All addresses in Update Leave (TEA 6) enumeration areas were invited by an in-person lister to respond by internet, paper, or phone. This table is the daily and cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.Daily – percentage of housing units whose self-responses were received on a particular date.Cumulative – percentage of housing units whose self-responses were received from the start of the census through a particular date.Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

All addresses in Self Response (TEA 1) enumeration areas were invited by mail to respond by internet, paper, or phone. This table is the daily and cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.Daily – percentage of housing units whose self-responses were received on a particular date.Cumulative – percentage of housing units whose self-responses were received from the start of the census through a particular date.Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail.The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts.The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate.More information about these data is available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review FAQs.Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data.Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR).1. Population Density: 2020 Population per square mile,2. Poverty Rate: Percentage of population under 100% FPL,3. Median Household income: Based on countywide median HH income of $71,538.4. Highschool Education Attainment: Percentage of 18 years and older population without high school graduation.5. English Speaking Ability: Percentage of 18 years and older population with less or none English speaking ability. 6. Household without Internet Access: Percentage of HH without internet access.7. Non-Hispanic White Population: Percentage of Non-Hispanic White population.8. Non-Hispanic African-American Population: Percentage of Non-Hispanic African-American population.9. Non-Hispanic Asian Population: Percentage of Non-Hispanic Asian population.10. Hispanic Population: Percentage of Hispanic population.

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

All addresses in Self Response (TEA 1) and Update Leave (TEA 6) enumeration areas were invited to respond by internet, paper, or phone. The table is the cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation at the start of NRFU (August 10) and the end of response processing (December 1)..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.Start of NRFU – self-responses received by August 10.Final – self-responses received by the end of response processing (December 1).Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement Files are an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official "production settings," the final set of algorithmic parameters and privacy-loss budget allocations that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite - Redistricting and Demographic and Housing Characteristics File - Production Settings (2023-04-03). These statistical queries, called "noisy measurements" were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016]; see also Dwork C. and Roth, A. [2014]) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023]), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement Files (2023-04-03) have been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004). The data include zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product- planning/2010-demonstration-data-products/04 Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census. The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics, including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence, after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints--information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) --are provided. These data are available for download (i.e. not restricted access). Due to their size, they must be downloaded through the link on this

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

Abstract

The 2000 Republic of Palau Census of Population and Housing was the second census collected and processed entirely by the republic itself. This monograph provides analyses of data from the most recent census of Palau for decision makers in the United States and Palau to understand current socioeconomic conditions. The 2005 Census of Population and Housing collected a wide range of information on the characteristics of the population including demographics, educational attainments, employment status, fertility, housing characteristics, housing characteristics and many others.

Geographic coverage

National

Analysis unit

• Household;
• Individual.

Universe

The 1990, 1995 and 2000 censuses were all modified de jure censuses, counting people and recording selected characteristics of each individual according to his or her usual place of residence as of census day. Data were collected for each enumeration district - the households and population in each enumerator assignment - and these enumeration districts were then collected into hamlets in Koror, and the 16 States of Palau.

Kind of data

Census/enumeration data [cen]

Sampling procedure

No sampling - whole universe covered

Mode of data collection

Face-to-face [f2f]

Research instrument

The 2000 censuses of Palau employed a modified list-enumerate procedure, also known as door-to-door enumeration. Beginning in mid-April 2000, enumerators began visiting each housing unit and conducted personal interviews, recording the information collected on the single questionnaire that contained all census questions. Follow-up enumerators visited all addresses for which questionnaires were missing to obtain the information required for the census.

Cleaning operations

The completed questionnaires were checked for completeness and consistency of responses, and then brought to OPS for processing. After checking in the questionnaires, OPS staff coded write-in responses (e.g., ethnicity or race, relationship, language). Then data entry clerks keyed all the questionnaire responses. The OPS brought the keyed data to the U.S. Census Bureau headquarters near Washington, DC, where OPS and Bureau staff edited the data using the Consistency and Correction (CONCOR) software package prior to generating tabulations using the Census Tabulation System (CENTS) package. Both packages were developed at the Census Bureau's International Programs Center (IPC) as part of the Integrated Microcomputer Processing System (IMPS).

