This dataset represents predictions made to individual, local NHDPlusV2 stream segments. Attributes were calculated for every local NHDPlusV2 stream segment. (See Supplementary Info for Glossary of Terms). These predictions were made to provide estimates of reference-condition stream temperatures in support of the 2008-2009 and 2013-2014 (forthcoming) National Rivers and Streams Assessments. These predictions were based on a set of published models (Hill et al. 2013; http://www.journals.uchicago.edu/doi/abs/10.1899/12-009.1). From Hill et al. (2013): "We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual stream temperature. These models used a set of least-disturbed USGS stations and sites to model stream temperatures from a set of landscape metrics. To build reference-condition models, we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. These data are summarized to produce local stream segment-level metrics as a continuous data type.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States MBA Forecast: Consumer Price Index (CPI): sa: Annual: YoY data was reported at 2.500 % in 2020. This records an increase from the previous number of 2.300 % for 2019. United States MBA Forecast: Consumer Price Index (CPI): sa: Annual: YoY data is updated yearly, averaging 1.850 % from Dec 2013 (Median) to 2020, with 8 observations. The data reached an all-time high of 2.500 % in 2020 and a record low of 1.300 % in 2016. United States MBA Forecast: Consumer Price Index (CPI): sa: Annual: YoY data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.I009: Consumer Price Index: Urban: sa: Forecast: Mortgage Bankers Association.

According to a survey conducted in 2023, higher education employees in the United States were most likely to believe that by 2026, AI tools will be used more for learning analytics, with ** percent of respondents sharing this belief. In that same year, ** percent also believed that academic dishonesty will have increased over the next two years.

The inflation rate in the United States is expected to decrease to 2.1 percent by 2029. 2022 saw a year of exceptionally high inflation, reaching eight percent for the year. The data represents U.S. city averages. The base period was 1982-84. In economics, the inflation rate is a measurement of inflation, the rate of increase of a price index (in this case: consumer price index). It is the percentage rate of change in prices level over time. The rate of decrease in the purchasing power of money is approximately equal. According to the forecast, prices will increase by 2.9 percent in 2024. The annual inflation rate for previous years can be found here and the consumer price index for all urban consumers here. The monthly inflation rate for the United States can also be accessed here. Inflation in the U.S.Inflation is a term used to describe a general rise in the price of goods and services in an economy over a given period of time. Inflation in the United States is calculated using the consumer price index (CPI). The consumer price index is a measure of change in the price level of a preselected market basket of consumer goods and services purchased by households. This forecast of U.S. inflation was prepared by the International Monetary Fund. They project that inflation will stay higher than average throughout 2023, followed by a decrease to around roughly two percent annual rise in the general level of prices until 2028. Considering the annual inflation rate in the United States in 2021, a two percent inflation rate is a very moderate projection. The 2022 spike in inflation in the United States and worldwide is due to a variety of factors that have put constraints on various aspects of the economy. These factors include COVID-19 pandemic spending and supply-chain constraints, disruptions due to the war in Ukraine, and pandemic related changes in the labor force. Although the moderate inflation of prices between two and three percent is considered normal in a modern economy, countries’ central banks try to prevent severe inflation and deflation to keep the growth of prices to a minimum. Severe inflation is considered dangerous to a country’s economy because it can rapidly diminish the population’s purchasing power and thus damage the GDP .

The purpose of this study was to ascertain whether election night reporting of presidential election results affected voter turnout in the 1980 United States election. The study gathered information on what time of day respondents voted, whether they had heard early reports of election results, and when they heard such reports. The dataset also includes variables used to assess likelihood of voting, including education, region, partisan strength, and feelings of citizen duty, as well as vote validation variables indicating the respondent's registration status and whether he or she voted. This study used part of the sample from the AMERICAN NATIONAL ELECTION STUDY, 1980 (ICPSR 7763). A brief telephone interview was conducted in January 1981 with individuals who participated in that study's Minor Panel (C1-C4) and Traditional Time Series samples (C3-C3po), and who agreed to be reinterviewed and could be reached by telephone. Vote validation variables and variables used to assess the likelihood of voting were drawn from the Integrated File of ICPSR 7763. This dataset can be merged with the entire Integrated File to permit analysis using the full data gathered for these respondents. Merging instructions are included in the machine-readable documentation for this study. Demographic information collected on respondents includes age, educational attainment, and political party affiliation.

This data release contains the associated data described in the related primary publication, “Predicting Flood Damage Probability Across the Conterminous United States” (Collins et al. [2022], see Cross Reference section). Publicly available geospatial datasets and random forest algorithms were used to analyze the spatial distribution and underlying drivers of flood damage probability caused by excessive rainfall and overflowing water bodies across the conterminous United States. Datasets contain input files for predictor and response variables used in the analysis and output files of flood damage probabilities generated from the analysis.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation prediction layer (PAE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the likelihood of elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

Surveys from swing states conducted the day before the 2024 election indicated an extremely close contest between Trump and Harris. Trump held a slight lead over of Harris in the majority of swing states.

