As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

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.

👨‍💻 Author: Azhar Saleem

"https://github.com/azharsaleem18" target="_blank"> https://img.shields.io/badge/GitHub-Profile-blue?style=for-the-badge&logo=github" alt="GitHub Profile"> "https://www.kaggle.com/azharsaleem" target="_blank"> https://img.shields.io/badge/Kaggle-Profile-blue?style=for-the-badge&logo=kaggle" alt="Kaggle Profile"> "https://www.linkedin.com/in/azhar-saleem/" target="_blank"> https://img.shields.io/badge/LinkedIn-Profile-blue?style=for-the-badge&logo=linkedin" alt="LinkedIn Profile">
"https://www.youtube.com/@AzharSaleem19" target="_blank"> https://img.shields.io/badge/YouTube-Profile-red?style=for-the-badge&logo=youtube" alt="YouTube Profile"> "https://www.facebook.com/azhar.saleem1472/" target="_blank"> https://img.shields.io/badge/Facebook-Profile-blue?style=for-the-badge&logo=facebook" alt="Facebook Profile"> "https://www.tiktok.com/@azhar_saleem18" target="_blank"> https://img.shields.io/badge/TikTok-Profile-blue?style=for-the-badge&logo=tiktok" alt="TikTok Profile">
"https://twitter.com/azhar_saleem18" target="_blank"> https://img.shields.io/badge/Twitter-Profile-blue?style=for-the-badge&logo=twitter" alt="Twitter Profile"> "https://www.instagram.com/azhar_saleem18/" target="_blank"> https://img.shields.io/badge/Instagram-Profile-blue?style=for-the-badge&logo=instagram" alt="Instagram Profile"> "mailto:azharsaleem6@gmail.com"> https://img.shields.io/badge/Email-Contact%20Me-red?style=for-the-badge&logo=gmail" alt="Email Contact">

Dataset Overview

Welcome to the Google Places Comprehensive Business Dataset! This dataset has been meticulously scraped from Google Maps and presents extensive information about businesses across several countries. Each entry in the dataset provides detailed insights into business operations, location specifics, customer interactions, and much more, making it an invaluable resource for data analysts and scientists looking to explore business trends, geographic data analysis, or consumer behaviour patterns.

Key Features

• Business Details: Includes unique identifiers, names, and contact information.
• Geolocation Data: Precise latitude and longitude for pinpointing business locations on a map.
• Operational Timings: Detailed opening and closing hours for each day of the week, allowing analysis of business activity patterns.
• Customer Engagement: Data on review counts and ratings, offering insights into customer satisfaction and business popularity.
• Additional Attributes: Links to business websites, time zone information, and country-specific details enrich the dataset for comprehensive analysis.

Potential Use Cases

This dataset is ideal for a variety of analytical projects, including: - Market Analysis: Understand business distribution and popularity across different regions. - Customer Sentiment Analysis: Explore relationships between customer ratings and business characteristics. - Temporal Trend Analysis: Analyze patterns of business activity throughout the week. - Geospatial Analysis: Integrate with mapping software to visualise business distribution or cluster businesses based on location.

Dataset Structure

The dataset contains 46 columns, providing a thorough profile for each listed business. Key columns include:

• business_id: A unique Google Places identifier for each business, ensuring distinct entries.
• phone_number: The contact number associated with the business. It provides a direct means of communication.
• name: The official name of the business as listed on Google Maps.
• full_address: The complete postal address of the business, including locality and geographic details.
• latitude: The geographic latitude coordinate of the business location, useful for mapping and spatial analysis.
• longitude: The geographic longitude coordinate of the business location.
• review_count: The total number of reviews the business has received on Google Maps.
• rating: The average user rating out of 5 for the business, reflecting customer satisfaction.
• timezone: The world timezone the business is located in, important for temporal analysis.
• website: The official website URL of the business, providing further information and contact options.
• category: The category or type of service the business provides, such as restaurant, museum, etc.
• claim_status: Indicates whether the business listing has been claimed by the owner on Google Maps.
• plus_code: A sho...

Data includes reviews of different restaurants on Google Maps. There are 1100 comments in total and pictures of each comment in the data set. The data is labeled according to 4 classes (Taste, Menu, Indoor atmosphere, Outdoor atmosphere) for the artificial intelligence to predict. The dataset has been prepared in a way that can be used in both text processing and image processing fields.