The goal of census data processing is to produce a set of data that described the population as clearly and accurately as possible. To meet this objective, crew leaders reviewed and edited questionnaires during field data collection to ensure consistency, completeness, and acceptability. Census clerks also reviewed questionnaires for omissions, certain inconsistencies, and population coverage. Census personnel conducted a telephone or personal visit follow-up to obtain missing information. The follow-ups considered potential coverage errors as well as questionnaires with omissions or inconsistencies beyond the completeness and quality tolerances specified in the review procedures.

Following field operations, census staff assigned remaining incomplete information and corrected inconsistent information on the questionnaires using imputation procedures during the final automated edit of the data. The use of allocations, or computer assignments of acceptable data, occurred most often when an entry for a given item was lacking or when the information reported for a person or housing unit on an item was inconsistent with other information for that same person or housing unit. In all of Palau’s censuses, the general procedure for changing unacceptable entries was to assign an entry for a person or housing unit that was consistent with entries for persons or housing units with similar characteristics. The assignment of acceptable data in place of blanks or unacceptable entries enhanced the usefulness of the data.

Sampling error estimates

Human and machine-related errors occur in any large-scale statistical operation. Researchers generally refer to these problems as non-sampling errors. These errors include the failure to enumerate every household or every person in a population, failure to obtain all required information from residents, collection of incorrect or inconsistent information, and incorrect recording of information. In addition, errors can occur during the field review of the enumerators' work, during clerical handling of the census questionnaires, or during the electronic processing of the questionnaires. To reduce various types of non-sampling errors, Census office personnel used several techniques during planning, data collection, and data processing activities. Quality assurance methods were used throughout the data collection and processing phases of the census to improve the quality of the data.

Census staff implemented several coverage improvement programs during the development of census enumeration and processing strategies to minimize under-coverage of the population and housing units. A quality assurance program improved coverage in each census. Telephone and personal visit follow-ups also helped improve coverage. Computer and clerical edits emphasized improving the quality and consistency of the data. Local officials participated in post-census local reviews. Census enumerators conducted additional re-canvassing where appropriate.

All addresses in Self Response (TEA 1) and Update Leave (TEA 6) enumeration areas were invited to respond by internet, paper, or phone. The table is the county-level self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for the nation at the start of NRFU (August 10, 2020) and the end of response processing (December 1, 2020). Self-response, return, and UAA rates from the 2010 Census at the NRFU cut date (April 19, 2010) and the end of response processing (September 7, 2010) are included to compare rates between censuses..For more information about the different types of enumeration areas, go to the 2020 Census Type of Enumeration (TEA) viewer page by clicking here: Type of Enumeration Area..Self-response rates presented in this table may differ from those presented in the self-response map that was updated daily during the 2020 Census. The map used raw data as it was being processed in real-time while these rates used post processed data..To read the report that provides background information about the rate, go to the Evaluations, Experiments, and Assessment page on census.gov by clicking here: Evaluations Experiments and Assessments..Key Column Terms:.NRFU (2020) – self-responses received by August 10, 2020.NRFU (2010) – self-responses received by April 19, 2010.Final (2020) – self-responses received by the end of response processing (December 1, 2020).Final (2010) – self-responses received by the end of response processing (September 7, 2010).Internet – percentage of housing units providing a self-response by internet questionnaire.Paper – percentage of housing units providing a self-response by paper questionnaire.CQA – percentage of housing units providing a self-response by phone.Total – percentage of housing units providing a self-response by internet, paper, or phone.Self-Response Rate – percentage of addresses in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.Return Rate – percentage of occupied housing units in Self Response (TEA 1) or Update Leave (TEA 6) areas providing a sufficient self-response by internet, paper, or phone.UAA Rate – percentage of addresses in Self Response areas (TEA 1) identified as undeliverable as addressed (UAA).NOTE: The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. (CBDRB-FY24-0271).Source: U.S. Census Bureau, 2020 Census

The Supplemental Fraud Survey (SFS) obtained additional information about fraud-related victimizations so that policymakers; academic researchers; practitioners at the federal, state, and local levels; and special interest groups who are concerned with these crimes can make informed decisions concerning policies and programs. The SFS asked questions related to victims' experiences with fraud. These responses are linked to the National Crime Victimization Survey (NCVS) survey instrument responses for a more complete understanding of the individual victim's circumstances. The 2017 Supplemental Fraud Survey (SFS) was the first implementation of this supplement to the annual NCVS to obtain specific information about fraud-related victimization and disorder on a national level. Since the SFS is a supplement to the NCVS, it is conducted under the authority of Title 34, United States Code, section 10132. Only Census employees sworn to preserve confidentiality may see the completed questionnaires.

