The ratio between the annual average population and the land area of the region. The land area concept (excluding inland waters) should be used wherever available; if not available then the total area, including inland waters (area of lakes and rivers) is used.

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 layer was created by Duncan Smith and based on work by the European Commission JRC and CIESIN. A description from his website follows:--------------------A brilliant new dataset produced by the European Commission JRC and CIESIN Columbia University was recently released- the Global Human Settlement Layer (GHSL). This is the first time that detailed and comprehensive population density and built-up area for the world has been available as open data. As usual, my first thought was to make an interactive map, now online at- http://luminocity3d.org/WorldPopDen/The World Population Density map is exploratory, as the dataset is very rich and new, and I am also testing out new methods for navigating statistics at both national and city scales on this site. There are clearly many applications of this data in understanding urban geographies at different scales, urban development, sustainability and change over time.

Population density by NUTS 3 region

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.

Copernicus Land Monitoring Service	Resolution	Comment
Coastal LU/LC	1:10.000	A Copernicus hotspot product to monitor landscape dynamics in coastal zones
EU-Hydro - Coastline	1:30.000	EU-Hydro is a dataset for all European countries providing the coastline
Natura 2000	1: 100000	A Copernicus hotspot product to monitor important areas for nature conservation
European Settlement Map	10m	A spatial raster dataset that is mapping human settlements in Europe
Imperviousness Density	10m	The percentage of sealed area
Impervious Built-up	10m	The part of the sealed surfaces where buildings can be found
Grassland 2018	10m	A binary grassland/non-grassland product
Tree Cover Density 2018	10m	Level of tree cover density in a range from 0-100%

Joint Research Center	Resolution	Comment
Global Human Settlement Population Grid GHS-POP)	250m	Residential population estimates for target year 2015
GHS settlement model layer (GHS-SMOD)	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	10m	Built-up grid derived from Sentinel-2 global image composite for reference year 2018
ENACT 2011 Population Grid (ENACT-POP R2020A)	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)	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)	1km	Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data	Resolution	Comment
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 ansd sex statistics interesected 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

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.

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.

The world's population first reached one billion people in 1803, and reach eight billion in 2023, and will peak at almost 11 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 live 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 decade 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.

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.

Detailed information on genetic distances and linkage phase between adjacent SNP markers in the paternal linkage map constructed for P. tremula intra-specific cross. (xlsx, 428Â kb) (XLSX 463Â kb)

This dataset provides all output data generated in the standard settings of HANZE v2.0 model. The 100-m pan-European maps (GeoTIFF) provide gridded totals of five variables for years 1870-2020 for 42 countries. The rasters are group in five ZIP files:

• CLC: land cover/use (Corine Land Cover classification; legend files are included in a separate ZIP)

• Pop: population

• GDP: gross domestic product (2020 euros)

• FA: fixed asset value (2020 euros)

• imp: imperviousness density (%)

Two additional CSV files contain uncertainty estimates of population, GDP and fixed asset value per NUTS3 region and flood hazard zone. The files provide 5th, 20th, 50th, 80th and 95th percentile for all timesteps, separately for coastal and riverine floods.

Two further Excel files contain subnational and national-level statistical data on population, land use and economic variables.

For detailed description of the files, see the documentation provided with the code.

This version replaces the airport list, which was previously incorrectly taken from HANZE v1, and adds land cover/use legend files for ArcGIS and QGIS.

This dataset features three gridded population dadasets of Germany on a 10m grid. The units are people per grid cell.

Datasets

DE_POP_VOLADJ16: This dataset was produced by disaggregating national census counts to 10m grid cells based on a weighted dasymetric mapping approach. A building density, building height and building type dataset were used as underlying covariates, with an adjusted volume for multi-family residential buildings.

DE_POP_TDBP: This dataset is considered a best product, based on a dasymetric mapping approach that disaggregated municipal census counts to 10m grid cells using the same three underyling covariate layers.

DE_POP_BU: This dataset is based on a bottom-up gridded population estimate. A building density, building height and building type layer were used to compute a living floor area dataset in a 10m grid. Using federal statistics on the average living floor are per capita, this bottom-up estimate was created.

Please refer to the related publication for details.

Temporal extent

The building density layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: http://doi.org/10.1594/PANGAEA.920894)

The building height layer is representative for ca. 2015 (doi: 10.5281/zenodo.4066295)

The building types layer is based on Sentinel-2 time series data from 2018 and Sentinel-1 time series data from 2017 (doi: 10.5281/zenodo.4601219)

The underlying census data is from 2018.

Data format

The data come in tiles of 30x30km (see shapefile). The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems.

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

Census data were provided by the German Federal Statistical Offices.

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).

Snapshot img

Digital Map Market Size 2024-2028

The digital map market size is forecast to increase by USD 19.75 billion at a CAGR of 26.06% between 2023 and 2028.

What will be the Size of the Digital Map Market During the Forecast Period?