The dataset contains the following columns: business_name, author_name, text, photo, rating, rating_category

IMPORTANT: The rating_category column is related to the photo of the review. If you want to use this dataset for NLP, you need to label it yourself. I will label it for you when I am available.

Under proposed Alternatives 3 and 4 in the draft Environmental Assessment for the proposed rule (Permits for Incidental Take of Eagles and Eagle Nests), wind facilities would be eligible for a general permit or a specific permit based, in part, on eagle abundance data. This map of the coterminous U.S. shows where projects would be eligible to self-certify for a general permit if they meet all of the criteria, or apply for a specific permit. Existing projects that are only eligible for specific permits may be determined to qualify for a general permit upon Service review of their permit application. See the Federal Register Notice for information about submitting comments on the proposed rule and draft environmental review. Permit eligibility zones are based on eBird relative abundance estimates for bald eagles and golden eagles throughout the coterminous United States at 8 km2 resolution for each of five periods (breeding, fall, migration, spring, winter). Using multiple seasons of data provides better measures of relative abundance for mapping purposes (Johnston et al. 2020), and our goal was to identify locations with high relative abundance of eagles in any one season. For each eagle species we evaluated non-zero relative abundance data for each season using custom R scripts in Program R (R Core Team 2021). We identified cells that had relative abundance values in each season that were at or above the relevant quantile threshold value. We combined these species-specific, seasonal layers into a single layer that indicates cells that did not exceed the relative abundance thresholds in any season and cells that exceeded one or more threshold. We then created maps of the permit eligibility zones (i.e., grid cells that did not exceed the relative abundance threshold in any season) for several scenarios for each eagle species. This map includes a combined proposed general permit zone consisting of areas where golden eagle abundance is 50% or less than golden eagle abundance elsewhere in the lower 48 states within each season and bald eagle abundance is 95% or less than it is elsewhere in the lower 48 states in each season. This raster layer uses an equal-area sinusoidal projection that is used by eBird status and trend products, including relative abundance data (this projection is also used for NASA MODIS data). Note: We advise that users reproject any layers they wish to conduct spatial overlay analyses with into the same sinusoidal projection as this layer to ensure the most accurate geospatial representation of the data. Cell values of 0 indicates cells where a wind project may be eligible for self-certified general permit if other qualification criteria are met under the proposed regulation. Cell values of 1 indicates cells where a wind project would be eligible for a simplified specific permit. Additional technical details about map development can be found in Appendix A of the draft Environmental Assessment for the Proposed Eagle Incidental Take Rule revision.

Geospatial Services Land management within the US Forest Service and on the 900,000+ acre Monongahela National Forest (NF) is driven by a wide mix of resource and societal demands that prove a challenge in fulfilling the Forest Service’s mission of “Caring for the Land and Serving the People.” Programmatically, the 2006 Land and Resource Management Plan guide natural resource management activities on lands administered by the Monongahela National Forest. The Forest Plan describes management direction and practices, resource protection methods and monitoring, desired resource conditions, and the availability and suitability of lands for resource management. Technology enables staff to address these land management issues and Forest Plan direction by using a science-based approach to facilitate effective decisions. Monongahela NF geospatial services, using enabling-technologies, incorporate key tools such as Environmental Systems Research Institute’s ArcGIS desktop suite and Trimble’s global positioning system (GPS) units to meet program and Forest needs. Geospatial Datasets The Forest has a broad set of geospatial datasets that capture geographic features across the eastern West Virginia landscape. Many of these datasets are available to the public through our download site. Selected geospatial data that encompass the Monongahela National Forest are available for download from this page. A link to the FGDC-compliant metadata is provided for each dataset. All data are in zipped format (or available from the specified source), in one of two spatial data formats, and in the following coordinate system: Coordinate System: Universal Transverse Mercator Zone: 17 Units: Meters Datum: NAD 1983 Spheroid: GRS 1980 Map files – All map files are in pdf format. These maps illustrate the correlated geospatial data. All maps are under 1 MB unless otherwise noted. Metadata file – This FGDC-compliant metadata file contains information pertaining to the specific geospatial dataset. Shapefile – This downloadable zipped file is in ESRI’s shapefile format. KML file – This downloadable zipped file is in Google Earth’s KML format. Resources in this dataset:Resource Title: Monongahela National Forest Geospatial Data. File Name: Web Page, url: https://www.fs.usda.gov/detail/mnf/landmanagement/gis/?cid=stelprdb5108081 Selected geospatial data that encompass the Monongahela National Forest are available for download from this page.

