Inspire-compliant view service for the INSPIRE-identified spatial data topics of the spatial information and planning system (RIPS). The spatial data for the object type waste landfill are displayed.

Abstract Paper aims The purpose of the study is to present a proposal for implementation of Voluntary Delivery Points (VDP) of expanded polystyrene (EPS) in Florianópolis city, Brazil. Originality Transportation, uncertainty of financial return, and lack of population's awareness are barriers for EPS recycling. This paper seeks to overcome these difficulties to give a better destination for EPS other than landfill. A method is proposed for the implementation of VDP to receive this type of waste. As far as the authors know, no previous research has proposed this analysis for Styrofoam® RL. Research method The research method consists of a survey application with the population and spatial analysis using heat maps of the public equipment on QGIS® software. Main findings It was possible to verify the generation capability of EPS waste in Florianópolis, the lack of population’s awareness about EPS recycling, and suitable points for the implementation of VDP to aid in the RL. Implications for theory and practice This paper seeks to encourage researchers and practitioners to develop solutions for EPS RL. The main contribution of this article is presenting a practical alternative method that aims to overcome one of the main barriers of EPS RL, which is low-density waste transportation, dealing with consumer's responsibility for the waste generated, their awareness on the issue and their active contribution to a proper destination of EPS waste.

Spatial information about the seafloor is critical for decision-making by marine resource science, management and tribal organizations. Coordinating data needs can help organizations leverage collective resources to meet shared goals. To help enable this coordination, the National Oceanic and Atmospheric Administration (NOAA) National Centers for Coastal Ocean Science (NCCOS) developed a spatial framework, process and online application to identify common data collection priorities for seafloor mapping, sampling and visual surveys off the US Caribbean territories of Puerto Rico and the US Virgin Islands. Fifteen participants from local federal, state, and academic institutions entered their priorities in an online application, using virtual coins to denote their priorities in 2.5x2.5 kilometer (nearshore) and 10x10 kilometer (offshore) grid size. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Results were analyzed and mapped using statistical techniques to identify significant relationships between priorities, reasons for those priorities and data needs. Fifteen high priority locations were broadly identified for future mapping, sampling and visual surveys. These locations include: (1) a coastal location in northwest Puerto Rico (Punta Jacinto to Punta Agujereada), (2) a location approximately 11 km off Punta Agujereada, (3) coastal Rincon, (4) San Juan, (5) Punta Arenas (west of Vieques Island), (6) southwest Vieques, (7) Grappler Seamount, (8) southern Virgin Passage, (9) north St. Thomas, (10) east St. Thomas, (11) south St. John, (12) west offshore St. Croix, (13) west nearshore St. Croix, (14) east nearshore St. Croix, and (15) east offshore St. Croix. Participants consistently selected (1) Biota/Important Natural Area, (2) Commercial Fishing and (3) Coastal/Marine Hazards as their top reasons (i.e., justifications) for prioritizing locations, and (1) Benthic Habitat Map and (2) Sub-bottom Profiles as their top data or product needs. This ESRI shapefile summarizes the results from this spatial prioritization effort. This information will enable US Caribbean organization to more efficiently leverage resources and coordinate their mapping of high priority locations in the region.

This effort was funded by NOAA’s NCCOS and supported by CRCP. The overall goal of the project was to systematically gather and quantify suggestions for seafloor mapping, sampling and visual surveys in the US Caribbean territories of Puerto Rico and the US Virgin Islands. The results are will help organizations in the US Caribbean identify locations where their interests overlap with other organizations, to coordinate their data needs and to leverage collective resources to meet shared goals.

