These data represent the predicted (modeled) prevalence of adults (Age 18+) that Delayed Medical Care Because of Cost among all adults for each census tract in Colorado. Delayed medical care is defined as needing to see a doctor within the past 12 months but not able to do so because of cost(s).The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Number of delays by length of delay and reason for delay at the monthly census point.

This dataset contains data from the P.L. 94-171 2020 Census Redistricting Program. The 2020 Census Redistricting Data Program provides states the opportunity to delineate voting districts and to suggest census block boundaries for use in the 2020 Census redistricting data tabulations (Public Law 94-171 Redistricting Data File). In addition, the Redistricting Data Program will periodically collect state legislative and congressional district boundaries if they are changed by the states. The program is also responsible for the effective delivery of the 2020 Census P.L. 94-171 Redistricting Data statutorily required by one year from Census Day. The program ensures continued dialogue with the states in regard to 2020 Census planning, thereby allowing states ample time for their planning, response, and participation. The U.S. Census Bureau will deliver the Public Law 94-171 redistricting data to all states by Sept. 30, 2021. COVID-19-related delays and prioritizing the delivery of the apportionment results delayed the Census Bureau’s original plan to deliver the redistricting data to the states by April 1, 2021.

Data in this dataset contains information on population, diversity, race, ethnicity, housing, household, vacancy rate for 2020 for various geographies (county, MCD, Philadelphia Planning Districts (referred to as county planning areas [CPAs] internally, Census designated places, tracts, block groups, and blocks)

For more information on the 2020 Census, visit https://www.census.gov/programs-surveys/decennial-census/about/rdo/summary-files.html

PLEASE NOTE: 2020 Decennial Census data has had noise injected into it because of the Census's new Disclosure Avoidance System (DAS). This can mean that population counts and characteristics, especially when they are particularly small, may not exactly correspond to the data as collected. As such, caution should be exercised when examining areas with small counts. Ron Jarmin, acting director of the Census Bureau posted a discussion of the redistricting data, which outlines what to expect with the new DAS. For more details on accuracy you can read it here: https://www.census.gov/newsroom/blogs/director/2021/07/redistricting-data.html

This feature layer includes historical (2009, 2013 - 2016) census subdivision level hours of delay for Annual Total Traffic Delay, Annual Total Passenger Car Delay, and Annual Total Truck Delay, as well as normalized delay based on the length of highways in each census subdivisionField Definition:TotDelay09 -Total Hours of Delay in 2009TotDelay13 -Total Hours of Delay in 2013TotDelay14 -Total Hours of Delay in 2014TotDelay15 - Total Hours of Delay in 2015TotDelay16 - Total Hours of Delay in 2016TotDel09KM - Total Hours of Delay per Kilometre in 2009TotDel13KM - Total Hours of Delay per Kilometre in 2013TotDel14KM -Total Hours of Delay per Kilometre in 2014TotDel15KM -Total Hours of Delay per Kilometre in 2015TotDel16KM -Total Hours of Delay per Kilometre in 2016PasDelay09 - Passenger Vehicle Hours of Delay in 2009PasDelay13 - Passenger Vehicle Hours of Delay in 2013PasDelay14 - Passenger Vehicle Hours of Delay in 2014PasDelay15 - Passenger Vehicle Hours of Delay in 2015PasDelay16 - Passenger Vehicle Hours of Delay in 2016PasDel09KM - Passenger Vehicle Hours of Delay per Kilometre in 2009PasDel13KM - Passenger Vehicle Hours of Delay per Kilometre in 2013PasDel14KM - Passenger Vehicle Hours of Delay per Kilometre in 2014PasDel15KM - Passenger Vehicle Hours of Delay per Kilometre in 2015PasDel16KM - Passenger Vehicle Hours of Delay per Kilometre in 2016TrkDelay09 - Truck Vehicle Hours of Delay in 2009TrkDelay13 - Truck Vehicle Hours of Delay in 2013TrkDelay14 - Truck Vehicle Hours of Delay in 2014TrkDelay15 - Truck Vehicle Hours of Delay in 2015 TrkDelay16 - Truck Vehicle Hours of Delay in 2016TrkDel09KM - Truck Vehicle Hours of Delay per Kilometre in 2009TrkDel13KM - Truck Vehicle Hours of Delay per Kilometre in 2013TrkDel14KM - Truck Vehicle Hours of Delay per Kilometre in 2014TrkDel15KM - Truck Vehicle Hours of Delay per Kilometre in 2014TrkDel16KM - Truck Vehicle Hours of Delay per Kilometre in 2016TotDelay09 – 16, Heures totales de retard; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « TotDelay13 » indique le total d’heures de retard en 2013)TotDel(09 – 16)KM, Heures totales de retard par kilomètre; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard par kilomètre est estimé (ex. : « TotDel13KM » indique le total d’heures de retard par kilomètre en 2013)PasDelay09 – 16, Heures de retard pour les véhicules de tourisme; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « PasDelay13 » indique les heures de retard pour les véhicules de tourisme en 2013)PasDel (09 – 16) KM, Heures de retard pour les véhicules de tourisme par kilomètre; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « PasDel13KM » indique les heures de retard par kilomètre pour les véhicules de tourisme en 2013)TrkDelay09 – 16, Heures de retard pour les véhicules utilitaires; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « TrkDelay13 » indique les heures de retard pour les véhicules utilitaires en 2013)TrkDel (09 – 16) KM, Heures de retard pour les véhicules utilitaires par kilomètre; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « TrkDel13KM » indique les heures de retard par kilomètre pour les véhicules utilitaires en 2013)

Policy design for delayed retirement and second child policy.

