IRS US Income Data by Zip Code

Number of Returns, Adjusted Gross Income, Total Income, and Taxable Income

By Jon Loyens [source]

About this dataset

This dataset provides a unique insight into the US income patterns in 2013, by zip code. With this data, you can explore how taxes and adjusted gross income (AGI) vary according to geographic area. The data includes total and average incomes reported, number of returns filed in each ZIP code and taxable incomes reported. This dataset is ideal for studying how economic trends have shifted geographically over time or examining regional economic disparities within the US. In addition, this dataset has been cleansed from data removed from items such as ZIP codes with fewer than 100 returns or those identified as a single building or nonresidential ZIP codes that were categorized as “other” (99999) by the IRS. Finally, dollar amounts for all variables are in thousands for better accuracy

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Research Ideas

• Using this dataset to identify potential locations for commercial developments by maping taxable incomes, total income amounts, and average incomes in different zip codes.
• Comparing the number of returns with total income, taxes payable, and income variance between different zip codes to gain insights into areas with higher financial prosperity or disparities between zip codes due to wider economic trends.
• Analyzing average adjusted gross incomes on a state-by-state basis to identify states where high net worth citizens or individuals earning high wages live in order to target marketing campaigns or develop high-end service offerings

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: IRSIncomeByZipCode.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abbreviation for the state in which the zip code is located. (String) | | ZIPCODE | The five-digit US zip code. (Integer) | | Number of returns | The total number of tax returns filed in the zip code. (Integer) | | Adjusted gross income (AGI) | The total amount of adjusted gross income reported in the zip code. (Integer) | | Avg AGI | The average amount of adjusted gross income reported in the zip code. (Integer) | | Number of returns with total income | The total number of returns with total income reported in the zip code. (Integer) | | Total income amount | The total amount of income reported in the zip code. (Integer) | | Avg total income | The average amount of total income reported in the zip code. (Integer) | | Number of returns with taxable income | The total number of returns with taxable income reported in the zip code. (Integer) | | Taxable income amount | The total amount of taxable income reported in the zip code. (Integer) | | Avg taxable income | The average amount of taxable income reported in the zip code. (Integer) |

File: IRSIncomeByZipCode_NoStateTotalsNoSmallZips.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abb...

Income and Urban vs. Rural Population Dataset

Overview

This dataset provides insights into the population distribution and income levels across counties in the United States, with a classification of counties as either "Urban" or "Rural." The data was sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).

Methodology

1. Data Source:

   • API Endpoint: U.S. Census Bureau ACS 2023 API
   • Variables:
     • B01003_001E: Total population.
     • B19013_001E: Median household income.

2. Processing:

   • Counties were classified as "Urban" if their population was above the median population; otherwise, they were classified as "Rural."
   • FIPS codes were generated by concatenating State and County FIPS codes.

3. Columns:

   • County: County name.
   • State: State name.
   • FIPS: Combined state and county FIPS code.
   • State FIPS Code: State's Federal Information Processing Standard code.
   • County FIPS Code: County's FIPS code.
   • Total Population: Total population of the county.
   • Median Household Income: Median household income for the county.
   • Urban-Rural: Classification based on population (Urban or Rural).

Usage

This dataset can be used for: - Urban vs. rural demographic and economic analysis. - Income distribution studies. - Data visualization and mapping using FIPS codes.

License

This dataset is provided under the public domain. Proper attribution to the U.S. Census Bureau is appreciated.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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 roughly 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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Adjusted Income and Poverty in the US

Changes in People and Health Insurance

By U.S. Census Bureau [source]

About this dataset

The U.S. Bureau of the Census' Current Population Survey, Annual Social and Economic Supplements, presents an insightful look into American society at any given time period. Through it's annual data, one can understand the makeup of a nation across a multitude of parameters--including income level distribution measures, poverty status characteristics and health insurance coverage broken down by age, race/ethnicity and gender.

This chart series is like a snapshot into America's past--allowing us to monitor both current progress made in regards to economic stability while also reflecting on the growth (or lack thereof) achieved over different decades in terms of racial discrepancies in poverty levels as well as an individual's ability present etc to maintain financial health. The series looks at data collected from 1959-2015; providing information on number/percentage all noninstitutionalized population (15+ years old) who are below or above poverty thresholds as well as median earnings for male/female earners adjusted for real inflation values (based on current dollars). Insights such as these enable us to gain key information about how economic disparities have fared during each year throughout this half century time span and how policy changes have impacted the overall wellbeing on a national level since then

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How to use the dataset

Introduction

This dataset contains information on the equivalence-adjusted income and poverty in the US from 1967 to 2015. It includes information on the population without health insurance coverage by state, total workers and full-time, year-round workers by sex and female-to-male earnings ratio, selected measures of equivalence-adjusted income dispersion, people in poverty by selected characteristics, and measures of income inequality. This guide will explain how to use this dataset effectively for analysis.

