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 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.

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

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

Job Data Scraper Project

This project is designed to scrape job listing data from a public website, allowing you to extract and organize important job-related information. It can be used to build a database of job listings or to gain insights into the job market.

Extracted Features

The following features are extracted from each job listing:

• Identifiers:

  • _version_: The version identifier of the job data.
  • guid: A unique identifier for the job listing.
  • reqid: The job requisition ID.
  • buid: A unique business ID.
  • id: A general identifier for the job.

• Geographical Information:

  • GeoLocation: Geographic coordinates of the job location.
  • country_exact: The exact country where the job is located.
  • city_exact: The exact city of the job location.
  • state_exact: The state or region of the job location.
  • postal_code: The postal code of the job location.

• Chronological Information:

  • date_added: The date when the job was added to the listing.
  • date_new: The date when the job became new.
  • date_updated: The last date when the job was updated.
  • salted_date: A derived date attribute.

• Company Information:

  • company_exact: The exact name of the company offering the job.
  • company_member: A specific identifier related to the company's status.

• Additional Information:

  • federal_contractor: Indicates whether the company is a federal contractor.
  • is_posted: Shows whether the job is currently posted.
  • network: Information about the company's network.
  • on_sites: Information about the onsite locations for the job.

• Job Details:

  • title_exact: The exact title of the job.
  • score: A score associated with the job listing.
  • description: The full job description.

Libraries and Skills Used

The project uses the following libraries and skills to achieve its purpose:

• Libraries:

  • requests: To send HTTP requests and fetch job data.
  • csv: To create and write data into a CSV file.
  • os: To check for the existence of files and other operating system operations.
  • random.choice: For rotating user agents to avoid detection when sending HTTP requests.

• Skills:

  • Data scraping to collect information from a public website.
  • Data extraction to pull specific features from JSON responses.
  • Data organization to structure and save data into a CSV file.
  • Error handling to manage failed HTTP requests and retry logic.
  • Pagination handling to iterate through job listings.

Applications

This project can be used to create a database of job listings, track job trends, or analyze the job market. The extracted data provides a comprehensive view of job details, company information, and location-based features. With these capabilities, this project can serve as a valuable tool for job seekers, recruiters, and business analysts.

PETERS, Shanan E.1, ROSS, Ian2, CZAPLEWSKI, John3 and LIVNY, Miron2, (1)Department of Geoscience, University of Wisconsin–Madison, 1215 W. Dayton St, Madison, WI 53706, (2)Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, (3)Department of Geoscience, University of Wisconsin-Madison, 1215 W Dayton St, Madison, WI 53706

Modern scientific databases simplify access to data and information, but a large body of knowledge remains within the published literature and is therefore difficult to access and leverage at scale in scientific workflows. Recent advances in machine reading and learning approaches to converting unstructured text, tables, and figures into structured knowledge bases are promising, but these software tools cannot be deployed for scientific research purposes without access to new and old publications and computing resources. Automation of such approaches is also necessary in order to keep pace with the ever-growing scientific literature. GeoDeepDive bridges the gap between scientists needing to locate and extract information from large numbers of publications and the millions of documents that are distributed by multiple different publishers every year. As of August 2018, GeoDeepDive (GDD) had ingested over 7.4 million full-text documents from multiple commercial, professional society, and open-access publishers. In accordance with GDD-negotiated publisher agreements, original documents and citation metadata are stored locally and prepared for common data mining activities by running software tools that parse and annotate their contents linguistically (natural language processing) and visually (optical character recognition). Vocabularies of terms in domain-specific databases can be labeled throughout the full-text of documents, with results exposed to users via an API. New vocabularies and versions of parsing and annotation tools can be deployed rapidly across all original documents using the distributed computing capacities provided by HTCondor. Downloading, storing, and pre-processing original PDF content from distributed publishers and making these data products available to user applications provides new mechanisms for discovering and using information in publications, augmenting existing databases with new information, and reducing time-to-science.

