Introduction

Google Maps Statistics: Google Maps has changed how we used to navigate or explore the world. In 2024, it will most certainly become the ultimate mapping service, getting so much more than most other services and boasting so many more users. This article will discuss some of the Google Maps statistics its global coverage, technology achievements, and downloads.

Statistics illustrates consumption, production, prices, and trade of Maps and hydrographic or similar charts; (printed other than in book form), including wall maps, topographical plans and similar in the World from 2007 to 2024.

The global HD maps for autonomous vehicles market was sized at almost *** billion U.S. dollars in 2020. It is forecast that in 2028, the market will surpass the size of **** billion U.S. dollars, growing at a steady CAGR of around ** percent.

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.

Agro-MAPS consists of selected agricultural land-use statistics (crop production, area harvested and crop yield) aggregated by sub-national administrative districts for selected years. The full Agro-MAPS database currently contains data for 134 countries - 130 countries at admin1 level; 59 countries at admin2 level. These countries represent 92% of the world land surface. Users can interactively query and display Agro-MAPS data as maps, for a given country or region (Africa, Asia, North America, Latin America & the Caribbean, Asia, Near East in Asia, Oceania).

GMIA shows the amount of area equipped for irrigation around the year 2005 in percentage of the total area on a raster with a spatial resolution of 5 arc-minutes (about 10 km at the equator). Additional map layers show the percentage of the area equipped for irrigation that was actually used for irrigation and the percentages of the area equipped for irrigation that was irrigated with groundwater, surface water or non-conventional sources of water. The data layer on area equipped for irrigation was developed by combining sub-national irrigation statistics with geospatial information on the position and extent of irrigation schemes to compute the irrigation density (fraction of 5 arc-minute cells equipped for irrigation). The information for the additional layers on area actually irrigated or on the water source for irrigation was derived from statistical survey data (e.g. census reports) only, therefore the accuracy at pixel level is limited. GIS users can import the map as either ASCII grids or as shape files.

As of March 2021, Waze was the mobile GPN navigation app found to collect the largest amount of data from global iOS users, with 21 data points collected across all examined segments. Maps.me collected a total of 20 data points from its users, including five data points on contact information. Hiking and trail GPS map Gaia followed, with 13 data points, respectively.

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.

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meter Source Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary Sphere Extent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer? This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro. In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend. To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth. Different Classifications Available to Map Five processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display. Using Time By default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year. In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change. Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009. This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover. Land Cover Processing To provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015. Source data The datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.php CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%) 50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

As of November 2020, Japan had mapped nearly ** percent of it's exclusive economic zone (EEZ). An EEZ is the sea zone stretching *** nautical miles (nmi) from the coast of a state. The Seabed 2030 project aims to map the world's ocean floor by the year 2030 using crowdsource datasets.

The global production of monoammonium phosphate, commonly known as MAP, reached roughly 29.2 million metric tons in 2022, up from 16.5 million metric tons in 2009. MAP is commonly used as a source of phosphorous and nitrogen for plant growth.

This map package contains data on global water access percentage change from 1990-2010 for rural and urban populations. Improved water source, rural and urban (% of rural and urban population with access). Access to an improved water source refers to the percentage of the population with reasonable access to an adequate amount of water from an improved source, such as a household connection, public standpipe, borehole, protected well or spring, and rainwater collection. Unimproved sources include vendors, tanker trucks, and unprotected wells and springs. Reasonable access is defined as the availability of at least 20 liters a person a day from a source within one kilometer of the dwelling.Water access of rural and urban populations in percentage for 1990, 2000 and 2010 are also included in the data. Map Package published and hosted by Esri Canada © 2013. Content Source(s): Countries, Esri Maps and DataImproved water source, rural and urban (% of rural and urban populations with access), World Bank DataPopulation, CIA, The World Factbook Coordinate System: Web Mercator Auxiliary Sphere (WKID 102100) Update Frequency: As Required Publication Date: August 2013

Global Map of Irrigation Areas - Version 5 Grid with percentage of area equipped for irrigation with a spatial resolution of 5 arc minutes or 0.083333 decimal degrees. This dataset is developed in the framework of the AQUASTAT Programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations and the Rheinische Friedrich-Wilhems University, Germany. The map shows the amount of area equipped for irrigation around the year 2005 in percentage of the total area on a raster with a resolution of 5 minutes. Additional map layers show the percentage of the area equipped for irrigation that was actually used for irrigation and the percentages of the area equipped for irrigation that was irrigated with groundwater, surface water or non-conventional sources of water. In details, the following products have been released and made available for download:

• Area equipped for irrigation expressed as percentage of total area: total=aei, surface water=aeisw, groundwater=aeigw, non-conventional sources of water=aeinc (ASCII-grid);

• Area actually irrigated expressed as percentage of area equipped for irrigation (ASCII-grid);

• Area equipped for irrigation expressed in hectares per cell (ASCII-grid);

• Irrigated areas v.5 (ESRI shapefile);

• High and low resolution images (PDF);

• Quality Assessment (Excel)

Due to the map generation method, the quality of the map can never be uniform. The overall quality of the map depends heavily on the individual quality of the data for the different countries.

Data revision: 2013-10-07

Supplemental Information:

The maps are generated as a grid with a cellsize of 5 arc minutes. For the GIS-users the maps are distributed in two different formats: as a zipped ASCII-grid that can be easily imported in most GIS-software that support rasters or grids; and, to accommodate people who use GIS-software that doesn't support rasters or grids, as a zipped ESRI shape file. The non-GIS-users can download the map as PDF-file in two different resolutions.

