), key players (Google, TomTom, Mapbox), and regional growth. Discover key insights into this rapidly expanding industry.

Google Data for Market Intelligence, Business Validation & Lead Enrichment Google Data is one of the most valuable sources of location-based business intelligence available today. At Canaria, we’ve built a robust, scalable system for extracting, enriching, and delivering verified business data from Google Maps—turning raw location profiles into high-resolution, actionable insights.

Our Google Maps Company Profile Data includes structured metadata on businesses across the U.S., such as company names, standardized addresses, geographic coordinates, phone numbers, websites, business categories, open hours, diversity and ownership tags, star ratings, and detailed review distributions. Whether you're modeling a market, identifying leads, enriching a CRM, or evaluating risk, our Google Data gives your team an accurate, up-to-date view of business activity at the local level.

This dataset is updated daily and is fully customizable, allowing you to pull exactly what you need, whether you're targeting a specific geography, industry segment, review range, or open-hour window.

What Makes Canaria’s Google Data Unique? • Location Precision – Every business record is enriched with latitude/longitude, ZIP code, and Google Plus Code to ensure exact geolocation • Reputation Signals – Review tags, star ratings, and review counts are included to allow brand sentiment scoring and risk monitoring • Diversity & Ownership Tags – Capture public-facing declarations such as “women-owned” or “Asian-owned” for DEI, ESG, and compliance applications • Contact Readiness – Clean, standardized phone numbers and domains help teams route leads to sales, support, or customer success • Operational Visibility – Up-to-date open hours, categories, and branch information help validate which locations are active and when

Our data is built to be matched, integrated, and analyzed—and is trusted by clients in financial services, go-to-market strategy, HR tech, and analytics platforms.

What This Google Data Solves Canaria Google Data answers critical operational, market, and GTM questions like:

• Which businesses are actively operating in my target region or category? • Which leads are real, verified, and tied to an actual physical branch? • How can I detect underperforming companies based on review sentiment? • Where should I expand, prospect, or invest based on geographic presence? • How can I enhance my CRM, enrichment model, or targeting strategy using location-based data?

Key Use Cases for Google Maps Business Data Our clients leverage Google Data across a wide spectrum of industries and functions. Here are the top use cases:

Lead Scoring & Business Validation • Confirm the legitimacy and physical presence of potential customers, partners, or competitors using verified Google Data • Rank leads based on proximity, star ratings, review volume, or completeness of listing • Filter spammy or low-quality leads using negative review keywords and tag summaries • Validate ABM targets before outreach using enriched business details like phone, website, and hours

Location Intelligence & Market Mapping • Visualize company distributions across geographies using Google Maps coordinates and ZIPs • Understand market saturation, density, and white space across business categories • Identify underserved ZIP codes or local business deserts • Track presence and expansion across regional clusters and industry corridors

Company Risk & Brand Reputation Scoring • Monitor Google Maps reviews for sentiment signals such as “scam”, “spam”, “calls”, or service complaints • Detect risk-prone or underperforming locations using star rating distributions and review counts • Evaluate consistency of open hours, contact numbers, and categories for signs of listing accuracy or abandonment • Integrate risk flags into investment models, KYC/KYB platforms, or internal alerting systems

CRM & RevOps Enrichment • Enrich CRM or lead databases with phone numbers, web domains, physical addresses, and geolocation from Google Data • Use business category classification for segmentation and routing • Detect duplicates or outdated data by matching your records with the most current Google listing • Enable advanced workflows like field-based rep routing, localized campaign assignment, or automated ABM triggers

Business Intelligence & Strategic Planning • Build dashboards powered by Google Maps data, including business counts, category distributions, and review activity • Overlay business presence with population, workforce, or customer base for location planning • Benchmark performance across cities, regions, or market verticals • Track mobility and change by comparing past and current Google Maps metadata

DEI, ESG & Ownership Profiling • Identify minority-owned, women-owned, or other diversity-flagged companies using Google Data ownership attributes • Build datasets aligned with supplier diversity mandates or ESG investment strategies • Segment location insights by ownership type ...

The Google Reviews dataset is perfect for obtaining comprehensive insights into businesses and their customer feedback globally. Easily filter by location, business type, or reviewer details to extract the precise data you need. The Google Reviews dataset includes key data points such as URL, place ID, place name, country, address, review ID, reviewer name, total reviews and photos by the reviewer, reviewer profile URL, and more. This dataset provides valuable information for sentiment analysis, business comparisons, and customer behavior studies.

Khulna Places: Multimodal Google Maps Dataset 🇧🇩

This dataset contains structured and raw information about places and businesses in Khulna, Bangladesh, collected from Google Maps.
It is designed to support exploratory data analysis, machine learning, NLP, computer vision, and multimodal research.

📂 Dataset Contents

The dataset includes five files, each serving a different purpose:

1. khulna.json

Raw, unprocessed Google Maps place data. Includes full nested information such as images, reviews, opening hours, geolocation, and additional metadata.
Recommended for advanced users who want complete data fidelity.

