These ESRI shape files are of National Park Service tract and boundary data that was created by the Land Resources Division. Tracts are numbered and created by the regional cartographic staff at the Land Resources Program Centers and are associated to the Land Status Maps. This data should be used to display properties that NPS owns and properties that NPS may have some type of interest such as scenic easements or right of ways.

This dataset provides information about the number of properties, residents, and average property values for White House Road cross streets in Riva, MD.

Traffic analytics, rankings, and competitive metrics for whitehouse.gov as of February 2026

Content

Inside this dataset you'll find the salaries of over 350 Trump officials from the year 2017.

Acknowledgements

This dataset comes from https://data.world/carlvlewis/white-house-salaries-jan-jun-30-17.

This dataset provides information about the number of properties, residents, and average property values for White House Road cross streets in Moneta, VA.

About this dataset

Context

The dataset tabulates the data for the White House, TN population pyramid, which represents the White House population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for White House, TN, is 31.5.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for White House, TN, is 19.2.
• Total dependency ratio for White House, TN is 50.7.
• Potential support ratio, which is the number of youth (working age population) per elderly, for White House, TN is 5.2.

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 for the White House population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the White House for the selected age group is shown in the following column.
• Population (Female): The female population in the White House for the selected age group is shown in the following column.
• Total Population: The total population of the White House for the selected age group is shown in the following column.

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 White House Population by Age. You can refer the same here

Procurement intelligence dataset for City of White House including contracts, vendors, RFPs, and meeting signals.

This dataset provides information about the number of properties, residents, and average property values for White House Road cross streets in Upper Marlboro, MD.

Why?

Inspired by an interest in scraping and organizing data released by the federal government, this is an easy-to-navigate CSV of all of the documents released by the Obama White House, now found on obamawhitehouse.archives.gov.

This includes the full content of each of the documents released (wherever possible), along with link references to each document.

Further, each of them is broken down by document type as well.

Comprehensive dataset containing 31 verified White House locations in United States with complete contact information, ratings, reviews, and location data.

About this dataset

Context

The dataset tabulates the White House population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of White House. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 8,639 (62.26% of the total population). 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 cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the White House population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in White House is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the White House is shown in the following column.

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 White House Population by Age. You can refer the same here

Comprehensive dataset tracking The White House's posting patterns by hour, day, and week

This dataset provides information about the number of properties, residents, and average property values for White House Lane cross streets in Center Cross, VA.

About this dataset

Context

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

Key observations

The percent distribution of White House population by race (across all racial categories recognized by the U.S. Census Bureau): 87.14% are white, 2.56% are Black or African American, 0.90% are Asian, 1.42% are some other race and 7.99% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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 White House
• Population: The population of the racial category (excluding ethnicity) in the White House is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of White House 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 White House Population by Race & Ethnicity. You can refer the same here

Comprehensive dataset containing 10 verified White House locations in Bulgaria with complete contact information, ratings, reviews, and location data.

The President of the United States manages the operations of the Executive branch of Government through Executive orders. After the President signs an Executive order, the White House sends it to the Office of the Federal Register (OFR). The OFR numbers each order consecutively as part of a series, and publishes it in the daily Federal Register shortly after receipt.

This dataset contains the predicted prices of the asset White House over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Contaminant testing results for White House, TN. 83 contaminants tested across 1 water system, with 2 found above health guidelines.

This dataset contains the predicted prices of the asset White House Tech Support over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Data Overview

Data comes from Office of Management and Budget, President’s Budget from white house official website on https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/budget/fy2018/hist05z2.xls

It consists of useful information about BUDGET AUTHORITY BY AGENCY in the range 1976–2022

Note: Data starting 2017, the value is estimated value

Acknowledge

I would like to clarify that I'm just making the dataset widely open to Kaggle community without any intention to upset certain group or organization. This dataset is available here

License

Public Domain Dedication and License (PDDL)

Ideas for using this dataset

• Time series analysis to estimate the total budget received in the future
• Making various visualization for the data