The American Community Survey (ACS) 5 Year 2013-2017 socioeconomic estimate data is a subset of information derived from the following census tables:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By Tenure;B17021 - Poverty Status Of Individuals In The Past 12 Months By Living Arrangement;B19001 - Household Income In The Past 12 Months;B19013 - Median Household Income In The Past 12 Months;B19025 - Aggregate Household Income In The Past 12 Months;B19113 - Median Family Income In The Past 12 Months;B19202 - Median Non-family Household Income In The Past 12 Months;B23001 - Sex By Age By Employment Status For The Population 16 Years And Over;B25014 - Tenure By Occupants Per Room;B25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into Unit;B25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 Months;C24010 - Sex By Occupation For The Civilian Employed Population 16 Years And Over;B20004 - Median Earnings In the Past 12 Months (In 2015 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and Over;B23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, and;B24021 - Occupation By Median Earnings In The Past 12 Months (In 2015 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs

Data Dictionary: DD_ACS 5-Year Socioeconomic Estimate Data by State Data Updated: BienniallyDate of Coverage: 2013 - 2017

The synthetic population was generated from the 2010-2014 ACS PUMS housing and person files.

United States Department of Commerce. Bureau of the Census. (2017-03-06).
American Community Survey 2010-2014 ACS 5-Year PUMS File [Data set].
Ann Arbor, MI: Inter-university Consortium of Political and Social
Research [distributor]. http://doi.org/10.3886/E100486V1

Outputs

There are 17 housing files
- repHus0.csv, repHus1.csv, ... repHus16.csv
and 32 person files
- rep_recode_ACSpus0.csv, rep_recode_ACSpus1.csv, ... rep_recode_ACSpus31.csv.

Files are split to be roughly equal in size. The files contain data for the entire country. Files are not split along any demographic characteristic. The person files and housing files must be concatenated to form a complete person file and a complete housing file, respectively.

If desired, person and housing records should be merged on 'id'. Variable description is below.

Data Dictionary
See [2010-2014 ACS PUMS data dictionary](http://doi.org/10.3886/E100486V1). All variables from the ACS PUMS housing files are present in the synthetic housing files and all variables from the ACS PUMS person files are present in the synthetic person files. Variables have not been modified in any way. Theoretically, variables like `person weight` no longer have any use in the synthetic population.

See README.md for more details.

analyze the survey of income and program participation (sipp) with r if the census bureau's budget was gutted and only one complex sample survey survived, pray it's the survey of income and program participation (sipp). it's giant. it's rich with variables. it's monthly. it follows households over three, four, now five year panels. the congressional budget office uses it for their health insurance simulation . analysts read that sipp has person-month files, get scurred, and retreat to inferior options. the american community survey may be the mount everest of survey data, but sipp is most certainly the amazon. questions swing wild and free through the jungle canopy i mean core data dictionary. legend has it that there are still species of topical module variables that scientists like you have yet to analyze. ponce de león would've loved it here. ponce. what a name. what a guy. the sipp 2008 panel data started from a sample of 105,663 individuals in 42,030 households. once the sample gets drawn, the census bureau surveys one-fourth of the respondents every four months, over f our or five years (panel durations vary). you absolutely must read and understand pdf pages 3, 4, and 5 of this document before starting any analysis (start at the header 'waves and rotation groups'). if you don't comprehend what's going on, try their survey design tutorial. since sipp collects information from respondents regarding every month over the duration of the panel, you'll need to be hyper-aware of whether you want your results to be point-in-time, annualized, or specific to some other period. the analysis scripts below provide examples of each. at every four-month interview point, every respondent answers every core question for the previous four months. after that, wave-specific addenda (called topical modules) get asked, but generally only regarding a single prior month. to repeat: core wave files contain four records per person, topical modules contain one. if you stacked every core wave, you would have one record per person per month for the duration o f the panel. mmmassive. ~100,000 respondents x 12 months x ~4 years. have an analysis plan before you start writing code so you extract exactly what you need, nothing more. better yet, modify something of mine. cool? this new github repository contains eight, you read me, eight scripts: 1996 panel - download and create database.R 2001 panel - download and create database.R 2004 panel - download and create database.R 2008 panel - download and create database.R since some variables are character strings in one file and integers in anoth er, initiate an r function to harmonize variable class inconsistencies in the sas importation scripts properly handle the parentheses seen in a few of the sas importation scripts, because the SAScii package currently does not create an rsqlite database, initiate a variant of the read.SAScii function that imports ascii data directly into a sql database (.db) download each microdata file - weights, topical modules, everything - then read 'em into sql 2008 panel - full year analysis examples.R< br /> define which waves and specific variables to pull into ram, based on the year chosen loop through each of twelve months, constructing a single-year temporary table inside the database read that twelve-month file into working memory, then save it for faster loading later if you like read the main and replicate weights columns into working memory too, merge everything construct a few annualized and demographic columns using all twelve months' worth of information construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half, again save it for faster loading later, only if you're so inclined reproduce census-publish ed statistics, not precisely (due to topcoding described here on pdf page 19) 2008 panel - point-in-time analysis examples.R define which wave(s) and specific variables to pull into ram, based on the calendar month chosen read that interview point (srefmon)- or calendar month (rhcalmn)-based file into working memory read the topical module and replicate weights files into working memory too, merge it like you mean it construct a few new, exciting variables using both core and topical module questions construct a replicate-weighted complex sample design with a fay's adjustment factor of one-half reproduce census-published statistics, not exactly cuz the authors of this brief used the generalized variance formula (gvf) to calculate the margin of error - see pdf page 4 for more detail - the friendly statisticians at census recommend using the replicate weights whenever possible. oh hayy, now it is. 2008 panel - median value of household assets.R define which wave(s) and spe cific variables to pull into ram, based on the topical module chosen read the topical module and replicate weights files into working memory too, merge once again construct a replicate-weighted complex sample design with a...

