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

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

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

The Department of Information Technology and Telecommunications, GIS Unit, has created a series of Map Tile Services for use in public web mapping & desktop applications. The link below describes the Basemap, Labels, & Aerial Photographic map services, as well as, how to utilize them in popular JavaScript web mapping libraries and desktop GIS applications. A showcase application, NYC Then&Now (https://maps.nyc.gov/then&now/) is also included on this page.

GapMaps Live is a simple to use location intelligence platform available in over 25 countries globally which allows you to visualise your own data integrated with the best Map Data available so your team can make faster, smarter and more confident retail location decisions.

In May 2025, Gaode Map was the most popular map and navigation app in China with more than *** million monthly active users. The second spot was taken by Baidu Map which acquired around *** million monthly active users, followed by Tencent Map with a wide margin.

This map charts out EPA Level 3 ecoregions that are considered deserts in North America. Furthermore, decades of climate data from NOAA have been clipped and measured for each desert, the data was then used to generate Walter-Leith Climate Summary Diagrams which are a double-y axis chart that normalizes precipitation and temperature to identify months of general humidity or aridity.Artwork/Illustrations of Desert Flora & Fauna by Marissa WallData:CMAP Precipitation & Temperature data provided by the NOAA PSL, Boulder, Colorado, USA from their website at https://psl.noaa.gov

Top Importers of Printed Maps, Charts and Atlases, 2016 Discover more data with ReportLinker!

The top of the Upper Cretaceous Dakota Sandstone is present in the subsurface throughout the Uinta and Piceance basins of UT and CO and is easily recognized in the subsurface from geophysical well logs. This digital data release captures in digital form the results of two previously published contoured subsurface maps that were constructed on the top of Dakota Sandstone datum; one of the studies also included a map constructed on the top of the overlying Mancos Shale. A structure contour map of the top of the Dakota Sandstone was constructed as part of a U.S. Geological Survey Petroleum Systems and Geologic Assessment of Oil and Gas in the Uinta-Piceance Province, Utah and Colorado (Roberts, 2003). This surface, constructed using data from oil and gas wells, from digital geologic maps of Utah and Colorado, and from thicknesses of overlying stratigraphic units, depicts the overall configuration of major structural trends of the present-day Uinta and Piceance basins and was used to ...

This web map contains the new Hybrid Reference Layer vector tile layer, which is designed to be used to overlay imagery. The vector tile layer is similar in content and style to the popular Imagery with Labels map, which is delivered as a map service with raster tiles, with additional labels for transportation features.The 'Imagery with Labels' basemap contains the World Imagery map service and the World Boundaries and Places map service, so when you use that basemap you get boundaries and places, but you don't get highways and streets at small scales or street labels at large scale.If you prefer a map that uses raster tiles for both boundary and transportation features, you can use the Imagery with Labels and Transportation map.

The global High Accuracy Map market size is valued at USD XXX million in 2025 and is projected to reach USD XXX million by 2033, exhibiting a CAGR of XX% during the forecast period. The market growth is primarily driven by the increasing adoption of autonomous vehicles, the growing demand for location-based services, and the rising popularity of intelligent transportation systems. The automotive industry is a major end-user of high-accuracy maps, as they are essential for the development of autonomous vehicles. The increasing adoption of smartphones and tablets has led to a surge in the demand for location-based services. These services require high-accuracy maps to provide users with precise location information. In addition, the rising popularity of intelligent transportation systems is also contributing to the growth of the high-accuracy map market. Intelligent transportation systems use high-accuracy maps to improve traffic flow, reduce congestion, and enhance safety. The key players in the high-accuracy map market include HERE Global B.V., Momenta, Emapgo, TomTom, Zenrin, Hyundai Mnsof, Baidu, AutoNavi, Navinfo, KOTEI Information Technology, Careland, Huawei, KuanDeng Technology, Leador, Beijing Lingtu Software Technology Co., Ltd., and ZTEmap. These players are investing heavily in research and development to improve the accuracy and precision of their maps.

These data were compiled to demonstrate new predictive mapping approaches and provide comprehensive gridded 30-meter resolution soil property maps for the Colorado River Basin above Hoover Dam. Random forest models related environmental raster layers representing soil forming factors with field samples to render predictive maps that interpolate between sample locations. Maps represented soil pH, texture fractions (sand, silt clay, fine sand, very fine sand), rock, electrical conductivity (ec), gypsum, CaCO3, sodium adsorption ratio (sar), available water capacity (awc), bulk density (dbovendry), erodibility (kwfact), and organic matter (om) at 7 depths (0, 5, 15, 30, 60, 100, and 200 cm) as well as depth to restrictive layer (resdept) and surface rock size and cover. Accuracy and error estimated using a 10-fold cross validation indicated a range of model performances with coefficient of variation (R2) for models ranging from 0.20 to 0.76 with mean of 0.52 and a standard deviation of 0.12. Models of pH, om and ec had the best accuracy (R2 > 0.6). Most texture fractions, CaCO3, and SAR models had R2 values from 0.5-0.6. Models of kwfact, dbovendry, resdept, rock models, gypsum and awc had R2 values from 0.4-0.5 excepting near surface models which tended to perform better. Very fine sands and 200 cm estimates for other models generally performed poorly (R2 from 0.2-0.4), and sample size for the 200 cm models was too low for reliable model building. More than 90% of the soils data used was sampled since 2000, but some older samples are included. Uncertainty estimates were also developed by creating relative prediction intervals, which allow end users to evaluate uncertainty easily.

