The shaded Census Tracts represent the lowest 20% for selected Miami-Dade County social, demographic and economic variables that were highly correlated with low response rates in the 2010 Census. The darker the shading, the greater the potential that the response rate is affected by more of the selected six variables

Dynamics of societal material stocks such as buildings and infrastructures and their spatial patterns drive surging resource use and emissions. Building up and maintaining stocks requires large amounts of resources; currently stock-building materials amount to almost 60% of all materials used by humanity. Buildings, infrastructures and machinery shape social practices of production and consumption, thereby creating path dependencies for future resource use. They constitute the physical basis of the spatial organization of most socio-economic activities, for example as mobility networks, urbanization and settlement patterns and various other infrastructures.

This dataset features a detailed map of material stocks for the whole of Austria on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), crowd-sourced geodata (OSM) and material intensity factors.

Temporal extent The map is representative for ca. 2018.

Data format Per federal state, 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.

The dataset features

area and mass for different street types

area and mass for different rail types

area and mass for other infrastructure

area, volume and mass for different building types

Masses are reported as total values, and per material category.

Units

area in m²

height in m

volume in m³

mass in t for infrastructure and buildings

Further information For further information, please see the publication or contact Helmut Haberl (helmut.haberl@boku.ac.at). A web-visualization of this dataset is available here. 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.

Publication Haberl, H., Wiedenhofer, D., Schug, F., Frantz, D., Virág, D., Plutzar, C., Gruhler, K., Lederer, J., Schiller, G. , Fishman, T., Lanau, M., Gattringer, A., Kemper, T., Liu, G., Tanikawa, H., van der Linden, S., Hostert, P. (accepted): High-resolution maps of material stocks in buildings and infrastructures in Austria and Germany. Environmental Science & Technology

Funding This research was primarly funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). ML and GL acknowledge funding by the Independent Research Fund Denmark (CityWeight, 6111-00555B), ML thanks the Engineering and Physical Sciences Research Council (EPSRC; project Multi-Scale, Circular Economic Potential of Non-Residential Building Scale, EP/S029273/1), JL acknowledges funding by the Vienna Science and Technology Fund (WWTF), project ESR17-067, TF acknowledges the Israel Science Foundation grant no. 2706/19.

The China County-Level Data on Provincial Economic Yearbooks, Keyed To 1:1M GIS Map consists of socioeconomic and boundary data for the administrative regions of China for 1990 and 1991. The socioeconomic data includes natural resources, population, employment, investment, wage, public finance, price, people's livelihood, agriculture, industry, energy, production, transportation, telecommunication, construction, trade, tourism, environmental protection, education, science, patents, culture, sports, health care, and social welfare. The boundary data are at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project, University of Michigan Center of China Studies (CCS), and the Center for International Earth Science Information Network (CIESIN).

The geology data set for this map includes arcs, polygons, and labels that outline and describe the general geologic age and type of bedrock of Iran. The geologic provinces data set includes arcs, polygons, and labels of geologic and petroleum provinces interpreted and designated by R.M. Pollastro from a number of literature and map resources to assist in the assessment of oil and gas resources for the USGS World Energy Project. The oil and gas field centerpoints data set is a point coverage that marks the approximate centerpoints of oil and gas fields in Iran. Political boundaries are provided to show the general location of country and/or other reference 'political' boundaries.

2020 Census Response Probability Map SeriesThis Map Series was derived from the U.S. Census Planning Database for projecting the initial response to the 2020 Census. The map highlights seven key variables that were highly correlated with low response rates in Miami-Dade County. It is intended to serve as a tool to prioritize outreach efforts in order to maximize the response rate to the upcoming Census.

This world map shows the member economies of Asia-Pacific Economic Cooperation (APEC).

Part of the Integrated Vulnerability Assessment in the Arab Region, this 1km pixel resolution raster dataset provides a representation of Adaptive Capacity to climate change, for the Economic Resources dimension, in the Middle East and North Africa Region. Vulnerability is a concept used to express the complex interaction of climate change effects and the susceptibility of a system to its impacts. The integrated vulnerability assessment methodology is based on an understanding of vulnerability as a function of a system’s climate change exposure, sensitivity and adaptive capacity to cope with climate change effects, consistent with the approach put forward by the Intergovernmental Panel on Climate Change (IPCC) in its Fourth Assessment Report (AR4). Combining exposure, sensitivity and adaptive capacity allows assessing the vulnerability of a system to climate change. Within this conceptual framework, Adaptive Capacity refers to “the ability of a system to adjust to climate change (including climate variability and extremes), to moderate potential damages, to take advantage of opportunities, or to cope with the consequences” as defined in the IPCC AR4. Adaptive Capacity was categorized into six dimensions. The Economic Resources dimension, together with the institutions can be classified as action devices that describe the enabling environment that allow a society to adapt. Economic Resources indicators include: Age Dependency Ratio; Food imports as Percentage of Merchandise Exports; GDP per Capita; Official Development Assistance Index. Indicators were assumed to retain the same values for the reference period and future periods, and raster grid pixel values were classified according to level of Adaptive Capacity, from low 1 to high 10.