The American Community Survey (ACS) 5 Year 2013-2017 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

Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by Tract Date of Coverage: 2013-2017 Data Updated: Biennially

The U.S. Department of Housing and Urban Development (HUD) periodically receives "custom tabulations" of Census data from the U.S. Census Bureau that are largely not available through standard Census products. These datasets, known as "CHAS" (Comprehensive Housing Affordability Strategy) data, demonstrate the extent of housing problems and housing needs, particularly for low income households. The primary purpose of CHAS data is to demonstrate the number of households in need of housing assistance. This is estimated by the number of households that have certain housing problems and have income low enough to qualify for HUD’s programs (primarily 30, 50, and 80 percent of median income). CHAS data provides counts of the numbers of households that fit these HUD-specified characteristics in a variety of geographic areas. In addition to estimating low-income housing needs, CHAS data contributes to a more comprehensive market analysis by documenting issues like lead paint risks, "affordability mismatch," and the interaction of affordability with variables like age of homes, number of bedrooms, and type of building. This dataset is a special tabulation of the 2016-2020 American Community Survey (ACS) and reflects conditions over that time period. The dataset uses custom HUD Area Median Family Income (HAMFI) figures calculated by HUD PDR staff based on 2016-2020 ACS income data. CHAS datasets are used by Federal, State, and Local governments to plan how to spend, and distribute HUD program funds. To learn more about the Comprehensive Housing Affordability Strategy (CHAS), visit: https://www.huduser.gov/portal/datasets/cp.html, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs Data Dictionary: DD_ACS 5-Year CHAS Estimate Data by State Date of Coverage: 2016-2020

The American Community Survey (ACS) 5 Year 2013-2017 housing estimate data is a subset of information derived from the following census tables:B25002 - Occupancy Status; B25009 - Tenure By Household Size;B25021 - Median Number Of Rooms By Tenure; B25024 - Units In Structure;B25032 - Tenure by Units In Structure; B25036 - Tenure By Year Structure Built;B25037 - Median Year Structure Built By Tenure; B25041 – Bedrooms;B25042 - Tenure By Bedrooms;B25056 - Contract Rent;B25058 - Median Contract Rent;B25068 - Bedrooms By Gross Rent;B25077 - Median Value;B25097 - Mortgage Status By Median Value (Dollars), and;B25123 - Tenure By Selected Physical And Financial Conditions.

To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs

Data Dictionary: DD_ACS 5-Year Housing Estimate Data by State Date of Coverage: 2013-2017 Data Updated: Biennially

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the place 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 Place Date of Coverage: 2016-2020

