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.

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.

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.

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.

Discover the booming HD Live Map market, projected to reach $6,016.4 million by 2025 with a strong CAGR. This in-depth analysis explores market drivers, trends, restraints, and key players like TomTom, Google, and Baidu, across commercial, military, and other applications. Learn about regional market shares and future growth potential.

The real-time maps market is experiencing robust growth, driven by the increasing adoption of connected vehicles, the proliferation of smartphones with advanced location services, and the rising demand for precise navigation and location-based services across various sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. Key growth drivers include the integration of real-time maps into autonomous driving systems, the expansion of smart city initiatives reliant on accurate location data, and the growing popularity of location-based mobile applications. Companies like TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Sanborn, and Baidu are key players in this dynamic market, continually innovating to provide enhanced map features and data accuracy. Competitive pressures are high, with a focus on data quality, coverage, and the integration of advanced technologies like AI and machine learning for improved traffic prediction and route optimization. While the market presents significant opportunities, challenges remain. Data security and privacy concerns, the need for continuous map updates to account for dynamic road conditions, and the high infrastructure costs associated with data collection and processing are some of the key restraints. Market segmentation is primarily based on technology (cloud-based vs. on-premise), application (automotive, navigation, logistics), and geography. North America and Europe currently hold a significant market share, but the Asia-Pacific region is poised for rapid growth fueled by increased smartphone penetration and burgeoning e-commerce activities that heavily rely on accurate location data. The future of the real-time maps market hinges on the continuous improvement of map accuracy, the integration of advanced technologies, and the effective addressal of data privacy and security concerns.

This map data product delivers high-precision, real-time, and historical GPS event records across North America. It is designed for organizations that require granular spatial data for applications such as mapping, movement tracking, retail analytics, and infrastructure planning.

Data Contents: Latitude & longitude coordinates Timestamp (epoch & human-readable date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate) Optional metadata: IP address, mobile carrier, device model

Access & Delivery: Available via API with custom polygon queries (up to 10,000 tiles) for targeted location insights. Data can be delivered hourly or daily in JSON, CSV, or Parquet formats, through AWS S3, Google Cloud Storage, or direct API access. Historical coverage extends back to September 2024, with 95% of events delivered within 3 days for near-real-time analysis.

Compliance & Flexibility: GDPR and CCPA compliant Credit-based query pricing for scalability Custom schema mapping and folder structure available

Applications: Map creation and enhancement POI visitation analytics Urban mobility and transit modeling Retail site selection and catchment area mapping Real estate and zoning analysis Geospatial risk and environmental planning

Dataset Summary
This dataset provides the most accurate and comprehensive geospatial information on wind turbines in South Africa as of 2025. It includes precise turbine coordinates, detailed technical attributes, and spatially harmonized metadata across 42 wind farms. The dataset contains 1,487 individual turbine entries with validated information on turbine type, rated capacity, rotor diameter, commissioning year, and administrative regions. It was compiled by integrating OpenStreetMap (OSM) data, satellite imagery from Google and Bing, a RetinaNet-based deep learning model for coordinate correction, and manual verification.

Data Structure

• Format: GeoJSON

• Coordinate Reference System (CRS): WGS 84 (EPSG:4326)

• Number of features: 1,487

• Geometry type: Point (turbine locations)

• Key attributes:

  • id: Unique internal identifier

  • osm_id: Reference ID from OpenStreetMap

  • gid, country, type1, name1, type2, name2: Administrative region (based on GADM)

  • farm_name: Name of the wind farm

  • commissioning_year: Year the turbine was commissioned

  • number_of_turbines: Total number of turbines at the wind farm

  • total_farm_capacity: Total installed capacity of the wind farm (MW)

  • capacity_per_turbine: Rated power per turbine (MW)

  • turbine_type: Manufacturer and model of the turbine

  • geometry: Point geometry (longitude, latitude)