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

Geophysical maps of the Northern TerritoryNTGS produces four Territory-wide geophysical products: * Magnetic map of the Northern Territory (2004) * Radiometric map of the Northern Territory (2004) * Elevation map of the Northern Territory (2004) * Gravity map of the Northern Territory (2004).These maps represent the culmination of several decades of work since the Bureau of Mineral Resources [BMR, now Geoscience Australia (GA)] first acquired reconnaissance airborne magnetic and radiometric data in the Northern Territory at Rum Jungle in 1952. Dates of first release: Magnetic Map - 2000; Elevation Map - 2002; Radiometric Map - 2003; Gravity Map - 2004.Ninety-one percent of the area of the NT is covered by data from 53 separate NTGS and Geoscience Australia semi-detailed airborne surveys flown since 1974. These data were acquired along flight lines spaced 150-500 m apart. The remaining portion of the NT is made up of reconnaissance BMR surveys, which typically employed flight line spacings of around 1.5-3.2 km.The Magnetic and Radiometric maps make use of data from all 53 surveys, whereas the Elevation Map only includes, where available, GPS-derived height data from surveys flown since 1993. Backdrop to the Elevation Map is provided by the GEODATA 9 Second DEM, resampled to 100 m. The Gravity Map incorporates data primarily from the 11 km grid of stations collected by BMR in the 1960s and 1970s, augmented by many detailed private sector surveys (interpolated to 200 m), two 4 km datasets collected jointly by NTGS and GA in the Tanami and Tennant regions, and more recently (2003) a 2 km airborne dataset.All four maps are updated as new semi-regional surveys are acquired by NTGS and added to the public domain database. They are currently available in several forms: * Hardcopy at 1:2.5M scale. Total Magnetic Intensity, Elevation and Gravity data are rendered in pseudocolour with the intensity stretched and synthetically sun-illuminated. The three elements of Radiometric data, potassium-thorium-uranium, are assigned to red-green-blue, respectively, whereas the intensity is the total count, high-pass filtered. * Digitally, as an ER Mapper .ERS (approximately 700 Mb uncompressed) and MapInfo .TAB, on CD-ROM from the Minerals and Energy Information Centre. Grid-merging of individual datasets has been undertaken using a mesh size of 100 m. * Digitally, as an ER Mapper compressed .ECW (geolocated image) on Image Web Server.Download the: * MapInfo Tab Dataset (2.71MB) * 1:2.5M Elevation Map of the NT, (March 2004) (pdf, 6.13MB) * 1:2.5M Geological Map of the NT, (March 2004) (pdf, 5.13MB) * 1:2.5M Gravity Map of the NT, (March 2004) (pdf, 8.16MB) * 1:2.5M Magnetic Map of the NT, (March 2004) (pdf, 6.49MB) * 1:2.5M Radiometric Map of the NT, (March 2004) (pdf, 19.7MB)Other publications

This geologic map was prepared as a part of a continuing study of digital methods and techniques as applied to complex geologic maps. The digital geologic map was created from the orginal linework used to prepare the published paper Geologic Map of Arkansas. Consequently, this geologic map database can be queried in many ways to produce a variety of geologic maps. The analog source of data was Haley, B.R.; assisted by Glick, E.E., Bush, W.V., Clardy, B.F., Stone, C.G., Woodward, M.B., and Zachry, D.L., 1993, Geologic Map of Arkansas: U.S. Geol. Survey, Special Geologic Map, scale 1:500,000.