The American Community Survey (ACS) 5 Year 2009-2013 socioeconomic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include:B08013 - Aggregate Travel Time To Work Of Workers By Sex;B08303 - Travel Time To Work;B17019 - Poverty Status In The Past 12 Months Of Families By Household Type By TenureB17021 - Poverty Status Of Individuals In The Past 12 Months By Living ArrangementB19001 - Household Income In The Past 12 MonthsB19013 - Median Household Income In The Past 12 MonthsB19025 - Aggregate Household Income In The Past 12 MonthsB19113 - Median Family Income In The Past 12 MonthsB19202 - Median Nonfamily Household Income In The Past 12 MonthsB23001 - Sex By Age By Employment Status For The Population 16 Years And OverB25014 - Tenure By Occupants Per RoomB25026 - Total Population in Occupied Housing Units by Tenure by year Householder Moved into UnitB25106 - Tenure By Housing Costs As A Percentage Of Household Income In The Past 12 MonthsC24010 - Sex By Occupation For The Civilian Employed Population 16 Years And OverB20004 - Median Earnings In the Past 12 Months (In 2009 Inflation-Adjusted Dollars) by Sex by Educational Attainment for the Population 25 Years and OverB23006 - Educational Attainment by Employment Status for the Population 25 to 64 Years, andB24021 - Occupation By Median Earnings In The Past 12 Months (In 2012 Inflation-Adjusted Dollars) For The Full-Time, Year-Round Civilian Employed Population 16 Years And Over.

To download additional socioeconomic information, visit: https://www.census.gov/programs-surveys/acs.Data Dictionary available for download by clicking on the following link: Data Dictionary – 2009-2013 ACS 5-Year Socioeconomic Estimate Data by Tract.

Data Current as of: 03//2017

Separate tables are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs)There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary.NOTE: Much of the data is from 5-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers. More information about the data table and a data dictionary can be found in the About/Attachments section.

Data Sources, including links; Data Dictionary; 2009-2013 American Community Survey, Block group-level Population Data; 2010 Decennial Census, Block group-level Population Data; 2008 National Emissions Inventory, Facility-level Data; 2011 National Emissions Inventory, Facility-level Data; 2014 National Emissions Inventory, Facility-level Data; 2010 Rural-Urban Commuting Area Codes, Tract-level Data; 2011 PM 2.5 Daily Average Fused Air Quality Surface Using Downscaling (FAQSD) Output, mean Tract-level Data, CONUS. This dataset is associated with the following publication: Mikati, I., A. Benson, T. Luben, J. Sacks, and J. Richmond-Bryant. Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status. American Journal of Public Health. American Public Health Association, Washington, DC, USA, 108(4): 480-485, (2018).

Dataset SummaryAbout this data:This feature layer symbolizes the relative population counts for the City's 12 Data Divisions, aggregating the tract-level estimates from the the Census Bureau's American Community Survey 2021 five-year samples.If you click on each Data Division, you can view other Census demographic information about that Data Division in addition to the population count.About the Census Data:The data comes from the U.S. Census Bureau's American Community Survey's 2017-2021 five-year samples. The American Community Survey (ACS) is an ongoing survey conducted by the federal government that provides vital information annually about America and its population. Information from the survey generates data that help determine how more than $675 billion in federal and state funds are distributed each year.For more information about the Census Bureau's ACS data and process of constructing the survey, visit the ACS's About page.About the City's Data Divisions:As a planning analytic tool, an interdepartmental working group divided Rochester into 12 “data divisions.” These divisions are well-defined and static so they are positioned to be used by the City of Rochester for statistical and planning purposes. Census data is tied to these divisions and serves as the basis for analyses over time. As such, the data divisions are designed to follow census boundaries, while also recognizing natural and human-made boundaries, such as the River, rail lines, and highways. Historical neighborhood boundaries, while informative in the division process, did not drive the boundaries. Data divisions are distinct from the numerous neighborhoods in Rochester. Neighborhood boundaries, like quadrant boundaries, police precincts, and legislative districts often change, which makes statistical analysis challenging when looking at data over time. The data division boundaries, however, are intended to remain unchanged. It is hoped that over time, all City data analysts will adopt the data divisions for the purpose of measuring change over time throughout the city.Dictionary: Division: The name of the data division. Total_Popu: The total population of the division. The population is calculated from the Census Bureau’s American Community Survey 2021 five-year samples. Percentage: Represents the percentage of City of Rochester residents which live in the division. Area_in_Sq: The total area in square miles of a given division. Source:City of Rochester Office of Innovation

This dataset contains two tables on the percent of household overcrowding (> 1.0 persons per room) and severe overcrowding (> 1.5 persons per room) for California, its regions, counties, and cities/towns. Data is from the U.S. Department of Housing and Urban Development (HUD), Comprehensive Housing Affordability Strategy (CHAS) and U.S. Census American Community Survey (ACS). The table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity: Healthy Communities Data and Indicators Project of the Office of Health Equity. Residential crowding has been linked to an increased risk of infection from communicable diseases, a higher prevalence of respiratory ailments, and greater vulnerability to homelessness among the poor. Residential crowding reflects demographic and socioeconomic conditions. Older-adult immigrant and recent immigrant communities, families with low income and renter-occupied households are more likely to experience household crowding. A form of residential overcrowding known as "doubling up"—co-residence with family members or friends for economic reasons—is the most commonly reported prior living situation for families and individuals before the onset of homelessness. More information about the data table and a data dictionary can be found in the About/Attachments section.The household crowding table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf
The format of the household overcrowding tables is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

