About this dataset

Context

The dataset tabulates the population of Miles by race. It includes the population of Miles across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Miles across relevant racial categories.

Key observations

The percent distribution of Miles population by race (across all racial categories recognized by the U.S. Census Bureau): 65.50% are white, 1.17% are Black or African American, 2.51% are some other race and 30.83% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Miles
• Population: The population of the racial category (excluding ethnicity) in the Miles is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Miles total population. 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 Miles Population by Race & Ethnicity. You can refer the same here

These data represent the centerline and measured increments at hundredths, tenths and whole miles, along the centerline of the Colorado River beginning at Glen Canyon Dam near Page, Arizona and terminating near the inflow s of Lake Mead in the Grand Canyon region of Arizona, USA. The centerline was digitized using Color Infra-Red (CIR) orthophotography collected in March 2000 as source information and a LiDAR-derived river shoreline representing 8,000 cubic feet per second (CFS)as the defined extent of the river. Every effort was made to follow the main flow of the river while keeping the line approximately equidistant from both shorelines. The centerline feature class has been created to more accurately map locations along the Colorado River downstream of the Glen Canyon Dam. River miles and river kilometers were developed from measurements along this line. The incremental point feature classes were derived from the centerline of the Colorado River datasets. Specifically, the points were generated from nodes extracted from the centerline endpoints of the tenth mile line feature class. The Grand Canyon Monitoring and Research Center (GCMRC) river mileage was cross-checked with commercially available river guides and always fell within one mile of the guides, usually corresponding within a half mile. Additionally, these data were subjected to internal review by GCMRC scientists and commercial boatmen with decades of river travel experience on the Colorado River. River Mile 0 was measured from the USGS concrete gage and cableway at Lees Ferry, Arizona -- as per the Colorado River Compact of 1922 -- with negative river mile numbers used in Glen Canyon and positive river mile numbers downstream in Marble and Grand Canyons. These data were updated in March 2015 using newer ortho-rectified imagery collected in May of 2009 and 2013, both at approximately 8,000 CFS. Due to extended drought conditions that have persisted in the U.S. Southwest, lake levels have dropped dramatically, especially at Lake Mead. A stretch of the Colorado River corridor that was part of Lake Mead in year 2000 has returned to a flowing river once again, and with a different channel that has not previously existed. All changes to the original centerline are downstream of River Mile 260 which is just upstream of Quartermaster Canyon in western Grand Canyon. New river miles and river kilometers were developed from this updated centerline.

About this dataset

Context

The dataset tabulates the population of Miles by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Miles across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 52.81% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Miles is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Miles total population. 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 Miles Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Long Branch population by age. The dataset can be utilized to understand the age distribution and demographics of Long Branch.

Content

The dataset constitues the following three datasets

• Long Branch, PA Age Group Population Dataset: A complete breakdown of Long Branch age demographics from 0 to 85 years, distributed across 18 age groups
• Long Branch, PA Age Cohorts Dataset: Children, Working Adults, and Seniors in Long Branch - Population and Percentage Analysis
• Long Branch, PA Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

Summary:

Estimated stand-off distance between ADS-B equipped aircraft and obstacles. Obstacle information was sourced from the FAA Digital Obstacle File and the FHWA National Bridge Inventory. Aircraft tracks were sourced from processed data curated from the OpenSky Network. Results are presented as histograms organized by aircraft type and distance away from runways.

Description:

For many aviation safety studies, aircraft behavior is represented using encounter models, which are statistical models of how aircraft behave during close encounters. They are used to provide a realistic representation of the range of encounter flight dynamics where an aircraft collision avoidance system would be likely to alert. These models currently and have historically have been limited to interactions between aircraft; they have not represented the specific interactions between obstacles and aircraft equipped transponders. In response, we calculated the standoff distance between obstacles and ADS-B equipped manned aircraft.

For robustness, this assessment considered two different datasets of manned aircraft tracks and two datasets of obstacles. For robustness, MIT LL calculated the standoff distance using two different datasets of aircraft tracks and two datasets of obstacles. This approach aligned with the foundational research used to support the ASTM F3442/F3442M-20 well clear criteria of 2000 feet laterally and 250 feet AGL vertically.