Request Free Sample

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. The proliferation of connected devices, including PDAs, Cortana, Siri, Amazon Echo, and Google Now, has increased the demand for digital maps in real-time mapping applications and map analytics. Real-time tracking systems are gaining popularity in sectors such as energy & power, automobile, telecommunication, and transportation, providing valuable spatial data on terrain, roads, buildings, rivers, and other features. APIs enable seamless integration of digital maps into various applications, enhancing user experience and ROI.
The internet has made digital maps accessible from anywhere, further fueling market growth. Overall, the market is poised for significant expansion, offering numerous opportunities for businesses and innovators alike.

How is this Digital Map Industry segmented and which is the largest segment?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Geography

  APAC

    China
    India
    Japan


  North America

    US


  Europe

    Germany


  South America



  Middle East and Africa

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period.

Digital maps play a crucial role in various industries, particularly in automotive applications for driver assistance systems. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. The increasing use of connected cars and the development of Long-Term Evolution (LTE) technologies are driving the demand for digital maps. These maps provide real-time traffic information, helping drivers navigate urban areas with high population density and traffic congestion more efficiently. Additionally, digital maps are essential for transportation route planning, public services, agriculture, and conservation efforts. In agriculture, digital maps help determine soil types, nutrient levels, and crop yields.

Waste reduction and the protection of sensitive ecosystems and habitats are also facilitated by digital maps. Overall, digital maps offer valuable insights for urban planning, emergency situations, and various industries, making them an indispensable tool for businesses and individuals alike.

Get a glance at the Digital Map Industry report of share of various segments. Request Free Sample

The navigation segment was valued at USD 4.58 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contribute 43% to the growth of the global market during the forecast period.

Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions, Request Free Sample

In the Asia-Pacific (APAC) region, the market for digital maps is experiencing growth due to the increasing use of Internet of Things (IoT) devices and real-time mapping technologies. Countries such as Japan, China, and South Korea, along with a few Southeast Asian nations, are key contributors to this market expansion. IoT devices, including GPS-enabled PDAs, professional assistants, and smart home devices, are being integrated into digital maps to provide real-time data. This data can be used to develop real-time dashboards, enabling organizations and local governments to effectively manage traffic, monitor oil field equipment, and more.

The growing digital connectivity landscape in APAC is fueling the demand for digital maps and related technologies, including APIs, SDKs, and mapping solutions from providers such as Nearmap, ESRI, and INRIX.

Digital Map Market Dynamics

Our digital map market researchers analyzed the data with 2023 as the base year, along with the key drivers, trends, and challenges. A holistic analysis of drivers will help companies refine their marketing strategies to gain a competitive advantage.

What are the key market drivers leading to the rise In the adoption of Digital Map Industry?

Adoption of intelligent PDAs is the key driver of the market.

The markets encompass a range of advanced technologies and applications that leverage Geographic Information Systems (

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).

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.

This dataset features detailed maps of material stock and population, as well as the distribution of jobs, for Austria on a 30m grid. The data is based on recent maps of material stock and building volume (compare to Haberl et al. 2021, doi: 10.1021/acs.est.0c05642), recent and historic census data, and a time series of Landsat TM, ETM+, and OLI Earth Observation data.

Temporal extent

The data contains annual maps from 1985 to 2018.

Data format and units

Per Austrian federal state, the data come in tiles of 30x30km. The projection is EPSG:3035. The images are compressed GeoTiff files (.tif). There is a mosaic in GDAL Virtual format (.vrt), which can readily be opened in most Geographic Information Systems. Please consider the generation of image pyramids before using *.vrt files.

All image data has 34 bands, where band 1 is data for 1985, and band 34 is data for 2018.

The dataset features

• population (Scaled by 100 to reduce data storage size. Divide by 100 to get people per cell)
• jobs (Scaled by 100 to reduce data storage size. Divide by 100 to get jobs per cell)
• mass (in tons) of …
  • total material stock
    • material stock in buildings
      • in commercial and industrial buildings
      • in multi-family residential buildings
      • in high-rise buildings
      • in single-family residential buildings
      • in lightweight buildings
    • material stock in road infrastructure
    • material stock in rail infrastructure
    • material stock in other infrastructure

Further information

For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Funding

This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

This metadata corresponds to the EUNIS Littoral biogenic habitat (salt marshes) types, predicted distribution of habitat suitability dataset.

Littoral habitats are those formed by animals such as worms and mussels or plants (salt marshes).

The verified littoral biogenic habitat samples used are derived from the Braun-Blanquet database (http://www.sci.muni.cz/botany/vegsci/braun_blanquet.php?lang=en) which is a centralised database of vegetation plots and comprises copies of national and regional databases using a unified taxonomic reference database. The geographic extent of the distribution data are all European countries except Armenia and Azerbaijan.

