Context

The United States Census is a decennial census mandated by Article I, Section 2 of the United States Constitution, which states: "Representatives and direct Taxes shall be apportioned among the several States ... according to their respective Numbers."
Source: https://en.wikipedia.org/wiki/United_States_Census

Content

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole.

The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys.

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Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:census_bureau_usa

https://cloud.google.com/bigquery/public-data/us-census

Dataset Source: United States Census Bureau

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by Steve Richey from Unsplash.

Inspiration

What are the ten most populous zip codes in the US in the 2010 census?

What are the top 10 zip codes that experienced the greatest change in population between the 2000 and 2010 censuses?

https://cloud.google.com/bigquery/images/census-population-map.png" alt="https://cloud.google.com/bigquery/images/census-population-map.png"> https://cloud.google.com/bigquery/images/census-population-map.png

The Decennial Census provides population estimates and demographic information on residents of the United States.

The Census Summary Files contain detailed tables on responses to the decennial census. Data tables in Summary File 1 provide information on population and housing characteristics, including cross-tabulations of age, sex, households, families, relationship to householder, housing units, detailed race and Hispanic or Latino origin groups, and group quarters for the total population. Summary File 2 contains data tables on population and housing characteristics as reported by housing unit.

Researchers at NYU Langone Health can find guidance for the use and analysis of Census Bureau data on the Population Health Data Hub (listed under "Other Resources"), which is accessible only through the intranet portal with a valid Kerberos ID (KID).

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

The United States census count (also known as the Decennial Census of Population and Housing) is a count of every resident of the US. The census occurs every 10 years and is conducted by the United States Census Bureau. Census data is publicly available through the census website, but much of the data is available in summarized data and graphs. The raw data is often difficult to obtain, is typically divided by region, and it must be processed and combined to provide information about the nation as a whole. The United States census dataset includes nationwide population counts from the 2000 and 2010 censuses. Data is broken out by gender, age and location using zip code tabular areas (ZCTAs) and GEOIDs. ZCTAs are generalized representations of zip codes, and often, though not always, are the same as the zip code for an area. GEOIDs are numeric codes that uniquely identify all administrative, legal, and statistical geographic areas for which the Census Bureau tabulates data. GEOIDs are useful for correlating census data with other censuses and surveys. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Census statistics play a key role in public policy decisions and social science research. However, given the risk of revealing individual information, many statistical agencies are considering disclosure control methods based on differential privacy, which add noise to tabulated data. Unlike other applications of differential privacy, however, census statistics must be postprocessed after noise injection to be usable. We study the impact of the U.S. Census Bureau’s latest disclosure avoidance system (DAS) on a major application of census statistics, the redrawing of electoral districts. We find that the DAS systematically undercounts the population in mixed-race and mixed-partisan precincts, yielding unpredictable racial and partisan biases. While the DAS leads to a likely violation of the “One Person, One Vote” standard as currently interpreted, it does not prevent accurate predictions of an individual’s race and ethnicity. Our findings underscore the difficulty of balancing accuracy and respondent privacy in the Census.