About this dataset

Context

The dataset tabulates the population of Real County by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Real County. The dataset can be utilized to understand the population distribution of Real County by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Real County. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Real County.

Key observations

Largest age group (population): Male # 65-69 years (195) | Female # 45-49 years (159). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

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

Age groups:

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Real County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Real County is shown in the following column.
• Population (Female): The female population in the Real County is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Real County for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Real County Population by Gender. You can refer the same here

Abstract

The survey was one of three components of a World Bank project implemented to provide information on the size and composition of the civil service, improve systems and control mechanisms, institutional capacity, and provide information on policy-formulation and decision-making processes. Other components included a census of Guatemalan civil servants and contractors, and the continuous updating and use of this information to strengthen checks and improve transparency, and a new policy framework aimed at strengthening the institutional capacity of the Guatemalan civil service.

The aim of the survey was to assess the characteristics and quality of human resource management in the public administration, as well as to capture the attitudes, motivations, and experiences of public officials. In particular, the survey focused on the priority areas for reform identified by the Government of Guatemala and the World Bank. The data collected was used to support the World Bank’s diagnostic of key problem areas in the human resource management of the public administration in Guatemala. It was used to inform the design of institution-level interventions, as well as the new public policy framework.

Geographic coverage

The target population were civil servants across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Analysis unit

Public servants (managers and non-managers) across 18 institutions in Guatemala at the central, and their respective departmental and municipal branches.

Kind of data

Aggregate data [agg]

Sampling procedure

The sample frame used comes from the frame used for the Human Resources National Census. It has the list of positions in all the units of the 18 institutions selected for this study. The sample size for the managerial level was calculated with a 95% confidence level and a 5% margin error for each institution. For the non-managers, it was calculated with the same confidence level and margin error. The sample sizes are adjusted so the sample would have an even number for each study domain for the experiment which will assign a different questionnaire to half of the respondents.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The survey questionnaire comprises following modules: 1- Pre-interview questions, 2- Demographic and work history information, 3- Management practices, 4- Performance evaluation, 5- perceptions about discrimination, 6- Human resources management practices, 7- Perceptions of the national office of the civil service, 8- Perception of acts of corruption, and 9- Review of surveys.

The questionnaire was prepared in English and Spanish.

Response rate

Response rate was 96%.

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, 2023 American Community Survey 1-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.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...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..An * indicates that the estimate is significantly different (at a 90% confidence level) than the estimate from the most current year. A "c" indicates the estimates for that year and the current year are both controlled; a statistical test is not appropriate..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..In the review of the 2011 Puerto Rico Community Survey (PRCS) estimates, the Census Bureau noted fluctuations in Puerto Rico's estimate of total housing units between 2009 and 2011. These fluctuations stem from large changes to the Puerto Rico sampling frame as a result of 2010 Census housing unit listing and enumeration activities combined with the absence of independent housing unit controls for the weighting process. Data users should exercise caution when comparing 2009, 2010, and 2011 PRCS estimates. For more information, go to User Notes..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column 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..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2000 data. Boundaries for urban areas have not been updated since Census 2000. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2011 American Community Survey (ACS) data generally reflect the December 2009 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..This table contains new estimates for health insurance coverage status by employment status in 2010..The health insurance coverage category names were modified in 2010. See ACS Health Insurance Definitions for a list of the insurance type definitions..Logical coverage edits applying a rules-based assignment of Medicaid, Medicare and military health coverage were added as of 2009 -- please see http://www.census.gov/hhes/www/hlthins/publications/coverage_edits_final.pdf for more details. The corresponding 2008 data table in American FactFinder does not incorporate these edits and is therefore not comparable to this table in 2009 or 2010. Select geographies of 2008 data comparable to the 2009 and 2010 tables are accessible at http://www.census.gov/hhes/www/hlthins/data/acs/2008/re-run.html..Census occupation codes for 2010 and later years are based on the 2010 revision of the Standard Occupational Classification (SOC). Occupation data from 2010 and later years are not strictly comparable to data from prior to 2010. For more information on the Census occupation code changes, please visit our website at http://www.census.gov/hhes/www/ioindex/..Industry codes are 4-digit codes and are based on the North American Industry Classification System 2007. The Industry categories adhere to the guidelines issued in Clarificati...