There were four main steps in the US Caribbean spatial prioritization process. The first step was to identify the technical advisory team, which included the 4 CRCP members: 2 from each the Puerto Rico and USVI regions. This advisory team recommended 33 organizations to participate in the prioritization. Each organization was then requested to designate a single representative, or respondent, who would have access to the web tool. The respondent would be responsible for communicating with their team about their needs and inputting their collective priorities. Step two was to develop the spatial framework and an online application. To do this, the US Caribbean was divided into 4 sub regions: nearshore and offshore for both Puerto Rico and USVI. The total inshore regions had 2,387 square grid cells approximately 2.5x2.5 km in size. The total offshore regions consisted of 438 square grid cells 10x10 km in size. Existing relevant spatial datasets (e.g., bathymetry, protected area boundaries, etc.) were compiled to help participants understand information and data gaps and to identify areas they wanted to prioritize for future data collections. These spatial datasets were housed in the online application, which was developed using Esri’s Web AppBuilder. In step three, this online application was used by 15 participants to enter their priorities in each subregion of interest. Respondents allocated virtual coins in the grid cells to denote their priorities for each region. Respondents were given access to all four regions, despite which territory they represented, but were not required to provide input into each region. Grid cells with more coins were higher priorities than cells with fewer coins. Participants also reported why these locations were important and what data types were needed. Coin values were standardized across the nearshore and offshore zones and used to identify spatial patterns across the US Caribbean region as a whole. The number of coins were standardized because each subregion had a different number of grid cells and participants. Standardized coin values were analyzed and mapped using statistical techniques, including hierarchical cluster analysis, to identify significant relationships between priorities, reasons for those priorities and data needs. This ESRI shapefile contains the 2.5x2.5 km and 10x10 km grid cells used in this prioritization effort and associated the standardized coin values overall, as well as by organization, justification and product. For a complete description of the process and analysis please see: Kraus et al. 2020.

This layer of the map based index (GeoIndex) shows the location of waste sites within England and Wales. The information is taken from an index listing of some 3500 waste sites in England and Wales identified by BGS as part of a survey carried out on behalf of the Department of the Environment in 1973. The index has been corrected and updated to a limited extent, but the data itself has not been changed. The data was collected in 1972 and the information reflects the knowledge at that time. It does not reflect current interpretation. Not all authorities made returns and there are not records for all of the sites listed. However, the data is an invaluable source of information about pre-1974 sites. The records themselves contain interpretations of the geology, ground and surface water risk assessments and information about the quantities and types of waste. Data visible at all map scales.

Solid wastes deposited in the coastal zone that date from an era of lax environmental regulations continue to pose significant challenges for regulators and coastal managers worldwide. The increasing risk of contaminant release from these legacy disposal sites, due to a range of factors including rising sea levels, associated saline intrusion, and greater hydrological extremes, have been highlighted by many researchers. Given this widespread challenge, and the often-limited remedial funds available, there is a pressing need for the development of new advanced site prioritization protocols to limit potential pollution risks to sensitive ecological or human receptors. This paper presents a multi-criteria decision analysis that integrates the principles of Conceptual Site Models (Source-Pathway-Receptor) at a national scale in England and Wales to identify legacy waste sites where occurrence of pollutant linkages are most likely. A suite of spatial data has been integrated in order to score potential risks associated with waste type (Source), likelihood of pollutant release relating to current and future flood and erosion climate projections, alongside current management infrastructure (Pathway), and proximity to sensitive ecological features or proxies of human use in coastal areas (Receptors). Of the 30,281 legacy waste deposits identified in England and Wales, 3,219 were located within the coastal zone, with coastal areas containing a density of legacy wastes (by area) 10.5 times higher than inland areas. Of these, 669 were identified as priority sites in locations without existing coastal defences or flood management infrastructure, with 2550 sites identified in protected areas where contaminant transfer risks could still be apparent. The majority (63%) of the priority sites have either undefined source terms, or are classified as mixed wastes. Mining and industrial wastes were also notable waste categories, and displayed strong regional distributions in the former mining areas of north-east and south-west of England, south Wales, and post-industrial estuaries. The large-scale screening process presented here could be used by environmental managers as a foundation to direct more high-resolution site assessment and remedial work at priority sites, and can be used as a tool by governments for directing funding to problematic sites. List of Acronyms.

The Agri-Environmental Spatial Data (AESD) product from the Census of Agriculture provides a large selection of farm-level variables from the Census of Agriculture and uses alternative data sources to improve the spatial distribution of the production activities. Therefore, the AESD database offers clients the possibility to better analyze the impact of agriculture activities on the environment and produce key indicators, or for any applications where accurate location of activities matters. Variables are offered using two types of physical boundaries: by Soil Landscape of Canada polygons and by Sub-sub-drainage areas (watersheds). The focus of the redistribution of the data is on the field crops and land use variables, but the database includes all census variables related to crops, livestock and management practices. This frame can also be used to extract Census of Agriculture data by custom geographic areas. Also, users interested in this version of the Census of Agriculture database using administrative types of regions can request it. In both cases, please contact Statistics Canada. This file was produced by Statistics Canada, Agriculture Division, Remote Sensing and Geospatial Analysis section, 2022, Ottawa.

