Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

A complete list of live websites using the Mw Google Maps technology, compiled through global website indexing conducted by WebTechSurvey.

A complete list of live websites using the Basic Google Maps Placemarks technology, compiled through global website indexing conducted by WebTechSurvey.

The global navigation map market is experiencing robust growth, driven by the increasing adoption of navigation systems in automobiles, smartphones, and other smart devices. The market's expansion is fueled by several key factors, including the rising demand for advanced driver-assistance systems (ADAS), the proliferation of connected cars, and the growing penetration of smartphones with integrated GPS capabilities. Furthermore, the continuous improvement in map accuracy, detail, and features like real-time traffic updates and augmented reality overlays significantly enhance user experience and fuel market demand. Major players like Google, TomTom, and HERE Technologies are investing heavily in research and development to improve map data accuracy, expand coverage, and integrate innovative functionalities. This competitive landscape fosters innovation and contributes to the overall market growth. We project a steady Compound Annual Growth Rate (CAGR) of approximately 15% between 2025 and 2033, based on current market trends and technological advancements. This growth will be particularly pronounced in developing economies experiencing rapid urbanization and infrastructure development, where accurate navigation maps are crucial for efficient transportation and logistics. The market segmentation reveals a diverse landscape. While automotive navigation remains a substantial segment, the increasing use of navigation apps on smartphones and the burgeoning market for location-based services are driving significant growth in other segments. Regional variations exist, with North America and Europe currently holding significant market shares due to high smartphone penetration and advanced infrastructure. However, rapidly developing regions in Asia-Pacific and Latin America are poised for substantial growth in the coming years. Challenges such as data security concerns, the need for continuous map updates to reflect dynamic road networks, and the high cost of data acquisition and processing are potential restraints. However, ongoing technological advancements and strategic partnerships between map providers and automotive manufacturers are mitigating these challenges, ensuring sustained market expansion.

The global Digital HD Map market is experiencing robust growth, projected to reach $1558.9 million in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 24.4% from 2025 to 2033. This expansion is driven by the increasing demand for precise location data across various sectors. The automotive industry, particularly autonomous vehicles, is a major catalyst, relying heavily on highly detailed and accurate maps for navigation and safety features. Furthermore, the burgeoning use of augmented reality (AR) and virtual reality (VR) applications, coupled with the expanding smart city initiatives globally, fuels the market's growth trajectory. The rise of advanced driver-assistance systems (ADAS) and the integration of digital maps into connected car platforms also contribute significantly to this market's expansion. Competition within the market is fierce, with established players like Google, TomTom, and HERE Technologies competing alongside emerging innovative companies. The market segmentation by map type (2D HD Map, 3D HD Map) and application (Commercial Use, Military Use, Others) reflects the diverse range of applications and associated technological advancements shaping this dynamic landscape. Different regions contribute varying levels of market share, with North America and Asia-Pacific anticipated to lead due to significant technological advancements and higher adoption rates. The market's growth is not without its challenges. Data acquisition and maintenance costs remain a significant hurdle, especially for maintaining the accuracy and timeliness of high-resolution map data. Ensuring data security and privacy, particularly with the increased use of location data in various applications, presents another substantial challenge. Regulatory frameworks governing the use and collection of such data vary across different geographies, creating complexities for businesses operating internationally. Despite these challenges, the long-term prospects for the Digital HD Map market remain positive, driven by continuous technological innovations, increasing investment in autonomous driving technologies, and the expanding need for precise location intelligence across diverse industry verticals. The market is expected to see further consolidation through mergers and acquisitions as companies strive to enhance their capabilities and market share.

This map shows site address points in the City of San José and provides tools to select and download address data. Addresses are part of the City's Master Address Database (MAD), which is a comprehensive database containing authoritative physical/site addresses. Mailing addresses may differ from site addresses.This map was created for the City of San José public maps gallery. The maps gallery is a collection of maps created and maintained by the Enterprise GIS team. Maps gallery maps showcase programs, projects, and spatial information derived from City data. The information in these maps is publicly shared for the purpose of transparency and accessibility. Many maps from the maps gallery are also embedded with the City website. Much of the data that supports these maps can be directly downloaded from the Open GIS Data Portal. City of San Jose Website: https://www.sanjoseca.gov/City of San Jose Maps Gallery: https://gis.sanjoseca.gov/apps/mapsgallery/City of San Jose Open GIS Data Portal: https://gisdata-csj.opendata.arcgis.com/

The USGS Topo base map service from The National Map is a combination of contours, shaded relief, woodland and urban tint, along with vector layers, such as geographic names, governmental unit boundaries, hydrography, structures, and transportation, to provide a composite topographic base map. Data sources are the National Atlas for small scales, and The National Map for medium to large scales.

The Python code provided generates polygonal maps resembling geographical landscapes, where certain areas may represent features like lakes or inaccessible regions. These maps are generated with specified characteristics such as regularity, gap density, and gap scale.