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since late January, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

United States Data

Data on cumulative coronavirus cases and deaths can be found in two files for states and counties.

Each row of data reports cumulative counts based on our best reporting up to the moment we publish an update. We do our best to revise earlier entries in the data when we receive new information.

Both files contain FIPS codes, a standard geographic identifier, to make it easier for an analyst to combine this data with other data sets like a map file or population data.

State-Level Data

State-level data can be found in the us-states.csv file.

date,state,fips,cases,deaths
2020-01-21,Washington,53,1,0
...

County-Level Data

County-level data can be found in the us-counties.csv file.

date,county,state,fips,cases,deaths
2020-01-21,Snohomish,Washington,53061,1,0
...

In some cases, the geographies where cases are reported do not map to standard county boundaries. See the list of geographic exceptions for more detail on these.

Github Repository

This dataset contains COVID-19 data for the United States of America made available by The New York Times on github at https://github.com/nytimes/covid-19-data

Abstract

Toxic intensities and risk for 246 Toxic Release Inventory (TRI) chemicals. Similar to the IPPS data, these intensities can be used to estimate toxic chemical load given employment, value of output, or value added.

The ToxInt database has been produced by the World Bank's Economics of Industrial Pollution research team, in collaboration with the Center for Economic Studies of the U.S. Census Bureau http://www.census.gov/. The dataset provides pollution intensities and the corresponding toxic risks for 246 chemicals in the U.S. EPA’s Toxic Release Inventory http://go.worldbank.org/QQREY33ET0 (TRI).

The IPPS project has aimed to establish initial benchmarks of pollution intensity and toxic risk in manufacturing sectors in the developing world. We have always assumed that further and more detailed analysis would refine, and in some cases alter, these first-order attempts to understand magnitudes of environmental degredation and health risk. Some colleagues in academia have expressed concern about the IPPS's reliance on acute toxicity measures to the exclusion of chronic toxicity measures, and its use of mass-only measures to identify environmental risk by chemical. For our part, we believe that IPPS should be viewed as a useful tool, rather than a final answer, for those involved in international risk assessment work.

The U.S. EPA has also been seeking to incorporate chemical risk assessment into its project work. The EPA maintains an Integrated Risk Information System (IRIS) http://www.epa.gov/ngispgm3/iris/index.html database on human health effects that may result from exposure to various chemicals in the environment. IRIS was initially developed for EPA staff, in response to a growing demand for consistent information on chemical substances for use in risk assessments, decision-making and regulatory activities. The information in IRIS is intended for those without extensive training in toxicology, but with some knowledge of health sciences.

EPA's Sector Facility Indexing Project (SFIP) provides another approach to risk assessment. The SFIP couples emissions data from the Toxics Release Inventory (TRI) with toxicity weighting factors. The result is an index which accounts for both emissions volume and risk in assessing toxic pollution. On April 29, 1997, a Subcommittee of the EPA's Science Advisory Board's Environmental Engineering Committee met to review the technical aspects of the SFIP. To learn more about this and other aspects of the EPA's current work on chemical risk, please visit them at http://www.epa.gov/science1/pifs.htm.

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Abstract

Rwanda Interim Demographic and Health Survey (RIDHS) follows the Demographic and Health Surveys (RDHS) that were successfully conducted in 1992, 2000, and 2005, and is part of a broad, worldwide program of socio-demographic and health surveys conducted in developing countries since the mid-1980s. RIDHS collected the indicators on fertility, family planning and maternal and child health which the survey normally provides. In addition, RIDHS integrated a malaria module and tests for the prevalence of malaria and anemia among women and children, thus determining the prevalence of malaria and anemia for women and children at the national level.

