This geodatabase includes spatial datasets that represent the High Plains aquifer in the States of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. Included are: (1) polygon extents; datasets that represent the aquifer system extent, (2) raster datasets for the altitude of the top and bottom surfaces of the High Plains aquifer, (3) altitude contours of the top surface and of the bottom surface used to generate the surface rasters. The altitude contours are supplied for reference. The extent of the High Plains aquifer is from the digital dataset U.S. Geological Survey Data Series 543 (USGS DS 543), and as a references, the digital version of the aquifer extent presented in the Groundwater Atlas of the United States (the U.S. Geological Survey Hydrologic Atlas 730-D, -E, and -C, (USGS HA 730-D, -E, -C). The altitude contours for the top surface of the High Plains aquifer are from digital datasets of U.S. Geological Survey Open-File Report 99-263 (USGS OFR 99-263), using the 1980 water-level data. The altitude contours for the bottom surface of the High Plains aquifer are from the U.S. Geological Survey Open-File Report 98-393 (USGS OFR 98-393). The altitude of the bottom surface, or base, was originally from the High Plains Regional Aquifer-System Analysis study. The resultant top and bottom altitude values were interpolated into surface rasters within a GIS using tools that create hydrologically correct surfaces from contour data, derive the altitude from the thickness (depth from the land surface), and merge the subareas into a single surface. The primary tool was an enhanced version of "Topo to Raster" used in ArcGIS, ArcMap, Esri 2014. The raster surfaces were corrected for the areas where the altitude of an underlying layer of the aquifer exceeded the altitude of an overlying layer.

The U.S. Interagency Elevation Inventory (USIEI) displays high-accuracy topographic and bathymetric data for the United States and its territories. The project is a collaborative effort between the National Oceanic and Atmospheric Administration, the U.S. Geological Survey, the Federal Emergency Management Agency, the U.S. Department of Agriculture - Natural Resources Conservation Service and U.S. Forest Service, the National Park Service, and the U.S. Army Corps of Engineers. This resource is a comprehensive, nationwide listing of known high-accuracy topographic data, including lidar and IfSAR, and bathymetric data, including NOAA hydrographic surveys, multibeam data, and bathymetric lidar. This zip file contains the attribute information and footprints about the data sets that are displayed in the Topographic Lidar, Topobathy Shoreline Lidar, IfSAR Data, and Bathymetric Lidar layers in the USIEI viewer. This does not include the elevation data itself. The data are provided in Esri file geodatabase format (gdb) and in the open format of OGC GeoPackage (gpkg). The data is also available via this map service: https://coast.noaa.gov/arcgis/rest/services/USInteragencyElevationInventory/USIEIv2/MapServer. The data is updated quarterly. The information provided for each elevation data set includes many attributes such as vertical accuracy, point spacing, and date of collection. A direct link to access the data or information about the contact organization is also available through the inventory. The footprints in this data set are generalized to represent the coverage of the collection. If the exact data coverage is needed, please contact the data provider for an authoritative footprint. The fields in the gdb and gpkg are in four tables. The fields in each table are listed in the Entity Attribute Overview field.

Digital elevation model used for the conservation assessment of Greater Sage-grouse and sagebrush habitat conducted by the Western Association of Fish and Wildlife Agencies. Digital elevation models were downloaded from the USGS National Elevation Dataset (NED) which was developed by merging the highest-resolution, best quality elevation data available across the United States into a seamless raster format to provide 1:24,000-scale Digital Elevation Model (DEM) data for the conterminous US.

The USGS NHDPlus High Resolution service, NHDPlus_HR, a part of The National Map, is a comprehensive set of digital spatial data comprising a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream network navigation, analysis, and data display. NHDPlus High Resolution (NHDPlus HR) is a scalable geospatial hydrography framework built from the high resolution National Hydrography Dataset, nationally complete Watershed The USGS NHDPlus High Resolution service, NHDPlus_HR, a part of The National Map, is a comprehensive set of digital spatial data comprising a nationally seamless network of stream reaches, elevation-based catchment areas, flow surfaces, and value-added attributes that enhance stream network navigation, analysis, and data display. NHDPlus High Resolution (NHDPlus HR) is a scalable geospatial hydrography framework built from the high resolution National Hydrography Dataset, nationally complete Watershed Boundary Dataset, and 3D Elevation Program (3DEP) ? arc-second (10 meter ground spacing) digital elevation model data. The National Map download client allows free downloads of public domain NHDPlus HR data in Esri File Geodatabase format. For additional information on the NHDPlus HR, go to https://www.usgs.gov/national-hydrography/national-hydrography-dataset. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.Use Constraints: _ None. All data are open and non-proprietary. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of this data may no longer represent actual conditions. Users should not use this data for critical applications without a full awareness of its limitations. This dataset is not intended to be used for site-specific regulatory determinations. Acknowledgment of the U.S. Geological Survey would be appreciated for products derived from these data.