The Traffic Volume Maps show the Annual Average Daily Traffic (AADT) numbers displayed at various locations on Maryland's roadways. Traffic counts are reported as the number of vehicles expected to pass a given location. This is an average day of the year for all traffic-counting stations in the state. Traffic volume data is collected from more than 8,700 Program Count stations and 91 Automatic Traffic Recorders (ATRs) located throughout Maryland.Program Count data is collected (both directions) at regular locations on either a three (3) year or six (6) year cycle depending on type of roadway. Growth factors are applied to counts which were not taken during the current year and the counts are factored based on the past yearly growth of an associated ATR. Counters are placed for 48 hours on a Monday or Tuesday and are picked up that Thursday or Friday, respectively. The ATR and toll count data is collected on a continuous basis. The toll station data is provided by Maryland Transportation Authority.Traffic Volume Maps are updated and published annually in March. Please see the Traffic Volume Map Introduction (PDF, ~148 kb) and ATR and Toll Locations (PDF, ~32 kb) for more information about the Traffic Volume Maps.The entire current Maryland Traffic Volume Map (~9.4 MB) is available as a PDF file.Traffic Volume Maps from previous years back to 1980 are available as PDF files.Please Note: The PDF file sizes for the Traffic Volume Maps vary. It may take a few seconds or a few minutes to display.MDOT SHA Website

Discover the booming Spain Location-Based Services (LBS) market! Explore its €880 million 2025 valuation, 13.75% CAGR, key drivers, and leading companies. Understand market segmentation and future trends for LBS in Spain. Recent developments include: February 2023: Mercedes-Benz and Google unveiled an extensive and visionary partnership aimed at revolutionizing the automotive industry and elevating the digital luxury car experience to new heights. In an industry-first move, Mercedes-Benz is set to develop its distinct navigation system, harnessing the advanced capabilities of the Google Maps Platform to craft an unparalleled driving experience. This groundbreaking collaboration will grant Mercedes-Benz exclusive access to Google's cutting-edge geospatial technologies, providing users with an array of exceptional features. These include comprehensive location data, automatic route optimization, up-to-the-minute traffic updates, and even predictive traffic insights, among other remarkable functionalities., January 2023: Mapbox, the leading platform for mapping and location services, joined forces with Toyota Motor Europe to introduce Cloud Navigation powered by Mapbox Dash. This transformative partnership brings an unprecedented level of real-time information to Toyota's Yaris, Yaris Cross, and Aygo X models, enhancing the driving experience in terms of efficiency, convenience, and safety. Alongside precision lane-level navigation, drivers can access a wealth of features such as live parking availability, speed limit alerts, and warnings for speed cameras. Furthermore, an upcoming pilot program will enable Toyota drivers to conveniently handle parking and fuel payments directly through their infotainment systems, further streamlining the driving experience.. Key drivers for this market are: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Potential restraints include: Growing Demand for Geo-based Marketing, Emerging Use-cases for LBS due to High Penetration of Social Media and Location-based App Adoption. Notable trends are: Indoor Location Segment is Expected to Hold Significant Share of the Market.