This map shows the subcrop pattern of the Mesozoic rock units present at the top Mesozoic unconformity (also commonly referred to as the base Tertiary unconformity) in Cook Inlet basin, Alaska. The subcrop is projected onto the top Mesozoic unconformity depth surface of Cook Inlet basin, Alaska, published by Shellenbaum and others (2010). Publicly available geologic and geophysical data from multiple sources were collected, interpreted, and integrated into the subcrop map. Formation picks at the top Mesozoic unconformity were determined for 109 wells. Mesozoic horizons from two regional marine two-dimensional (2-D) seismic datasets (approximately 3,300 miles) were interpreted. Eight map units were established for the Mesozoic subcrop map: Kaguyak-Matanuska Formations, undivided; Naknek Formation; Chinitna Formation-Tuxedni Group, undivided; Talkeetna Formation; Talkeetna-Border Ranges ultramafic and mafic complex (BRUMC), undivided; Pogibshi-Port Graham formations, undivided; plutonic rocks, undivided; and metamorphic rocks, undivided. This map was prepared as part of a multi-year effort by the Alaska Department of Natural Resources.

Top Exporters of Printed Maps, Charts and Atlases, 2016 Discover more data with ReportLinker!

The typical statewide or county-wide red/blue map (shown at left) depicts presidential voting results on a winner-take-all basis, so they award an entire geographical area to the Republican or Democratic candidate no matter how close the actual vote tally The large map in the attachment factors in both the percentage of the popular vote won by each candidate as well as the population density of each county. So, the sparsely populated Great Plains and Rocky Mountain West are shown in a much lighter color than the Eastern Seaboard, and the map as a whole is more purple than either red or blue. Perhaps the United States is less divided than some maps would lead us to believe.

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

This web map depicts GIS data for known Stormwater Infrastructure in the City of SeaTac, Washington. The information is based on the best available knowledge collected from construction as-builts and field inspections, with a focus on mapping features in the public right-of-way. The stormwater infrastructure contains the following datasets: discharge points, catch basins and manholes, pipes and ditches, misc structures, water quality facilities points and polygons, and access risers. The data is being continually updated as newer information becomes available.Incorporated in February 1990, the City of SeaTac is located in the Pacific Northwest, approximately midway between the cities of Seattle and Tacoma in the State of Washington. SeaTac is a vibrant community, economically strong, environmentally sensitive, and people-oriented. The City boundaries surround the Seattle-Tacoma International Airport, (approximately 3 square miles in area) which is owned and operated by the Port of Seattle. For additional information regarding the City of SeaTac, its people, or services, please visit https://www.seatacwa.gov. For additional information regarding City GIS data or maps, please visit https://www.seatacwa.gov/our-city/maps-and-gis.

The topographic map on a scale of 1:10,000 is the basic scale of Brandenburg's topographical maps. The earth's surface is relatively complete (only slightly generalized) and geometrically exact to scale. It is the cartographic implementation of a comprehensive topographical survey of the country (photogrammetric aerial image evaluation, incorporation of topographical additional information, topographical field comparison). The historical editions of the TK10 are available from different years from 1992 (basic up-to-dateness of individual sheets older). From 2002, the TK10 (ATKIS) was created by deriving from the basic landscape model (basic DLM). In different map layouts and representations, the historical map sheets depict a piece of Brandenburg's contemporary history. They are available in analogue plot output (paper) and are available free of charge as downloads. When using the data, the license conditions must be observed.

The topographic maps (TK) are generated from digital landscape and terrain models as well as the official property cadastre information system ALKIS and visualised according to the nationwide signature catalogue of the presentation editions ‘basemap.de P10’ grid. The TKs are available nationwide and in the uniform geodetic reference system and map projection for the state of Brandenburg. They are available as analogue map prints (plots), as raster data and as web services. When using the data, the license conditions must be observed.

Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.

The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).

The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":

0: Open land (OL) [#440154] 1: Scrub (S) [#404387] 2: Young regenerating forest (YRF) [#29788e] 3: Low density forest (LDF) [#22a884] 4: Medium density forest (MDF) [#7ad251] 5: High density forest (HDF) [#fde725] 10: Oil palm [#fcffa4] 11: Coconut [#a4feff] 50: Urban [#fa0000] 255: No data

Citation: Use of these data require citation of this dataset and the original research articles. These citations are as follows:

Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.

Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. Remote Sensing of Environment, 261, 112479.

Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448

This map intends to highlight a pervasive issue for the tribal areas of northeastern Arizona; the roadway network relies heavily upon dirt and gravel surfaces for both local roads and regional connections. Paired with the extreme seasonal weather conditions of northern Arizona, this roadway network experiences poor resilience and disproportionate damage from heavy rains, flash flooding, snow, and ice. This results in greater risks to residents, travelers, school buses, and emergency response; roads are often washed out or impassable due to weather, causing schools not to run bus routes (resulting in forced absenteeism for students), emergency response to take longer, and individuals to face barriers to getting to and from work, school, and other essential locations. By showing this issue spatially, I hope to spark data-driven discussion about mitigation of these conditions through strategic prioritization of and investment in countermeasures to improve resilience, safety, and mobility.Datasets Used: DEM - United States Geological Survey (USGS), Precipitation and Watershed Data - University of Arizona, Multispectral Ground Imagery - Landsat 9 (USGS/NASA) Roadway Network - OpenStreetMaps, Tribal/State Boundaries - U.S. Census Bureau (TIGER/LINE)

This dataset is a series of digital map-posters accompanying the AdaptNRM Guide: Helping Biodiversity Adapt: supporting climate adaptation planning using a community-level modelling approach.

These represent supporting materials and information about the community-level biodiversity models applied to climate change. Map posters are organised by four biological groups (vascular plants, mammals, reptiles and amphibians), two climate change scenario (1990-2050 MIROC5 and CanESM2 for RCP8.5), and five measures of change in biodiversity.

The map-posters present the nationally consistent data at locally relevant resolutions in eight parts – representing broad groupings of NRM regions based on the cluster boundaries used for climate adaptation planning (http://www.environment.gov.au/climate-change/adaptation) and also Nationally.

Map-posters are provided in PNG image format at moderate resolution (300dpi) to suit A0 printing. The posters were designed to meet A0 print size and digital viewing resolution of map detail. An additional set in PDF image format has been created for ease of download for initial exploration and printing on A3 paper. Some text elements and map features may be fuzzy at this resolution.

Each map-poster contains four dataset images coloured using standard legends encompassing the potential range of the measure, even if that range is not represented in the dataset itself or across the map extent.

Most map series are provided in two parts: part 1 shows the two climate scenarios for vascular plants and mammals and part 2 shows reptiles and amphibians. Eight cluster maps for each series have a different colour theme and map extent. A national series is also provided. Annotation briefly outlines the topics presented in the Guide so that each poster stands alone for quick reference.

An additional 77 National maps presenting the probability distributions of each of 77 vegetation types – NVIS 4.1 major vegetation subgroups (NVIS subgroups) - are currently in preparation.

Example citations:

Williams KJ, Raisbeck-Brown N, Prober S, Harwood T (2015) Generalised projected distribution of vegetation types – NVIS 4.1 major vegetation subgroups (1990 and 2050), A0 map-poster 8.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

Williams KJ, Raisbeck-Brown N, Harwood T, Prober S (2015) Revegetation benefit (cleared natural areas) for vascular plants and mammals (1990-2050), A0 map-poster 9.1 - East Coast NRM regions. CSIRO Land and Water Flagship, Canberra. Available online at www.AdaptNRM.org and https://data.csiro.au/dap/.

This dataset has been delivered incrementally. Please check that you are accessing the latest version of the dataset. Lineage: The map posters show case the scientific data. The data layers have been developed at approximately 250m resolution (9 second) across the Australian continent to incorporate the interaction between climate and topography, and are best viewed using a geographic information system (GIS). Each data layers is 1Gb, and inaccessible to non-GIS users. The map posters provide easy access to the scientific data, enabling the outputs to be viewed at high resolution with geographical context information provided.

Maps were generated using layout and drawing tools in ArcGIS 10.2.2

A check list of map posters and datasets is provided with the collection.

Map Series: 7.(1-77) National probability distribution of vegetation type – NVIS 4.1 major vegetation subgroup pre-1750 #0x

8.1 Generalised projected distribution of vegetation types (NVIS subgroups) (1990 and 2050)

9.1 Revegetation benefit (cleared natural areas) for plants and mammals (1990-2050)

9.2 Revegetation benefit (cleared natural areas) for reptiles and amphibians (1990-2050)

10.1 Need for assisted dispersal for vascular plants and mammals (1990-2050)

10.2 Need for assisted dispersal for reptiles and amphibians (1990-2050)

11.1 Refugial potential for vascular plants and mammals (1990-2050)

11.1 Refugial potential for reptiles and amphibians (1990-2050)

12.1 Climate-driven future revegetation benefit for vascular plants and mammals (1990-2050)

12.2 Climate-driven future revegetation benefit for vascular reptiles and amphibians (1990-2050)