Snow and ice-covered Mount Baker in northern Washington, is the highest peak in the North Cascades (3,286 meters or 10,781 feet) and the northernmost volcano in the conterminous United States. It is the only U.S. volcano in the Cascade Range that has been affected by both alpine and continental glaciation. The stratovolcano is composed mainly of andesite lava flows and breccias formed prior to the most recent major glaciation (Fraser Glaciation), which occurred between about 25,000 and 10,000 years ago. The most recent major eruption at Mount Baker (6,700 years ago) was accompanied by a major flank-collapse event that caused lahars to rush down the Nooksack River and then eastward into Baker Lake. In 1975-76, Sherman Crater immediately south of the summit, exhibited signs of renewed volcanic activity as a result of magma intruding into the volcano but not erupting. The DEM (digital elevation model) of Mount Baker covers approximately 201 square miles and is the product of high-precision airborne lidar (Light Detection and Ranging) surveys performed between 08/26/15 and 09/27/15 by Quantum Spatial under contract with the USGS. The DEM, represents the ground surface beneath forest cover. This release includes two raster datasets in .tif format, (1) a DEM dataset (mt_baker_dem.zip, 1.40 GB), and (2) a hillshade raster (mt_baker_hillshade.zip, 573 MB).

United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data was reported at 2.264 % in 2010. This records an increase from the previous number of 2.246 % for 2000. United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data is updated yearly, averaging 2.264 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 2.329 % in 1990 and a record low of 2.246 % in 2000. United States US: Urban Population Living in Areas Where Elevation is Below 5 meters: % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Land Use, Protected Areas and National Wealth. Urban population below 5m is the percentage of the total population, living in areas where the elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted Average;

About this dataset

Context

The dataset tabulates the High Point population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of High Point across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of High Point was 116,926, a 1.18% increase year-by-year from 2022. Previously, in 2022, High Point population was 115,557, an increase of 0.36% compared to a population of 115,140 in 2021. Over the last 20 plus years, between 2000 and 2023, population of High Point increased by 30,747. In this period, the peak population was 116,926 in the year 2023. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the High Point is shown in this column.
• Year on Year Change: This column displays the change in High Point population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for High Point Population by Year. You can refer the same here

Glacier Peak is a 3,214 m (10,544 ft.) stratovolcano composed mainly of dacite. The volcano is located in the Glacier Peak Wilderness Area, in the Mt. Baker-Snoqualmie National Forest, about 100 km (65 mi) northeast of Seattle and 110 km (70 mi) south of the International Boundary with Canada. Since the continental ice sheets receded from the region approximately 15,000 years ago, Glacier Peak has erupted repeatedly during at least six episodes. Two of these eruptions were among the largest in the Cascades during this time period. This DEM (digital elevation model) of Glacier Peak is the product of high-precision airborne lidar (Light Detection and Ranging) surveys performed during August-November, 2014 and June, 2015 by Quantum Spatial under contract with the USGS. This digital map, totaling approximately 475 square miles, represents the ground surface beneath forest cover and contributes to natural hazard monitoring efforts, the study of regional geology, volcanic landforms, and landscape modification during and after future volcanic eruptions, both at Glacier Peak or elsewhere globally. This release is comprised of a DEM dataset accompanied by a hillshade raster, each divided into 18 tiles. Each tile’s bounding rectangle is identical to the extent of the USGS 7.5 minute topographic quadrangles covering the same area. The names of the DEM tiles are eleven characters long (e.g., dem_xxxxxx). The prefix, "dem", indicates the file is a DEM and the last seven characters correspond to the map reference code of the quadrangle defining the tile's spatial extent. Hillshade tile names are denoted by the prefix "hs", but are otherwise identical to the DEM they are derived from.

The High Resolution National Hydrography Dataset Plus (NHDPlus HR) is an integrated datset of geospatial data layers, including the most current National Hydrography Dataset (NHD), the 10-meter 3D Elevation Program Digital Elevation Model (3DEP DEM), and the National Watershed Boundary Dataset (WBD). The NHDPlus HR combines the NHD, 3DEP DEMs, and WBD to create a stream network with linear referencing, feature naming, "value added attributes" (VAAs), elevation-derived catchments, and other features for hydrologic data analysis. The stream network with linear referencing is a system of data relationships applied to hydrographic systems so that one stream reach "flows" into another and "events" can be tied to and traced along the network. The VAAs provide capabilities for upstream and downstream navigation with linear referencing, analysis, and modeling. The elevation derived catchments are used to associate other landscape attributes, such as land cover, with stream ...