The two datasets of processed tracks of ADS-B equipped aircraft curated from the OpenSky Network. It is likely that rotorcraft were underrepresented in these datasets. There were also no considerations for aircraft equipped only with Mode C or not equipped with any transponders. The first dataset was used to train the v1.3 uncorrelated encounter models and referred to as the “Monday” dataset. The second dataset is referred to as the “aerodrome” dataset and was used to train the v2.0 and v3.x terminal encounter model. The Monday dataset consisted of 104 Mondays across North America. The other dataset was based on observations at least 8 nautical miles within Class B, C, D aerodromes in the United States for the first 14 days of each month from January 2019 through February 2020. Prior to any processing, the datasets required 714 and 847 Gigabytes of storage. For more details on these datasets, please refer to "Correlated Bayesian Model of Aircraft Encounters in the Terminal Area Given a Straight Takeoff or Landing" and “Benchmarking the Processing of Aircraft Tracks with Triples Mode and Self-Scheduling.”

Two different datasets of obstacles were also considered. First was point obstacles defined by the FAA digital obstacle file (DOF) and consisted of point obstacle structures of antenna, lighthouse, meteorological tower (met), monument, sign, silo, spire (steeple), stack (chimney; industrial smokestack), transmission line tower (t-l tower), tank (water; fuel), tramway, utility pole (telephone pole, or pole of similar height, supporting wires), windmill (wind turbine), and windsock. Each obstacle was represented by a cylinder with the height reported by the DOF and a radius based on the report horizontal accuracy. We did not consider the actual width and height of the structure itself. Additionally, we only considered obstacles at least 50 feet tall and marked as verified in the DOF.

The other obstacle dataset, termed as “bridges,” was based on the identified bridges in the FAA DOF and additional information provided by the National Bridge Inventory. Due to the potential size and extent of bridges, it would not be appropriate to model them as point obstacles; however, the FAA DOF only provides a point location and no information about the size of the bridge. In response, we correlated the FAA DOF with the National Bridge Inventory, which provides information about the length of many bridges. Instead of sizing the simulated bridge based on horizontal accuracy, like with the point obstacles, the bridges were represented as circles with a radius of the longest, nearest bridge from the NBI. A circle representation was required because neither the FAA DOF or NBI provided sufficient information about orientation to represent bridges as rectangular cuboid. Similar to the point obstacles, the height of the obstacle was based on the height reported by the FAA DOF. Accordingly, the analysis using the bridge dataset should be viewed as risk averse and conservative. It is possible that a manned aircraft was hundreds of feet away from an obstacle in actuality but the estimated standoff distance could be significantly less. Additionally, all obstacles are represented with a fixed height, the potentially flat and low level entrances of the bridge are assumed to have the same height as the tall bridge towers. The attached figure illustrates an example simulated bridge.

It would had been extremely computational inefficient to calculate the standoff distance for all possible track points. Instead, we define an encounter between an aircraft and obstacle as when an aircraft flying 3069 feet AGL or less comes within 3000 feet laterally of any obstacle in a 60 second time interval. If the criteria were satisfied, then for that 60 second track segment we calculate the standoff distance to all nearby obstacles. Vertical separation was based on the MSL altitude of the track and the maximum MSL height of an obstacle.

For each combination of aircraft track and obstacle datasets, the results were organized seven different ways. Filtering criteria were based on aircraft type and distance away from runways. Runway data was sourced from the FAA runways of the United States, Puerto Rico, and Virgin Islands open dataset. Aircraft type was identified as part of the em-processing-opensky workflow.

• All: No filter, all observations that satisfied encounter conditions
• nearRunway: Aircraft within or at 2 nautical miles of a runway
• awayRunway: Observations more than 2 nautical miles from a runway
• glider: Observations when aircraft type is a glider
• fwme: Observations when aircraft type is a fixed-wing multi-engine
• fwse: Observations when aircraft type is a fixed-wing single engine
• rotorcraft: Observations when aircraft type is a rotorcraft

License

This dataset is licensed under Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International(CC BY-NC-ND 4.0).

This license requires that reusers give credit to the creator. It allows reusers to copy and distribute the material in any medium or format in unadapted form and for noncommercial purposes only. Only noncommercial use of your work is permitted. Noncommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. Exceptions are given for the not for profit standards organizations of ASTM International and RTCA.

