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.

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 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.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

As of 2025, Asia was the most densely populated region of the world, with nearly 156 inhabitants per square kilometer, whereas Oceania's population density was just over five inhabitants per square kilometer.

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Description of the containing files inside the Dataset.

The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.

* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

* Malta was added to the dataset

Copernicus Land Monitoring Service:

Coastal LU/LC

Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones

EU-Hydro - Coastline

Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline

Natura 2000

Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation

European Settlement Map

Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe

Imperviousness Density

Resolution 10m; The percentage of sealed area

Impervious Built-up

Resolution 10m; The part of the sealed surfaces where buildings can be found

Grassland 2018

Resolution 10m; A binary grassland/non-grassland product

Tree Cover Density 2018

Resolution 10m; Level of tree cover density in a range from 0-100%

Joint Research Center:

Global Human Settlement Population Grid
GHS-POP)

Resolution 250m; Residential population estimates for target year 2015

GHS settlement model layer
(GHS-SMOD)

Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities

GHS-BUILT

Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018

ENACT 2011 Population Grid

(ENACT-POP R2020A)

Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations

JRC Open Power Plants Database (JRC-PPDB-OPEN)

Europe's open power plant database

GHS functional urban areas
(GHS-FUA R2019A)

Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)

GHS Urban Centre Database
(GHS-UCDB R2019A)

Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data:

Open Street Map (OSM)

BF, Transportation Network, Utilities Network, Places of Interest

CEMS

Data from Rapid Mapping activations in Europe

GeoNames

Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.

Global Administrative Areas

Administrative areas of all countries, at all levels of sub-division

NUTS3 Population Age/Sex Group

Eurostat population by age and sex statistics interescted with the NUTS3 Units

FLOPROS

A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

With a growing emphasis on the societal benefits gained through recreation outdoors, a method is needed to identify which spaces are most valuable for providing those benefits. Social media platforms offer a wealth of useful information on where people prefer to enjoy the outdoors. We combined geotagged images from Flickr with several environmental metrics in a Maxent model to calculate the probability of a photograph being taken (the potential supply of recreational amenity). We then built a set of population density kernels to express the potential demand of recreational amenity. Linear regression was used to compare supply and demand layers to visitation records from 540 recreation sites across Europe. The result was a map estimating the number of visitors/km2/year. Our analysis showed that natural areas near population centres deliver more recreational benefit than attractive sites in remote locations. The former should therefore be prioritised by planners and policymakers seeking to protect or improve recreational amenity.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

CORINE Land Cover (CLC) database

Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/

Terms of Use: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

natura/*

Natura 2000 natural protection areas

Source: https://www.eea.europa.eu/data-and-maps/data/natura-10

Terms of Use: https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

gebco/GEBCO_2014_2D.nc

GEBCO bathymetric dataset

Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/

Terms of Use: https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

Population and GDP data for Swiss Cantons

Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html

Terms of Use:

https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html

https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html

nama_10r_3popgdp.tsv.gz

Population by NUTS3 region

Source: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en

Terms of Use:

https://ec.europa.eu/eurostat/about/policies/copyright

GDP_per_capita_PPP_1990_2015_v2.nc

Gross Domestic Product per capita (PPP) from years 1999 to 2015

Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer

Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015"

Source: https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4

ppp_2019_1km_Aggregated.tif

The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Source: https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777

License: Creative Commons Attribution 4.0 International Licens

data/bundle/era5-HDD-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level heating degree days in Europe for 1941-2023. Used for rescaling heat demand in weather years not covered by energy balance statistics.

data/bundle/era5-runoff-per-country.csv

• Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875- License: CC-BY 4.0- Contains country-level daily sum of runoff in Europe for 1941-2023. Used for rescaling hydro-electricity availability in weather years not covered by EIA hydro-generation statistics.

shipdensity_global.zip

Global Shipping Traffic Density

Creative Commons Attribution 4.0

https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density

seawater_temperature.nc

Global Ocean Physics Reanalysis

Seawater temperature at 5m depth

Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services

License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence

The European Copernicus Coastal Flood Awareness System (ECFAS) project will contribute to the evolution of the Copernicus Emergency Monitoring Service by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS will provide a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development will run from January 2021-December 2022. The ECFAS project is a collaboration between Istituto Universitario di Studi Superiori IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and is funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

This project has received funding from the European Union’s Horizon 2020 programme

Description of the containing files inside the Dataset.