A data set of cross-nationally comparable microdata samples for 15 Economic Commission for Europe (ECE) countries (Bulgaria, Canada, Czech Republic, Estonia, Finland, Hungary, Italy, Latvia, Lithuania, Romania, Russia, Switzerland, Turkey, UK, USA) based on the 1990 national population and housing censuses in countries of Europe and North America to study the social and economic conditions of older persons. These samples have been designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. A common set of nomenclatures and classifications, derived on the basis of a study of census data comparability in Europe and North America, was adopted as a standard for recoding. This series was formerly called Dynamics of Population Aging in ECE Countries. The recommendations regarding the design and size of the samples drawn from the 1990 round of censuses envisaged: (1) drawing individual-based samples of about one million persons; (2) progressive oversampling with age in order to ensure sufficient representation of various categories of older people; and (3) retaining information on all persons co-residing in the sampled individual''''s dwelling unit. Estonia, Latvia and Lithuania provided the entire population over age 50, while Finland sampled it with progressive over-sampling. Canada, Italy, Russia, Turkey, UK, and the US provided samples that had not been drawn specially for this project, and cover the entire population without over-sampling. Given its wide user base, the US 1990 PUMS was not recoded. Instead, PAU offers mapping modules, which recode the PUMS variables into the project''''s classifications, nomenclatures, and coding schemes. Because of the high sampling density, these data cover various small groups of older people; contain as much geographic detail as possible under each country''''s confidentiality requirements; include more extensive information on housing conditions than many other data sources; and provide information for a number of countries whose data were not accessible until recently. Data Availability: Eight of the fifteen participating countries have signed the standard data release agreement making their data available through NACDA/ICPSR (see links below). Hungary and Switzerland require a clearance to be obtained from their national statistical offices for the use of microdata, however the documents signed between the PAU and these countries include clauses stipulating that, in general, all scholars interested in social research will be granted access. Russia requested that certain provisions for archiving the microdata samples be removed from its data release arrangement. The PAU has an agreement with several British scholars to facilitate access to the 1991 UK data through collaborative arrangements. Statistics Canada and the Italian Institute of statistics (ISTAT) provide access to data from Canada and Italy, respectively. * Dates of Study: 1989-1992 * Study Features: International, Minority Oversamples * Sample Size: Approx. 1 million/country Links: * Bulgaria (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02200 * Czech Republic (1991), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06857 * Estonia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06780 * Finland (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06797 * Romania (1992), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06900 * Latvia (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/02572 * Lithuania (1989), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03952 * Turkey (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/03292 * U.S. (1990), http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06219

This feature layer includes historical (2009, 2013 – 2016) census division level hours of delay on highways for Annual Total Traffic Delay, Annual Total Passenger Car Delay, and Annual Total Truck Delay, as well as normalized delay based on the length of highways.Field Definition:TotDelay09 -Total Hours of Delay in 2009TotDelay13 -Total Hours of Delay in 2013TotDelay14 -Total Hours of Delay in 2014TotDelay15 - Total Hours of Delay in 2015TotDelay16 - Total Hours of Delay in 2016TotDel09KM - Total Hours of Delay per Kilometre in 2009TotDel13KM - Total Hours of Delay per Kilometre in 2013TotDel14KM -Total Hours of Delay per Kilometre in 2014TotDel15KM -Total Hours of Delay per Kilometre in 2015TotDel16KM -Total Hours of Delay per Kilometre in 2016PasDelay09 - Passenger Vehicle Hours of Delay in 2009PasDelay13 - Passenger Vehicle Hours of Delay in 2013PasDelay14 - Passenger Vehicle Hours of Delay in 2014PasDelay15 - Passenger Vehicle Hours of Delay in 2015PasDelay16 - Passenger Vehicle Hours of Delay in 2016PasDel09KM - Passenger Vehicle Hours of Delay per Kilometre in 2009PasDel13KM - Passenger Vehicle Hours of Delay per Kilometre in 2013PasDel14KM - Passenger Vehicle Hours of Delay per Kilometre in 2014PasDel15KM - Passenger Vehicle Hours of Delay per Kilometre in 2015PasDel16KM - Passenger Vehicle Hours of Delay per Kilometre in 2016TrkDelay09 - Truck Vehicle Hours of Delay in 2009TrkDelay13 - Truck Vehicle Hours of Delay in 2013TrkDelay14 - Truck Vehicle Hours of Delay in 2014TrkDelay15 - Truck Vehicle Hours of Delay in 2015 TrkDelay16 - Truck Vehicle Hours of Delay in 2016TrkDel09KM - Truck Vehicle Hours of Delay per Kilometre in 2009TrkDel13KM - Truck Vehicle Hours of Delay per Kilometre in 2013TrkDel14KM - Truck Vehicle Hours of Delay per Kilometre in 2014TrkDel15KM - Truck Vehicle Hours of Delay per Kilometre in 2014TrkDel16KM - Truck Vehicle Hours of Delay per Kilometre in 2016Sommaire régional des heures de retard sur les autoroutes, par division de recensement basé sur les données GPS des véhicules de tourisme et utilitaires (2009, 2013-2016).Cette couche comprend les heures de retard de circulation sur les autoroutes, par division de recensement (2009, 2013-2016) pour le retard total annuel, le retard total annuel des voitures de tourisme et le retard total annuel des camions, ainsi que les retards normalisés en fonction de la longueur des autoroutes.TotDelay09 – 16, Heures totales de retard; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « TotDelay13 » indique le total d’heures de retard en 2013)TotDel(09 – 16)KM, Heures totales de retard par kilomètre; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard par kilomètre est estimé (ex. : « TotDel13KM » indique le total d’heures de retard par kilomètre en 2013)PasDelay09 – 16, Heures de retard pour les véhicules de tourisme; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « PasDelay13 » indique les heures de retard pour les véhicules de tourisme en 2013)PasDel (09 – 16) KM, Heures de retard pour les véhicules de tourisme par kilomètre; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « PasDel13KM » indique les heures de retard par kilomètre pour les véhicules de tourisme en 2013)TrkDelay09 – 16, Heures de retard pour les véhicules utilitaires; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « TrkDelay13 » indique les heures de retard pour les véhicules utilitaires en 2013)TrkDel (09 – 16) KM, Heures de retard pour les véhicules utilitaires par kilomètre; les chiffres « 09 – 16 » représentent l’année pour laquelle le retard est estimé (ex. : « TrkDel13KM » indique les heures de retard par kilomètre pour les véhicules utilitaires en 2013)

