Comprehensive dataset containing 28 verified High Peak locations in United States with complete contact information, ratings, reviews, and location data.

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 (NAD83). All bare earth elevation values are in meters and are referenced to the North American Vertical Datum of 1988 (NAVD88). Each tile is distributed in the UTM Zone in which it lies. If a tile crosses two UTM zones, it is delivered in both zones. The one-meter DEM is the highest resolution standard DEM offered in the 3DEP product suite. Other 3DEP products are nationally seamless DEMs in resolutions of 1/3, 1, and 2 arc seconds. These seamless DEMs were referred to as the National Elevation Dataset (NED) from about 2000 through 2015 at which time they became the seamless DEM layers under the 3DEP program and the NED name and system were retired. Other 3DEP products include five-meter DEMs in Alaska as well as various source datasets including the lidar point cloud and interferometric synthetic aperture radar (Ifsar) digital surface models and intensity images. All 3DEP products are public domain.

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.

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

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;

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).

A detailed airborne gravity gradiometry, magnetic, and radiometric survey of Mountain Pass, California was flown by CGG Canada Services Ltd. (CGG). The high-resolution helicopter survey was flown at a flight-line spacing of 100 and 200 m, a flight-line azimuth of 70 degrees, a nominal flight-line elevation above ground of 70 m, and consists of about 1,814 line-kilometers. Tie lines were spaced at a 1-km interval with a flight-line azimuth of 160 degrees. Data were collected using a HeliFALCON airborne gravity gradiometry system, Scintrex CS-3 cesium magnetometer, Radiation Solutions RS-500 spectrometer, and Riegl LMS-Q1401-80n laser scanner and processed by CGG. Gravity gradiometry data include corrections for residual aircraft motion, self gradient, terrain corrections, and tie-line and micro-levelling. Magnetic data were corrected by the contractor for diurnal variations of the Earth’s magnetic field, tie-line leveled, micro-leveled, and an International Geomagnetic Reference Field of the Earth was removed. Radiometric data include corrections for aircraft and cosmic background radiation, radon background, Compton scattering effects, and variations in altitude. Data are provided in ASCII (.csv) and Geosoft database (.gdb) format, database channels and descriptions are listed in the survey report, and grids of gravity and hillshade are in ASCII Grid eXchange Format (.gxf). Maps and grids of magnetic and radiometric data were released by Ponce and Denton (2018a-d). References: Ponce, D.A., and Denton, K.M., 2018a, Aeromagnetic map of Mountain Pass and vicinity, California and Nevada: U.S. Geological Survey Scientific Investigations Map 3412-B, 6 p., 1 pl., scale 1:62,500, https://doi.org/10.3133/sim3412B. Ponce, D.A., and Denton, K.M., 2018b, High-resolution aeromagnetic survey of Mountain Pass, California: U.S. Geological Survey data release, https://doi.org/doi:10.5066/P92XVOOF. Ponce, D.A., and Denton, K.M., 2018c, Airborne radiometric maps of Mountain Pass, California: U.S. Geological Survey Scientific Investigations Map 3412-C, 6 p., 1 pl., scale 1:62,500, https://doi.org/10.3133/sim3412C. Ponce, D.A., and Denton, K.M., 2018d, High-resolution airborne radiometric survey of Mountain Pass, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9ENLS6D.

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 ...

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 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.

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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• Promote your products and services using geotargeting strategies.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas: 1. Gaining a Competitive Edge: Utilize point of interest (POI) data to analyze competitors, identify high-opportunity areas, and attract more customers. 2. Enhancing Customer Journeys: Leverage location intelligence to provide personalized, real-time recommendations that boost customer engagement. 3. Optimizing Store Expansion: Select the most profitable locations by analyzing foot traffic, demographics, and competitor insights. 4. Streamlining Deliveries: Improve fulfillment accuracy through address validation, reducing failed shipments and increasing customer satisfaction. 5. Driving Smarter Campaigns: Use geospatial insights to effectively target the right audiences, enhance outreach, and maximize campaign impact.

Unemployment Rate in the United States increased to 4.30 percent in August from 4.20 percent in July 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.

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.

Comprehensive dataset containing 6 verified High Point locations in Tennessee, United States with complete contact information, ratings, reviews, and location data.

Polygon shapefile showing the footprint boundaries, source agency origins, and resolutions of compiled bathymetric digital elevation models (DEMs) used to construct a continuous, high-resolution DEM of the southern portion of San Francisco Bay.

The High Accuracy Elevation Data Project collected elevation data (meters) on a 400 meter topographic grid with a vertical accuracy of +/- 15 centimeters to define the topography in South Florida. The data are referenced to the horizontal datum North American Datum 1983 (NAD 83) and the vertical datum North American Vertical Datum 1988 (NAVD 88). The High Accuracy Elevation Data Project began with a pilot study in FY 1995 to determine if the then state-of-the-art GPS technology could be used to perform a topographic survey that would meet the vertical accuracy requirements of the hydrologic modeling community. The initial testing platform was from a truck and met the accuracy requirements. Data were collected in areas near Homestead, Florida. The data are available for the areas shown on the USGS High Accuracy Elevation Data graphic at http://sofia.usgs.gov/exchange/desmond/desmondelev.html.

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), is producing detailed geologic maps of the coastal sea floor. Imagery, originally collected by NOAA for charting purposes, provides a fundamental framework for research and management activities along this part of the Massachusetts coastline, shows the composition and terrain of the seabed, and provides information on sediment transport and benthic habitat. Interpretive data layers were derived from the combined single-beam and multibeam echo-sounder data and sidescan-sonar data collected in the vicinity of Edgartown Harbor, Massachusetts. During August 2008 seismic-reflection profiles (Boomer and Chirp) were acquired, and during September 2008 bottom photographs and surficial sediment data were acquired as part of two ground-truth reconnaissance surveys.

The U.S. Geological Survey (USGS), in cooperation with the National Oceanic and Atmospheric Administration (NOAA) and the Massachusetts Office of Coastal Zone Management (MA CZM), is producing detailed geologic maps of the coastal sea floor. Imagery, originally collected by NOAA for charting purposes, provides a fundamental framework for research and management activities along this part of the Massachusetts coastline, shows the composition and terrain of the seabed, and provides information on sediment transport and benthic habitat. Interpretive data layers were derived from the combined single-beam and multibeam echo-sounder data and sidescan-sonar data collected in the vicinity of Edgartown Harbor, Massachusetts. During August 2008 seismic-reflection profiles (Boomer and Chirp) were acquired, and during September 2008 bottom photographs and surficial sediment data were acquired as part of two ground-truth reconnaissance surveys.