The dataset was divided at European country level, except the Adriatic area which was extracted as a region and not on a country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the abovementioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layers includes information fro the whole Europe and the second layer has only the information regaridng the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standars. Below there are tables which present the dataset.

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

Research data and scientific software related to a study exploring the statistical relations between the concentration of nine heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, V, Zn) and N in moss specimens collected in 2010 throughout Europe and a set potential explanatory variables (such as the atmospheric deposition calculated by use of two chemical transport models, distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils). Statistical analysis and modelling relies on Random Forest (RF). RF-models in conjunction with a Geographical Information System (GIS) were then used for mapping spatial patterns of element concentrations in moss across Europe.

Snapshot img

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 applications,

Description

This dataset includes the inputs and outputs generated in the spatial modeling of CES using social media data for eight mountain parks in Spain and Portugal (Aigüestortes, Sierra de Guadarrama, Ordesa, Peneda-Gerês, Picos de Europa, Sierra de las Nieves, Sierra Nevada and Teide). This spatial modeling is addressed in the article in preparation entitled: "What drives cultural ecosystem services in mountain protected areas? An AI-assisted answer using social media."

The variables used as inputs to generate the models come from different sources:

-CES presence points come from social media photos (Flickr and Twitter) labeled using AI models and validated by experts. The models used for automatic labeling were Dino v2 and OPENAI's GPT 4.1 model. Consensus was sought on the labels from these two label sources, which showed F1 values above 0.75, and these labels were used as presence data.

The environmental variables used are mainly derived from:

• OpenStreetMap (OSM) https://www.openstreetmap.org/

• Variables derived from remote sensing

• Topographic variables

• Current and future climate variables derived from CHELSA (https://chelsa-climate.org/)

• Landscape metrics (calculated with Fragstats software https://www.fragstats.org/)

• Viewshed

• Land use and land cover maps (https://land.copernicus.eu/en/products/corine-land-cover)

The models were generated with the maximum entropy (MaxEnt) algorithm using the biomod2 R package, leveraging its suitability for presence-only data, low sample sizes, and mixed predictor types. To address sampling bias, we generated 10 pseudo-absence replicates based on the “target-group background” method. Models were evaluated using AUC-ROC and True Skill Statistic (TSS), with performance validation via 10-fold cross-validation, resulting in 100 runs per model. Ensemble models were created from runs with AUC-ROC > 0.6, using the median for spatial projections of CES and the coefficient of variation to estimate uncertainty. We implemented two modelling approaches: one assuming consistent CES preferences across parks, and another assuming park-specific preferences shaped by local environmental contexts.

Table 1. Categories used in social media photo tagging: Stoten, based on the scientific framework proposed by Moreno-Llorca et al. (2020) (https://doi.org/10.1016/j.scitotenv.2020.140067).

Stoten

Cultural

Fauna/Flora

Gastronomy

Nature & Landscape

Not relevant

Recreational

Religious

Rural tourism

Sports

Sun and beach

Urban

Table 2. Table of contents of the dataset

Folder

format

Description

Inputs

Base layers

by National Park

100-meter grid

grid_wgs84_atrib

.shp

100 x 100 meter grid for each of the studied national parks that cover the study area

Biosphere Reserve

MAB_wgs84

.shp

Biosphere reserve layers present in each of the national parks studied

Municipality

Municipality

.shp

Layers of municipalities that overlap with the park area, biosphere reserve, Natura 2000 and the socioeconomic influence area with a 100-meter buffer

