Available data for gross domestic product (GDP) and population density are useful for defining divisions in socio-economic gradients across Europe, since economic power and human population pressure are recognised as two of the most critical factors causing ecosystem changes. To overcome both the limitations in data availability and in the distortions caused by using administrative regions, we decided to base the socio-economic dimension on an economic density indicator, defined as the income generated per square kilometre (EUR km-2), which can be mapped at a 1km2 spatial resolution. Economic density forms an integrative indicator that is based on two key drivers that were identified above: economic power and human population pressure. The indicator, which has been used to rank countries by their level of development, can be considered a crude measure for impacts on the environment caused by economic activity. An economic density map (EUR km-2) at 1 km2 spatial resolution was constructed by multiplying economic power (EUR person-1) with population density (person km-2). Subsequent logarithmic divisions resulted in an aggregated map of four economic density zones. Although the map has a fine spatial resolution it has to be realised that they form a spatial disaggregation of coarser census statistics. Importantly, the finer resolution discerns regional gradients in human activity that are required for many environmental studies, whilst broad gradients in economic activity is also treated consistently across Europe. GDP and population density data used were for the year 2001. The dataset consists of GeoTiff files of the economic density map and the four economic density zones.

This map shows the population density in North-Eastern Europe in 2011. This map is extracted from the cartographic atlas made on the occasion of the merger of the Alsace, Champagne-Ardenne and Lorraine Regions in January 2016. It is available on the website of the Grand Est Region. This map was designed for A3 format, landscape.

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).

This dataset contains the interpolated values of bird density and bird flight speed (N-S and E-W) resulting from the methodology presented in [reference].The methodology is explained in less detail at rafnuss-postdoc.github.io/BMM. The resulting interpolation is a probability distribution (define the probability of each value to occurs). Only the median, quantile 10 and 90 are given in this file.

The spatio-temporal grid has a resolution of 0.2° in latitude (43°-68°) and longitude (-5°-30°) and 15 minutes in time (19 September to 10 October 2016), resulting in 127x176x2017 nodes. Over this large data cube, the estimation are only computed at the nodes located (1) over land, (2) within 200km of the nearest radar and (3) during nighttime.

The same dataset can be visualised on a dedicated web interface: www.bmm.raphaelnussbaumer.com and data can be queried on a API (documentation).

The csv file is structured as a table with the following columns:

• density_estimation: Median bird density [bird/km^2] (quantile 50)
• density_quantile10: Quantile 10 of bird density [bird/km^2]
• density_quantile90: Quantile 90 of bird density [bird/km^2]
• speedu_estimation: Mean bird speed east(+)/west(-) [m/s]
• speedu_std: Standard deviation of bird speed east(+)/west(-) [m/s]
• speedv_estimation: Mean bird speed north(+)/south(-) [m/s]
• speedv_std: Standard deviation of bird speed north(+)/south(-) [m/s]
• latitude
• longitude
• time

This dataset contains the modeling results GIS data (maps) of the study “Sustainable Human Population Density in Western Europe between 560.000 and 360.000 years ago” by Rodríguez et al. (2022). The NPP data (npp.zip) was computed using an empirical formula (the Miami model) from palaeo temperature and palaeo precipitation data aggregated for each timeslice from the Oscillayers dataset (Gamisch, 2019), as defined in Rodríguez et al. (2022, in review). The Population densities file (pop_densities.zip) contains the computed minimum and maximum population densities rasters for each of the defined MIS timeslices. With the population density value Dc in logarithmic form log(Dc). The Species Distribution Model (sdm.7z) includes input data (folder /data), intermediate results (folder /work) and results and figures (folder /results). All modelling steps are included as an R project in the folder /scripts. The R project is subdivided into individual scripts for data preparation (1.x), sampling procedure (2.x), and model computation (3.x). The habitat range estimation (habitat_ranges.zip) includes the potential spatial boundaries of the hominin habitat as binary raster files with 1=presence and 0=absence. The ranges rely on a dichotomic classification of the habitat suitability with a threshold value inferred from the 5% quantile of the presence data. The habitat suitability (habitat_suitability.zip) is the result of the Species Distribution Modelling and describes the environmental suitability for hominin presence based on the sites considered in this study. The values range between 0=low and 1=high suitability. The dataset includes the mean (pred_mean) and standard deviation (pred_std) of multiple model runs.

The Vessel Density maps in the EU are created since the 2019 by Cogea for the European Marine Observation and Data Network (EMODnet). The dataset is updated every year and is available for viewing and download on EMODnet Human Activities web portal (www.emodnet-humanactivities.eu). The maps are based on AIS data yearly purchased from Collecte Localisation Satellites (CLS) and ORBCOMM. The maps, GeoTIFF format, show shipping density in 1x1km cells of a grid covering all EU waters and some neighbouring areas. Density is expressed as hours per square kilometre per month. The following ship types are available:0 Other, 1 Fishing, 2 Service, 3 Dredging or underwater ops, 4 Sailing, 5 Pleasure Craft, 6 High speed craft, 7 Tug and towing, 8 Passenger, 9 Cargo, 10 Tanker, 11 Military and Law Enforcement, 12 Unknown and All ship types. This particular layer refers to EMODnet ship type "All" that includes all the AIS ship Type codes.