Software Geographic Information Systems Market Outlook

As of 2023, the Software Geographic Information Systems (GIS) market size was valued at approximately USD 9.1 billion and is projected to reach around USD 18.6 billion by 2032, reflecting a robust Compound Annual Growth Rate (CAGR) of 8.5%. This remarkable growth is primarily driven by the increasing demand for spatial data across various industries, coupled with the advancement in geospatial technologies. The growing integration of GIS with mainstream business operations for better decision-making and the surge in urbanization and smart city initiatives are significant factors propelling the market forward. The continuous evolution in software capabilities, including enhanced data visualization and integration capabilities, further contributes to the rising adoption of GIS solutions worldwide.

One of the pivotal growth drivers of the Software GIS market is the expanding requirement for spatial data and analytics to enhance operational efficiency across multiple industry verticals. Industries such as urban planning, transportation, agriculture, and natural resources management are increasingly relying on GIS solutions for data-driven decision-making. The ability of GIS to provide real-time, location-based insights is revolutionizing how businesses plan, manage resources, and optimize their operations. Moreover, the rapid digitization and adoption of IoT (Internet of Things) technologies are also bolstering the demand for GIS software, as businesses seek to leverage interconnected devices for better data collection and analysis. The integration of GIS with IoT platforms allows for more comprehensive and precise spatial insights, thus driving market growth.

Another significant factor contributing to the growth of the Software GIS market is the advancement in cloud computing technologies. The shift from traditional on-premises deployment to cloud-based GIS solutions is gaining traction due to the numerous advantages offered by the cloud. Cloud-based GIS provides enhanced scalability, flexibility, and cost-effectiveness, making it an attractive option for businesses of all sizes. Additionally, cloud solutions facilitate easier collaboration and data sharing among different stakeholders, fostering a more integrated approach to spatial data management. The growing investment in cloud infrastructure by major players in the technology sector further supports the widespread adoption of cloud-based GIS solutions, enabling businesses to harness the power of spatial data in a more efficient and streamlined manner.

Furthermore, the increasing emphasis on environmental conservation and sustainable development is driving the demand for GIS applications in environmental monitoring and management. GIS software is extensively used for mapping and analyzing environmental data, helping organizations to monitor changes in land use, assess natural resource availability, and evaluate the impact of human activities on the environment. As governments and organizations worldwide strive to meet sustainability goals and address climate change challenges, GIS solutions are becoming indispensable tools for informed decision-making and strategic planning. The integration of GIS with emerging technologies such as AI and machine learning is also enhancing the capabilities of these systems, enabling more sophisticated analysis and predictive modeling.

The application of GIS in Transportation is becoming increasingly significant as the demand for efficient and sustainable transport systems grows. GIS technology enables transportation planners and operators to analyze spatial data in real-time, optimizing route planning and improving logistics operations. By integrating GIS with technologies like GPS and telematics, transportation systems can provide more accurate and timely information, enhancing decision-making processes. This integration is crucial for managing transportation networks effectively, reducing costs, and improving service delivery. As urban areas continue to expand and the need for smart transportation solutions rises, GIS in Transportation is expected to play a pivotal role in shaping the future of mobility.

Component Analysis

The Software segment of the GIS market is experiencing significant growth, driven by the continuous innovation and development of advanced GIS software solutions. Software providers are focusing on enhancing the functionality and usability of their products, incorporating features such as 3D visualization, real-time data process

This is a polygon coverage representing the wetlands cited in the "A Directory of Important Wetlands in Australia" Third Edition (EA, 2001), plus various additions for wetlands listed after 2001. This dataset includes attribute information showing the wetlands type and criteria for listing for each wetland.

This coverage is a compilation of various data sources and has been collected using a variety of methods. This dataset should therefore be used as an indicative guide only to wetland boundaries and locations. The data has been collated by the Australian Government Department of Sustainability, Environment, Water, Population and Communities from various datasets including those supplied by the relevant State agencies.