National park limit

National_park_limit

.shp

Boundaries of each of the national parks studied

Natura 2000

RN2000

.shp

Layers of the Natura 2000 for each of the national parks studied

Socioeconomic influence area

AIS

.shp

Area of socioeconomic influence of each of the parks studied

Readme

.txt

File containing layer metadata, including download locations and descriptions of shape attributes.

by National Park

Accessibility

.tif

Accessibility variables that include routes, streets, parking, and train tracks

Climate

.tif

Chelsea-derived climate variable layers and solar radiation layers

Ecosystem functioning

.tif

Layers derived from remote sensing that are related with the functional attributes of ecosystems

Ecosystem structure

.tif

Landscape and spectral diversity metrics

Geodiversity

.tif

Topographic and derived variables

Land use Land cover

.tif

Layers related to land use and cover

Tourism and Culture

.tif

Layers related to infrastructure associated with tourism such as bars, restaurants, lodgings and places of cultural interest such as monuments

Scripts

Modeling to get output data

Biomod_modelling_by_park

.R

Script used for modeling CES using data from social media by fitting one ENM for each park and CES.

Biomod_modelling_all_parks

.R

Script used for modeling CES using data from social media by fitting one ENM for each CES.

Modeling to get output data

Downloading and processing variables

EFAS

EFAs code

.js

GEE scripts used to download the Ecosystem Functional Attributes (EFAs) (Paruelo et al.2001; Alcaraz-Segura et al. 2006) derived from Sentinel 2 dataset for each of the national parks studied

OSM

1) Download layers

.py

Python scripts used to download the OpenStreetMap layers of interest for each of the national parks studied.

2) Join layers

.py

Scripts used to merge OSM layers belonging to the same category. e.g., primary, secondary, and tertiary highways.

3) Count point

.py

Scripts used to count the number of points in each of the 100 grid cells for each park, used in case of point type data

4) Presence and absence

.py

Scripts used to assess presence in each of the cells of the 100-square grid for each park, used in the case of data types such as points, lines, and polygons.

Remote sensing

Canopy

.js

GEE scripts used to download the canopy (https://gee-community-catalog.org/projects/canopy/) downloaded and cropped for each of the national parks studied

ESPI

.js

GEE scripts used to download the ESPI index (Ecosystem Service Provision Index) downloaded and cropped for each of the national parks studied

European disturbance map

.js

GEE scripts used to download European disturbance maps (//https://www.eea.europa.eu/data-and-maps/figures/biogeographical-regions-in-europe-2)

downloaded and cropped for each of the national parks studied

LST

.js

GEE scripts used to download LST maps (from Landsat Collection)

downloaded and cropped for each of the national parks studied

Night lights

.js

GEE scripts used to download nighttime light maps (https://developers.google.com/earth-engine/datasets/catalog/NOAA_VIIRS_DNB_ANNUAL_V22)

downloaded and cropped for each of the national parks studied

Population density

.js

GEE scripts used to download population density maps (https://developers.google.com/earth-engine/datasets/catalog/CIESIN_GPWv411_GPW_Population_Density)

downloaded and cropped for each of the national parks studied

Soil groups

.js

GEE scripts used to download Hydrologic Soil Group maps (https://gee-community-catalog.org/projects/hihydro_soil/)

downloaded and cropped for each of the national parks studied

Solar radiation

.js

GEE scripts used to download solar radiation maps (https://globalsolaratlas.info/support/faq)

downloaded and cropped for each of the national parks studied

RGB diversity

Seasonal KMeans clustering

.js

GEE scripts were used to calculate seasonal clusters using Sentinel 2 RGB bands with GEE's .wekaKMeans algorithm. These layers were downloaded and cropped for each of the national parks studied.

Colour diversity analysis

.R

R script used to calculate spectral diversity (Shannon, Simpson and inverse Simpson) using the cluster layers and RGB bands derived from Sentinel 2.