MIT is releasing this dataset in good faith to promote open and transparent research of the low altitude airspace. Given the limitations of the dataset and a need for more research, a more restrictive license was warranted. Namely it is based only on only observations of ADS-B equipped aircraft, which not all aircraft in the airspace are required to employ; and observations were source from a crowdsourced network whose surveillance coverage has not been robustly characterized.

As more research is conducted and the low altitude airspace is further characterized or regulated, it is expected that a future version of this dataset may have a more permissive license.

Distribution Statement

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited.

© 2021 Massachusetts Institute of Technology.

Delivered to the U.S. Government with Unlimited Rights, as defined in DFARS Part 252.227-7013 or 7014 (Feb 2014). Notwithstanding any copyright notice, U.S. Government rights in this work are defined by DFARS 252.227-7013 or DFARS 252.227-7014 as detailed above. Use of this work other than as specifically authorized by the U.S. Government may violate any copyrights that exist in this work.

This material is based upon work supported by the Federal Aviation Administration under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Federal Aviation Administration.

This document is derived from work done for the FAA (and possibly others); it is not the direct product of work done for the FAA. The information provided herein may include content supplied by third parties. Although the data and information contained herein has been produced or processed from sources believed to be reliable, the Federal Aviation Administration makes no warranty, expressed or implied, regarding the accuracy, adequacy, completeness, legality, reliability or usefulness of any information, conclusions or recommendations provided herein. Distribution of the information contained herein does not constitute an endorsement or warranty of the data or information provided herein by the Federal Aviation Administration or the U.S. Department of Transportation. Neither the Federal Aviation Administration nor the U.S. Department of

This dataset presents a rich collection of physicochemical parameters from 147 reservoirs distributed across the conterminous U.S. One hundred and eight of the reservoirs were selected using a statistical survey design and can provide unbiased inferences to the condition of all U.S. reservoirs. These data could be of interest to local water management specialists or those assessing the ecological condition of reservoirs at the national scale. These data have been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. This dataset is not publicly accessible because: It is too large. It can be accessed through the following means: https://portal-s.edirepository.org/nis/mapbrowse?scope=edi&identifier=2033&revision=1. Format: This dataset presents water quality and related variables for 147 reservoirs distributed across the U.S. Water quality parameters were measured during the summers of 2016, 2018, and 2020 – 2023. Measurements include nutrient concentration, algae abundance, dissolved oxygen concentration, and water temperature, among many others. Dataset includes links to other national and global scale data sets that provide additional variables.

USHAP (USHighAirPollutants) is one of the series of long-term, full-coverage, high-resolution, and high-quality datasets of ground-level air pollutants for the United States. It is generated from the big data (e.g., ground-based measurements, satellite remote sensing products, atmospheric reanalysis, and model simulations) using artificial intelligence by considering the spatiotemporal heterogeneity of air pollution. This is the big data-derived seamless (spatial coverage = 100%) daily, monthly, and yearly 1 km (i.e., D1K, M1K, and Y1K) ground-level PM2.5 dataset in the United States from 2000 to 2020. Our daily PM2.5 estimates agree well with ground measurements with an average cross-validation coefficient of determination (CV-R2) of 0.82 and normalized root-mean-square error (NRMSE) of 0.40, respectively. All the data will be made public online once our paper is accepted, and if you want to use the USHighPM2.5 dataset for related scientific research, please contact us (Email: weijing_rs@163.com; weijing@umd.edu). Wei, J., Wang, J., Li, Z., Kondragunta, S., Anenberg, S., Wang, Y., Zhang, H., Diner, D., Hand, J., Lyapustin, A., Kahn, R., Colarco, P., da Silva, A., and Ichoku, C. Long-term mortality burden trends attributed to black carbon and PM2.5 from wildfire emissions across the continental USA from 2000 to 2020: a deep learning modelling study. The Lancet Planetary Health, 2023, 7, e963–e975. https://doi.org/10.1016/S2542-5196(23)00235-8 More air quality datasets of different air pollutants can be found at: https://weijing-rs.github.io/product.html