Sanitary landfills and uncontrolled dumpsites are plastic wastes (PWs) reservoirs containing ∼60% of all the plastics ever made, amounting to 5,000 × 106 tons as of 2017. The distribution, long-term behavior, and release of macro- and microplastics (MPs) from disposal sites are critical to global plastics pollution, but are poorly understood and lack systematic assessments. We review comprehensively the available knowledge in the three aspects herein. The spatial and temporal distribution of PW in 616 municipal solid waste (MSW) samples retrieved from 275 disposal sites in 56 countries are summarized. The weight percentages of PW (%PW) generally decrease with increasing year of disposal and disposal depth. Other influential factors are disposal duration and country income level. The %PW values in different disposal sites show high regionality and spatial variability and heterogeneity. Disposal sites mostly have harsh temperature and stress, reactive liquids, and microbial activities, which are conducive to long-term processes of PW and MPs. The major processes are chemical degradation, dissolution, leaching and adsorption, biological degradation, mechanical wearing, pneumatic and hydrological transport and deposition, and conglomeration. PW leaves disposal sites via recycling, scavenging, mining, wind and surface runoff, coastal erosion and flooding, and slope failure. The release and removal pathways of PW from disposal sites have been recognized only qualitatively. In addition, the sources, presences, and secondary generation of MPs in disposal sites have been studied occasionally, whereas the transport and fate of MPs within and from disposal sites remain largely unstudied.

Environmental conditions and anthropogenic impacts are key influences on ecological processes and associated ecosystem services. Effective management of Tonga’s marine ecosystems therefore depends on accurate and up-to-date knowledge of environmental and anthropogenic variables. Although many types of environmental and anthropogenic data are now available in global layers, they are often inaccessible to end users, particularly in developing countries with limited accessibility and analytical training. Furthermore, the resolution of many global layers might not be sufficient to make informed local decisions. Although the near-shore marine ecosystem of Tonga is extensive, the resources available for its management are limited and little is known about its current ecological state. Here we provide a marine socio-environmental dataset covering Tonga’s near-shore marine ecosystem as compiled from various global layers, remote sensing projects, local ministries, and the 2016 national census. The dataset consists of 11 environmental and 6 anthropogenic variables summarized in ecologically relevant ways, spatially overlaid across the near-shore marine ecosystem of Tonga. The environmental variables selected include: bathymetry, coral reef density, distance from deep water, distance from land, distance from major terrestrial inputs, habitat, land area, net primary productivity, salinity, sea surface temperature, and wave energy. The anthropogenic variables selected include: fishing pressure, management status, distance to fish markets, distance from villages, population pressure, and a socioeconomic development index based on population density, growth, mean age, mean education level, and unemployment. This extensive and accessible dataset will be a useful tool for future assessment and management of marine ecosystems in Tonga.

Beginning with the discovery of a "curious valley" in 1903 by Captain Scott, the McMurdo Dry Valleys (MDV) in Antarctica have been impacted by humans, although there were only three brief visits prior to 1950. Since the late 1950's, human activity in the MDV has become commonplace in summer, putting pressure on the region's fragile ecosystems through camp construction and inhabitation, cross-valley transport on foot and via vehicles, and scientific research that involves sampling and deployment of instruments. Historical photographs, put alongside information from written documentation, offer an invaluable record of the changing patterns of human activity in the MDV. Photographic images often show the physical extent of field camps and research sites, the activities that were taking place, and the environmental protection measures that were being followed. Historical photographs of the MDV, however, are scattered in different places around the world, often in private collections, and there is a real danger that many of these photos may be lost, along with the information they contain. This project will collect and digitize historical photographs of sites of human activity in the MDV from archives and private collections in the United States, New Zealand, and organize them both chronologically and spatially in a GIS database. Sites of past human activities will be re-photographed to provide comparisons with the present, and re-photography will assist in providing spatial data for historical photographs without obvious location information. The results of this analysis will support effective environmental management into the future. The digital photo archive will be openly available through the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) website (www.mcmlter.org), where it can be used by scientists, environmental managers, and others interested in the region.