At the African Union and European Union Commission College meeting in Addis Abeba, Ethiopia (April 25-26, 2013) the Atlas was introduced by EU Commissioner Hedegaard (Climate Action) on behalf of the European Commission President José Manuel Barroso. The atlas is available for download from this site (see links below). Physical copies of the book are available through the EU Publications Office. You can order the Soil Atlas from the EU Bookshop of the Publication Office in Luxembourg at the price of 25 EUR. An introduction to the Soil Atlas of Africa was presented at the European Parliament's session 'Land and soil degradation post Rio+20', on November 9th 2012 (hosted by Sandrine Bélier, MEP) Download the Soil Atlas of Africa Download the PDF version of Soil Atlas of Africa. You are invited to download all the 3 parts as the total size of the Atlas is more than 500 MB. Part 1 Pages 1-78, Size: 254MB Part 2 Pages 79-128, Size: 95MB Part 3 Pages 129 - 176, Size: 175MB Download the data of the Soil Atlas of Africa The data underpinning the Soil Atlas of Africa will gradually be made available. In a first instance, a shapefile is available describing the dominant WRB Reference Soil Group and associated qualifiers. Data forthe main soil properties (such as texture, pH, organic carbon, carbonates, etc...) will become available in the near future. In order to obtain ther data, a Request Form (see above tab) should be submitted. The continent of Africa contains all but one of the WRB Reference Soil Groups and illustrates a great soil diversity (see page 61 of the Atlas). It is important to notice that over 60% of the soil types represent hot, arid or immature soil assemblages: Arenosols (22%), Leptosols (17%), Cambisols (11%), Calcisols (6%), Regosols (2%) and Solonchaks/Solonetz (2%). A further 20% or so are soils of a tropical or subtropical character: Ferralsols (10%), Plinthosols (5%), Lixisols (4%) and Nitisols (2%). A considerable area (6%) is occupied by a further 16 reference groups that cover an area of less that 1% of the African land mass. This fact illustrates that a considerable number of soil types are associated with local soil-forming factors such as volcanic activity, accumulations of gypsum or silica, waterlogging, etc. What is interesting is that unlike the other continents, Africa does not exhibit large expanses of prairie or steppe-type soils (i.e. Kastanozems, Chernozems and Phaeozems). The new harmonised soil map shows the soil classes at continental scale. The map has been produced for the Soil Atlas of Africa [1]. Since the map is displayed in the Atlas in a series of map sheets at the scale 1:3 M, its harmonisation was done accordingly [2]. The map has been derived from the Harmonized World Soil Database (HWSD) [3]. The original data were updated and modified according to the World Reference Base for Soil Resources classification system [4]. The corrections concerned boundary issues, areas with no information, soil patterns, river and drainage networks, and dynamic features such as sand dunes, water bodies and coastlines. In comparison to the initial map derived from HWSD, the new map represents a correction of 13% of the soil data for the continent. Additional information is provided in the references below and the metadata. The WRB system recommends that the Reference Siol Groups (RSGs) with prefix qualifiers be used for small-scale maps (i.e. smaller than 1:1 M) (IUSS Working Group WRB, 2010). This recommendation has been followed in the construction of the legend: one or two prefix qualifiers are put with each RSG to define the soil types. Each polygons contains three fields: the code and the name of the Reference Soil Group. Projection: WGS 1984Legend: You can find them in the page 64 and 65 of the Soil Atlas of Africa. Download the Legends from page 64 and 65 of the Atlas (part a, part b). JRC releases E-BOOK versions of the Soil Atlas of Africa As an alternative to the printed and pdf versions of the Soil Atlas of Africa, the JRC's Soil Resource Assessment Project (IES Land Resource Management Unit) is pleased to announce that the publication is now available in e-Book formats for both ePUB apps and Kindle devices or apps. The e-Books format (i.e. Electronic or Digital Books) has brought about a technological revolution in the world of publishing by allowing users to 'read' documents as books on a wide range of dedicated hardware devices (e.g. Kindle) and programmes/applications (e.g. iBooks, Google Books, Kindle apps, Kobo, etc.). This allows readers to take hundreds of books or very large books with them on small computing devices such as dedicated eReaders, tablets or Smart Phones running dedicated apps. For example, our soil Atlas of Africa is in laid out in A3 format, contains 176 pages and weighs over 2.5 kg – so it is quite an imposing publication! By using the e-Book version, it will fit in your pocket and you can take it with you wherever you go! In addition, e-Book files are much smaller than conventional print-quality pdf files, which makes them quicker and much easier to download, which is especially useful where the Internet connection may be poor or is accessed via cell phone networks. To use these files, please select and download the relevant file format and open it with a dedicated reader (See hints below). Download the ePUB versionDownload the Kindle version How to read the ePUB file? The easiest way to open an ePUB file is to download it onto your computer (PC or Mac), Smart Phone or Cloud Storage (e.g. Dropbox, GoogleDrive, iCloud). Then open with, or export to, a suitable app or programme (there are many ePUB apps for Windows, Mac and Android operating systems, many of which are free). (Please note that Amazon Kindles cannot read ePUB files.) How to read the MOBI file on Kindle devices? The MOBi file format is designed for Kindle readers and apps. Either download the file to your computer or Cloud Storage. Connect your Kindle device to your computer with a USB cable and copy the file to the 'documents' folder on the internal storage of the Kindle. You will then see the file as a new book on your device. Please remember to 'eject' your Kindle device when you are finished. For future reference, documents that are less than 50 MB can be emailed directly to your Kindle account. How to read the ePUB and MOBI files on iPads? To transfer files to iPads, you will have to use a) either the file sharing options of iTunes. Connect your iPad to your PC or Mac so iTunes opens, open up your iPad in iTunes, go to apps, scroll down then find Kindle or any other eBook app, click on it and you'll have a box to the right, drag the mobi or ePUB file there and they will be copied into the Kindle or ePUB app on the iPad; or b) the export function of the Cloud Storage system to upload to your tablet.Additional information on eBooks and associated file formats can be found on: http://en.wikipedia.org/wiki/E-book References - Documentation Jones, A., Breuning-Madsen, H., Brossard, M., Dampha, A., Deckers, J., Dewitte, O., Gallali, T., Hallett, S., Jones, R., Kilasara, M., Le Roux, P., Michéli, E., Montanarella, L., Spaargaren, O., Thiombiano, L., Van Ranst, E., Yemefack, M., Zougmore, R., (eds.), 2013. Soil Atlas of Africa. European Commission, Publications Office of the European Union, Luxembourg. 176 pp. ISBN 978-92-79-26715-4, doi 10.2788/52319 Dewitte, O., Jones, A., Spaargaren, O., Breuning-Madsen, H., Brossard, M., Dampha, A., Deckers, J., Gallali, T., Hallett, S., Jones, R., Kilasara, M., Le Roux, P., Michéli, E., Montanarella, L., Thiombiano, L., Van Ranst, E., Yemefack, M., Zougmore, R., 2013. Harmonisation of the soil map of Africa at the continental scale. Geoderma, 211-212, 138-153 FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). Food and Agriculture Organization of the United Nations, Rome, Italy and IIASA, Laxenburg, Austria Spaargaren O, Schad P, Micheli E (2010): Guidelines for constructing small-scale map legends using the WRB. FAO, Rome. IUSS Working Group WRB, 2006. World Reference Base for Soil Resources 2006. World Soil Resources Report No. 103. Food and Agriculture Organization of the United Nations, Rome ISBN-10: 9251055114 What is special about soil in Africa? The first ever SOIL ATLAS OF AFRICA uses striking maps, informative texts and stunning photographs to answer and explain these and other questions. Leading soil scientists from Europe and Africa have collaborated to produce this unique document. Using state-of-the-art computer mapping techniques, the Soil Atlas of Africa shows the changing nature of soil across the continent. It explains the origin and functions of soil, describes the different soil types that can be found in Africa and their relevance to both local and global issues. The atlas also discusses the principal threats to soil and the steps being taken to protect soil resources. The Soil Atlas of Africa is more than just a normal atlas. It presents a new and comprehensive interpretation of an often neglected natural resource. The Soil Atlas of Africa is an essential reference to a non-renewable resource that is fundamental for life on this planet. The Soil Atlas of Africa – highlighting a forgotten resource? In most people's mind, soil would not figure highly in a list of the natural resources of Africa. However, healthy and fertile soils are the cornerstones of food security, key environmental services, social cohesion and the economies of most African countries. Unfortunately, soil in Africa tends only to reach public awareness when it fails – often with catastrophic consequences as seen by the famine episodes of the Sahel in the 1980s and more recently in Niger and the Horn of Africa. Soil is the foundation to many of the Millennium Development Goals. In addition to providing the medium for food, fodder and fuel wood production (around 98% of the calories consumed in Africa are derived from

