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 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.

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.

We are developing a set of NASA Extensions to the Google Maps API—and soon to other frameworks such as OpenLayers as well—that will make these platforms more useful to NASA scientists and our colleagues elsewhere.

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 the booming Google Maps Platform consulting market: market size, growth drivers, key trends like AI integration, and restraints, with insights for large enterprises and SMEs.

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).

http://www.thethinkingstick.com/images/2015/03/vpq.gif" alt="enter image description here">

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.

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 the dynamic Custom Digital Map Service market, driven by automotive innovation and location intelligence. Discover market size, CAGR, key drivers, and future trends for 2025-2033.

Dataset used for CIL ETH Zürich road segmentation semester Project. Data from Google Maps API. Data from 13 major cities. You find satellite images, map images road segmentation and lower resolution road segmentation (16x16 pixel blocks coloured as road if at least 25% road was present).

Sample EV-to-RCS data obtained using Google Maps API.

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.

Context

This is a supplementary dataset to the Data Science for Good: Kiva Crowdfunding challenge. In the Kiva challenge, the kiva_loans.csv file contains a large record of loans with the borrower's locations. This dataset provides the latitude and longitude of these locations.

The original dataset also includes another file loan_themes_by_region.csv, which provides some additional information on geographical locations of the loan themes offered. However, there are significantly more borrower locations than loan theme locations, and these two locations are not always the same. This dataset tries to solve this problem by directly obtaining the geocode of all borrower locations via Google Maps Geocoding API.

Content

There are four columns in the CSV. "Region" and "country" match the corresponding fields in kiva_loans.csv. "Latitude" and "longitude" are self-explanatory. Queries without valid results from the Google Maps API are indicated by latitude=-999, longitude=-999.

The geocodes are not manually validated and should be used with caution. Bad query results may happen due to mistakes in the original dataset or Google Maps' autocorrection.

Acknowledgements

The building of this dataset uses the following API: https://developers.google.com/maps/documentation/geocoding/intro

Inspiration

This dataset can help participants in the Kiva challenge by allowing them to compare location proximity and visualise data on a world/regional map when analysing Kiva's loans. The original purpose of the dataset is for me to visualise loan type clustering results on a world map and find similarities in borrower needs between remote, disjoint regions, however I hope the community will find better, more creative uses for this tiny dataset.

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live Map Data as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live Map Data include:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live Map Data include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

This dataset contains over 5000 reviews collected from multiple branches of Easy Bakery. The data was collected using SerpApi, a real-time API to access Google search results. The dataset offers opportunities for various analysis tasks.

Spatial dispersion regression results.

http://www.openstreetmap.org/images/osm_logo.png" alt=""/> OpenStreetMap (openstreetmap.org) is a global collaborative mapping project, which offers maps and map data released with an open license, encouraging free re-use and re-distribution. The data is created by a large community of volunteers who use a variety of simple on-the-ground surveying techniques, and wiki-syle editing tools to collaborate as they create the maps, in a process which is open to everyone. The project originated in London, and an active community of mappers and developers are based here. Mapping work in London is ongoing (and you can help!) but the coverage is already good enough for many uses.

Browse the map of London on OpenStreetMap.org

Downloads:

The whole of England updated daily:

• england.osm.bz2 ~185M - .osm formatted raw XML data (compressed)
• england.shp.zip ~156M - ESRI shapefiles

For more details of downloads available from OpenStreetMap, including downloading the whole planet, see 'planet.osm' on the wiki.

Data access APIs:

Download small areas of the map by bounding-box. For example this URL requests the data around Trafalgar Square:
http://api.openstreetmap.org/api/0.6/map?bbox=-0.13062,51.5065,-0.12557,51.50969

Data filtered by "tag". For example this URL returns all elements in London tagged shop=supermarket:
http://www.informationfreeway.org/api/0.6/*[shop=supermarket][bbox=-0.48,51.30,0.21,51.70]

The .osm format

The format of the data is a raw XML represention of all the elements making up the map. OpenStreetMap is composed of interconnected "nodes" and "ways" (and sometimes "relations") each with a set of name=value pairs called "tags". These classify and describe properties of the elements, and ultimately influence how they get drawn on the map. To understand more about tags, and different ways of working with this data format refer to the following pages on the OpenStreetMap wiki.

• .osm - About the raw XML data format of OpenStreetMap
• Downloading data - More details of the API and extract download options
• Xapi - The extended api for filtering by tag
• Map Features - A list of tags, and what map features they represent
• Converting map data between formats - catalogue of tools for converting from .osm to other formats

Simple embedded maps

Rather than working with raw map data, you may prefer to embed maps from OpenStreetMap on your website with a simple bit of javascript. You can also present overlays of other data, in a manner very similar to working with google maps. In fact you can even use the google maps API to do this. See OSM on your own website for details and links to various javascript map libraries.

