The number of Twitter users in the United States was forecast to continuously increase between 2024 and 2028 by in total 4.3 million users (+5.32 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 85.08 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Canada and Mexico.

The number of Reddit users in the United States was forecast to continuously increase between 2024 and 2028 by in total 10.3 million users (+5.21 percent). After the ninth consecutive increasing year, the Reddit user base is estimated to reach 208.12 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like Mexico and Canada.

What is this?

A community effort is a must in order to make a better, more accurate vision model, as I simply cannot tag thousands of images. If you would provide 50 corrections and 20 more people do so as well, it would help a lot. If 100 ppl would help with 50 corrections each, we might have a high-accuracy functioning uncensored vision model. The best format would be to name the output and images with the same name, like: 1.png 1.txt

2.png 2.txt

The best approach is probably… See the full description on the dataset page: https://huggingface.co/datasets/SicariusSicariiStuff/X-Ray_Community_Tagging.

PerCapita_CO2_Footprint_InDioceses_FULLBurhans, Molly A., Cheney, David M., Gerlt, R.. . “PerCapita_CO2_Footprint_InDioceses_FULL”. Scale not given. Version 1.0. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.MethodologyThis is the first global Carbon footprint of the Catholic population. We will continue to improve and develop these data with our research partners over the coming years. While it is helpful, it should also be viewed and used as a "beta" prototype that we and our research partners will build from and improve. The years of carbon data are (2010) and (2015 - SHOWN). The year of Catholic data is 2018. The year of population data is 2016. Care should be taken during future developments to harmonize the years used for catholic, population, and CO2 data.1. Zonal Statistics: Esri Population Data and Dioceses --> Population per dioceses, non Vatican based numbers2. Zonal Statistics: FFDAS and Dioceses and Population dataset --> Mean CO2 per Diocese3. Field Calculation: Population per Diocese and Mean CO2 per diocese --> CO2 per Capita4. Field Calculation: CO2 per Capita * Catholic Population --> Catholic Carbon FootprintAssumption: PerCapita CO2Deriving per-capita CO2 from mean CO2 in a geography assumes that people's footprint accounts for their personal lifestyle and involvement in local business and industries that are contribute CO2. Catholic CO2Assumes that Catholics and non-Catholic have similar CO2 footprints from their lifestyles.Derived from:A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of resultshttp://ffdas.rc.nau.edu/About.htmlRayner et al., JGR, 2010 - The is the first FFDAS paper describing the version 1.0 methods and results published in the Journal of Geophysical Research.Asefi et al., 2014 - This is the paper describing the methods and results of the FFDAS version 2.0 published in the Journal of Geophysical Research.Readme version 2.2 - A simple readme file to assist in using the 10 km x 10 km, hourly gridded Vulcan version 2.2 results.Liu et al., 2017 - A paper exploring the carbon cycle response to the 2015-2016 El Nino through the use of carbon cycle data assimilation with FFDAS as the boundary condition for FFCO2."S. Asefi‐Najafabady P. J. Rayner K. R. Gurney A. McRobert Y. Song K. Coltin J. Huang C. Elvidge K. BaughFirst published: 10 September 2014 https://doi.org/10.1002/2013JD021296 Cited by: 30Link to FFDAS data retrieval and visualization: http://hpcg.purdue.edu/FFDAS/index.phpAbstractHigh‐resolution, global quantification of fossil fuel CO2 emissions is emerging as a critical need in carbon cycle science and climate policy. We build upon a previously developed fossil fuel data assimilation system (FFDAS) for estimating global high‐resolution fossil fuel CO2 emissions. We have improved the underlying observationally based data sources, expanded the approach through treatment of separate emitting sectors including a new pointwise database of global power plants, and extended the results to cover a 1997 to 2010 time series at a spatial resolution of 0.1°. Long‐term trend analysis of the resulting global emissions shows subnational spatial structure in large active economies such as the United States, China, and India. These three countries, in particular, show different long‐term trends and exploration of the trends in nighttime lights, and population reveal a decoupling of population and emissions at the subnational level. Analysis of shorter‐term variations reveals the impact of the 2008–2009 global financial crisis with widespread negative emission anomalies across the U.S. and Europe. We have used a center of mass (CM) calculation as a compact metric to express the time evolution of spatial patterns in fossil fuel CO2 emissions. The global emission CM has moved toward the east and somewhat south between 1997 and 2010, driven by the increase in emissions in China and South Asia over this time period. Analysis at the level of individual countries reveals per capita CO2 emission migration in both Russia and India. The per capita emission CM holds potential as a way to succinctly analyze subnational shifts in carbon intensity over time. Uncertainties are generally lower than the previous version of FFDAS due mainly to an improved nightlight data set."Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/Esri Gridded Population Data 2016DescriptionThis layer is a global estimate of human population for 2016. Esri created this estimate by modeling a footprint of where people live as a dasymetric settlement likelihood surface, and then assigned 2016 population estimates stored on polygons of the finest level of geography available onto the settlement surface. Where people live means where their homes are, as in where people sleep most of the time, and this is opposed to where they work. Another way to think of this estimate is a night-time estimate, as opposed to a day-time estimate.Knowledge of population distribution helps us understand how humans affect the natural world and how natural events such as storms and earthquakes, and other phenomena affect humans. This layer represents the footprint of where people live, and how many people live there.Dataset SummaryEach cell in this layer has an integer value with the estimated number of people likely to live in the geographic region represented by that cell. Esri additionally produced several additional layers World Population Estimate Confidence 2016: the confidence level (1-5) per cell for the probability of people being located and estimated correctly. World Population Density Estimate 2016: this layer is represented as population density in units of persons per square kilometer.World Settlement Score 2016: the dasymetric likelihood surface used to create this layer by apportioning population from census polygons to the settlement score raster.To use this layer in analysis, there are several properties or geoprocessing environment settings that should be used:Coordinate system: WGS_1984. This service and its underlying data are WGS_1984. We do this because projecting population count data actually will change the populations due to resampling and either collapsing or splitting cells to fit into another coordinate system. Cell Size: 0.0013474728 degrees (approximately 150-meters) at the equator. No Data: -1Bit Depth: 32-bit signedThis layer has query, identify, pixel, and export image functions enabled, and is restricted to a maximum analysis size of 30,000 x 30,000 pixels - an area about the size of Africa.Frye, C. et al., (2018). Using Classified and Unclassified Land Cover Data to Estimate the Footprint of Human Settlement. Data Science Journal. 17, p.20. DOI: http://doi.org/10.5334/dsj-2018-020.What can you do with this layer?This layer is unsuitable for mapping or cartographic use, and thus it does not include a convenient legend. Instead, this layer is useful for analysis, particularly for estimating counts of people living within watersheds, coastal areas, and other areas that do not have standard boundaries. Esri recommends using the Zonal Statistics tool or the Zonal Statistics to Table tool where you provide input zones as either polygons, or raster data, and the tool will summarize the count of population within those zones. https://www.esri.com/arcgis-blog/products/arcgis-living-atlas/data-management/2016-world-population-estimate-services-are-now-available/