U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 km2 in the Northern Basin and Range Ecoregion to a high of 78,782 km2 in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it is collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format. U.S. Geological Survey scientists, funded by the Climate and Land Use Change Research and Development Program, developed a dataset of 2006 and 2011 land use and land cover (LULC) information for selected 100-km2 sample blocks within 29 EPA Level 3 ecoregions across the conterminous United States. The data was collected for validation of new and existing national scale LULC datasets developed from remotely sensed data sources. The data can also be used with the previously published Land Cover Trends Dataset: 1973-2000 (http:// http://pubs.usgs.gov/ds/844/), to assess land-use/land-cover change in selected ecoregions over a 37-year study period. LULC data for 2006 and 2011 was manually delineated using the same sample block classification procedures as the previous Land Cover Trends project. The methodology is based on a statistical sampling approach, manual classification of land use and land cover, and post-classification comparisons of land cover across different dates. Landsat Thematic Mapper, and Enhanced Thematic Mapper Plus imagery was interpreted using a modified Anderson Level I classification scheme. Landsat data was acquired from the National Land Cover Database (NLCD) collection of images. For the 2006 and 2011 update, ecoregion specific alterations in the sampling density were made to expedite the completion of manual block interpretations. The data collection process started with the 2000 date from the previous assessment and any needed corrections were made before interpreting the next two dates of 2006 and 2011 imagery. The 2000 land cover was copied and any changes seen in the 2006 Landsat images were digitized into a new 2006 land cover image. Similarly, the 2011 land cover image was created after completing the 2006 delineation. Results from analysis of these data include ecoregion based statistical estimates of the amount of LULC change per time period, ranking of the most common types of conversions, rates of change, and percent composition. Overall estimated amount of change per ecoregion from 2001 to 2011 ranged from a low of 370 square km in the Northern Basin and Range Ecoregion to a high of 78,782 square km in the Southeastern Plains Ecoregion. The Southeastern Plains Ecoregion continues to encompass the most intense forest harvesting and regrowth in the country. Forest harvesting and regrowth rates in the southeastern U.S. and Pacific Northwest continued at late 20th century levels. The land use and land cover data collected by this study is ideally suited for training, validation, and regional assessments of land use and land cover change in the U.S. because it’s collected using manual interpretation techniques of Landsat data aided by high resolution photography. The 2001-2011 Land Cover Trends Dataset is provided in an Albers Conical Equal Area projection using the NAD 1983 datum. The sample blocks have a 30-meter resolution and file names follow a specific naming convention that includes the number of the ecoregion containing the block, the block number, and the Landsat image date. The data files are organized by ecoregion, and are available in the ERDAS Imagine (.img) format.

Dataset Card for "wikitext"

  Dataset Summary

The WikiText language modeling dataset is a collection of over 100 million tokens extracted from the set of verified Good and Featured articles on Wikipedia. The dataset is available under the Creative Commons Attribution-ShareAlike License. Compared to the preprocessed version of Penn Treebank (PTB), WikiText-2 is over 2 times larger and WikiText-103 is over 110 times larger. The WikiText dataset also features a far larger… See the full description on the dataset page: https://huggingface.co/datasets/Salesforce/wikitext.

The Northeast U.S. Shelf (NES) Long-Term Ecological Research (LTER) project integrates observations, experiments, and models to understand and predict how planktonic food webs are changing, and how those changes impact the productivity of higher trophic levels. The NES-LTER is co-located with the Northeast U.S. Continental Shelf Large Marine Ecosystem, spanning the Middle Atlantic Bight and Gulf of Maine. Our focal cross-shelf transect extends about 150 km southward from Martha's Vineyard, MA, to just beyond the shelf break.

Other-Long-Term-Assets Time Series for Tanger Factory Outlet Centers Inc. Tanger Inc. (NYSE: SKT) is a leading owner and operator of outlet and open-air retail shopping destinations, with over 44 years of expertise in the retail and outlet shopping industries. Tanger's portfolio of 37 outlet centers and three open-air lifestyle centers includes 16 million square feet well positioned across tourist destinations and vibrant markets in 21 U.S. states and Canada. A publicly traded REIT since 1993, Tanger continues to innovate the retail experience for its shoppers with over 3,000 stores operated by more than 700 different brand name companies. Tanger is furnishing a Form 8-K with the Securities and Exchange Commission ("SEC") that includes a supplemental information package for the quarter ended June 30, 2025.