See full Data Guide here. Quaternary Geology Feature Set is 1:24,000-scale data that illustrates the geologic features formed in Connecticut during the Quaternary Period, which spans from 2.588 ± 0.005 million years ago to the present and includes the Pleistocene (glacial) and Holocene (postglacial) Epochs. The Quaternary Period has been a time of development of many details of the Connecticut landscape and all surficial deposits. At least twice in the last Pleistocene, continental ice sheets swept across Connecticut from the north. Their effects are of pervasive importance to present-day occupants of the land.

The Quaternary Geology information illustrates the geologic history and the distribution of depositional environments during the emplacement of unconsolidated glacial and postglacial surficial deposits and the landforms resulting from those events in Connecticut. These deposits range from a few feet to several hundred feet in thickness, overlie the bedrock surface and underlie the organic soil layer of Connecticut. Quaternary Geology is mapped without regard for any organic soil layer that may overly the deposit.

For additional documentation including a description of the unconsolidated glacial and postglacial surficial deposits shown on the map, refer to the CT ECO Complete Resource Guide for Quaternary Geology.

The Connecticut Quaternary Geology information was initially compiled at 1:24,000 scale (1 inch = 2,000 feet) then recompiled for a statewide 1:125,000-scale map, Quaternary Geology Map of Connecticut and Long Island Sound Basin (PDF, 56 Mb) Stone, J.R., Schafer, J.P., London, E.H. and Thompson, W.B., 1992, U.S. Geological Survey Special Map, 2 sheets, scale 1:125,000, and pamphlet, 71 p. A companion map, the Surficial Materials Map of Connecticut (PDF, 26 Mb) Stone, J.R., Schafer, J.P., London, E.H., DiGiacomo-Cohen, M.L., Lewis, R.L., and Thompson, W.B., 2005, U.S. Geological Survey Scientific Investigation Map 2784, 2 sheets, scale 1:125,000, emphasizes the surface and subsurface texture (grain-size distribution) of these materials. The quaternary geology and surficial material features portrayed on these two maps are very closely related; each contributes to the interpretation of the other.