This data includes offices where Forest Service employees work or where IT equipment is housed. There is no Personally Identifiable Information (PII) data in this dataset, nor telework locations. It includes owned, leased and shared offices. Shared offices are buildings owned or leased by another entity (i.e. a university, other federal agency, etc.) but one or more Forest Service employee(s) work at the building or IT equipment is housed at the building.Depicts the spatial locations for Office locations from the Forest Service CIO Asset Management Office. It includes owned, leased and shared offices. Data is collected, maintained and stewarded by the CIO Asset Management Office. EDW data loading tools extract the office location data from the CIO Asset Mgt. database. Latitude and longitude values are validated and then converted to spatial point data. Spatial point data and associated attributed data describing the office location are inserted into the Office Location Feature class in the Enterprise Data Warehouse. Changes to the Office Location data are checked daily by EDW data loading tools. Data is updated weekly. Data is visible at all scales and zoom levels. Metadata and Downloads.

The ViTexOCR script presents a new method for extracting navigation data from videos with text overlays using optical character recognition (OCR) software. Over the past few decades, it was common for videos recorded during surveys to be overlaid with real-time geographic positioning satellite chyrons including latitude, longitude, date and time, as well as other ancillary data (such as speed, heading, or user input identifying fields). Embedding these data into videos provides them with utility and accuracy, but using the location data for other purposes, such as analysis in a geographic information system, is not possible when only available on the video display. Extracting the text data from imagery using software allows these videos to be located and analyzed in a geospatial context. The script allows a user to select a video, specify the text data types (e.g. latitude, longitude, date, time, or other), text color, and the pixel locations of overlay text data on a sample video frame. The script’s output is a data file containing the retrieved geospatial and temporal data. All functionality is bundled in a Python script that incorporates a graphical user interface and several other software dependencies.

Boston From Location Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Building footprint and height data were obtained from the latest global 3D building database. The building footprint data originated from Microsoft and Google datasets. Building height information was estimated using an XGBoost machine learning regression model that integrates multi-source remote sensing features. The height estimation model was trained using datasets from ONEGEO Map, Microsoft, Baidu, and EMU Analytics, utilizing 2020 data for the final estimations. Validation of this database demonstrates that the height estimation models perform exceptionally well at a global scale across both the Northern and Southern Hemispheres. The estimated heights closely match reference height data, especially for landmark buildings, showcasing superior accuracy compared to other global height products. The 3D building data that support this dataset are available in Zenodo DOI:10.5194/essd-16-5357-2024 (Che, Y., Li, X., Liu, X., Wang, Y., Liao, W., Zheng, X., Zhang, X., Xu, X., Shi, Q., Zhu, J., Yuan, H., and Dai, Y. 3D-GloBFP: the first global three-dimensional building footprint dataset. Earth System Science Data)Based on the 3D building database, we verify the locations and boundaries of individual cultural heritage sites and their buffer zones using UNESCO's heritage map platform (https://whc.unesco.org/), and categorize heritage into three groups for data extraction:Broad Scale Sites: For sites encompassing continuous building clusters or portions of cities (e.g., City of Bath), we extract buildings within the designated buffer zones provided by the UNESCO platform.Single Building Sites: For individual monuments or structures (e.g., Tower of London), we precisely extract the building footprints based on their exact coordinates.Multiple Dispersed Buildings: For sites consisting of multiple, non-contiguous structures (e.g., Wooden Churches of Southern Małopolska, Poland), we identify each location using the platform’s data and verify them through Google Maps before extracting the relevant buildings.A few linear heritage sites, such as extensive archaeological routes spanning over a thousand kilometers, are excluded due to the complexities associated with their vast spatial extent and the variability of climate conditions across different segments.The effective data coverage varies across continents: Europe and North America have an effective rate of 82.5%, Asia and the Pacific 68.3%, Latin America and the Caribbean 75.7%, Arab States 76.5%, and Africa 49.2%. This variability reflects differences in data availability. In less developed regions, remote sensing data tends to overlook non-urban heritage sites, and soil and rock structures common in Africa and Southeast Asia are more difficult to detect using satellite remote sensing techniques, leading to lower effective data coverage in these regions.This dataset accompanies the following published article:Chen, Zihua, Gao, Qian, Wu, Yajing, Li, Jiaxin, Li, Xiaowei, Li, Xiao, Wang, Zhenbo, & Cui, Haiyang (2025). World Cultural Heritage sites are under climate stress and no emissions mitigation pathways can uniformly protect them. Communications Earth & Environment, 6:628. https://doi.org/10.1038/s43247-025-02603-8