Publication Abstract
Accurate and detailed spatial data on wind energy infrastructure is essential for renewable energy planning, grid integration, and system analysis. However, publicly available datasets often suffer from limited spatial accuracy, missing attributes, and inconsistent metadata. To address these challenges, this study presents a harmonized and spatially refined dataset of wind turbines in South Africa, combining OpenStreetMap (OSM) data with high-resolution satellite imagery, deep learning-based coordinate correction, and manual curation. The dataset includes 1487 turbines across 42 wind farms, representing over 3.9 GW of installed capacity as of 2025. Of this, more than 3.6 GW is currently operational. The Geo-Coordinates were validated and corrected using a RetinaNet-based object detection model applied to both Google and Bing satellite imagery. Instead of relying solely on spatial precision, the curation process emphasized attribute completeness and consistency. Through systematic verification and cross-referencing with multiple public sources, the final dataset achieves a high level of attribute completeness and internal consistency across all turbines, including turbine type, rated capacity, and commissioning year. The resulting dataset is the most accurate and comprehensive publicly available dataset on wind turbines in South Africa to date. It provides a robust foundation for spatial analysis, energy modeling, and policy assessment related to wind energy development. The dataset is publicly available.

Citation Notification

If you use this dataset, please cite the following publication:

Kleebauer, M.; Karamanski, S.; Callies, D.; Braun, M. A Wind Turbines Dataset for South Africa: OpenStreetMap Data, Deep Learning Based Geo-Coordinate Correction and Capacity Analysis. ISPRS Int. J. Geo-Inf. 2025, 14, 232. https://doi.org/10.3390/ijgi14060232

This map data product delivers accurate, real-time, and historical GPS event data from across Africa, including major cities, rural regions, and transit corridors. The dataset is built for mapping, spatial analysis, mobility research, and commercial decision-making.

Data Attributes Latitude & longitude coordinates Timestamp (epoch & human-readable date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate)

Optional: IP address, mobile carrier, device model

Access & Delivery Data is available via API with polygon-based querying (up to 10,000 tiles) for precise POI or region targeting. Delivery options include hourly or daily updates in JSON, CSV, or Parquet formats, through AWS S3, Google Cloud, or direct API access. Historical coverage extends back to September 2024, and 95% of events are available within 3 days for near-real-time analysis.

Compliance & Customization GDPR & CCPA compliant sourcing Credit-based pricing for scalable usage Custom schema mapping & folder structures on request Applications Base mapping and geospatial visualization Infrastructure planning and asset tracking Retail site selection and catchment analysis Transport route optimization Urban mobility and zoning analysis Risk and environmental planning

The professional map services market is booming, projected to reach $625.6 million by 2025 with a 7% CAGR. Discover key trends, leading companies, and regional insights in this comprehensive market analysis. Learn about the impact of AI, IoT, and autonomous vehicles on this rapidly growing sector.

The automotive navigation maps market is booming, projected to reach $69.57 billion by 2033, driven by ADAS, autonomous vehicles, and connected car growth. Explore market trends, key players (Google, HERE, TomTom), and regional insights in this comprehensive analysis.

This dataset consists of map files which show bus routes covering the Greater Manchester area. The dataset is available in MapInfo .tab, Google .kml, and ESRI .shp file formats. Please acknowledge the source of this information using the following attribution statement: Contains Transport for Greater Manchester data. Contains OS data © Crown copyright and database right 2025.

This map data product delivers precise, high-quality GPS event datasets covering the entirety of South America, from major urban centers to rural areas and transportation corridors. It is purpose-built for organizations needing reliable geospatial data for mapping, infrastructure planning, mobility studies, and commercial decision-making.

Data Attributes Latitude & longitude coordinates Timestamp (epoch & formatted date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate) Optional: IP address, device model, mobile carrier

Access & Delivery Query Method: Polygon queries (up to 10,000 tiles) for precision targeting Delivery Cadence: Hourly or daily Formats: JSON, CSV, Parquet (Snappy/GZIP compression)

Endpoints: AWS S3, Google Cloud, or API Historical Coverage: Since September 2024 Freshness: 95% of events available within 3 days

Compliance & Customization Fully GDPR and CCPA compliant Credit-based pricing to scale usage Optional custom schema mapping and folder structure

Applications Urban planning & smart city development Retail location and catchment area analysis Infrastructure monitoring and asset tracking Environmental and land use mapping Logistics and transport route optimization Risk assessment and disaster planning

Discover the booming digital map ecosystem market, projected to reach $450 billion by 2033. Explore key drivers, regional trends, and leading companies shaping this rapidly evolving landscape, including autonomous vehicle integration and LBS advancements. Learn more about market size, CAGR, and segmentation analysis in this comprehensive report.

description: The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve.; abstract: The location of each site is shown on a Google Map. Data are available as a Google Map with links to Station Information and Data for each site. Data are available for 58 sites along I-75 and for 28 sites along State Road 29 in Big Cypress National Preserve.