Post processing

Align_and_Clip_rasters

.py

Python scripts used to align and clip the downloaded layers to a 100-meter grid reference layer for each of the national parks studied.

Outputs

CES projections

proj_Aiguestortes_Sports_ensemble

.tif

Spatial projections for the best models obtained for each CES and park

References:

Alcaraz-Segura, D., Paruelo, J., and Cabello, J. 2006: Identification of current ecosystem functional types in the Iberian Peninsula, Global Ecol. Biogeogr., 15, 200–212, https://doi.org/10.1111/j.1466-822X.2006.00215.x

Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M., 2017. Climatologies at high resolution for the earth’s land surface areas. Sci Data 4, 170122. https://doi.org/10.1038/sdata.2017.122

Lobo, J.M., Jiménez-Valverde, A., Hortal, J., 2010. The uncertain nature of absences and their importance in species distribution modelling. Ecography 33, 103–114. https://doi.org/10.1111/j.1600-0587.2009.06039.x

Paruelo, J. M., Jobbágy, E. G., and Sala, O. E. 2001: Current Distribution of Ecosystem Functional Types in Temperate South America, Ecosystems, 4, 683–698, https://doi.org/10.1007/s10021-001-0037-9

Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026

Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S., 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19, 181–197. https://doi.org/10.1890/07-2153.1

Thuiller, W., Georges, D., Gueguen, M., Engler, R., Breiner, F., Lafourcade, B., Patin, R., 2023. biomod2: Ensemble Platform for Species Distribution Modeling.

Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C.G., Sousa-Guedes, D., Martínez-Freiría, F., Real,

The world's population first reached one billion people in 1805, and reached eight billion in 2022, and will peak at almost 10.2 billion by the end of the century. Although it took thousands of years to reach one billion people, it did so at the beginning of a phenomenon known as the demographic transition; from this point onwards, population growth has skyrocketed, and since the 1960s the population has increased by one billion people every 12 to 15 years. The demographic transition sees a sharp drop in mortality due to factors such as vaccination, sanitation, and improved food supply; the population boom that follows is due to increased survival rates among children and higher life expectancy among the general population; and fertility then drops in response to this population growth. Regional differences The demographic transition is a global phenomenon, but it has taken place at different times across the world. The industrialized countries of Europe and North America were the first to go through this process, followed by some states in the Western Pacific. Latin America's population then began growing at the turn of the 20th century, but the most significant period of global population growth occurred as Asia progressed in the late-1900s. As of the early 21st century, almost two-thirds of the world's population lives in Asia, although this is set to change significantly in the coming decades. Future growth The growth of Africa's population, particularly in Sub-Saharan Africa, will have the largest impact on global demographics in this century. From 2000 to 2100, it is expected that Africa's population will have increased by a factor of almost five. It overtook Europe in size in the late 1990s, and overtook the Americas a few years later. In contrast to Africa, Europe's population is now in decline, as birth rates are consistently below death rates in many countries, especially in the south and east, resulting in natural population decline. Similarly, the population of the Americas and Asia are expected to go into decline in the second half of this century, and only Oceania's population will still be growing alongside Africa. By 2100, the world's population will have over three billion more than today, with the vast majority of this concentrated in Africa. Demographers predict that climate change is exacerbating many of the challenges that currently hinder progress in Africa, such as political and food instability; if Africa's transition is prolonged, then it may result in further population growth that would place a strain on the region's resources, however, curbing this growth earlier would alleviate some of the pressure created by climate change.

This dataset features a map of building types for Germany on a 10m grid based on Sentinel-1A/B and Sentinel-2A/B time series. A random forest classification was used to map the predominant type of buildings within a pixel. We distinguish single-family residential buildings, multi-family residential buildings, commercial and industrial buildings and lightweight structures. Building types were predicted for all pixels where building density > 25 %. Please refer to the publication for details.