This study is an investigation of the agrarian landscape in the western part of Ostergotland County (in Sweden). The study was published in the form of a dissertation in Human Geography written (in German) by Steffan Helmfrid, and defended in 1962. Two maps were produced as a part of the dissertation, based on historical material. These maps show the agrarian landscape as it was around 1640. Helmfrid's analogue process for producing these maps was unique for the 1960s. These two maps are made available here.

Two maps are made available here:

1) Name: No Title (Working version) scale: 1:50,000 coordinate system: no coordinates file format: tiff (.tif) file size: 198 MB file name: helmfrid_orginal_photoshop.tif

2) Name: "The Agrarian Landscape on the Vadstena plains around the year 1640" scale: 1:100,000 coordinate system: RT 90 2.5 gon V (EPSG 3021) file format: geotiff (.tif) file size: 40 MB file name: helmfrid_rectify.tif

The first map is an unpublished working version that was used as the basis for the second map which is the published version contained in Helmfrid's dissertation. The working version has a larger-scale, which means it is more detailed than the second map.

The second map was digitized and rectified in 2009 by Johan Berg, also from Stockholm University. This was done as part of a project on land relations during the Younger Iron Age in western Ostergotland. (See link to separate data , 2019-102, in SND's catalog). The working version (the first map) was digitized in 2019 when it was decided to publish Helmfrid's maps via the Swedish National Data Service.

The working version was based on a compilation of 400 smaller village and farm maps, all from the 1640s. These sources can be found in the following historical property maps (in Swedish: geometriska jordeböcker): D5, D6, D7, D8 and D10 (See https://riksarkivet.se/visa-kartsamlingar -- "D" refers to maps from Ostergotland county). All the original historical maps were signed by one surveyor -- Larson Groth.

The rectified map can be opened in most Geographic Information System (GIS) programs. The map can also be opened in image editing software, but without coordinates. The working version (the first map) is also a raster file, but because the working version was never rectified, it has been saved as a normal tiff file (.tif), not a geotiff. It can be opened in image editing programs like Photoshop or opensource Gimp.

More information on how Helmfrid produced these maps and a link and reference to his dissertation can be found (in Swedish) in the attached documentation file.

Web map of Manitoba's forest inventory in 2021. This web map is used within Manitoba's Five Year Report on the Status of Forestry, 2016 - 2021 story map. The web map includes all of the Forest Resource Inventory (FRI) and Forest Land Inventory (FLI) interpreted data that forms Manitoba's 'green zone'. For Manitoba's northern portion where FRI/FLI data does not exist (white zone), North American Land Change Monitoring System (NALCMS) data from 2015 was updated and used.

This dataset was generated by the TU Wien Department of Geodesy and Geoinformation.European Sentinel-1 forest type and tree cover density maps represent first continental-scale forest layers based on Sentinel-1 C-Band Synthetic Aperture Radar (SAR) backscatter data. For the year 2017 they cover the majority of European continent with 10 m and 100 m sampling for forest type and tree cover density, respectively. The maps were derived using the method described in https://www.tandfonline.com/doi/full/10.1080/01431161.2018.1479788.The forest type map shows the dominant forest type class (coniferous, broadleaf). Tree cover density map shows the percentage of forest canopy cover within the 100 m pixel.Please be referred to our peer-reviewed article at https://doi.org/10.3390/rs13030337 for details and accuracy assessment accross Europe.Dataset RecordThe forest type and tree cover density maps are sampled at 10 m and 100 m pixel spacing respectively, georeferenced to the Equi7Grid and divided into square tiles of 100km extent ("T1"-tiles). With this setup, the forest maps consist of 728 tiles over the European continent, with data volumes of 3.12 GB and 378.3 MB.The tiles' file-format is a LZW-compressed GeoTIFF holding 16-bit integer values, with tagged metadata on encoding and georeference. Compatibility with common geographic information systems as QGIS or ArcGIS, and geodata libraries as GDAL is given.In this repository, we provide each forest map as tiles, whereas two zipped dataset-collections are available for download below.Code AvailabilityFor the usage of the Equi7Grid we provide data and tools via the python package available on GitHub at https://github.com/TUW-GEO/Equi7Grid. More details on the grid reference can be found in https://www.sciencedirect.com/science/article/pii/S0098300414001629.AcknowledgementsThe computational results presented have been achieved using the Vienna Scientific Cluster (VSC).