Data Overview

The datasets contain both summary statistics and detailed breakdowns for different categories throughout the years 1967 to 2015. In Table A1 you can find data on population without health insurance coverage by state during that time period. Table A4 contains total numbers of workers as well as real median earning details organized by sex and male/female earning ratios over time period in question. The tables A3 through 5 include more specific details related to measurements of Equivalence Adjusted Income Dispersion such as Gini Coefficient values.. Both table 2 & 3 provides detail breakdowns relating to Income distribution measurements between 2014 & 2015 along with other related statistical figures regarding individuals below poverty line during this time period based upon age , race , Hispanic Origin factors.

Data Cleaning/Preparation Specifics

This dataset follows a similar notation used throughout each table so it shouldn't be difficult understand what is being represented .However representing individual components like Gini Coefficient (TableA3) or Female ratio Vs Male earnings remains abstract in comparison especially when attempting visualization techniques (Charting). In order for users not familiar with certain terms like “Equivalence -Adjusted Income Dispersion” it would need explaining thoroughly or these terms should at least be highlighted & avoid confusing readers . Level out Missing Data that is within range statistically makes sense according “Census Technical Docs” . For example missing value data pertaining Individual Poverty estimates have based upon qualification requirements where numbers are rounded up after exchange calculations ( See official Raw Data column Notes available under Sources ).

Visualization Strategies

For effective visualization there needs be understanding between what counts supplied are actually representing For example: Column such as Difference Between Female & Male Earnings shown TableA4 helps gauge pay gap but difference between % Measures significantly important when charting any changes overtime diagrams or identifying movements visually from various bar /line graphs dealing this type data set . Other numerical aspects such Gender Ratio

Research Ideas

• Tracking changes in poverty levels over time by state and ethnicity
• Examining the impact of government programs like the EITC and CTC on pov...

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Data is collected because of public interest in how the City’s budget is being spent on salary and overtime pay for all municipal employees. Data is input into the City's Personnel Management System (“PMS”) by the respective user Agencies. Each record represents the following statistics for every city employee: Agency, Last Name, First Name, Middle Initial, Agency Start Date, Work Location Borough, Job Title Description, Leave Status as of the close of the FY (June 30th), Base Salary, Pay Basis, Regular Hours Paid, Regular Gross Paid, Overtime Hours worked, Total Overtime Paid, and Total Other Compensation (i.e. lump sum and/or retro payments). This data can be used to analyze how the City's financial resources are allocated and how much of the City's budget is being devoted to overtime. The reader of this data should be aware that increments of salary increases received over the course of any one fiscal year will not be reflected. All that is captured, is the employee's final base and gross salary at the end of the fiscal year. In very limited cases, a check replacement and subsequent refund may reflect both the original check as well as the re-issued check in employee pay totals.

NOTE 1: To further improve the visibility into the number of employee OT hours worked, beginning with the FY 2023 report, an updated methodology will be used which will eliminate redundant reporting of OT hours in some specific instances. In the previous calculation, hours associated with both overtime pay as well as an accompanying overtime “companion code” pay were included in the employee total even though they represented pay for the same period of time. With the updated methodology, the dollars shown on the Open Data site will continue to be inclusive of both types of overtime, but the OT hours will now reflect a singular block of time, which will result in a more representative total of employee OT hours worked. The updated methodology will primarily impact the OT hours associated with City employees in uniformed civil service titles. The updated methodology will be applied to the Open Data posting for Fiscal Year 2023 and cannot be applied to prior postings and, as a result, the reader of this data should not compare OT hours prior to the 2023 report against OT hours published starting Fiscal Year 2023. The reader of this data may continue to compare OT dollars across all published Fiscal Years on Open Data.
NOTE 2: As a part of FISA-OPA’s routine process for reviewing and releasing Citywide Payroll Data, data for some agencies (specifically NYC Police Department (NYPD) and the District Attorneys’ Offices (Manhattan, Kings, Queens, Richmond, Bronx, and Special Narcotics)) have been redacted since they are exempt from disclosure pursuant to the Freedom of Information Law, POL § 87(2)(f), on the ground that disclosure of the information could endanger the life and safety of the public servants listed thereon. They are further exempt from disclosure pursuant to POL § 87(2)(e)(iii), on the ground that any release of the information would identify confidential sources or disclose confidential information relating to a criminal investigation, and POL § 87(2)(e)(iv), on the ground that disclosure would reveal non-routine criminal investigative techniques or procedures. Some of these redactions will appear as XXX in the name columns.