This layer is a component of ENOW_Counties.

This map service presents spatial information about the Economics: National Ocean Watch (ENOW) data in the Web Mercator projection. The ENOW data provides time-series data on the ocean and Great Lakes economy, which includes six economic sectors dependent on the oceans and Great Lakes, and measures four economic indicators: Establishments, Employment, Wages, and Gross Domestic Product (GDP). The annual time-series data are available for about 400 coastal counties, 30 coastal states, 8 regions, and the nation. The service was developed by the National Oceanic and Atmospheric Administration (NOAA), but may contain data and information from a variety of data sources, including non-NOAA data. NOAA provides the information “as-is” and shall incur no responsibility or liability as to the completeness or accuracy of this information. NOAA assumes no responsibility arising from the use of this information. The NOAA Office for Coastal Management will make every effort to provide continual access to this service but it may need to be taken down during routine IT maintenance or in case of an emergency. If you plan to ingest this service into your own application and would like to be informed about planned and unplanned service outages or changes to existing services, please register for our Data Services Newsletter (http://coast.noaa.gov/digitalcoast/publications/subscribe). For additional information, please contact the NOAA Office for Coastal Management (coastal.info@noaa.gov).

© NOAA Office for Coastal Management

This dataset is linked to the KML (Keyhole Markup Language) file list. The format is a markup language based on the XML (eXtensible Markup Language) syntax standard, which uses markup structure and contains nested elements and attributes. Developed and maintained by Keyhole, a company under Google, it is used to express geographical annotations. Documents written in KML language are KML files, which use the XML file format and are used in Google Earth-related software (Google Earth, Google Map, Google Maps for mobile) to display geographical data (including points, lines, polygons, polyhedra, and models). Many GIS-related systems now also use this format for exchanging geographical data, and the fields and encoding of this data are all in UTF-8. For more details, please visit the Geographical Information Center (http://gic.wra.gov.tw/).

Spatial coverage index compiled by East View Geospatial of set "Venezuela State Natural Resources Maps". Source data from FEP (publisher). Type: Thematic - Economic Geography. Scale: Varies. Region: South America.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

Alaska Regional Development Organizations (ARDORs), their contact information, and their Comprehensive Economic Development Strategies (CEDS).The mission of the ARDORs Program is to encourage the formation of regional development organizations to prepare and implement regional development strategies (Alaska Statute 44.33.896). Through regional development strategies, local knowledge, and coordinated implementation, ARDORs champion economic development planning for Alaska’s regions and communities by leveraging baseline support provided by the State of Alaska.ARDORs develop customized work plans that contain goals, objectives, and strategies for addressing regional economic development needs including: Facilitating development of a healthy regional economy that results in sustainable business growth, new business investment, and economic diversification. Identifying and working to eliminate regional economic development barriers. Developing and implementing a comprehensive economic development strategy. Coordinating regional planning efforts that result in new employment and business opportunities. Working to enable multiple communities to collaborate and pool limited resources. Strengthening partnerships with public, private, and non-government organizations. Providing technical assistance to encourage business startup, retention, and expansion.Source: Alaska Department of Commerce, Community & Economic Development