The once-a-decade decennial census was conducted in April 2010 by the U.S. Census Bureau. This count of every resident in the United States was mandated by Article I, Section 2 of the Constitution and all households in the U.S. and individuals living in group quarters were required by law to respond to the 2010 Census questionnaire. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute billions in federal funds to local communities. The questionnaire consisted of a limited number of questions but allowed for the collection of information on the number of people in the household and their relationship to the householder, an individual's age, sex, race and Hispanic ethnicity, the number of housing units and whether those units are owner- or renter-occupied, or vacant. The first wave of results for sub-state geographic areas in New Mexico was released on March 15, 2011, through the Redistricting Data (PL94-171) Summary File. This batch of data covers the state, counties, places (both incorporated and unincorporated communities), tribal lands, school districts, neighborhoods (census tracts and block groups), individual census blocks, and other areas. The Redistricting products provide counts by race and Hispanic ethnicity for the total population and the population 18 years and over, and housing unit counts by occupancy status. The 2010 Census Redistricting Data Summary File can be used to redraw federal, state and local legislative districts under Public Law 94-171. This is an important purpose of the file and, indeed, state officials use the Redistricting Data to realign congressional and state legislative districts in their states, taking into account population shifts since the 2000 Census. More detailed population and housing characteristics will be released in the summer of 2011. The data in these particular RGIS Clearinghouse tables are for all Census Tracts in New Mexico. There are two data tables. One provides total counts by major race groups and by Hispanic ethnicity, while the other provides proportions of the total population for these same groups. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Approximate Monte Carlo (AMC) method seed Privacy Protected Microdata File (PPMF0) and PPMF replicates (PPMF1, PPMF2, ..., PPMF25) are a set of microdata files intended for use in estimating the magnitude of error(s) introduced by the 2020 Decennial Census Disclosure Avoidance System (DAS) into the Redistricting and DHC products. The PPMF0 was created by executing the 2020 DAS TopDown Algorithm (TDA) using the confidential 2010 Census Edited File (CEF) as the initial input; the replicates were then created by executing the 2020 DAS TDA repeatedly with the PPMF0 as its initial input. Inspired by analogy to the use of bootstrap methods in non-private contexts, U.S. Census Bureau (USCB) researchers explored whether simple calculations based on comparing each PPMFi to the PPMF0 could be used to reliably estimate the scale of errors introduced by the 2020 DAS, and generally found this approach worked well.

The PPMF0 and PPMFi files contained here are provided so that external researchers can estimate properties of DAS-introduced error without privileged access to internal USCB-curated data sets; further information on the estimation methodology can be found in Ashmead et. al 2024.

The 2010 DHC AMC seed PPMF0 and PPMF replicates have been cleared for public dissemination by the USCB Disclosure Review Board (CBDRB-FY24-DSEP-0002). The 2010 PPMF0 included in these files was produced using the same parameters and settings as were used to produce the 2010 Demonstration Data Product Suite (2023-04-03) PPMF, but represents an independent execution of the TopDown Algorithm. The PPMF0 and PPMF replicates contain all Person and Units attributes necessary to produce the Redistricting and DHC publications for both the United States and Puerto Rico, and include geographic detail down to the Census Block level. They do not include attributes specific to either the Detailed DHC-A or Detailed DHC-B products; in particular, data on Major Race (e.g., White Alone) is included, but data on Detailed Race (e.g., Cambodian) is not included in the PPMF0 and replicates.

The 2020 AMC replicate files for estimating confidence intervals for the official 2020 Census statistics are available.

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people by contacting over 3.5 million households across the country. The resulting data provides incredibly detailed demographic information across the US aggregated at various geographic levels which helps determine how more than $675 billion in federal and state funding are distributed each year. Businesses use ACS data to inform strategic decision-making. ACS data can be used as a component of market research, provide information about concentrations of potential employees with a specific education or occupation, and which communities could be good places to build offices or facilities. For example, someone scouting a new location for an assisted-living center might look for an area with a large proportion of seniors and a large proportion of people employed in nursing occupations. Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their homes, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. For more information, see the Census Bureau's ACS Information Guide . This public dataset is hosted in Google BigQuery as part of the Google Cloud Public Datasets Program , with Carto providing cleaning and onboarding support. It is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Annual Resident Population Estimates, Estimated Components of Resident Population Change, and Rates of the Components of Resident Population Change; for the United States, States, Metropolitan Statistical Areas, Micropolitan Statistical Areas, Counties, and Puerto Rico: April 1, 2010 to July 1, 2019 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through March. // Note: Total population change includes a residual. This residual represents the change in population that cannot be attributed to any specific demographic component. // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // The Office of Management and Budget's statistical area delineations for metropolitan, micropolitan, and combined statistical areas, as well as metropolitan divisions, are those issued by that agency in September 2018. // Current data on births, deaths, and migration are used to calculate population change since the 2010 Census. An annual time series of estimates is produced, beginning with the census and extending to the vintage year. The vintage year (e.g., Vintage 2019) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the entire estimates series is revised. Additional information, including historical and intercensal estimates, evaluation estimates, demographic analysis, research papers, and methodology is available on website: https://www.census.gov/programs-surveys/popest.html.