This data set represents the amount of clay sediment in the soil, in percent times 1000, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents 1990 block group population density, in people per square kilometer, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents the presence or absence of poorly sorted glacial till east of the Rocky Mountains in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents the percent of woody wetlands and emergent herbaceous wetlands land cover in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents predicted nitrate concentration in shallow, recently recharged ground water, in milligrams per liter, in the conterminous United States, and was generated by a national nonlinear regression model based on 16 input parameters. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is a national map of nitrate concentration (in milligrams/liter) in shallow, recently recharged ground water as predicted by the GWAVA-S model. The data set is one of 17 spatial data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). This data set represents the model output, which is depicted in figure 2 of Nolan and Hitt (2006) that shows predicted nitrate concentration in milligrams per liter in shallow, recently recharged ground water. The model results can be used to indicate areas of the Nation that may be vulnerable to nitrate contamination. For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents "water input," the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter, in the conterminous United States.

The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water.

Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation.

One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking.

This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006).

For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1).

Table 1.-- Parameters of nonlinear regression model for nitrate in shallow ground water (GWAVA-S) and corresponding input spatial data sets. [kg, kilograms; km2, square kilometers.]

Nitrogen Source Factors Data Set Name 1 farm fertilizer (kg/hectare) gwava-s_ffer 2 confined manure (kg/hectare) gwava-s_conf 3 orchards/vineyards (percent) gwava-s_orvi 4 population density (people/km2) gwava-s_popd 5 cropland/pasture/fallow (percent) gwava-s_crpa

Transport to Aquifer Factors 6 water input (km2/cm) gwava-s_wtin 7 carbonate rocks (yes/no) gwava-s_crox 8 basalt and volcanic rocks (yes/no) gwava-s_vrox 9 drainage ditch (km2) gwava-s_ddit 10 slope (percent x 1000) gwava-s_slop 11 glacial till (yes/no) gwava-s_gtil 12 clay sediment (percent x 1000) gwava-s_clay

Attenuation Factors 13 fresh surface water withdrawal gwava-s_swus for irrigation (megaliters/day) 14 irrigation tailwater recovery (km2) gwava-s_twre 15 histosol soil type (percent) gwava-s_hist 16 wetlands (percent) gwava-s_wetl

"Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare.

"Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare.

"Orchards/vineyards" is the percent of orchards/vineyards land cover classification.

"Population density" is 1990 block group population density, in people per square kilometer.

"Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications.

"Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter.

"Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks.

"Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks.

"Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers.

"Slope" is the soil surface slope, in percent times 1000.

"Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains.