Help build the map!

The OpenStreetMap project aims to attract large numbers of contributors who all chip in a little bit to help build the map. Although the map editing tools take a little while to learn, they are designed to be as simple as possible, so that everyone can get involved. This project offers an exciting means of allowing local London communities to take ownership of their part of the map.

Read about how to Get Involved and see the London page for details of OpenStreetMap community events.

Real-time pollen levels and air quality data from Google Maps API

Documented March 19, 2023

!!NEW!!!

GeoDAR reservoirs were registered to the drainage network! Please see the auxiliary data "GeoDAR-TopoCat" at https://zenodo.org/records/7750736. "GeoDAR-TopoCat" contains the drainage topology (reaches and upstream/downstream relationships) and catchment boundary for each reservoir in GeoDAR, based on the algorithm used for Lake-TopoCat (doi:10.5194/essd-15-3483-2023).

Documented April 1, 2022

Citation

Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, M. S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: georeferenced global dams and reservoirs database for bridging attributes and geolocations. Earth System Science Data, 14, 1869–1899, 2022, https://doi.org/10.5194/essd-14-1869-2022.

Please cite the reference above (which was fully peer-reviewed), NOT the preprint version. Thank you.

Contact

Dr. Jida Wang, jidawang@ksu.edu, gdbruins@ucla.edu

Data description and components

Data folder “GeoDAR_v10_v11” (.zip) contains two consecutive, peer-reviewed versions (v1.0 and v1.1) of the Georeferenced global Dams And Reservoirs (GeoDAR) dataset:

• GeoDAR_v10_dams (in both shapefile format and the comma-separated values (csv) format): GeoDAR version 1.0, including 22,560 dam points georeferenced based on the World Register of Dams (WRD), the International Commission on Large Dams (ICOLD; https://www.icold-cigb.org; last access on March 13th, 2019).
• GeoDAR_v11_dams (in both shapefile and csv): GeoDAR version 1.1 dam points, including 24,783 dams which harmonized GeoDAR_v10_dams and the Global Reservoir and Dam Database (GRanD) v1.3 (Lehner et al., 2011).
• GeoDAR_v11_reservoirs (in shapefile): GeoDAR version 1.1 reservoirs, including 21,515 reservoir polygons retrieved by associating GeoDAR_v11_dams with GRanD v1.3 reservoirs, HydroLAKES v1.0 (Messager et al., 2016), and the UCLA Circa 2015 Lake Inventory (Sheng et al., 2016). The reservoir retrieval follows a one-to-one relationship between dams and reservoirs.

As by-products of GeoDAR harmonization, folder “GeoDAR_v10_v11” also contains:

• GRanD_v13_issues.csv: This file contains the original records of all 7,320 dam points in GRanD v1.3, with 94 of them marked by our identified issues and suggested corrections. These 94 records are placed at the beginning of this table. They include 89 records showing possible georeferencing and/or attribute errors, and another 5 records documented as subsumed or replaced. Our added fields start from column BG and include:
  • “Issue”: main issue(s) of this record
  • “Description”: more detailed explanation of the issue
  • “Lat_corrected”: suggested correction for latitude (if any) in decimal degree
  • “Lon_corrected”: suggested correction for longitude (if any) in decimal degree
  • “Correction_source”: correction source(s)
  • “Harmonized”: whether this GRanD dam was harmonized in GeoDAR v1.1 and the reason.
• Wada_et_al_2017_harmonized.csv: This csv file contains the original records of all 139 georeferenced large dams/reservoirs in Wada et al. (2017; doi:10.1007/s10712-016-9399-6), with our revised storage capacities and spatial coordinates for data harmonization. Our added fields start from column E and include:
  • Revised_capacity_km3: Our revised reservoir storage capacity in cubic kilometers used for harmonization
  • Revised_lat: Revised latitude in decimal degree
  • Revised_lon: Revised longitude in decimal degree
  • Verification_notes: Description of the issues, verification sources, and other information used for harmonization.

Attribute description

Pollenradar Wien presents the allergen-producing trees and their surroundings in Vienna.

The app provides valuable information for anyone who is planning excursions, sports or outdoor picnics, and is allergic to pollen.

The app allows the creation of a personal allergy profile, the flowering times of the trees are visualized in the allergy profile.

The app was developed on the basis of the Open Government data of the city of Vienna.

The data is visualized via Google Maps API.

The app needs internet access.