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the county level..The American Community Survey (ACS) 5 Year 2016-2020 demographic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include: B01001 - Sex By Age;

B03002 - Hispanic Or Latino Origin By Race; B11001 - Household Type (Including Living Alone); B11005 - Households By Presence Of People Under 18 Years By Household Type; B11006 - Households By Presence Of People 60 Years And Over By Household Type; B16005 - Nativity By Language Spoken At Home By Ability To Speak English For The Population 5 Years And Over; B25010 - Average Household Size Of Occupied Housing Units By Tenure, and; B15001 - Sex by Educational Attainment for the Population 18 Years and Over; To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by County Date of Coverage: 2016-2020

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

This layer shows Population. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2017-2021ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 16, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Palm Beach, FL, 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, Palm Beach did not include 6, or 7-person households. Across the different household sizes in Palm Beach the mean income is $224,707, and the standard deviation is $67,003. The coefficient of variation (CV) is 29.82%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Palm Beach, there were 3 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $121,669. It then further increased to $270,229 for 5-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/palm-beach-fl-median-household-income-by-household-size.jpeg" alt="Palm Beach, FL median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Palm Beach median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Solana Beach, CA, 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, Solana Beach did not include 6, or 7-person households. Across the different household sizes in Solana Beach the mean income is $194,660, and the standard deviation is $67,812. The coefficient of variation (CV) is 34.84%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Solana Beach, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• 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 $110,893. It then further increased to $241,500 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 Solana Beach median household income. You can refer the same here

This layer shows Population. This is shown by state and county boundaries. This service contains the 2018-2022 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the point by Population Density and size of the point by Total Population. The size of the symbol represents the total count of housing units. Population Density was calculated based on the total population and area of land fields, which both came from the U.S. Census Bureau. Formula used for Calculating the Pop Density (B01001_001E/GEO_LAND_AREA_SQ_KM). To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2018-2022ACS Table(s): B01001, B09020Data downloaded from: Census Bureau's API for American Community Survey Date of API call: January 18, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