Two datasets are presented. The blgdens100m.zip shows the EU-building density map in 100 m × 100 m resolution raster, (building area in m2 per 10 000 m2 or hectare) for all Europe. The second dataset RooftopPVpotential_EU.zip (vector) provides the modelled rooftop area and the technical and economic solar PV potential for Europe at regional level.

Maps with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

land and atmosphere. The European Space Agency is now studying concepts for the Next Generation Sentinel-3 Topography mission (S3NGT) mission that would launch in the 2032+ time period. In order to meet the primary objectives of the S3NGT mission requirement document a complex analysis of river and lake targets is required to size the satellite mass memory and downlink system.

In the context of Service Level Agreement between JRC and EFSA, a series of datasets have been developed as support to the FATE and the ECOREGION EFSA PPR Working Groups. This dataset contains the topsoil bulk density expressed in kg.m^-3 This metadata record is adapted from the orginal one received from JRC.

The Global Human Settlement Layer (GHSL) project is supported by European Commission, Joint Research Center and Directorate-General for Regional and Urban Policy. The GHSL produces new global spatial information, evidence-based analytics, and knowledge describing the human presence in the planet.

The GHSL relies on the design and implementation of new spatial data mining technologies allowing to process automatically and extract analytics and knowledge from large amount of heterogeneous data including: global, fine-scale satellite image data streams, census data, and crowd sources or volunteering geographic information sources. Spatial data reporting objectively and systematically about the presence of population and built-up infrastructures are necessary for any evidence-based modelling or assessing of i) human and physical exposure to threats as environmental contamination and degradation, natural disasters and conflicts, ii) impact of human activities on ecosystems, and iii) access to resources.

This spatial raster dataset depicts the distribution and density of residential population, expressed as the number of people per cell. Resident population from censuses for year 2011 provided by Eurostat were disaggregated from source zones to grid cells, informed by land use and land cover from Corine Land Cover Refined 2006 and by the distribution and density of built-up as mapped in the European Settlement Map 2016 layer.

Map represents the calculated (surface) heat-flow density (HFD) in mW/m2 with topographic correction. It is made with data from 119 boreholes from the measured temperatures in the available boreholes and measured thermal conductivity on cored rock samples from the same boreholes. The pattern of the HFD isolines is affected by numerous parameters, particularly the thermal conductivity of rocks, rock permeability and fracturing, fluid content of the rocks, and all are reflected in the measured temperature gradient in the boreholes.

Map of the forest biomass density of Europe for the year 2020 matching the harmonized forest biomass statistics at the administrative unit. The map represents the dry aboveground forest biomass density in units of tons/ha. The map is available in GeoTIFF format at the spatial resolution of 100 x 100 m in ETRS89 LAEA (EPSG:3035) coordinate reference system for the extent of Europe (Biomass_Map_2020.tif)

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Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

Activation date: 2014-04-22
Event type: Other

Activation reason:
Service Request: The nation-wide asset mapping for Finland provides a detailed regional geospatial dataset for the quick and uncomplicated calculation of potential damages either in the preparedness phase or during the immediate response phase of crises caused by natural hazard events. The concept follows the Basic European Asset Map (BEAM) data model developed under the Copernicus precursor project SAFER (Services and Applications for Emergency Response) and extended in the FP7 project IncREO (Increasing Resilience through Earth Observation).BEAM Finland is a comprehensive dataset comprising of a set of spatialized economic indicator values and a population density value. All economic attributes are expressed in EURO/m². By using GIS methods for intersecting BEAM data with hazard intensity information and appropriate vulnerability functions quick regional estimates can be made for exposure of assets and population, damage assessments and cost/benefit analysis.The wall-to-wall map and vector dataset depicts assets for various economic categories as well as for population density. The data are derived by combining socioeconomic data and land use/cover data. Fourteen distinct contributing attributes for the asset mapping are provided (e.g. buildings, households, industry, agriculture, etc.). Assets information is made available not only as a cumulative layer of different types of assets (e.g. private households, industry, commerce, vehicles, agriculture, etc.), but as accessible single contributing layers as well, each of them expressing its value.

This dataset contains 23 30m resolution raster data of continental Europe land use / land cover classes extracted from OpenStreetMap, as well as administrative areas, and a harmonized building dataset based on OpenStreetMap and Copernicus HRL Imperviousness data.