This map data product provides accurate, real-time, and historical GPS event records across Europe. Ideal for applications in mapping, spatial analytics, and movement tracking, the dataset delivers location intelligence with granular detail and high data quality.

Data Composition Each record contains: GPS coordinates (latitude, longitude) Timestamp (epoch & date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate) Optional: IP address, carrier, device model

Access & Delivery The dataset is available via API with polygon queries (up to 10,000 tiles), enabling targeted spatial analysis for POIs, regions, or entire cities. Data can be delivered hourly or daily in JSON, CSV, or Parquet formats, via AWS S3, Google Cloud, or direct API access. Historical backfill is available from September 2024.

Key Attributes

Real-time updates with 95% of events available within 3 days Custom schema mapping & folder structures GDPR & CCPA compliant data sourcing and opt-out processes Credit-based pricing for scalability Applications Map creation and enhancement Urban mobility mapping Retail catchment analysis Transport route optimization POI mapping and visitation analytics Geospatial risk and impact studies

This is a data output from the GeoCrimeData project (http://geocrimedata.blogspot.com/). It contains Open Street Map data with derived measures of road integration (which can be used as a proxy for traffic volume). The data were derived from Open Street Map downloaded provided on the ShareGeo repository (e.g. for England: http://www.sharegeo.ac.uk/handle/10672/28) For more information about how the data was created, see: https://docs.google.com/document/d/16eNQKKxlLlh8H2Gayz86F68ZsTXUF72kJ1qiW2VUu7A/edit For other GeoCrimeData written material, see: https://docs.google.com/document/d/1gJ9B4BZNvL3w2DPfyv9vu-7P7_tnVf3F3H3rvygr1cc. Map data (c) OpenStreetMap contributors, CC-BY-SA This dataset was derived from OpenStreetMap. Access and use constraints are based on conditions set out in the OpenStreetMap Licence Agreement which can be found at http://wiki.openstreetmap.org/wiki/OpenStreetMap_License. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2011-11-10 and migrated to Edinburgh DataShare on 2017-02-21.

The Location as a Service (LaaS) industry is experiencing robust growth, projected to reach a market size of $50.85 billion in 2025, exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.11%. This expansion is driven by several key factors. The increasing adoption of mobile devices and the proliferation of location-based applications fuel the demand for precise and reliable location data. Furthermore, the rise of the Internet of Things (IoT) and the need for real-time location tracking across various industries, including logistics, transportation, and asset management, are significantly boosting market growth. The development of advanced technologies like GPS, Wi-Fi positioning, and sensor fusion is enhancing location accuracy and providing more sophisticated location intelligence. Finally, the growing focus on improving operational efficiency and enhancing customer experiences through location-based services is driving further adoption across diverse sectors. The LaaS market is segmented by various service types, including indoor positioning, map data services, location analytics, and geofencing. Major players like Ubiquicom, GL Communications Inc, HPE Aruba, IBM, Google, and Zebra Technologies are actively shaping the market landscape through technological innovations and strategic partnerships. While the industry faces challenges such as data privacy concerns and the need for consistent data quality across diverse platforms, the overall market trajectory remains strongly positive. The forecast period (2025-2033) is expected to witness continued growth driven by expanding applications in smart cities, autonomous vehicles, and augmented reality experiences. The competitive landscape is dynamic with ongoing mergers, acquisitions, and technological advancements fostering market evolution and increasing accessibility of LaaS solutions. Key drivers for this market are: Growing Demand for Geo-based Marketing, Technological Advancements Aided by Emergence of BLE and UWB for Indoor Services; Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Potential restraints include: Trade-offs Between Privacy/Security and Regulatory Constraints. Notable trends are: FMCG and E-Commerce Sector Expected to Witness Significant Growth.

The HD Map for Autonomous Driving market is booming, projected to reach $5832 million by 2035 with a 50.9% CAGR. Explore key trends, drivers, restraints, and leading companies shaping this rapidly evolving sector. Learn about market segmentation by application (L1/L2+, L3, others) and type (crowdsourcing, centralized).