The downloadable ZIP file contains Esri shapefiles and PDF maps. Contains the information used to determine the location of the new legislative and congressional district boundaries for the state of Idaho as adopted by Idaho's first Commission on Redistricting on March 9, 2002. Contains viewable and printable legislative and congressional district maps, viewable and printable reports, and importable geographic data files.These data were contributed to INSIDE Idaho at the University of Idaho Library in 2001. CD/DVD -ROM availability: https://alliance-primo.hosted.exlibrisgroup.com/permalink/f/m1uotc/CP71156191150001451These files were created by a six-person, by-partisan commission, consisting of six commission members, three democrats and three republicans. This commission was given 90 days to redraw congressional and legislative district boundaries for the state of Idaho. Due to lawsuits, the process was extended. This legislative plan was approved by the commission on March 9th, 2002 and was previously called L97. All digital data originates from TIGER/Line files and 2000 U.S. Census data.Frequently asked questions:How often are Idaho's legislative and congressional districts redrawn? Once every ten years after each census, as required by law, or when directed by the Idaho Supreme Court. The most recent redistricting followed the 2000 census. Redistricting is not expected to occur again in Idaho until after the 2010 census. Who redrew Idaho's legislative and congressional districts? In 2001, for the first time, Idaho used a citizens' commission to redraw its legislative and congressional district boundaries. Before Idaho voters amended the state Constitution in 1994 to create a Redistricting Commission, redistricting was done by a committee of the Idaho Legislature. The committee's new district plans then had to pass the Legislature before becoming law. Who was on the Redistricting Commission? Idaho's first Commission on Redistricting was composed of Co-Chairmen Kristi Sellers of Chubbuck and Tom Stuart of Boise and Stanley. The other four members were Raymond Givens of Coeur d'Alene, Dean Haagenson of Hayden Lake, Karl Shurtliff of Boise, John Hepworth of Buhl (who resigned effective December 4, 2001), and Derlin Taylor of Burley (who was appointed to replace Mr. Hepworth). What are the requirements for being a Redistricting Commissioner? According to Idaho Law, no person may serve on the commission who: 1. Is not a registered voter of the state at the time of selection; or 2. Is or has been within one (1) year a registered lobbyist; or 3. Is or has been within two (2) years prior to selection an elected official or elected legislative district, county or state party officer. (This requirement does not apply to precinct committeepersons.) The individual appointing authorities may consider additional criteria beyond these statutory requirements. Idaho law also prohibits a person who has served on the Redistricting Commission from serving in either house of the legislature for five years following their service on the commission. When did Idaho's first Commission on Redistricting meet? Idaho law allows the Commission only 90 days to conduct its business. The Redistricting Commission was formed on June 5, 2001. Its 90-day time period would expire on September 3, 2001. After holding hearings around the state in June and July, a majority of the Commission voted to adopt new legislative and congressional districts on August 22, 2001. On November 29th, the Idaho Supreme Court ruled the Commission's legislative redistricting plan unconstitutional and directed them to reconvene and adopt an alternative plan. The Commission did so, adopting a new plan on January 8, 2000. The Idaho Supreme Court found the Commission's second legislative map unconstitutional on March 1, 2002 and ordered the Commission to try again. The Commission adopted a third plan on March 9, 2002. The Supreme Court denied numerous challenges to this third map. It then became the basis for the 2002 primary and General elections and is expected to be used until the 2012 elections. What is the basic timetable for Idaho to redraw its legislative and congressional districts?Typically, and according to Idaho law, the Redistricting Commission cannot be formally convened until after Idaho has received the official census counts and not before June 1 of a year ending in one. Idaho's first Commission on redistricting was officially created on June 5, 2001. By law, a Commission then has 90 days (or until September 3, 2001 in the case of Idaho's first Commission) to approve new legislative and congressional district boundaries based on the most recent census figures. If at least four of the six commissioners fail to approve new legislative and congressional district plans before that 90-day time period expires, the Commission will cease to exist. The law is silent as to what happens next. Could you summarize the important dates for Idaho's first Commission on Redistricting one more time please? After January 1, 2001 but before April 1, 2001: As required by federal law, the Census Bureau must deliver to the states the small area population counts upon which redistricting is based. The Census Bureau determines the exact date within this window when Idaho will get its population figures. Idaho's were delivered on March 23, 2001. Why conduct a census anyway? The original and still primary reason for conducting a national census every ten years is to determine how the 435 seats in the United States House of Representatives are to be apportioned among the 50 states. Each state receives its share of the 435 seats in the U.S. House based on the proportion of its population to that of the total U.S. population. For example, the population shifts during the 1990's resulted in the Northeastern states losing population and therefore seats in Congress to the Southern and the Western states. What is reapportionment? Reapportionment is a federal issue that applies only to Congress. It is the process of dividing up the 435 seats in the U.S. House of Representatives among the 50 states based on each state's proportion of the total U.S. population as determined by the most recent census. Apportionment determines the each state's power, as expressed by the size of their congressional delegation, in Congress and, through the electoral college, directly affects the selection of the president (each state's number of votes in the electoral college equals the number of its representatives and senators in Congress). Like all states, Idaho has two U.S. senators. Based on our 1990 population of 1,006,000 people and our 2000 population of 1,293,953, and relative to the populations of the other 49 states, Idaho will have two seats in the U.S. House of Representatives. Even with the state's 28.5% population increase from 1990 to 2000, Idaho will not be getting a third seat in the U.S. House of Representatives. Assuming Idaho keeps growing at the same rate it did through the decade of the 1990's, it will likely be 30 or 40 years (after 3 or 4 more censuses) before Idaho gets a third congressional seat. What is redistricting? Redistricting is the process of redrawing the boundaries of legislative and congressional districts within each state to achieve population equality among all congressional districts and among all legislative districts. The U.S. Constitution requires this be done for all congressional districts after each decennial census. The Idaho Constitution also requires that this be done for all legislative districts after each census. The democratic principle behind redistricting is "one person, one vote." Requiring that districts be of equal population ensures that every elected state legislator or U.S. congressman represents very close to the same number of people in that state, therefore, each citizen's vote will carry the same weight. How are reapportionment and redistricting related to the census? The original and still primary reason for conducting a census every ten years is to apportion the (now) 435 seats in the U.S. House of Representatives among the several states. The census records population changes and is the legally recognized basis for redrawing electoral districts of equal population. Why is redistricting so important? In a democracy, it is important for all citizens to have equal representation. The political parties also see redistricting as an opportunity to draw districts that favor electing their members and, conversely, that are unfavorable for electing their political opposition. (It's for this reason that redistricting has been described as "the purest form of political bloodsport.") What is PL 94-171? Public Law (PL) 94-171 (Title 13, United States Code) was enacted by Congress in 1975. It was intended to provide state legislatures with small-area census population totals for use in redistricting. The law's origins lie with the "one person, one vote" court decisions in the 1960's. State legislatures needed to reconcile Census Bureau's small geographic area boundaries with voting tabulation districts (precincts) boundaries to create legislative districts with balanced populations. The Census Bureau worked with state legislatures and others to meet this need beginning with the 1980 census. The resulting Public Law 94-171 allows states to work voluntarily with the Census Bureau to match voting district boundaries with small-area census boundaries. With this done, the Bureau can report to those participating states the census population totals broken down by major race group and Hispanic origin for the total population and for persons aged 18 years and older for each census subdivision. Idaho participated in the Bureau's Census 2000 Redistricting Data Program and, where counties used visible features to delineate precinct boundaries, matched those boundaries with census reporting areas. In those instances where counties did not use visible features to