Detailed linkage and recombination rate maps are necessary to use the full potential of genome sequencing and population genomic analyses. We used a custom collared flycatcher 50 K SNP array to develop a high-density linkage map with 37 262 markers assigned to 34 linkage groups in 33 autosomes and the Z chromosome. The best-order map contained 4215 markers, with a total distance of 3132 cM and a mean genetic distance between markers of 0.12 cM. Facilitated by the array being designed to include markers from most scaffolds, we obtained a second-generation assembly of the flycatcher genome that approaches full chromosome sequences (N50 super-scaffold size 20.2 Mb and with 1.042 Gb (of 1.116 Gb) anchored to and mostly ordered and oriented along chromosomes). We found that flycatcher and zebra finch chromosomes are entirely syntenic but that inversions at mean rates of 1.5–2.0 event (6.6–7.5 Mb) per My have changed the organization within chromosomes, rates high enough for inversions to potentially have been involved with many speciation events during avian evolution. The mean recombination rate was 3.1 cM/Mb and correlated closely with chromosome size, from 2 cM/Mb for chromosomes >100 Mb to >10 cM/Mb for chromosomes <10 Mb. This size dependence seemed entirely due to an obligate recombination event per chromosome; if 50 cM was subtracted from the genetic lengths of chromosomes, the rate per physical unit DNA was constant across chromosomes. Flycatcher recombination rate showed similar variation along chromosomes as chicken but lacked the large interior recombination deserts characteristic of zebra finch chromosomes.

Polyploids account for approximately 70% of flowering plants, including many field, horticulture and forage crops. Cottons are a world-leading fiber and important oilseed crop, and a model species for study of plant polyploidization, cellulose biosynthesis and cell wall biogenesis. This study has addressed the concerns of physical mapping of polyploids with BACs and/or BIBACs by constructing a physical map of the tetraploid cotton, Gossypium hirsutum L. The physical map consists of 3,450 BIBAC contigs with an N50 contig size of 863 kb, collectively spanning 2,244 Mb. We sorted the map contigs according to their origin of subgenome, showing that we assembled physical maps for the A- and D-subgenomes of the tetraploid cotton, separately. We also identified the BIBACs in the map minimal tilling path, which consists of 15,277 clones. Moreover, we have marked the physical map with nearly 10,000 BIBAC ends (BESs), making one BES in approximately 250 kb. This physical map provides a line of evidence and a strategy for physical mapping of polyploids, and a platform for advanced research of the tetraploid cotton genome, particularly fine mapping and cloning the cotton agronomic genes and QTLs, and sequencing and assembling the cotton genome using the modern next-generation sequencing technology.

The size of the Nordics Location-Based Services market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 15.64% during the forecast period. Recent developments include: February 2023: Mercedes-Benz and Google unveiled an extensive and visionary partnership aimed at revolutionizing the automotive industry and elevating the digital luxury car experience to new heights. In an industry-first move, Mercedes-Benz is set to develop its distinct navigation system, harnessing the advanced capabilities of the Google Maps Platform to craft an unparalleled driving experience. This groundbreaking collaboration will grant Mercedes-Benz exclusive access to Google's cutting-edge geospatial technologies, providing users with an array of exceptional features. These include comprehensive location data, automatic route optimization, up-to-the-minute traffic updates, and even predictive traffic insights, among other remarkable functionalities., January 2023: Mapbox, the platform for mapping and location services, joined forces with Toyota Motor Europe to introduce Cloud Navigation powered by Mapbox Dash. This transformative partnership brings an unprecedented level of real-time information to Toyota's Yaris, Yaris Cross, and Aygo X models, enhancing the driving experience in terms of efficiency, convenience, and safety.. Key drivers for this market are: Growing Demand for Geo-based Marketing, 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: Indoor Location Segment is Expected to Hold a Significant Share of the Market.