The modelled suitability for EUNIS saltmarsh habitat types is an indication of where conditions are favourable for the habitat type based on sample plot data (Braun-Blanquet database) and the Maxent software package. The modelled suitability map may be used as a proxy for the geographical distribution of the habitat type. However, note that it is not representing the actual distribution of the habitat type. As predictors for the suitabilty modelling not only Climate and Soil parameters have been taken into account, but also so-called RS-EVB's, Remote Sensing-enabled Essential Biodiversity Variables like Landuse, Vegetation height, Phenology, LAI(Leave Area Index) and Population density. Because the EBV's are restricted by the extent of the Remote Sensing data (EEA38 countries and the United Kingdom) the modelling result does also not go beyond this boundary. The dataset is provided both in Geodatabase and Geopackage formats.

The Training map files show the modelled suitable distribution, omitting the 10% of occurrence records in the least suitable environment under the assumption that they are not representative of the overall suitable habitat distribution. The 10 percentile training presence is an arbitrary threshold which omits all regions with habitat suitability lower than the suitability values for the lowest 10% of occurrence records.

This is an assessment of pedestrian accessibility in the world's main urban centers, aggregated at country level. Indicators include the average walking time to different categories of destinations, as well as the proportion of inhabitants that can access each category of services within a 15 minutes walk. The data is produced and maintained by the UN's Sustainable Development Solutions Network (SDSN) as part of the SDG Transformation Center.Pedestrian accessibility is the extent to which the built environment supports walking access to destinations of interest. This measure is particularly useful for assessing spatial justice in cities, usually represented by underpriviledged communities which are pushed to live in deteriorated urban areas receiving a minor share of public investments and thus low levels of accessibility. Monitoring spatial indicators of pedestrian accessibility helps planners and policymakers evaluate the impacts of urban design and transport interventions and guides targeted interventions towards creating healthy, sustainable cities, and achieving the United Nations (UN) Sustainable Development Goals (SDGs).Data SourcesTwo main sources of data are behind this study. OpenStreetMap is used to collect data on pedestrian infrastructure and geographically allocated places of interest (POI): hospitals, schools, supermarkets, restaurants, schools, etc. Pedestrian infrastructure networks are returned by the OpenStreetMap API as networks of nodes and edges, where each node represents a street intersection and each edge represents a segment of road with walkable features. Data on population density for every city is retrieved from the European Commission's 2020 Global Human Settlement Layer (GHSL) . This data is retrieved in the form of a grid of 100m by 100m squares and their associated population density values covering the entire world.Geographical extentThe geographical extent of a particular city or region often varies according to different authorities and interpretations. Novel projects, such as the Global Human Settlements (GHS) Urban Centres Database (UCDB), seek to establish a consistent, shared geographic definition of “urban centres” globally. This study does not consider municipal boundaries for defining city borders. Rather, it considers "Functional Urban Areas" as defined by the OECD and the European Commission . The boundaries of Functional Urban Areas consider urbanization factors such as commuting flows and population density, and are less arbitrary than municipal boundaries. For this reason, cities presented here may have a different (and often bigger) shape expected.Accessibility analysisTo measure accessibility to services for each city, we perform a network analysis on the pedestrian street networks and POIs data to quantify and map accessibility to urban infrastructure at the street intersection level. For each 100m cell from the population grid data, the resulting "walking time" reflects the time that a person residing inside that cell would have to walk for, using the existing pedestrian infrastructure, to reach the first amenity from a given category of services. The analysis was performed using geopandas and pandana python packages.These calculations were performed for all cities where at least one POI could be identified for each square kilometer. This threshold is applied in order to enforce representativity and accuracy. These scores were then be generalized for each urban center, by taking the population weighted average of the accessibility score for each point in the population grid.Code for generating these results is publicly available at: https://github.com/sdsna/sdg-accessibilityThis methodology was expanded from Nicoletti, L., Verma, T., Sirenko, M. (2022). Disadvantaged Communities Have Lower Access to Urban Infrastructure. Environment and Planning B: Urban Analytics and City Science, 0(0) and the CityAccessMap project.

Global societal material stocks such as buildings and infrastructure accumulated rapidly within recent decades, along with population growth. Material stocks constitute the physical basis of most socio-economic activities and services, such as mobility, housing, health, or education. The dynamics of stock growth, and its relation to the population that demands those services, is an essential indicator for long-term societal resource use and patterns of emissions. The creation of societal material stock creates path dependencies for future resource use, with an important impact on how the transformation towards sustainable societies can succeed.

This dataset is a supplement to previously generated detailed maps of the distribution of material stocks, population and employment across Austria from 1985 to 2018 (10.5281/zenodo.7195101).

The data are aggregated tabular data used to create illustrations in an accompanying data article.

Data format and units

This dataset features:

• Tabular aggregated data of material stocks, population and employment on a municipality level from 1985 to 2018 (in administrative borders of 2018.
  • Note: Only layers used to create illustrations in the accompanying data article are presented as tabular data!
• Municipalities of Austria as as shape file (from https://www.data.gv.at/katalog/dataset/stat_gliederung-osterreichs-in-gemeinden14f53#resources)
• Annual population and employment numbers on a federal states level, extracted and aggregated from Statistik Austria (see readme.txt)

Further information

For further information, please see the publication or contact Franz Schug (fschug@wisc.edu). Visit our website to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.

Funding

This research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950).

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.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.