Total-Long-Term-Debt Time Series for Bloomin Brands Inc. Bloomin' Brands, Inc., through its subsidiaries, owns and operates casual, polished casual, and fine dining restaurants in the United States and internationally. The company operates through U.S. and International Franchise segments. Its restaurant portfolio has four concepts, including Outback Steakhouse, a casual steakhouse restaurant; Carrabba's Italian Grill that offers authentic Italian cuisine; Bonefish Grill; and Fleming's Prime Steakhouse & Wine Bar, a contemporary steakhouse. The company was founded in 1988 and is based in Tampa, Florida.

About this dataset

Context

The dataset tabulates the Groton Long Point population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Groton Long Point. The dataset can be utilized to understand the population distribution of Groton Long Point by age. For example, using this dataset, we can identify the largest age group in Groton Long Point.

Key observations

The largest age group in Groton Long Point, CT was for the group of age 75 to 79 years years with a population of 64 (12.38%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Groton Long Point, CT was the Under 5 years years with a population of 1 (0.19%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Groton Long Point is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Groton Long Point total population. 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 Groton Long Point Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Long Pine by race. It includes the population of Long Pine across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Long Pine across relevant racial categories.

Key observations

The percent distribution of Long Pine population by race (across all racial categories recognized by the U.S. Census Bureau): 96.39% are white, 0.66% are American Indian and Alaska Native, 0.33% are Asian and 2.62% are multiracial.

https://i.neilsberg.com/ch/long-pine-ne-population-by-race.jpeg" alt="Long Pine population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Long Pine
• Population: The population of the racial category (excluding ethnicity) in the Long Pine is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Long Pine total population. 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 Long Pine Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Long Branch, PA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Long Branch did not include 4, 6, or 7-person households. Across the different household sizes in Long Branch the mean income is $93,729, and the standard deviation is $74,979. The coefficient of variation (CV) is 80%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2023, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $29,167. It then further increased to $202,000 for 5-person households, the largest household size for which the bureau reported a median household income.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific household size.

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 Long Branch median household income. You can refer the same here

About this dataset

Context

The dataset tabulates the Long Prairie township population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Long Prairie township. The dataset can be utilized to understand the population distribution of Long Prairie township by age. For example, using this dataset, we can identify the largest age group in Long Prairie township.

Key observations

The largest age group in Long Prairie Township, Minnesota was for the group of age 55 to 59 years years with a population of 126 (11.28%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Long Prairie Township, Minnesota was the 85 years and over years with a population of 19 (1.70%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Long Prairie township is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Long Prairie township total population. 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 Long Prairie township Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the population of Far Hills by race. It includes the population of Far Hills across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Far Hills across relevant racial categories.

Key observations

The percent distribution of Far Hills population by race (across all racial categories recognized by the U.S. Census Bureau): 87.51% are white, 0.70% are Black or African American, 1.40% are Asian, 5.39% are some other race and 5% are multiracial.

https://i.neilsberg.com/ch/far-hills-nj-population-by-race.jpeg" alt="Far Hills population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Far Hills
• Population: The population of the racial category (excluding ethnicity) in the Far Hills is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Far Hills total population. 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 Far Hills Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset tabulates the Long Creek population by age. The dataset can be utilized to understand the age distribution and demographics of Long Creek.

Content

The dataset constitues the following three datasets

• Long Creek, OR Age Group Population Dataset: A complete breakdown of Long Creek age demographics from 0 to 85 years, distributed across 18 age groups
• Long Creek, OR Age Cohorts Dataset: Children, Working Adults, and Seniors in Long Creek - Population and Percentage Analysis
• Long Creek, OR Population Pyramid Dataset: Age Groups, Male and Female Population, and Total Population for Demographics Analysis

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/.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Long Pine. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Long Pine. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Long Pine, the median household income stands at $60,739 for householders within the 25 to 44 years age group, followed by $33,841 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $24,543.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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 Long Pine median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Far Hills. Based on the latest 2018-2022 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Far Hills. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2022

In terms of income distribution across age cohorts, in Far Hills, the median household income stands at $180,267 for householders within the 25 to 44 years age group, followed by $157,489 for the 45 to 64 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $109,766.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific age group

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 Far Hills median household income by age. You can refer the same here