The main objectives of the RIDHS were: • At the national level, gather data to determine demographic rates, particularly fertility and infant and child mortality rates, and analyze the direct and indirect factors that determine fertility and child mortality rates and trends. • Evaluate the level of knowledge and use of contraceptives among women and men. • Gather data concerning family health: vaccinations; prevalence and treatment of diarrhea, acute respiratory infections (ARI), and fever in children under the age of five; antenatal care visits; and assistance during childbirth. • Gather data concerning the prevention and treatment of malaria, particularly the possession and use of mosquito nets, and the prevention of malaria in pregnant women. • Gather data concerning child feeding practices, including breastfeeding. • Gather data concerning circumcision among men between the ages of 15 and 59. • Collect blood samples in all of the households surveyed for anemia testing of women age 15-49, pregnant women and children under age five. • Collect blood samples in all of the households surveyed for hemoglobin and malaria diagnostic testing of women age 15 to 49, pregnant women and children under age five.

Geographic coverage

National coverage

Analysis unit

Household Individual Woman age 15-49 Man age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the RIDHS is a two-stage stratified area sample. Clusters are the primary sampling units and are constituted from enumeration areas (EA). The EA were defined in the 2002 General Population and Housing Census (RGPH) (SNR, 2005).

These enumeration areas provided the master frame for the drawing of 250 clusters (187 rural and 63 urban), selected with a representative probability proportional to their size. Only 249 of these clusters were surveyed, because one cluster located in a refugee camp had to be eliminated from the sample. A strictly proportional sample allocation would have resulted in a very low number of urban households in certain provinces. It was therefore necessary to slightly oversample urban areas in order to survey a sufficient number of households to produce reliable estimates for urban areas. The second stage involved selecting a sample of households in these enumeration areas. In order to adequately guarantee the accuracy of the indicators, the total number drawn was limited to 30 households per cluster. Because of the nonproportional distribution of the sample among the different strata and the fact that the number of households was set for each cluster, weighting was used to ensure the validity of the sample at both national and provincial levels.

All women age 15-49 years who were either usual residents of the selected household or visitors present in the household on the night before the survey were eligible to be interviewed (7,528 women). In addition, a sample of men age 15-59 who were either usual residents of the selected household or visitors present in the household on the night before the survey were eligible for the survey (7,168 men). Finally, all women age 15-49 and all children under the age of five were eligible for the anemia and malaria diagnostic tests.

The sample for the 2007-08 RIDHS covered the population residing in ordinary households across the country. A national sample of 7,469 households (1,863 in urban areas and 5,606 in rural areas) was selected. The sample was first stratified to provide adequate representation from urban and rural areas as well as all the four provinces and the city of Kigali, the nation’s capital.

Sampling deviation

One cluster located in a refugee camp had to be eliminated from the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2007-08 RIDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. The content of these questionnaires was based on model questionnaires developed by the MEASURE DHS project.

Initial technical meetings that were held beginning in September 2007 allowed a wide range of government agencies as well as local and international organizations to contribute to the development of the questionnaires. Based on these discussions, the DHS model questionnaires were modified to reflect the needs of users and relevant issues in population, family planning, anemia, malaria and other health concerns in Rwanda. The questionnaires were then translated from French into Kinyarwanda. These questionnaires were finalized in December 2007 before the training of male and female interviewers.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. In addition, some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit such as the main source of drinking water, type of toilet facilities, materials used for the floor of the house, the main energy source used for cooking and ownership of various durable goods. Finally, the Household Questionnaire was also used to identify women and children eligible for the hemoglobin (anemia) and malaria diagnostic tests.