Features:

1. Polygon Generation:

   • The code utilizes the Shapely library to generate polygonal shapes within specified bounding boxes. These polygons serve as the primary representation of the map.

2. Gap Generation:

   • Within the generated polygons, the code introduces gaps to simulate features like lakes or inaccessible areas. These gaps are represented as holes within the central polygon.
3. Forest Generation
   • Within the generated polygons, the code introduces different forest areas. These forest are added like a new Feature inside the GEOJSON.

4. Parameterized Generation:

   • The generation process is parameterized, allowing control over features such as regularity (shape uniformity), gap density (homogeneity of gaps), and gap scale (size of gaps relative to the polygon).

Components:

1. PolygonGenerator Class:

   • Responsible for generating the outer polygon shape and introducing gaps to simulate features.
   • Offers methods to generate individual polygons with specified characteristics.

2. Parameter Ranges and Experimentation:

   • The code includes predefined ranges for regularity, gap density, vertex number, bounding box, forest density and forest scale range in 3 different CSV.
   • It conducts experiments by generating maps with different parameter combinations, offering insights into how these parameters affect the map's appearance.

Usage:

1. Map Generation:

   • Users can instantiate the PolygonGenerator class to generate individual polygons representing maps with specific features.
   • Parameters such as regularity, gap density, and gap scale can be adjusted to customize the map generation process.

2. Experimentation:

   • Users can experiment with different parameter combinations to observe the effects on map generation.
   • This allows for exploration and understanding of how different parameters influence the characteristics of generated maps.

Potential Applications:

• The code can be used in various applications requiring the generation of simulated landscapes, such as in gaming, geographical analysis, or educational tools.
• It provides a flexible and customizable framework for creating maps with specific features, allowing users to tailor the generated maps to their requirements.
• Can be applied to generate maps for drone scanning operations, facilitating optimized area division and efficient data collection.

This statistic illustrates the share of internet users who used online maps / navigation services on a smartphone in the past 4 weeks in the United States in 2022, by age. The results were sorted by age. In 2022, some ** percent of respondents aged 18 to 29 years stated they used online maps / navigation services on a smartphone in the past 4 weeks.

The Statista Global Consumer Survey offers a global perspective on consumption and media usage, covering the offline und online world of the consumer.

This web map references the live tiled map service from the OpenStreetMap (OSM) project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: https://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in ESRI products under a Creative Commons Attribution-ShareAlike license. Tip: This service is one of the basemaps used in the ArcGIS.com map viewer. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10. Tip: Here are some well known locations as they appear in this web map, accessed by launching the web map with a URL that contains location parameters: Athens, Cairo, Jakarta, Moscow, Mumbai, Nairobi, Paris, Rio De Janeiro, Shanghai

The dataset presents a list of laboratories set up in the humanities, digital humanities, and media studies within universities across the world in 1983-2018. The data are collected and organized in an interactive map designed in the digital StoryMapJS tool, creating a valuable visible representation of the laboratory concept from a geographical and historical perspective. Based on the interactive map, I analyze the history of the laboratory in the humanities within a global context from the 1980s to 2018. The dataset includes 214 laboratories.

Data collection

I identified laboratories by using different resources such as universities’ websites, articles, and research projects. Besides, I sent a questionnaire to the most relevant networks in October 2018 to identify even more labs created in (digital) humanities and media studies at universities.

Data organization

I collected data about each lab based on its website and other resources. I extracted the following data: year established, year ended (if applicable), lab’s name, university, city, country, affiliation and location (if provided), disciplines and keywords (based on labs’ statements and projects and aiming to situate a lab), selected projects (if provided), purpose (a short quotation of a lab’s statement published on its website), website, and geographical latitude and longitude. I organized all the data in chronological order according to year established in Google Sheets. Next, I used StoryMapJS, a free tool designed by the Northwestern University’s Knight Lab, to map my data.

The global mobile navigation software market is experiencing robust growth, driven by the increasing adoption of smartphones, the expansion of high-speed internet access, and the rising demand for real-time location-based services. The market is characterized by a diverse range of players, including established technology companies like Garmin and TomTom, alongside emerging players specializing in advanced mapping and navigation technologies. The integration of AI and machine learning is significantly enhancing navigation accuracy and user experience, leading to the development of features such as predictive routing, personalized recommendations, and augmented reality overlays. This technological advancement, coupled with the increasing demand for seamless in-car navigation systems and the growth of the ride-sharing industry, fuels market expansion. Furthermore, the incorporation of safety features, such as driver fatigue detection and advanced driver-assistance systems (ADAS), is driving further market growth. Competitive pressures are high, with companies constantly striving to improve their mapping data accuracy, update frequency, and user interface. The forecast period (2025-2033) anticipates continued growth, driven primarily by the proliferation of connected cars and the expansion of 5G networks. The market is segmented by features (e.g., real-time traffic updates, offline maps, voice navigation), operating system, pricing models, and geographic regions. North America and Europe currently hold significant market shares, but rapid growth is expected in Asia-Pacific and other emerging markets due to increasing smartphone penetration and infrastructure development. While challenges remain, such as data security concerns and the need for continuous map updates, the overall market outlook is positive, with considerable growth opportunities for companies capable of innovating and adapting to the evolving technological landscape. We estimate the market size in 2025 to be $15 billion, growing at a CAGR of 12% through 2033, based on current market trends and reported growth in related sectors.