Temporal extent

Sentinel-2 time series data are from 2018. Sentinel-1 time series data are from 2017.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (*.tif). Metadata are located within the Tiff, partly in the FORCE domain. There is a mosaic in GDAL Virtual format (*.vrt), which can readily be opened in most Geographic Information Systems. Building type values are categorical, according to the following scheme:

0 - No building

1 - Commercial and industrial buildings

2 - Single-family residential buildings

3 - Lightweight structures

4 - Multi-family residential buildings

Further information

For further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).
A web-visualization of this dataset is available here.

Publication

Schug, F., Frantz, D., van der Linden, S., & Hostert, P. (2021). Gridded population mapping for Germany based on building density, height and type from Earth Observation data using census disaggregation and bottom-up estimates. PLOS ONE. DOI: 10.1371/journal.pone.0249044

Acknowledgements

The dataset was generated by FORCE v. 3.1 (paper, code), which is freely available software under the terms of the GNU General Public License v. >= 3. Sentinel imagery were obtained from the European Space Agency and the European Commission.

Funding
This dataset was produced with funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This data set consists of 83 digital maps that were produced by the Food and Agriculture Organization of the United Nations (FAO) for the World Bank as part of a Global Farming Systems Study. The maps are distributed through the FAO-UN GeoNetwork Portal to Spatial Data and Information.

As part of the World Bank's review of its rural development strategy, the Bank sought the assistance of FAO in evaluating how farming systems might change and adapt over the next thirty years. Amongst other objectives, the World Bank asked FAO to provide guidance on priorities for investment in food security, poverty reduction, and economic growth, and in particular to identify promising approaches and technologies that will contribute to these goals. The results of the study are summarized in a set of seven documents, comprising six regional reports and a global overview. The global overview, which synthesizes the results of the six regional analyses as well as discussing global trends, cross-cutting issues and possible implementation modalities, presents an overview of the complete study. The global document is supplemented by two case study reports of development issues of importance to farming systems globally.

The six regions studied include:

East Asia Pacific East Europe and Central Asia Latin America and Caribbean Middle East and North Africa South Asia Sub-Saharan Africa

Map coverages for each region include the following:

Average precipitation Average temperature Elevation Irrigation intensity Land cover Length of growing period Livestock stocking density Major environmental constraints Major farming systems NOAA Satellite imagery (shaded relief imagery and ocean floor bathymetry) Permanent crop and arable land Rural population Slope Total population

The map coverages were prepared by FAO based on the following data sources:

Doll, P. and Siebert, S. 1999. A Digital Global Map of Irrigated Areas, Report No A9901, Centre for Environmental Systems Research, University of Kassel, Kassel, Germany.

Environmental Systems Research Institute (ESRI) Data and Maps 1999, Volume 1. World Worldsat Color Shaded Relief Image. Based on 1996 NOAA weather satellite images, with enhanced shaded relief imagery and ocean floor relief data (bathymetry) to provide a land and undersea topographic view. ESRI, Redlands, California, USA.

Food and Agriculture Organization of the United Nations (FAO), Land and Water Development Division (AGL) with the collaboration of the International Institute for Applied Systems Analysis (IIASA). 2000. Global Agro-Ecological Zones Study. FAO, Rome, Italy.

Gomes, R. 1999. Major Environmental Constraints for Agricultural Production Project. Based on FAOCLIM database, ARTEMIS NDVI imagery, and soil and terrain data provided by Soil Resources Management and Conservation Service. FAO-GIS. Food and Agriculture Organization of the United Nations (FAO), Environment and Natural Resources Service, Rome, Italy.

Leemans, R. and Cramer, W. 1991. The IIASA Database for Mean Monthly Values of Temperature, Precipitation and Cloudiness on a Global Terrestrial Grid. Research Report RR-91-18. November 1991. International Institute of Applied Systems Analyses, Laxenburg, pp. 61.

Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Oak Ridge National Laboratory (ORNL), Oak Ridge, Tennessee, USA.