The Women’s Questionnaire was used to collect information on women of reproductive age (15-49 years) and covered questions on the following topics: • Background characteristics • Marital status • Birth history • Knowledge and use of family planning methods • Fertility preferences • Antenatal and delivery care • Breastfeeding practices • Vaccinations and childhood illnesses

The Men’s Questionnaire was administered to all men age 15-59 years living in the selected households. The Men’s Questionnaire collected information similar to that of the Women’s Questionnaire, with the only difference being that it did not include birth history or questions on maternal and child health or nutrition. In addition, the Men’s Questionnaire also collected information on circumcision.

Cleaning operations

Data entry began on January 7, 2008, three weeks after the beginning of data collection activities in the field. Data were entered by a team of five data processing personnel recruited and trained by staff from ICF Macro. The data entry team was reinforced during this work with an additional staffer. Completed questionnaires were periodically brought in from the field to the National Institute of Statistics in Kigali, where assigned staff checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry staff. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ICF Macro MEASURE DHS program, and Serpro S.A. All questionnaires were entered twice to eliminate as many data entry errors as possible from the files. In addition, a quality control program was used to detect data collection errors for each team. This information was shared with field teams during supervisory visits to improve data quality. The data entry and internal consistency verification phase of the survey was completed on May 14, 2008.

Response rate

The response rate was high for both men (95.4 percent) and women (97.5 percent).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2007-08 RIDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2007-08 RIDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population

Release Date: 2021-03-11.Release Schedule:.The data in this file come from the 2017 Economic Census. For more information about economic census planned data product releases, see Economic Census: About: 2017 Release Schedules...Key Table Information:.Includes only establishments of firms with payroll...Data Items and Other Identifying Records:.Number of establishments.Number of establishments in business at end of year.Sales, value of shipments, or revenue ($1,000).Total under-roof floor space (1,000 square feet).Under-roof selling space (1,000 square feet).Sales, value of shipments, or revenue per square foot of under-roof selling space (dollars).Under-roof selling space as percent of total under-roof floor space (%).Response coverage of total under-roof floor space inquiry (%).Response coverage of under-roof selling space inquiry (%)..Geography Coverage:.The data are shown for employer establishments at the U.S. and state levels. For information about economic census geographies, including changes for 2017, see Economic Census: Economic Geographies...Industry Coverage:.The data are shown for 2017 NAICS codes 445110, Supermarkets and other grocery (except convenience) stores; 445120, Convenience stores; 452210, Department stores; and 452311, Warehouse clubs and supercenters. For information about NAICS, see Economic Census: Technical Documentation: Economic Census Code Lists...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/economic-census/data/2017/sector44/EC1744FLSPACE.zip..API Information:.Economic census data are housed in the Census Bureau API. For more information, see Explore Data: Developers: Available APIs: Economic Census..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only...To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Economic Census: Technical Documentation: Methodology...Symbols:.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised.s - Relative standard error exceeds 40%.For a complete list of symbols, see Economic Census: Technical Documentation: Data Dictionary.. .Source:.U.S. Census Bureau, 2017 Economic Census.For information about the economic census, see Business and Economy: Economic Census...Contact Information:.U.S. Census Bureau.For general inquiries:. (800) 242-2184/ (301) 763-5154. ewd.outreach@census.gov.For specific data questions:. (800) 541-8345.For additional contacts, see Economic Census: About: Contact Us.

Overview: FEMA and Argonne National Laboratory completed the first analysis of community resilience indicators in 2018 and repeated the process in 2022. The analysis process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the research team to ensure the methodologies are:

Quantitative, Data and methodology are publicly available, Calculated at the county level or lower, Examine generalized hazard risk (rather than a singular hazard), and Focused on pre-disaster community conditions.

After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visualization, conducts a correlation analysis, and creates a composite index called the "FEMA Community Resilience Challenges Index (CRCI)".

In 2022, the FEMA and Argonne research team updated the 2018 literature review and examined 14 methodologies published between 2003 and 2021. Examining the indicators used in these methodologies, the research team identified 22 indicators as commonly used (indicators used in five or more of the 14 methodologies). The research team produced the FEMA CRCI at the county and the census tract levels. More details on these indicators and the research process can be found in the FEMA CRCI Storymap. Data last updated on May 13, 2023. This is the latest available version of the CRCI. Questions or comments about this layer? Email the RAPT team at FEMA-TARequest@fema.dhs.gov

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structure ...