"Clay sediment" is the amount of clay sediment in the soil, in percent times 1000.

"Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day.

"Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers.

"Histosol soil type" is the amount of histosols soil taxonomic order, in percent.

"Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications.

Reference cited:

Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

This data set represents soil surface slope, in percent times 1000, in the conterminous United States. The data set was used as an input data layer for a national model to predict nitrate concentration in shallow ground water. Nolan and Hitt (2006) developed two national models to predict contamination of ground water by nonpoint sources of nitrate. The nonlinear approach to national-scale Ground-WAter Vulnerability Assessment (GWAVA) uses components representing nitrogen (N) sources, transport, and attenuation. One model (GWAVA-S) predicts nitrate contamination of shallow (typically less than 5 meters deep), recently recharged ground water, which may or may not be used for drinking. The other (GWAVA-DW) predicts ambient nitrate concentration in deeper supplies used for drinking. This data set is one of 17 data sets (1 output data set and 16 input data sets) associated with the GWAVA-S model. Full details of the model development are in Nolan and Hitt (2006). For inputs to the model, spatial attributes representing 16 nitrogen loading and transport and attenuation factors were compiled as raster data sets (1-km by 1-km grid cell size) for the conterminous United States (see table 1). >Table 1.-- Parameters of nonlinear regression model for nitrate in shallow > ground water (GWAVA-S) and corresponding input spatial data sets. > [kg, kilograms; km2, square kilometers.] > >Nitrogen Source Factors Data Set Name > 1 farm fertilizer (kg/hectare) gwava-s_ffer > 2 confined manure (kg/hectare) gwava-s_conf > 3 orchards/vineyards (percent) gwava-s_orvi > 4 population density (people/km2) gwava-s_popd > 5 cropland/pasture/fallow (percent) gwava-s_crpa > >Transport to Aquifer Factors > 6 water input (km2/cm) gwava-s_wtin > 7 carbonate rocks (yes/no) gwava-s_crox > 8 basalt and volcanic rocks (yes/no) gwava-s_vrox > 9 drainage ditch (km2) gwava-s_ddit > 10 slope (percent x 1000) gwava-s_slop > 11 glacial till (yes/no) gwava-s_gtil > 12 clay sediment (percent x 1000) gwava-s_clay > >Attenuation Factors > 13 fresh surface water withdrawal gwava-s_swus > for irrigation (megaliters/day) > 14 irrigation tailwater recovery (km2) gwava-s_twre > 15 histosol soil type (percent) gwava-s_hist > 16 wetlands (percent) gwava-s_wetl "Farm fertilizer" is the average annual nitrogen input from commercial fertilizer applied to agricultural lands, 1992-2001, in kilograms per hectare. "Confined manure" is the average annual nitrogen input from confined animal manure, 1992 and 1997, in kilograms per hectare. "Orchards/vineyards" is the percent of orchards/vineyards land cover classification. "Population density" is 1990 block group population density, in people per square kilometer. "Cropland/pasture/fallow" is the percent of cropland/pasture/fallow land cover classifications. "Water input" is the ratio of the total area of irrigated land to precipitation, in square kilometers per centimeter. "Carbonate rocks" is the presence or absence of Valley and Ridge carbonate rocks. "Basalt and volcanic rocks" is the presence or absence of basalt and volcanic rocks. "Drainage ditch" is the area of National Resources Inventory surface drainage, field ditch conservation practice, in square kilometers. "Slope" is the soil surface slope, in percent times 1000. "Glacial till" is the presence or absence of poorly sorted glacial till east of the Rocky Mountains. "Clay sediment" is the amount of clay sediment in the soil, in percent times 1000. "Fresh surface water withdrawal for irrigation" is the amount of fresh surface water withdrawal for irrigation, in megaliters per day. "Irrigation tailwater recovery" is the area of National Resources Inventory irrigation system, tailwater recovery conservation practice, in square kilometers. "Histosol soil type" is the amount of histosols soil taxonomic order, in percent. "Wetlands" is the percent of woody wetlands and emergent herbaceous wetlands land cover classifications. Reference cited: Nolan, B.T. and Hitt, K.J., 2006, Vulnerability of shallow ground water and drinking-water wells to nitrate in the United States: Environmental Science and Technology, vol. 40, no. 24, pages 7834-7840.

The number of Pinterest users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 0.3 million users (+3.14 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 9.88 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of LinkedIn users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 1.5 million users (+4.51 percent). After the eighth consecutive increasing year, the LinkedIn user base is estimated to reach 34.7 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

The number of Instagram users in the United Kingdom was forecast to continuously increase between 2024 and 2028 by in total 2.1 million users (+7.02 percent). After the ninth consecutive increasing year, the Instagram user base is estimated to reach 32 million users and therefore a new peak in 2028. Notably, the number of Instagram users of was continuously increasing over the past years.User figures, shown here with regards to the platform instagram, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).