The central question of this project can be reformulated as a hypothesis: Despite an overall increase in human activities in the MDV, the spatial range of these activities has become more confined over time as a result of an increased awareness of ecosystem fragility and efforts to manage the region. To address this hypothesis, the project will define the spatial distribution and temporal frequency of human activity in the MDV. Photographs and reports will be collected from archives with polar collections such as the National Archives of New Zealand in Wellington and Christchurch and the Byrd Polar Research Center in Ohio. Private photograph collections will be accessed through personal connections, social media, advertisements in periodicals such as The Polar Times, and other means. Re-photography in the field will follow established techniques and will create benchmarks for future research projects. The spatial data will be stored in an ArcGIS database for analysis and quantification of the human footprint over time in the MDV. The improved understanding of changing patterns of human activity in the MDV provided by this historical photo archive will provide three major contributions: 1) a fundamentally important historic accounting of human activity to support current environmental management of the MDV; 2) defining the location and type of human activity will be of immediate benefit in two important ways: a) places to avoid for scientists interested in sampling pristine landscapes, and, b) targets of opportunity for scientists investigating the long-term environmental legacy of human activity; and 3) this research will make an innovative contribution to knowledge of the environmental history of the MDV.

INDICATOR DEFINITION This indicator records the composition and weight of waste returned to Australia from Macquarie Island, Casey, Davis and Mawson stations. For the purposes of this indicator, waste is defined as materials that are no longer wanted, needed or used and which require treatment or disposal.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: PRESSURE

RATIONALE FOR INDICATOR SELECTION The data will assist Australia in meeting Article 17 of the Protocol on Environmental Protection to the Antarctic Treaty, namely, to report annually on the steps taken to implement the Protocol. This indicator will also assist in monitoring the extent to which waste minimisation is occurring, and furthering planning and policy measures appropriate to efficient and effective waste management practices. The data may be a valuable tool in evaluating the environmental impacts of operational and scientific activities, and the extent of the community adoption and economics of recycling.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Australian Antarctic continental stations and Macquarie Island station.

Frequency: Annual report.

Measurement technique: Data (waste weight and composition) are compiled from station cargo manifests and information supplied by the AAD's waste management contractor.

LINKS TO OTHER INDICATORS Amount of incinerated waste, Station person days, Field trips

The indicators (non-EPA owned, yet described in the Handbook and Manuscript) represent: 1) potential exposures due to extreme events (heat, floods, drought, and wildfire), 2) specific sources of contaminant releases (the different types of sites/waste facilities), 3) contaminant fate and transport (through water and wind), and 4) population sensitivity characteristics (demographics, socioeconomic conditions, existing health conditions) that indicate which individuals in the community may be impacted more by extreme events. The geospatial indicator data layers are at the Block Group level (U.S. Census Bureau, 2022), and each Block Group is considered to be a “community”. This dataset is not publicly accessible because: The indicator data are from external co-authors collected solely from coauthor resources. The indicator data were all generated using publicly available data sources, as documented in the External Review Draft of Handbook (research product attached). It can be accessed through the following means: This data can be accessed by contacting the external lead author on the Handbook and Manuscript (Dr. Paramita Sinha, RTI), who can provide all of the indicator data layers and associated metadata documentation. Format: The indicator data are from external co-authors collected solely from co-author resources. The format of the indicator data are geospatial data layers (e.g., Shapefiles, NetCDFs) with daily/hourly temporal resolution and Census Block Group level spatial resolution. There are over 250 individual geospatial data files which would be too large to upload and store. The data are already available from public data sources, as documented in the External Review Draft of Handbook. This dataset is associated with the following publications: Sinha, P., R. Truesdale, M. Fry, and S. Julius. Handbook on Indicators of Community Vulnerability to Extreme Events: Considering Sites and Waste Management Facilities. U.S. Environmental Protection Agency, Washington, DC, USA, 2023. Sinha, P., S. Julius, M. Fry, R. Truesdale, J. Cajka, M. Eddy , P. Doraiswamya, and D. Womacka. Assessing community vulnerability to extreme events in the presence of contaminated sites and waste management facilities: An indicator approach. Urban Climate. Elsevier Science, New York, NY, 53(101800): 1-30, (2024).