Export: Location of Producer: Chongqing data was reported at 5.379 USD bn in Mar 2025. This records an increase from the previous number of 4.337 USD bn for Feb 2025. Export: Location of Producer: Chongqing data is updated monthly, averaging 1.415 USD bn from Jan 1998 (Median) to Mar 2025, with 327 observations. The data reached an all-time high of 7.695 USD bn in Dec 2021 and a record low of 23.549 USD mn in Jan 1999. Export: Location of Producer: Chongqing data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under Global Database’s China – Table CN.JB: USD: Export by Location of Producer.

Overview: This dataset is designed for finetuning Large Language Models (LLMs) to extract geo-location information from text inputs. It consists of two JSON files: training.json and validation.json, which are used for training and validating the model, respectively.

Format: Each entry in the JSON files is a dictionary with two key-value pairs: - "input": A string containing a description of a location, often including hints about its geographical location, landmarks, and facilities. - "output": A string representing the extracted geo-location, typically a city or a specific place within a city.

Example Entry: { "input": "this is one of the high end malls in heart of chennai. can be easily accessed. you have all in one place with excellent infrastructure. the food court has all cuisines where one can relax/enjoy after a tiresome shopping. the multiplex inside with the big bazaar is an...", "output": "Chennai" }

Usage: The dataset is used to train and validate LLMs to identify and extract geo-location information from text. The models are finetuned using the PEFT-Lora approach, which is a practice for efficient finetuning of language models.

Applications: The finetuned models can be used in various applications, such as location-based services, geo-tagging content, and analyzing geographical references in text data.

Export: Location of Producer: Hebei: Qinhuangdao data was reported at 544.406 USD mn in Mar 2025. This records an increase from the previous number of 471.445 USD mn for Feb 2025. Export: Location of Producer: Hebei: Qinhuangdao data is updated monthly, averaging 270.225 USD mn from Jan 2006 (Median) to Mar 2025, with 231 observations. The data reached an all-time high of 821.678 USD mn in Mar 2023 and a record low of 63.368 USD mn in Jan 2006. Export: Location of Producer: Hebei: Qinhuangdao data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JB: USD: Export by Location of Producer.

Trip related italian location data. The data, collected completely anonymous, can be used: raw, to train AI algorithms, into other software related developing, trace back users' trips (thanks to our unique ID number, not linked in any way to user personal data), or for several further processings (e.g. for statistical purposes). The dataset can be also processeed to extract other meaningful location data such as POIs, or specific locations (E.g. Limited traffic zone entrances). Customizations are also available.

Export: Location of Exporter: Fujian data was reported at 13.151 USD bn in Mar 2025. This records an increase from the previous number of 8.542 USD bn for Feb 2025. Export: Location of Exporter: Fujian data is updated monthly, averaging 5.131 USD bn from Jan 1995 (Median) to Mar 2025, with 362 observations. The data reached an all-time high of 21.396 USD bn in Jun 2022 and a record low of 359.813 USD mn in Jan 1995. Export: Location of Exporter: Fujian data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under Global Database’s China – Table CN.JB: USD: Export by Location of Exporter.

Export: Location of Exporter: Hainan data was reported at 1.089 USD bn in Mar 2025. This records an increase from the previous number of 854.110 USD mn for Feb 2025. Export: Location of Exporter: Hainan data is updated monthly, averaging 149.207 USD mn from Jan 1995 (Median) to Mar 2025, with 362 observations. The data reached an all-time high of 1.885 USD bn in Jul 2024 and a record low of 33.225 USD mn in Feb 2002. Export: Location of Exporter: Hainan data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under Global Database’s China – Table CN.JB: USD: Export by Location of Exporter.

Files are located here, defining the locations of the pressure transducers on the HIRENASD model. These locations also correspond to the locations that analysts should extract the pressure coefficient data from their results for comparison with experimental data and comparison with other analyses.