United States FRBOP Forecast: Annual Housing Starts: Mean: Current data was reported at 1.264 USD mn in Dec 2018. This records a decrease from the previous number of 1.293 USD mn for Sep 2018. United States FRBOP Forecast: Annual Housing Starts: Mean: Current data is updated quarterly, averaging 1.400 USD mn from Sep 1981 (Median) to Dec 2018, with 150 observations. The data reached an all-time high of 2.083 USD mn in Jun 1986 and a record low of 0.558 USD mn in Jun 2009. United States FRBOP Forecast: Annual Housing Starts: Mean: Current data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.EA007: Private Housing Units: Started and Authorized: Forecast: Federal Reserve Bank of Philadelphia.

United States FRBOP Forecast: Non Farm Payroll: Mean: sa: Plus 2 Qtrs data was reported at 151,612.225 Person th in Mar 2019. This records an increase from the previous number of 150,894.356 Person th for Dec 2018. United States FRBOP Forecast: Non Farm Payroll: Mean: sa: Plus 2 Qtrs data is updated quarterly, averaging 136,547.365 Person th from Dec 2003 (Median) to Mar 2019, with 62 observations. The data reached an all-time high of 151,612.225 Person th in Mar 2019 and a record low of 130,825.930 Person th in Dec 2009. United States FRBOP Forecast: Non Farm Payroll: Mean: sa: Plus 2 Qtrs data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s United States – Table US.G030: Current Employment Statistics Survey: Employment: Non Farm: sa: Forecast.

Monthly and weekly soil moisture predictions in 2010 at 1-km spatial resolution using two different modeling methods integrated in the modular SOil Moisture SPatial Inference Engine (SOMOSPIE- Rorabaugh et al. 2019) (kernel-weighted k-nearest neighbors

Empirical models described in previous publications were developed and applied to estimate the probability of streamflow modification for every stream segment in the conterminous United States from 1980-2015. This metadata record documents 6 comma separated tables populated with predictions of streamflow modification (please see the Supplemental Element for citations or please refer to the cross-reference section). These data are based on watershed attributes computed for each NHDPlus v2.1 reach that were subsequently applied to previously published (and herein described) machine-learning models.

The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The whooping crane is a listed endangered species in North America, protected under federal legislation in the United States and Canada. The only self-sustaining and wild population of Whooping Cranes nests at and near Wood Buffalo National Park near the provincial border of Northwest Territories and Alberta, Canada. Birds from this population migrate through the Great Plains of North America and winter along the Gulf Coast of Texas at Aransas National Wildlife Refuge and surrounding lands. These data represent predictions from a resource selection function using GPS locations between 2010 and 2016 during migration. This surface represents predictions under drought conditions across the study area. Pixel values can be used to represent the relative probability of whooping crane use during migration, based on landscape conditions in 2020. These values should not be interpreted as absolute values of probability of use. See additional information about resource selection functions and interpretation of output as needed.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

These GIS layers provide the probability of observing the forecast of adjusted land elevation (PAE) with respect to predicted sea-level rise or the Northeastern U.S. for the 2020s, 2030s, 2050s and 2080s. These data are based on the following inputs: sea-level rise, vertical land movement rates due to glacial isostatic adjustment and elevation data. The output displays the highest probability among the five adjusted elevation ranges (-12 to -1, -1 to 0, 0 to 1, 1 to 5, and 5 to 10 m) to be observed for the forecast year as defined by a probabilistic framework (a Bayesian network), and should be used concurrently with the adjusted land elevation layer (AE), also available from http://woodshole.er.usgs.gov/project-pages/coastal_response/, which provides users with the forecast elevation range occurring when compared with the four other elevation ranges. These data layers primarily show the distribution of adjusted elevation range probabilities over a large spatial scale and should therefore be used qualitatively.

Forecast: Number of E-money Payments in United States 2024 - 2028 Discover more data with ReportLinker!

Inflation Expectations in the United States decreased to 3.20 percent in May from 3.60 percent in April of 2025. This dataset provides - United States Consumer Inflation Expectations- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Climate Prediction Center releases a US Hazards Outlook daily, Monday through Friday. The product highlights regions of anticipated hazardous weather during the next 3-7 and 8-14 days and examples include heavy snow, high winds, flooding, extreme heat and cold and severe thunderstorms. The product highlights regions of anticipated hazardous weather during the next 3-7 and 8-14 days. Three separate files are available for download for each time period. A soils shapefile (and KMZ) contain severe drought and enhanced wildfire risk hazards. A temperature file contains temperature, wind, and wave hazards, and a precipitation file contains rain, snow, and severe weather hazards. The contents of these file are mashed up to create one composite graphic per time period as well as being displayed on an interactive Google Map

In the United States in 2024, mandatory outlays for higher education amounted to about 125 billion U.S. dollars. Mandatory outlays for higher education are projected to be around 29 billion U.S. dollars in 2035.