GIS Software Market Outlook

The global Geographic Information System (GIS) software market size is projected to witness substantial growth over the forecast period, with a notable CAGR of 11.2% from 2024 to 2032. In 2023, the market size was valued at approximately USD 9.1 billion and is expected to reach around USD 23.5 billion by 2032. This growth trajectory is primarily driven by the increasing integration of GIS across various industries, advancements in spatial data analysis technologies, and heightened demand for location-based services. The rising need for urban planning and smart city projects also significantly contributes to the market's expansion, alongside growing investments in infrastructure development across the globe.

Several key factors underpin the robust growth of the GIS software market. Firstly, the surge in demand for spatial data analytics is transforming decision-making processes across sectors such as agriculture, construction, and transportation. GIS software enables organizations to visualize, analyze, and interpret data to understand spatial relationships, patterns, and trends. This capability is crucial for efficient resource management, strategic planning, and effective deployment of assets. Furthermore, the integration of GIS with artificial intelligence and machine learning technologies enhances predictive analytics, enabling more precise forecasting and decision-making, which drives further adoption in both private and public sectors.

Secondly, the expansion of smart city initiatives worldwide is propelling the demand for GIS software. As urban areas continue to grow, there is an increasing need for sophisticated tools that can aid in planning and managing complex infrastructural developments. GIS software plays a pivotal role in urban planning by providing detailed visualization and analysis of spatial data, thereby aiding in effective decision-making concerning transportation, utilities, land use, and environmental management. This is further bolstered by government initiatives aimed at improving urban infrastructure and sustainability, thus contributing significantly to market growth.

Additionally, the growing adoption of location-based services across various industries is another major driver for the GIS software market. These services leverage GIS technology to provide real-time data and analytics, which are essential for navigation, asset tracking, and location-based marketing. The transportation and logistics sectors, in particular, are extensively utilizing GIS for route optimization, fleet management, and logistics planning. Moreover, the proliferation of smartphones and mobile applications has accelerated the demand for these services, further spurring the growth of the GIS software market.

The regional outlook for the GIS software market highlights a varied growth trajectory across different geographies. North America currently holds a significant market share due to the presence of major GIS software vendors and early adoption of advanced technologies. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rapid urbanization, infrastructure development, and increasing investments in smart city projects in countries like China and India are key factors driving the market in this region. Europe also shows promising growth prospects, particularly with the European Union's emphasis on sustainable development and environmental management, which necessitates the use of GIS technology.

Component Analysis

The GIS software market segmentation by component includes both software and services. The software segment is anticipated to hold the largest market share, driven by the increasing adoption of advanced software solutions that offer comprehensive tools for data analysis, mapping, and visualization. Software platforms that integrate GIS with cloud computing, IoT, and AI are seeing heightened demand as they provide more robust, scalable, and efficient solutions for complex spatial data analysis. Companies are continuously innovating to enhance the functionalities of GIS software, which is further propelling the growth of this segment.

Within the software segment, desktop GIS applications continue to dominate due to their widespread use in detailed data analysis and map creation. However, WebGIS and mobile GIS applications are rapidly gaining traction owing to their accessibility and convenience, allowing users to analyze spatial data from anywhere and at any time. This shift is largely attributed to the growing need for real-time data access and the integration

UWWTD Treatment Plants, Dec. 2024 is one of the datasets produced within the frame of the reporting under 13th UWWTD Art.15 reporting period (UWWTD data call 2023).

The Urban Waste Water Treatment Directive (UWWTD) (91/271/EEC) obliges Member States to report data on the implementation of the Directive upon request from the European Commission bi-annually. Reported data include receiving areas as designated under UWWTD, agglomerations, urban waste water treatment plants serving the agglomerations and points of discharges.

Dataset UWWTD_TreatmentPlants contains urban waste water treatment plants and collecting systems without UWWTP, including their coordinates, capacity and actual load treated, type of treatment and data on the performance of plants.

The dataset is provided in GeoPackage and ESRI File geodatabase formats.

The published output contains data reported in 2024. Current output is provisional, as it is subject to the Commission's compliance check, following which some records may be amended and further information will be added.

Remark to the Spatial scope: out of EU27 countries, Romania and Slovenia are not included (data not reported yet).