As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

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.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

👨‍💻 Author: Azhar Saleem

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Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

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​.

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.

It starts with a simple habit: you open your browser and type a question. A few keystrokes later, Google gives you answers, videos, maps, and suggestions before you even finish your thought. For billions of people around the world, this daily interaction is second nature. But behind that blinking cursor...

Context

Realising which routes a taxi takes while going from one location to another gives us deep insights into why some trips take longer than others. Also, most taxis rely on navigation from Google Maps, which reinforces the use case of this dataset. On a deeper look, we can begin to analyse patches of slow traffic and number of steps during the trip (explained below).

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Content

The data, as we see it contains the following columns :

• trip_id, pickup_latitude, pickup_longitude (and equivalents with dropoff) are picked up from the original dataset.
• distance : Estimates the distance between the start and the end latitude, in miles.
• start_address and end_address are directly picked up from the Google Maps API
• params : Details set of parameters, flattened out into a single line. (Explained below)

Parameters

The parameters field is a long string of a flattened out JSON object. At its very basic, the field has space separated steps. The syntax is as follows :

Step1:{ ... }, Step2:{ ...

Each step denotes the presence of an intermediate point.

Inside the curly braces of each of the steps we have the distance for that step measured in ft, and the start and end location. The start and end location are surrounded by round braces and are in the following format :

Step1:{distance=X ft/mi start_location=(latitude, longitude) end_location ...}, ...

One can split the internal params over space to get all the required values.

Acknowledgements

All the credit for the data goes to the Google Maps API, though limited to 2000 queries per day. I believe that even that limited amount would help us gain great insights.

Future prospects

• More data : Since the number of rows processed are just 2000, with a good response we might be able to get more. If you feel like contributing, please have a look at the script here and try and run in for the next 2000 rows.

• Driver instructions : I did not include the driver instruction column in the data from the google API as it seemed to complex to use in any kind of models. If that is not the general opinion, I can add it here.

This is a GPS dataset acquired from Google.

Google tracks the user’s device location through Google Maps, which also works on Android devices, the iPhone, and the web. It’s possible to see the Timeline from the user’s settings in the Google Maps app on Android or directly from the Google Timeline Website. It has detailed information such as when an individual is walking, driving, and flying. Such functionality of tracking can be enabled or disabled on demand by the user directly from the smartphone or via the website. Google has a Take Out service where the users can download all their data or select from the Google products they use the data they want to download. The dataset contains 120,847 instances from a period of 9 months or 253 unique days from February 2019 to October 2019 from a single user. The dataset comprises a pair of (latitude, and longitude), and a timestamp. All the data was delivered in a single CSV file. As the locations of this dataset are well known by the researchers, this dataset will be used as ground truth in many mobility studies.

Please cite the following papers in order to use the datasets:

T. Andrade, B. Cancela, and J. Gama, "Discovering locations and habits from human mobility data," Annals of Telecommunications, vol. 75, no. 9, pp. 505–521, 2020. 10.1007/s12243-020-00807-x (DOI)and T. Andrade, B. Cancela, and J. Gama, "From mobility data to habits and common pathways," Expert Systems, vol. 37, no. 6, p. e12627, 2020.10.1111/exsy.12627 (DOI)

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

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

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.

Web map tile service (WMTS), which enables viewing of cadastral map both in digital and analogue form. Data are provided in a form of map tiles in WGS84/Pseudo-Mercator coordinate reference system. A Google Maps compatible scale set is used. The service fulfills the OGC WMTS 1.0.0 standard. The service is publicly available, free-of-charge and covers the whole territory of the Czech Republic. All layers except overview map are provided in levels 17 (1 : 4 265) to 25 (1 : 16).

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.

Offline Maps for Travel Market Outlook

According to our latest research, the offline maps for travel market size reached USD 4.21 billion globally in 2024, registering a robust growth trajectory. The market is expected to expand at a CAGR of 11.8% from 2025 to 2033, with the forecasted market value projected to reach USD 11.74 billion by 2033. This significant growth is primarily driven by rising global travel activity, increasing smartphone penetration, and growing concerns over connectivity and data privacy during travel. As per the latest research, the demand for reliable, data-independent navigation solutions is shaping the future of the offline maps for travel market.

One of the primary growth factors propelling the offline maps for travel market is the surge in international and domestic travel, particularly among millennials and Gen Z travelers. With the proliferation of budget airlines, improved visa policies, and the emergence of experiential travel trends, more individuals are exploring remote and off-the-grid destinations where internet connectivity is often unreliable or unavailable. In such scenarios, offline maps become indispensable tools, providing travelers with uninterrupted access to navigation, points of interest, and route planning. Furthermore, the increasing popularity of adventure tourism, including hiking, biking, and camping, is fueling the adoption of offline maps, as travelers seek to navigate challenging terrains with confidence, regardless of network availability.

Another key driver is the advancement in smartphone technology and the integration of sophisticated offline mapping functionalities within mobile applications. Modern navigation apps now offer features such as turn-by-turn directions, offline search, and real-time location tracking without the need for an active data connection. These innovations have significantly enhanced the user experience, making offline maps not only a backup solution but a primary navigation tool for many travelers. Additionally, heightened concerns over data privacy, particularly when using public or unsecured Wi-Fi networks abroad, have led to a preference for offline solutions that minimize data exposure and potential cyber threats. This shift is further supported by the growing awareness among travelers regarding the risks associated with sharing location data with third-party services.

The offline maps for travel market is also benefiting from strategic partnerships and collaborations between map developers, tourism boards, and local governments. Many destinations are now promoting the use of offline maps to enhance visitor experiences, reduce congestion at popular sites, and support sustainable tourism initiatives. For instance, tourism authorities are increasingly offering downloadable maps that highlight eco-friendly routes, cultural landmarks, and local businesses, thereby fostering economic growth within communities. These initiatives not only boost the adoption of offline maps but also align with broader trends in responsible and tech-enabled travel.

From a regional perspective, the Asia Pacific region is emerging as a major growth engine for the offline maps for travel market, driven by the rapid expansion of the travel and tourism sector in countries such as China, India, Japan, and Southeast Asia. The region's vast and diverse geography, coupled with varying levels of internet infrastructure, underscores the need for reliable offline navigation solutions. North America and Europe also continue to hold significant market shares, supported by high smartphone adoption rates, advanced digital ecosystems, and a strong culture of independent travel. Meanwhile, Latin America, the Middle East, and Africa are witnessing increasing uptake of offline maps, spurred by growing mobile internet penetration and the rising popularity of adventure and eco-tourism.

Product Type Analysis

The product type segment of the offline maps for travel market is categorized into navigation apps, downloadable map software, and dedicated GPS devices. Navigation apps have emerged as the leading product type, owing to their widespread availability, user-friendly interfaces, and seamless integration with smartphones. These apps, such as Google Maps, Maps.me, and Sygic, allow users to download maps for offline use, providing essential navigation features even in areas with limited or no connectivity. The convenience of having a comprehensive navigation tool on

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. 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.

Comprehensive business dataset extracted from Google Maps containing 21 data points per business including contact information, ratings, hours, and location data.

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.

Google Data with verified US business listings from Google Maps, including locations, reviews, hours, and ratings. This Google Data is updated weekly and fully customizable — ideal for lead scoring, market mapping, location analysis, and CRM enrichment.