Slingenbergh, J. Livestock Distribution, Production and Diseases: Towards a Global Livestock Atlas. Food and Agriculture Organization of the United Nations (FAO), AGAH, Rome, Italy. (aka Global Livestock Production and Health Atlas (GLiPHA))

U.S. Geological Survey, EROS Data Center. 1996. GTOPO30 Digital Data Set. EDC, Sioux Falls, South Dakota, USA.

In 1938, the year before the outbreak of the Second world War, the countries with the largest populations were China, the Soviet Union, and the United States, although the United Kingdom had the largest overall population when it's colonies, dominions, and metropole are combined. Alongside France, these were the five Allied "Great Powers" that emerged victorious from the Second World War. The Axis Powers in the war were led by Germany and Japan in their respective theaters, and their smaller populations were decisive factors in their defeat. Manpower as a resource In the context of the Second World War, a country or territory's population played a vital role in its ability to wage war on such a large scale. Not only were armies able to call upon their people to fight in the war and replenish their forces, but war economies were also dependent on their workforce being able to meet the agricultural, manufacturing, and logistical demands of the war. For the Axis powers, invasions and the annexation of territories were often motivated by the fact that it granted access to valuable resources that would further their own war effort - millions of people living in occupied territories were then forced to gather these resources, or forcibly transported to work in manufacturing in other Axis territories. Similarly, colonial powers were able to use resources taken from their territories to supply their armies, however this often had devastating consequences for the regions from which food was redirected, contributing to numerous food shortages and famines across Africa, Asia, and Europe. Men from annexed or colonized territories were also used in the armies of the war's Great Powers, and in the Axis armies especially. This meant that soldiers often fought alongside their former-enemies. Aftermath The Second World War was the costliest in human history, resulting in the deaths of between 70 and 85 million people. Due to the turmoil and destruction of the war, accurate records for death tolls generally do not exist, therefore pre-war populations (in combination with other statistics), are used to estimate death tolls. The Soviet Union is believed to have lost the largest amount of people during the war, suffering approximately 24 million fatalities by 1945, followed by China at around 20 million people. The Soviet death toll is equal to approximately 14 percent of its pre-war population - the countries with the highest relative death tolls in the war are found in Eastern Europe, due to the intensity of the conflict and the systematic genocide committed in the region during the war.

The earliest point where scientists can make reasonable estimates for the population of global regions is around 10,000 years before the Common Era (or 12,000 years ago). Estimates suggest that Asia has consistently been the most populated continent, and the least populated continent has generally been Oceania (although it was more heavily populated than areas such as North America in very early years). Population growth was very slow, but an increase can be observed between most of the given time periods. There were, however, dips in population due to pandemics, the most notable of these being the impact of plague in Eurasia in the 14th century, and the impact of European contact with the indigenous populations of the Americas after 1492, where it took almost four centuries for the population of Latin America to return to its pre-1500 level. The world's population first reached one billion people in 1803, which also coincided with a spike in population growth, due to the onset of the demographic transition. This wave of growth first spread across the most industrially developed countries in the 19th century, and the correlation between demographic development and industrial or economic maturity continued until today, with Africa being the final major region to begin its transition in the late-1900s.

In the middle of 2023, about 60 percent of the global population was living in Asia.The total world population amounted to 8.1 billion people on the planet. In other words 4.7 billion people were living in Asia as of 2023. Global populationDue to medical advances, better living conditions and the increase of agricultural productivity, the world population increased rapidly over the past century, and is expected to continue to grow. After reaching eight billion in 2023, the global population is estimated to pass 10 billion by 2060. Africa expected to drive population increase Most of the future population increase is expected to happen in Africa. The countries with the highest population growth rate in 2024 were mostly African countries. While around 1.47 billion people live on the continent as of 2024, this is forecast to grow to 3.9 billion by 2100. This is underlined by the fact that most of the countries wit the highest population growth rate are found in Africa. The growing population, in combination with climate change, puts increasing pressure on the world's resources.