SAND_GRAVEL_RESOURCES_IN is a polygon shapefile that identifies sand and gravel permissive tracts in the surficial unconsolidated deposits of Indiana. Permissive tracts have been defined by the U.S. Geological Survey (Open File Report 95-681, Singer, D.A., 1993, Basic concepts in three-part quantitative assessments of undiscovered mineral resources: Nonrenewable Resources, v. 2, no. 2, p. 69-81) as "areas where the geology permits the existence of mineral deposits (mineral resource potential) of a specific type... Noting that the possibility of the occurrence of the specified deposit type (mineral resource) outside the permissive tract should be negligible." This shapefile provides an approximate economic ranking of the sand and gravel resource potential of each mapped surficial unconsolidated deposit. The resource potential of each map unit within the mapped surficial unconsolidated deposits is ranked as a "potential," "low potential," or "not a potential" resource. The definitions of these rankings are derived from Gray, H. H., 1973, Properties and uses of geologic materials in Indiana: Indiana Geological Survey Regional Geologic Map Supplementary Chart 1 and Carr, D. D., and Webb, W. M., 1970, Sand and gravel resources of Indiana: Indiana Geological Survey Bulletin 42-D, 31 p. "Potential resource" indicates that the surficial unconsolidated deposits are likely to contain economic concentrations of sand and gravel, "low potential" indicates that the surficial unconsolidated deposits may contain economic concentrations of sand and gravel, and "not a potential resource" indicates that the surficial unconsolidated deposits do not contain economic concentrations of sand and gravel.

AR5 shows types of land resources on a scale of 1:5000 in areas below the forest border. The data set is adapted to use on scales from 1:5000 to 1:20 000. AR5 is derived from the earlier fields in Economic Mapping. The division into land use types is based on criteria for vegetation, natural drainage and cultural impact. The dataset consists of the following properties that can be used to produce map layers with predefined presentation rules (sld files): area type (ARTYPE), land conditions (ARGRUNNF), wood species (ARTRESLAG), arable land (ARDYRKING), forest land (ARSKOGBON). Area less than two acres is usually not separated as a separate figure. Agricultural land is separated down to 0.5 acres. The requirement for location accuracy is two meters. Municipalities are responsible for continuous updating. NIBIO makes periodic updates every 4-7 years. Bone and Agriculture maps are only available as a WMS service.

GeoTIFF of Wind Power Class values for the state of Alaska. This map shows the annual average wind power estimates at a height of 50 meters. It is a combination of high resolution and low resolution datasets produced by NREL and other organizations. The data was screened to eliminate areas unlikely to be developed onshore due to land use or environmental issues. In many states, the wind resource on this map is visually enhanced to better show distribution on ridge crests and other features.

New York State Regional Economic Development CouncilsA map service is also available - https://gisservices.its.ny.gov/arcgis/rest/services/Regional_Economic_Development_Councils/MapServerCurrent as of March 2023Please contact NYS ITS Geospatial Services at nysgis@its.ny.gov if you have any questions.

Appropriate for use in local and regional thematic analysis. Data are not accurate enough to be used as a geodetic or engineering base. Intended to include representation for each Conservation Area.

This data set shows state Conservation Areas in Illinois. Digitized from maps provided by DNR, county plat books, USGS TIGER files, and 1:24,000 quadrangle maps. Boundaries are approximate and do not indicate ownership or property boundaries. The Illinois Department of Conservation Land and Water Report of 30 June 1994 was used as a reference.

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A collection of over 75 charts and maps presenting key statistics on the farm sector, food spending and prices, food security, rural communities, the interaction of agriculture and natural resources, and more.

How much do you know about food and agriculture? What about rural America or conservation? ERS has assembled more than 75 charts and maps covering key information about the farm and food sectors, including agricultural markets and trade, farm income, food prices and consumption, food security, rural economies, and the interaction of agriculture and natural resources.

How much, for example, do agriculture and related industries contribute to U.S. gross domestic product? Which commodities are the leading agricultural exports? How much of the food dollar goes to farmers? How do job earnings in rural areas compare with metro areas? How much of the Nation’s water is used by agriculture? These are among the statistics covered in this collection of charts and maps—with accompanying text—divided into the nine section titles.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Ag and Food Sectors and the Economy Land and Natural Resources Farming and Farm Income Rural Economy Agricultural Production and Prices Agricultural Trade Food Availability and Consumption Food Prices and Spending Food Security and Nutrition Assistance For complete information, please visit https://data.gov.

This dataset is linked to the KML (Keyhole Markup Language) file list. This format is a markup language based on the XML (eXtensible Markup Language) syntax standard, which uses markup structure and includes nested elements and attributes. It was developed and maintained by Keyhole, a company owned by Google, to express geographic annotations. Documents written in the KML language are KML files, using the XML file format and are applied in Google Earth-related software (Google Earth, Google Map, Google Maps for mobile) to display geographic data (including points, lines, shapes, polygons, polyhedra, and models). Many GIS-related systems now also use this format for geospatial data exchange. The fields and encoding of this data in KML are all UTF-8. For more information, please visit the "Geographic Information Warehouse Center" (http://gic.wra.gov.tw/).