Industry By Median Earnings For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over Report based on US Census and American Community Survey Data.

Total-Other-Income-Expense-Net Time Series for American Healthcare REIT, Inc.. American Healthcare REIT, Inc., a Maryland-based self-managed REIT, owns and operates a diversified portfolio of clinical healthcare real estate across the U.S., U.K., and the Isle of Man. Its focus includes senior housing, skilled nursing facilities (SNFs), outpatient medical (OM) buildings, and other healthcare-related properties. The company utilizes a fully integrated management platform and operates senior housing under the RIDEA structure. In addition to owning and operating properties, it has originated and acquired secured loans and may pursue other real estate-related investments opportunistically. The REIT seeks income-generating assets and selectively develops healthcare properties. It has elected to be taxed as a REIT under the U.S. Internal Revenue Code and intends to maintain compliance with REIT requirements.

Discounts for Internet service through the Affordable Connectivity Program (ACP) ended June 1, 2024 due to lack of additional funding. Whether the program will receive additional funding in the future is uncertain. Please see ACP program information from the FCC for more details.The Affordable Connectivity Program (ACP) households data set summarizes household enrollments and subscriptions by month and zip code for beneficiary households located in Detroit zip codes. The Affordable Connectivity Program (ACP) is a U.S. government program to help low-income households pay for Internet services and connected devices. Households that participate in ACP receive discounts on qualifying broadband Internet services of up to $30 per month and can also receive a one-time discount of up to $100 to purchase a laptop, desktop computer, or tablet. Households can qualify for ACP based on participation in Lifeline or other service provider programs for low-income households, income at or below 200% of the federal poverty guidelines, participation in other Lifeline-qualifying programs such as SNAP or Medicaid, or participation in free and reduced-price school lunch and breakfast programs. Additionally, service providers can ask the FCC to approve an alternative verification process and use that approved process to check consumer eligibility. ACP program discounts first became available to eligible enrolled households on January 1, 2022. The ACP claims process is built on the Lifeline Claims System and this data set is derived from snapshots of all subscribers entered in the National Lifeline Accountability Database (NLAD) as of the first of each month. The ACP was created under the Infrastructure Investment and Jobs Act, also known as the Bipartisan Infrastructure Law, and is administered by the independent not-for-profit Universal Service Access Co. under the direction of the Federal Communications Commission (FCC). Eligible beneficiaries who participated in the Emergency Broadband Benefit (EBB) program that was funded by the Coronavirus Aid, Relief, and Economic Security (CARES) Act, were transitioned to ACP between January 1 and March 1, 2022. EBB was ACP's predecessor program and ran from May 12, 2021 until it was phased out on February 28, 2022. Due to the granularity of available data, households located in communities adjacent to Detroit that share a zip code such as Hamtramck and Highland Park are included in this data set.Fieldsprogram - Associated program for the data (ACP or EBB)data_month - Data month is associated with the subscriber snapshot for each claim month. If data month is listed as '5/1/2022', then the subscriber snapshot was captured on June 1, and the data represents the number of households in ACP as of June 1. This is the universe of subscribers that providers can claim for the May 2022 data month.zipcode - Zip code where the enrolled household is located.net_new_enrollments_alternative_verification_process - Difference between the current month Total Subscribers who qualified using an alternative verification process and prior month Total Subscribers who qualified using an alternative verification process.net_new_enrollments_verified_by_school - Difference between the current month Total Subscribers who qualified using school lunch program verification and prior month Total Subscribers who qualified using school lunch program verification.net_new_enrollments_lifeline - Difference between the current month Total Subscribers who qualified using the Lifeline program and prior month Total Subscribers who qualified using the Lifeline program.net_new_enrollments_national_verifier_application - Difference between the current month Total Subscribers who qualified using a National Verifier application and prior month Total Subscribers who qualified using a National Verifier application.net_new_enrollments_total - Difference between the total number of subscribers in the current and prior months. Calculated based on the sum of net new monthly enrollments verified by the school, lifeline, alternative verification process, and national verifier application programs.total_alternative_verification_process - Number of households in the ACP on the first of the month snapshot whose eligibility was determined via an FCC-approved alternative verification process. total_verified_by_school - Number of households in the ACP on the first of the month snapshot whose eligibility was verified based on participation in a school lunch program.total_lifeline - Number of households in the ACP on the first of the month snapshot whose eligibility was determined based on participation in Lifeline, a federal program that lowers the monthly cost of phone or Internet services.total_national_verifier_application - Number of households in the ACP on of the first of the month snapshot whose eligibility was determined via the National Eligibility Verifier (National Verifier) system.total_subscribers - Number of total households participating in ACP on the first of the month snapshot. If, for example, there were 100 subscribers enrolled as of the June 1, 2022 snapshot, then Total Subscribers for the 05/01/2022 (May 2022) data month would be 100.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