About this dataset

Context

The dataset presents median household incomes for various household sizes in Norton, Massachusetts, 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, all of the household sizes were found in Norton town. Across the different household sizes in Norton town the mean income is $163,137, and the standard deviation is $69,964. The coefficient of variation (CV) is 42.89%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Norton town, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $54,028. It then further increased to $270,229 for 7-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/norton-ma-median-household-income-by-household-size.jpeg" alt="Norton, Massachusetts median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Norton town median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Hastings-On-Hudson, NY, 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, Hastings-On-Hudson did not include 6, or 7-person households. Across the different household sizes in Hastings-On-Hudson the mean income is $197,105, and the standard deviation is $68,834. The coefficient of variation (CV) is 34.92%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Hastings-On-Hudson, there were 2 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• 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 $82,606. It then further increased to $250,001 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 Hastings-On-Hudson median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Seal Beach, CA, 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, Seal Beach did not include 7-person households. Across the different household sizes in Seal Beach the mean income is $171,560, and the standard deviation is $77,049. The coefficient of variation (CV) is 44.91%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Seal Beach, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• 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 $46,088. It then further increased to $250,001 for 6-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 Seal Beach median household income. You can refer the same here

About this dataset

Context

The dataset presents median household incomes for various household sizes in Aumsville, OR, 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, Aumsville did not include 7-person households. Across the different household sizes in Aumsville the mean income is $116,645, and the standard deviation is $77,120. The coefficient of variation (CV) is 66.12%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households. Please note that the U.S. Census Bureau uses $250,001 as a JAM value to report incomes of $250,000 or more. In the case of Aumsville, there were 1 household sizes where the JAM values were used. Thus, the numbers for the mean and standard deviation may not be entirely accurate and have a higher possibility of errors. However, to obtain an approximate estimate, we have used a value of $250,001 as the income for calculations, as reported in the datasets by the U.S. Census Bureau.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $70,336. It then further increased to $270,229 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/aumsville-or-median-household-income-by-household-size.jpeg" alt="Aumsville, OR median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 2022 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 Aumsville median household income. You can refer the same here