Citation:

Users are requested to refer to the map as follows: "Stefan Siebert, Verena Henrich, Karen Frenken and Jacob Burke (2013). Global Map of Irrigation Areas version 5. Rheinische Friedrich-Wilhelms-University, Bonn, Germany / Food and Agriculture Organization of the United Nations, Rome, Italy".

Contact points:

Metadata Contact: AQUASTAT

Data lineage:

Due to the map generation method, the quality of the map can never be uniform. The overall quality of the map depends heavily on the individual quality of the data for the different countries.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO

Online resources:

Global Map of Irrigation Areas (GMIA) on the AQUASTAT website

Download - Global area equipped for irrigation expressed as percentage of total area - Raster (ASCII-grid, 2.3 MB)

Download - Global area equipped for irrigation expressed in hectares per cell - Raster (ASCII-grid, 2.3 MB)

Download - Global Map of Irrigation Areas v.5 - Vector (ESRI shapefile, 4 MB)

Download - Global Map of Irrigation Areas v.5 - All files

Download - Global Map of Irrigation Areas v.5 - High resolution image (PDF, 3.1 MB)

Download - Global Map of Irrigation Areas v.5 - Low resolution image (PDF, 0.9 MB)

This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.CitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

Information on global cropland distribution is critical for agricultural monitoring and food security. We propose a new self-adapting statistics allocation model (SASAM) that fuses multiple existing maps with national and subnational statistics to develop new global cropland map. This synergy method is based on the agreement between input cropland maps, and is independent of training samples. The statistics of cropland area are used as a standard to rank the input cropland maps and build a score table to indicate the agreement among the input datasets. Statistics of national, first and second subnational levels are allocated to the pixels with higher agreement scores, and the multi-level allocation results are then integrated to obtain the extent of cropland. We applied SASAM to produce a global cropland synergy map with a spatial resolution of 500 m circa 2010. The synergy cropland map is a critical input for the Spatial Production Allocation Model (SPAM) to make global crop distribution (https://doi.org/10.7910/DVN/PRFF8V). It will be the vital baseline information for global land modelling, food production estimation, and food security monitoring to meet the Sustainable Development Goals pertaining to agriculture adopted by the United Nations.

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

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

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

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The mobile map market share is expected to increase by USD 6.73 billion from 2020 to 2025, and the market’s growth momentum will accelerate at a CAGR of 18.41%. This mobile map market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. The mobile map market report also offers information on several market vendors, including Alibaba Group Holding Ltd., Alphabet Inc., Apple Inc., CE Info. Pvt. Ltd., Environmental Systems Research Institute Inc., HERE Global BV, Microsoft Corp., NavInfo Co. Ltd., TomTom International BV, and Verizon Communications Inc. among others. Furthermore, this report extensively covers mobile map market segmentation by application (outdoor mobile map and indoor mobile map), end-user (automotive navigation, mobile and internet, and public sector and enterprise), and geography (APAC, North America, Europe, South America, and MEA).

What will the Mobile Map Market Size be During the Forecast Period?

Download the Free Report Sample to Unlock the Mobile Map Market Size for the Forecast Period and Other Important Statistics

Mobile Map Market: Key Drivers, Trends, and Challenges

Based on our research output, there has been a positive impact on the market growth during and post COVID-19 era. The increasing adoption of technologically advanced mobile devices is notably driving the mobile map market growth, although factors such as threat from open-source platform may impede market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the mobile map industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

Key Mobile Map Market Driver

The increasing adoption of technologically advanced mobile devices is one of the primary factors driving the mobile map market growth. The growing penetration of advanced mobile devices has increased the use of location-based services (LBS). To support this, mobile device manufacturers are introducing new devices that can integrate location-based applications such as GPS-enabled applications. In addition, individuals rely on such devices to obtain information such as traffic updates, directions to nearby locations, and real-time information such as weather forecasts. All these GPS-based applications are built on digital maps. Furthermore, the growth in connected devices will drive the demand for mobile maps globally to enable seamless navigation.

Key Mobile Map Market Trend

The development of indigenous mapping systems is one of the major mobile map market trends. This trend is growing significantly in Brazil, Russia, China, and India. Governments are encouraging regional mobile map makers to develop mobile map solutions that are country-specific. This trend is further supported by advanced mapping technology, which can develop accurate 3D digital maps.

Key Mobile Map Market Challenge

The growing popularity of open-source solutions has an adverse effect on the net sales of commercial mobile map solutions. The inflated cost of mobile map solutions has increased the demand for open-source mobile map applications in the market, especially in emerging countries such as China and India. These nations consist of many SMEs that require mobile map solutions but do not have sufficient capital to invest in customized mobile map technology. Therefore, open-source mobile map solutions have become a preferred choice among them. Many automobile companies also prefer open-source mobile map solutions in their vehicles. Large companies, as a part of the cost reduction, now prefer using open-source mobile map solutions compared with commercial mobile map solutions.

This mobile map market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Mobile Map Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alibaba Group Holding Ltd.
Alphabet Inc.
Apple Inc.
CE Info. Pvt. Ltd.
Environmental Systems Research Institute Inc.
HERE Global BV
Microsoft Corp.
NavInfo Co. Ltd.
TomTom International BV
Verizon Communications Inc.

This statistical study of the mobile map market encompasses successful business strategies deployed by the key vendors. The mobile mapping market is fragmented and the vendors are deploying growth strategies such as M&A activities to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in