2. khulna_all_in_one.csv

A flattened, single-table version of the dataset where each row represents one place.
Complex fields (images, reviews, opening hours, attributes) are stored as JSON-formatted strings.

3. places_core.csv

Clean, place-level structured data containing: - Place name and category - Description (when available) - Address and city - Latitude and longitude - Ratings and review counts - Business metadata

Suitable for EDA, ML modeling, and geospatial analysis.

4. place_images.csv

Image metadata associated with places, including: - Image URLs - Author names - Upload timestamps

This file enables multimodal learning and computer vision experiments. Note: Only image URLs are provided; images are not hosted in this dataset.

5. reviews.csv

User-generated reviews linked to places, containing: - Star ratings - Review text - Language - Publication timestamps

Useful for NLP tasks such as sentiment analysis and opinion mining.

🔍 Key Features

• Multi-category urban place data
• Geolocation (latitude & longitude)
• Ratings and popularity metrics
• User reviews for NLP
• Image URLs for multimodal research
• Real-world, imperfect data for practical ML work

🧠 Possible Use Cases

• Exploratory data analysis (EDA)
• Business category classification
• Sentiment analysis on reviews
• Multimodal learning (text + image)
• Geospatial analysis and mapping
• Recommendation systems
• Weakly supervised learning

⚠️ Notes

• Some fields may contain missing values, reflecting real-world data.
• Image descriptions are not provided; users may generate captions using computer vision models.
• JSON-like columns in CSV files can be parsed back into Python objects if needed.

📜 Disclaimer

This dataset is shared strictly for educational and research purposes. All data originates from publicly accessible sources.

🙌 Acknowledgement

If you use this dataset in a project or publication, please consider crediting the dataset source.

Unlock a top 3 position on Google Maps with a strong profile: accurate details, well-managed reviews, helpful photos and solid website signals.

The National Geologic Map Database (NGMDB) is a Congressionally mandated national archive of geoscience maps, reports, and stratigraphic information. The Geologic Mapping Act of 1992 and its Reauthorizations calls for the U.S. Geological Survey and the Association of American State Geologists (AASG) to cooperatively build this national archive, according to technical and scientific standards whose development is coordinated by the NGMDB. The NGMDB consists of a comprehensive set of publication citations, stratigraphic nomenclature, downloadable content in raster and vector formats, unpublished source information, and guidance on standards development. The NGMDB contains information on more than 110,000 maps and related geoscience reports published from the early 1800s to the present day, by more than 630 agencies, universities, associations, and private companies.

This case study document provides information on how Google Maps is using our open datasets and articulates citizen benefits.

The Overture Maps Foundation dataset in BigQuery provides a comprehensive and detailed collection of geospatial data aimed at fostering the development of high-quality maps and location-based applications. This dataset includes a wide range of map-related data such as road networks, administrative boundaries, points of interest, and natural features. The data is meticulously collected and updated through contributions from various stakeholders, including government agencies, private companies, and the open-source community. This ensures that the dataset remains current and accurate, supporting various applications from navigation systems to urban planning. The dataset spans multiple geographic regions and covers various aspects of mapping, from detailed street-level data to broader administrative boundaries. It is structured to facilitate easy querying and integration with other geospatial data, making it a valuable resource for developers, researchers, and analysts. For detailed information about the data schema, attributes, and usage, please refer to the data dictionary . This dataset is provided and maintained by CARTO . When using this dataset, please cite it as "Overture Maps Foundation, BigQuery Dataset." For more information on the dataset and citation guidelines, visit the Overture Maps Foundation citation page . This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Federal contract award data for Google Maps On-line Mapping API Service, awarded by ADMINISTRATIVE OFFICE OF THE US COURTS in the Other Computer Related Services sector. Sourced from SAM.gov federal procurement database.

Ini adalah dataset review google maps berbahasa Indonesia. Dataset ini berisi tentang ulasan atau review dari tempat wisata di Kabupaten Pacitan, Jawa Timur Indonesia.

Dataset ini terdiri dari beberapa bagian : - Bagian 'raw' berisi 5 file csv yang mentah. Data masih sangat mentah dan bisa dilakukan pemrosesan seperti pembersihan dan tokenisasi. - Bagian 'GMaps_Review_Classified' berisi data teks yang sudah diberikan label atau sudah diklasifikasi. Klasifikasi yang dilakukan adalah klasifikasi emosi yang merupakan hasil proyek dari author https://medium.com/@wildanabidd/klasifikasi-emosi-dan-pemodelan-topik-ulasan-wisatawan-di-pacitan-7be423c69dd5. - Bagian 'GMaps_Review_Cleaned' berisi data teks yang belum diklasifikasikan, disini anda bisa melakukan klasifikasi yang seperti dataset sebelumnya.

The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.