The dataset is related to student data, from an educational research study focusing on student demographics, academic performance, and related factors. Here’s a general description of what each column likely represents:

Sex: The gender of the student (e.g., Male, Female). Age: The age of the student. Name: The name of the student. State: The state where the student resides or where the educational institution is located. Address: Indicates whether the student lives in an urban or rural area. Famsize: Family size category (e.g., LE3 for families with less than or equal to 3 members, GT3 for more than 3). Pstatus: Parental cohabitation status (e.g., 'T' for living together, 'A' for living apart). Medu: Mother's education level (e.g., Graduate, College). Fedu: Father's education level (similar categories to Medu). Mjob: Mother's job type. Fjob: Father's job type. Guardian: The primary guardian of the student. Math_Score: Score obtained by the student in Mathematics. Reading_Score: Score obtained by the student in Reading. Writing_Score: Score obtained by the student in Writing. Attendance_Rate: The percentage rate of the student’s attendance. Suspensions: Number of times the student has been suspended. Expulsions: Number of times the student has been expelled. Teacher_Support: Level of support the student receives from teachers (e.g., Low, Medium, High). Counseling: Indicates whether the student receives counseling services (Yes or No). Social_Worker_Visits: Number of times a social worker has visited the student. Parental_Involvement: The level of parental involvement in the student's academic life (e.g., Low, Medium, High). GPA: The student’s Grade Point Average, a standard measure of academic achievement in schools.

This dataset provides a comprehensive look at various factors that might influence a student's educational outcomes, including demographic factors, academic performance metrics, and support structures both at home and within the educational system. It can be used for statistical analysis to understand and improve student success rates, or for targeted interventions based on specific identified needs.

Since the late 1950s, the USGS has maintained a long-term glacier mass-balance program at three North American glaciers. Measurements began on South Cascade Glacier, WA in 1958, expanding to Gulkana and Wolverine glaciers, AK in 1966, and later Sperry Glacier, MT in 2005. Additional measurements have been made on Lemon Creek Glacier, AK to compliment data collected by the Juneau Icefield Research Program (JIRP; Pelto and others, 2013). Direct field measurements are combined with weather data and imagery analyses to estimate the seasonal and annual mass balance at each glacier in both a conventional and reference surface format (Cogley and others, 2011). High-altitude measurements of meteorological data have been collected since the beginning of the USGS Benchmark Glacier Program adjacent to glaciers in order to support related science. This portion of the data release includes select weather data that has received basic quality control and assurance. Data is released at three different levels of processing, level 0, 1 and 2. Level 0 data contains compiled raw data, before QC procedures are applied, at the original timestep recorded by the instrument. Level 1 data has received a plausible value check, and minimal manual error identification (e.g. errors noted on field visits). Level 2 data has been through more extensive quality control procedures and is provided at both the original instrument timestep as well as aggregated hourly and daily values. However, beyond the procedures detailed in this document, no additional steps have been taken to manually assure quality of the data. Data outside the main record of temperature and precipitation at each site should be considered preliminary, and be utilized with increased scrutiny.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Xtract.io's bank location data delivers a comprehensive geographical snapshot of the United States banking infrastructure. This dataset provides financial institutions, market researchers, and business strategists with granular insights into the distribution of top banks and their ATM networks. By mapping precise locations, organizations can analyze market penetration, identify potential expansion opportunities, and develop targeted marketing strategies. The data supports competitive intelligence, demographic studies, and strategic planning across the financial services landscape.

Point of Interest (POI) data, also known as places data, provides the exact location of buildings, stores, or specific places. It has become essential for businesses to make smarter, geography-driven decisions in today's competitive landscape.

LocationsXYZ, the POI data product from Xtract.io, offers a comprehensive database of 6 million locations across the US, UK, and Canada, spanning 11 diverse industries, including:

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This collection contains 100 soundscape recordings of 10 minutes duration, which have been annotated with 10,296 bounding box labels for 21 different bird species from the Western United States. The data were recorded in 2015 in the southern end of the Sierra Nevada mountain range in California, USA. This collection has been featured as test data in the 2020 BirdCLEF and Kaggle Birdcall Identification competition and can primarily be used for training and evaluation of machine learning algorithms.

Data collection

The recordings were made in Sequoia and Kings Canyon National Parks, two contiguous national parks in the southern Sierra Nevada mountain range in California, USA. The focus of the acoustic study was the high-elevation region of the Parks; specifically, the headwater lake basins above 3,000 km in elevation. The original intent of the study was to monitor seasonal activity of birds and bats at lakes containing trout and lakes without trout, because the cascading impacts of trout on the adjacent terrestrial zone remain poorly understood. Soundscapes were recorded for 24 h continuously at 10 lakes (5 fishless, 5 fish-containing) throughout Sequoia and Kings Canyon National Parks during June-September 2015. Song Meter SM2+ units (Wildlife Acoustics, USA) powered by custom-made solar panels were used to obviate the need to swap batteries, due to the recording locations being extremely difficult to access. Song Meters continuously recorded mono-channel, 16-bits uncompressed WAVE files at 48 kHz sampling rate. For this collection, recordings were resampled at 32 kHz and converted to FLAC.