Separate tables are provided for three geographic levels:The seven counties in the CMAP region (with regional total)The 284 municipalities in the CMAP regionThe 77 Chicago community areas (CCAs)There is limited geographic availability (particularly at the CCA level) for some variables. Additional information on availability and data sources are found in the CDS Data Dictionary.NOTE: Much of the data is from 5-year American Community Survey, which is a sample-based data product. This means users must exercise caution when interpreting data from low-population municipalities, as the margins of error are often large compared to the estimate. Not sure which municipality or Chicago community area you want? Explore a community's data in the interactive dashboard.Are you looking for the PDF versions? Find and download the print-friendly Community Data Snapshots from the agency website.

American Community Survey statistics on income and poverty, and how many households experienced poverty in the past 12 months as of 2010. THis is an update from a previously uploaded dataset that required a data dictionary to decipher the cryptic field names. Descriptive names are included in this dataset.

Here are the raw data and R code used in the paper "A comparison of two neighborhood-level socioeconomic indices in the United States" by Boscoe and Li currently under review. The raw data and data dictionary are exactly as they were obtained from the National Historical Geographic Information System (NHGIS). The data comprise the 7 American Community Survey variables used to construct the Yost Index at the block group level for the period 2011-2015.

This table contains data on the percent of population aged 16 years or older whose commute to work is 10 or more minutes/day by walking or biking for California, its regions, counties, and cities/towns. Data is from the U.S. Census Bureau, American Community Survey, and from the U.S. Department of Transportation, Federal Highway Administration, and National Household Travel Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Active modes of transport, bicycling and walking alone and in combination with public transit, offer opportunities to incorporate physical activity into the daily routine. Physical activity is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Automobile commuting is associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Consequently the transition from automobile-focused transport to public and active transport offers environmental health benefits, including reductions in air pollution, greenhouse gases and noise pollution, and may lead to greater overall safety in transportation. More information about the data table and a data dictionary can be found in the About/Attachments section.

Definitions of “urban” and “rural” are abundant in government, academic literature, and data-driven journalism. Equally abundant are debates about what is urban or rural and which factors should be used to define these terms. Absent from most of this discussion is evidence about how people perceive or describe their neighborhood. Moreover, as several housing and demographic researchers have noted, the lack of an official or unofficial definition of suburban obscures the stylized fact that a majority of Americans live in a suburban setting. In 2017, the U.S. Department of Housing and Urban Development added a simple question to the 2017 American Housing Survey (AHS) asking respondents to describe their neighborhood as urban, suburban, or rural. This service provides a tract-level dataset illustrating the outcome of analysis techniques applied to neighborhood classification reported by the American Housing Survey (AHS) as either urban, suburban, or rural. To create this data, analysts first applied machine learning techniques to the AHS neighborhood description question to build a model that predicts how out-of-sample households would describe their neighborhood (urban, suburban, or rural), given regional and neighborhood characteristics. Analysts then applied the model to the American Community Survey (ACS) aggregate tract-level regional and neighborhood measures, thereby creating a predicted likelihood the average household in a census tract would describe their neighborhood as urban, suburban, and rural. This last step is commonly referred to as small area estimation. The approach is an example of the use of existing federal data to create innovative new data products of substantial interest to researchers and policy makers alike. If aggregating tract-level probabilities to larger areas, users are strongly encouraged to use occupied household counts as weights. We recommend users read Section 7 of the working paper before using the raw probabilities. Likewise, we recognize that some users may: prefer to use an uncontrolled classification, or prefer to create more than three categories. To accommodate these uses, our final tract-level output dataset includes the ";raw" probability an average household would describe their neighborhood as urban, suburban, and rural. These probability values can be used to create an uncontrolled classification or additional categories. The final classification is controlled to AHS national estimates (26.9% urban; 52.1% suburban, 21.0% rural). For more information about the 2017 AHS Neighborhood Description Study click on the following visit: https://www.hud.gov/program_offices/comm_planning/communitydevelopment/programs/ Data Dictionary: DD_Urbanization Perceptions Small Area Index.