The land use / land cover classes are:

1. buildings.commercial
2. buildings.industrial
3. buildings.residential
4. cemetery
5. construction.site
6. dump.site (landfill)
7. farmland
8. farmyard
9. forest
10. grass
11. greenhouse
12. harbour
13. meadow
14. military
15. orchard
16. quarry
17. railway
18. reservoir
19. road
20. salt
21. vineyard

The land use / land cover data was generated by extracting OSM vector layers from https://download.geofabrik.de/). These were then transformed into a 30 m density raster for each feature type. This was done by first creating a 10 m raster where each pixel intersecting a vector feature was assigned the value 100. These pixels were then aggregated to 10 m resolution by calculating the average of every 9 adjacent pixels. This resulted in a 0—100 density layer for the three feature types. Although the digitized building data from OSM offers the highest level of detail, its coverage across Europe is inconsistent. To supplement the building density raster in regions where crowd-sourced OSM building data was unavailable, we combined it with Copernicus High Resolution Layers (HRL) (obtained from https://land.copernicus.eu/pan-european/ high-resolution-layers), filling the non-mapped areas in OSM with the Impervious Built-up 2018 pixel values, which was averaged to 30 m. The probability values produced by the averaged aggregation were integrated in such a way that values between 0—100 refer to OSM (lowest and highest probabilities equal to 0 and 100 respectively), and the values between 101—200 refer to Copernicus HRL (lowest and highest probability equal to 200 and 101 respectively). This resulted in a raster layer where values closer to 100 are more likely to be buildings than values closer to 0 and 200. Structuring the data in this way allows us to select the higher probability building pixels in both products by the single boolean expression: Pixel > 50 AND pixel <150.

This dataset is part of the OpenStreetMap+ was used to pre-process the LUCAS/CORINE land use / land cover samples (https://doi.org/10.5281/zenodo.4740691) used to train machine learning models in Witjes et al., 2022 (https://doi.org/10.21203/rs.3.rs-561383/v4)

Each layer can be viewed interactively on the Open Data Science Europe data viewer at maps.opendatascience.eu.

WORKING VERSION.

All layers will be visible in this linked webgis app along with estimated error. <== To Be Updated in new final version, not yet so.

The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0008983 degrees ground sampling distance per cell, which corresponds to about 1 ha, i.e. ~100 m x ~100 m at the equator, but decreases in area with increasing latitude as the coordinate system is not equal-area, e.g. ~70 m west-est direction at 45° latitude and ~50 m west-est direction at 60° latitude.

canopyBulkDensity.tif - Canopy Bulk Density (CBD) - amount of canopy biomass per volume of canopy (kg/m3)

canopyBulkDensityRMSE.tif - Estimated root mean square of errors for CBD (kg/m3)

canopyBaseHeight.tif - Canopy Base Height (CBH) - height of canopy start measured from the ground (m)

canopyBaseHeightRMSE.tif - Estimated root mean square of errors for CBH (m)

Maps with worldwide wind speed and wind power density potential. The GIS data stems from the Global Wind Atlas (http://globalwindatlas.info/). The link provides poster size (.pdf) and midsize maps (.png).

This dataset (GIS maps)(2016) contains 7 soil property maps that have been derived using soil point data from the LUCAS 2009 soil survey (around 20,000 points) for EU-25, using hybrid approaches like regression kriging. Properties: clay, silt and sand content; coarse fragments; bulk density; USDA soil textural class; available water capacity. Resolution 500m.

This metadata refers to the HRL Forest 2018 primary status layer Tree Cover Density (TCD). The TCD raster product provides information on the proportional crown coverage per pixel at 10m spatial resolution and ranges from 0% (all non-tree covered areas) to 100%, whereby Tree Cover Density is defined as the "vertical projection of tree crowns to a horizontal earth’s surface“. The production of the High Resolution Forest layers was coordinated by the European Environment Agency (EEA) in the frame of the EU Copernicus programme.

The HRL Forest product consists of 3 types of (status) products and additional change products. The status products are available for 2012, 2015, and 2018 reference years: 1. Tree cover density (TCD) (level of tree cover density in a range from 0-100%) 2. Dominant leaf type (DLT) (broadleaved or coniferous majority) 3. Forest type product (FTY). The forest type product allows to get as close as possible to the FAO forest definition. In its original (10m (2018) / 20m (2012, 2015)) resolution it consists of two products: a dominant leaf type product that has a MMU of 0.5 ha, as well as a 10% tree cover density threshold applied, and 2) a support layer that maps (now only available on demand), based on the dominant leaf type product, trees under agricultural use and in urban context (derived from CLC and imperviousness 2009 data). For the final 100 m product trees under agricultural use and urban context from the support layer are removed. NEW for 2018: the 10m 2018 reference year FTY product now also has the agricultural/urban trees removed. In the past this was done only for the 100m product, now it is consistently applied for both the 10m and the 100m FTY products.

This dataset is provided as 10 meter rasters (fully conformant with the EEA reference grid) in 100 x 100 km tiles grouped according to the EEA38 countries and the United Kingdom.

You can find more information about the product here: https://land.copernicus.eu/en/products/high-resolution-layer-tree-cover-density/tree-cover-density-2018.