Map Gallery for overall maps of Neighborhood Associations and Organizations registered with the City of Bloomington Housing and Neighborhood Development Department (HAND) Related Maps Individual Neighborhood Maps Neighborhood Compliance Zone Maps

The underlying dataset for this daytime product is MODIS land surface temperature data (MOD11A2), which was gap-filled using the approach outlined in Weiss et al. (2014) to eliminate missing data caused by factors such as cloud cover. Gap-free outputs were then aggregated temporally and spatially to produce the monthly ≈5km product. This dataset was produced by Harry Gibson and Daniel Weiss of the Malaria Atlas Project (Big Data Institute, University of Oxford, United Kingdom, https://malariaatlas.org/).

This data set provides local LAI maps for the selected measured sites in Canada. These derived maps may also be useful for validating other LAI maps over these same sites given that the areas are protected from disturbance. The maps should be used for the given period of validity. The LAI data are suitable for use in modeling the carbon, water, energy, energy and trace gas exchange between the land surface and the atmosphere at regional scales. The data set may also be useful for monitoring changes in the land surface.The Leaf Area Index (LAI) maps are at 30-m resolution for the selected sites. LAI is defined here as half the total (all-sided) live foliage area per unit horizontal projected ground surface area. Overstory LAI corresponds to all tree foliage except for treeless areas where it corresponds to total foliage. The algorithms were developed from ground measurements and Landsat TM and ETM+ images (Fernandes et. al., 2003). A mask was developed using the Landsat ETM+/TM5 image and available land cover map to identify only those areas with land cover belonging to the sample land cover classes and with Landsat ETM+/TM5 spectral reflectance values that fell within the convex hull of the spectral reflectance values over the plots. LAI was mapped within the masked region using the Landsat ETM+/TM5 image and the developed transfer function. The final LAI map was scaled by a factor of 20 (offset 0). The LAI maps are in Tagged Image File Format (TIFF).

The self-driving 3D high-precision map market is experiencing rapid growth, driven by the increasing adoption of autonomous vehicles and advanced driver-assistance systems (ADAS). This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant expansion over the forecast period of 2025-2033. Let's assume, based on typical growth rates observed in similar technological sectors and considering the considerable investment in autonomous vehicle technology, a market size of $5 billion in 2025 and a CAGR of 25% is reasonable. This implies a market value exceeding $20 billion by 2033. Key drivers include the rising demand for safer and more efficient transportation solutions, advancements in sensor technologies (LiDAR, radar, cameras), and the continuous development of sophisticated mapping algorithms. The market is segmented by crowdsourcing model versus centralized model and by application, encompassing levels L1/L2+ and L3 driving automation, alongside other emerging applications. Major players like TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Baidu, and NVIDIA are actively shaping this landscape through innovative mapping solutions and strategic partnerships. The regional distribution shows significant concentration in North America and Asia Pacific, particularly in the United States and China, fueled by robust technological advancements and supportive government regulations. The market's growth trajectory is influenced by several trends, including the increasing availability of high-resolution satellite imagery and aerial photography for map creation, the development of real-time map updates based on vehicle data, and the integration of artificial intelligence (AI) for enhanced map accuracy and efficiency. However, challenges such as high data acquisition and processing costs, data privacy concerns, and the need for continuous map updates represent significant restraints. The future growth of the self-driving 3D high-precision map market will heavily depend on the continued progress in autonomous driving technology, the scalability of crowdsourcing solutions, and the ability to overcome regulatory and technological hurdles. The integration of 5G technology promises to further accelerate growth by enabling faster data transmission and real-time map updates, making the autonomous driving experience smoother and safer.

Auburn Maine's parcel Inquiry map with optional zoning and high-resolution aerial photography. Optional zoning layers. Map provides detailed assessing data for each parcel as well as links to WebPro assessing records and Google Street View. Users can search for parcels using parcel ID, location, or owner name. Advanced search options provide ability to select and buffer parcels with an optional export to csv file.

description: All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.; abstract: All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.

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In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Pigeon Point to Monterey map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://res1walrusd-o-twrd-o-tusgsd-o-tgov.vcapture.xyz/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://res1doid-o-torg.vcapture.xyz/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Pigeon Point to Monterey map area data layers. Data layers are symbolized as shown on the associated map sheets.