Export: Location of Exporter: Guangdong: Dongguan data was reported at 10.572 USD bn in Mar 2025. This records an increase from the previous number of 8.185 USD bn for Feb 2025. Export: Location of Exporter: Guangdong: Dongguan data is updated monthly, averaging 10.097 USD bn from Jan 2015 (Median) to Mar 2025, with 122 observations. The data reached an all-time high of 16.629 USD bn in Sep 2021 and a record low of 4.101 USD bn in Feb 2020. Export: Location of Exporter: Guangdong: Dongguan data remains active status in CEIC and is reported by General Administration of Customs. The data is categorized under China Premium Database’s International Trade – Table CN.JB: USD: Export by Location of Exporter.

Real, long-term data collected from three participants using a Place Lab client, from which the authors extract significant places. ; jhkang@cs.washington.edu

Location Data as a Service Market Outlook

According to our latest research, the global Location Data as a Service (LDaaS) market size reached USD 2.85 billion in 2024, reflecting robust adoption across a broad spectrum of industries. The market is projected to expand at a CAGR of 18.6% from 2025 to 2033, reaching a forecasted value of USD 14.78 billion by 2033. This remarkable growth is driven by the accelerating demand for location intelligence in advertising, logistics, and urban planning, as organizations increasingly recognize the value of real-time geospatial data in enhancing operational efficiency and customer engagement.

One of the most significant growth factors for the Location Data as a Service market is the proliferation of connected devices and the Internet of Things (IoT). With billions of smartphones, vehicles, and IoT sensors generating vast volumes of geolocation data, businesses are leveraging LDaaS platforms to extract actionable insights for strategic decision-making. The shift towards digital transformation, especially post-pandemic, has amplified the need for precise, real-time location data to optimize supply chains, personalize consumer experiences, and manage assets. Moreover, the integration of artificial intelligence and machine learning with location data services is enabling advanced analytics, predictive modeling, and automation, further fueling market expansion.

Another pivotal driver is the growing adoption of location-based advertising and marketing. Enterprises across retail, financial services, and entertainment sectors are utilizing LDaaS to target consumers with personalized offers, enhance foot traffic, and measure campaign effectiveness. The ability to analyze consumer movement patterns, visit frequencies, and dwell times empowers marketers to craft hyper-localized strategies, leading to improved ROI and customer loyalty. Furthermore, regulatory advancements and increased transparency in data collection practices are bolstering consumer trust, encouraging broader utilization of location data in compliance with privacy standards.

The rapid evolution of smart cities and intelligent transportation systems is also contributing substantially to the growth of the Location Data as a Service market. Governments and urban planners are deploying LDaaS solutions to monitor traffic flows, manage public transportation, and design infrastructure projects with precision. The integration of mapping, geocoding, and real-time analytics supports disaster response, resource allocation, and environmental monitoring, enabling cities to become more resilient and sustainable. As urbanization accelerates globally, the demand for scalable, cloud-based location data solutions is expected to surge, establishing LDaaS as a cornerstone of modern urban management.

From a regional perspective, North America currently dominates the LDaaS market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region’s leadership can be attributed to the presence of major technology firms, advanced telecommunications infrastructure, and high digital adoption rates. However, Asia Pacific is anticipated to exhibit the fastest growth over the forecast period, driven by rapid urbanization, expanding e-commerce, and increasing investments in smart city initiatives. Latin America and the Middle East & Africa are also showing promising potential, as governments and enterprises in these regions embrace digital transformation and data-driven decision-making.

Component Analysis

The Component segment of the Location Data as a Service market is bifurcated into Platform and Services. The platform sub-segment forms the backbone of LDaaS offerings, providing the core infrastructure for data collection, processing, storage, and analytics. These platforms are designed to handle vast streams of geospatial data from a variety of sources, including mobile devices, GPS sensors, and satellite feeds. With the increasing complexity of location data and the need for real-time processing, platforms are evolving to incorporate advanced features such as AI-powered analytics, seamless integration with third-party systems, and robust APIs, ensuring scalability and flexibility for diverse business needs.

The services sub-segment, comprising consulting, integration, support, and managed services, is