A broad and generalized selection of 2012-2016 US Census Bureau 2016 5-year American Community Survey race, ethnicity and citizenship data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of the household income, median household income by race and by age group, Social Security income, the GINI Index, per capita income, median family income, and median household earnings by age, and by education level, in New Mexico. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. NOTE: A '-666666666' entry indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.

US Census American Community Survey (ACS) 2018, 5-year estimates of the key economic characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2018 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Class Of Worker By Median Earnings For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over Report based on US Census and American Community Survey Data.

US Census American Community Survey (ACS) 2020, 5-year estimates of the key economic characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2020 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project GitHub page (https://github.com/ktalexan/OCACS-Geodemographics).

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2012-2016, to show household income numbers and ranges, by census tract in the Atlanta region. The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2012-2016). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, click here.Attributes: GEOID10 = 2010 Census tract identifier (combination of Federal Information Processing Series (FIPS) codes for state, county, and census tract) County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county) Area_Name = 2010 Census tract name- - - - - -Total_Population = # Total Population, 2016 Total_Population_MOE_2016 = # Total population (Margin of Error), 2016- - - - - -Total_households = Total households, 2016 Total_households_MOE = Total households (Margin of Error), 2016 HH_income_less_35k = # Household income less than $35,000, 2016 HH_income_less_35k_MOE = # Household income less than $35,000 (Margin of Error), 2016 Pct_HH_income_less_35k = % Household income less than $35,000, 2016 Pct_HH_income_less_35k_MOE = % Household income less than $35,000 (Margin of Error), 2016 HH_income_35k_75k = # Household income $35,000 to $74,999, 2016 HH_income_35k_75k_MOE = # Household income $35,000 to $74,999 (Margin of Error), 2016 Pct_HH_income_35k_75k = % Household income $35,000 to $74,999, 2016 Pct_HH_income_35k_75k_MOE = % Household income $35,000 to $74,999 (Margin of Error), 2016 HH_income_75k_200k = # Household income $75,000 to $199,999, 2016 HH_income_75k_200k_MOE = # Household income $75,000 to $199,999 (Margin of Error), 2016 Pct_HH_income_75k_200k = % Household income $75,000 to $199,999, 2016 Pct_HH_income_75k_200k_MOE = % Household income $75,000 to $199,999 (Margin of Error), 2016 HH_income_200k_more = # Household income $200,000 or more, 2016 HH_income_200k_more_MOE = # Household income $200,000 or more (Margin of Error), 2016 Pct_HH_income_200k_more = % Household income $200,000 or more, 2016 Pct_HH_income_200k_more_MOE = % Household income $200,000 or more (Margin of Error), 2016 Median_HH_income = Median household income, 2016 Median_HH_income_MOE = Median household income (Margin of Error), 2016 Median_HH_Income_2000 = Median Household Income, 2000 Chg_Median_HH_Income_2000_16 = Change, Median household income, 2000-2016 Average_Per_capita_Income = Per Capita Income (dollars), 2016 Average_Per_capita_Income_MOE = Per Capita Income (dollars) (Margin of Error), 2016- - - - - -Aggregate_Household_Income = Aggregate Household Income, 2016 Aggregate_Household_Income_MOE = Aggregate Household Income (Margin of Error), 2016 Mean_Household_Income = Mean Household Income, 2016 Mean_Household_Income_MOE = Mean Household Income (Margin of Error), 2016- - - - - -Planning_Region = Planning region designation for ARC purposes AcresLand = Land area within the tract (in acres) AcresWater = Water area within the tract (in acres) AcresTotal = Total area within the tract (in acres) SqMi_Land = Land area within the tract (in square miles) SqMi_Water = Water area within the tract (in square miles) SqMi_Total = Total area within the tract (in square miles) TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively. CountyName = County Name last_edited_date = Last date the feature was edited by ARC Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2012-2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

US Census American Community Survey (ACS) 2017, 5-year estimates of the key economic characteristics of ZIP Code Tabulation Areas geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2017 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Sex By Class Of Worker And Median Earnings For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over Report based on US Census and American Community Survey Data.

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.