Sampling and annotation protocol

A total of 100 10-minute segments of audio between July 9 and 12, 2015 from morning hours (06:10-09:10 PDT) from all 10 sites were selected at random. Annotators were asked to box every bird call they could recognize, ignoring those that are too faint or unidentifiable. Every sound that could not be confidently assigned an identity was reviewed with 1-2 other experts in bird identification. To minimize observer bias, all identifying information about the location, date and time of the recordings was hidden from the annotator. Raven Pro software was used to annotate the data. Provided labels contain full bird calls that are boxed in time and frequency. In this collection, we use eBird species codes as labels, following the 2021 eBird taxonomy (Clements list). Unidentifiable calls have been marked with “????” and were added as bounding box labels to the ground truth annotations. Parts of this dataset have previously been used in the 2020 BirdCLEF and Kaggle Birdcall Identification competition.

Files in this collection

Audio recordings can be accessed by downloading and extracting the “soundscape_data.zip” file. Soundscape recording filenames contain a sequential file ID, recording date and timestamp in PDT (UTC-7). As an example, the file “HSN_001_20150708_061805.flac” has sequential ID 001 and was recorded on July 8th 2015 at 06:18:05 PDT. Ground truth annotations are listed in “annotations.csv” where each line specifies the corresponding filename, start and end time in seconds, low and high frequency in Hertz and an eBird species code. These species codes can be assigned to scientific and common name of a species with the “species.csv” file. The approximate recording location with longitude and latitude can be found in the “recording_location.txt” file.

Acknowledgements

Compiling this extensive dataset was a major undertaking, and we are very thankful to the domain experts who helped to collect and manually annotate the data for this collection (individual contributors in alphabetic order): Anna Calderón, Thomas Hahn, Ruoshi Huang, Angelly Tovar

Our USA Point of Interest (POI) data supports various location intelligence projects and facilitates the development of precise mapping and navigation tools, location analysis, address validation, and much more. Gain access to highly accurate, clean, and USA scaled POI data featuring over 24 million verified locations across the United States of America. We have been providing this data to companies worldwide for 30 years.

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This is a 1 arc-second (approximately 30 m) resolution tiled collection of the 3D Elevation Program (3DEP) seamless data products . 3DEP data serve as the elevation layer of The National Map, and provide basic elevation information for Earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for global change research, hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. 3DEP data compose an elevation dataset that consists of seamless layers and a high resolution layer. Each of these layers consists of the best available raster elevation data of the conterminous United States, Alaska, Hawaii, territorial islands, Mexico and Canada. 3DEP data are updated continually as new data become available. Seamless 3DEP data are derived from diverse source data that are processed to a common coordinate system and unit of vertical measure. These data are distributed in geographic coordinates in units of decimal degrees, and in conformance with the North American Datum of 1983 (NAD 83). All elevation values are in meters and, over the conterminous United States, are referenced to the North American Vertical Datum of 1988 (NAVD 88). The vertical reference will vary in other areas. The elevations in these DEMs represent the topographic bare-earth surface. All 3DEP products are public domain.

This dataset includes data over Canada and Mexico as part of an international, interagency collaboration with the Mexico's National Institute of Statistics and Geography (INEGI) and the Natural Resources Canada (NRCAN) Centre for Topographic Information-Sherbrook, Ottawa. For more details on the data provenance of this dataset, visit here and here.

Click here for a broad overview of this dataset

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.

The United States has an average elevation of roughly 2,500 feet (763m) above sea level, however there is a stark contrast in elevations across the country. Highest states Colorado is the highest state in the United States, with an average elevation of 6,800 feet (2,074m) above sea level. The 10 states with the highest average elevation are all in the western region of the country, as this is, by far, the most mountainous region in the country. The largest mountain ranges in the contiguous western states are the Rocky Mountains, Sierra Nevada, and Cascade Range, while the Appalachian Mountains is the longest range in the east - however, the highest point in the U.S. is Denali (Mount McKinley), found in Alaska. Lowest states At just 60 feet above sea level, Delaware is the state with the lowest elevation. Delaware is the second smallest state, behind Rhode Island, and is located on the east coast. Larger states with relatively low elevations are found in the southern region of the country - both Florida and Louisiana have an average elevation of just 100 feet (31m) above sea level, and large sections of these states are extremely vulnerable to flooding and rising sea levels, as well as intermittent tropical storms.

Labor Force Participation Rate in the United States decreased to 62.30 percent in June from 62.40 percent in May of 2025. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.