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

2020 decennial census data for population, age, and race aggregated to the block level. Age and sex variables will not be released by the Census Bureau until late June 2022. This dataset will be updated once they are released.

A database based on a random sample of the noninstitutionalized population of the United States, developed for the purpose of studying the effects of demographic and socio-economic characteristics on differentials in mortality rates. It consists of data from 26 U.S. Current Population Surveys (CPS) cohorts, annual Social and Economic Supplements, and the 1980 Census cohort, combined with death certificate information to identify mortality status and cause of death covering the time interval, 1979 to 1998. The Current Population Surveys are March Supplements selected from the time period from March 1973 to March 1998. The NLMS routinely links geographical and demographic information from Census Bureau surveys and censuses to the NLMS database, and other available sources upon request. The Census Bureau and CMS have approved the linkage protocol and data acquisition is currently underway. The plan for the NLMS is to link information on mortality to the NLMS every two years from 1998 through 2006 with research on the resulting database to continue, at least, through 2009. The NLMS will continue to incorporate data from the yearly Annual Social and Economic Supplement into the study as the data become available. Based on the expected size of the Annual Social and Economic Supplements to be conducted, the expected number of deaths to be added to the NLMS through the updating process will increase the mortality content of the study to nearly 500,000 cases out of a total number of approximately 3.3 million records. This effort would also include expanding the NLMS population base by incorporating new March Supplement Current Population Survey data into the study as they become available. Linkages to the SEER and CMS datasets are also available. Data Availability: Due to the confidential nature of the data used in the NLMS, the public use dataset consists of a reduced number of CPS cohorts with a fixed follow-up period of five years. NIA does not make the data available directly. Research access to the entire NLMS database can be obtained through the NIA program contact listed. Interested investigators should email the NIA contact and send in a one page prospectus of the proposed project. NIA will approve projects based on their relevance to NIA/BSR''s areas of emphasis. Approved projects are then assigned to NLMS statisticians at the Census Bureau who work directly with the researcher to interface with the database. A modified version of the public use data files is available also through the Census restricted Data Centers. However, since the database is quite complex, many investigators have found that the most efficient way to access it is through the Census programmers. * Dates of Study: 1973-2009 * Study Features: Longitudinal * Sample Size: ~3.3 Million Link: *ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/00134

Starting in mid-July of 2020, despite many delays due to Covid-19, census takers began interviewing households who had not yet responded online or via the mail to the U.S. 2020 Census. The federal census, required by the United States’ Constitution, happens once every 10 years and each time, there are new variations in enumeration (counting) techniques and what statistical data to collect. There are processes around “how” to count and then also “what” to count; the data collected needs to be useful for governance and allocation yet also respectful of privacy and remain fair and impartial for the entire U.S. population. In 2019 and 2020, hundreds of thousands of temporary workers from local communities were hired to go out into the field as census takers as well as staff offices and provide supervision. This 22nd federal census count began in January 2020 with remote portions of Alaska, where the territory was still frozen and traversable. These employed citizens are just one aspect of how the census is truly a community event. Let’s dive into the history of the U.S. Census and also learn why this count is so important.

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter. -----------------------------------------This layer shows six different types of disability. Data is from US Census American Community Survey (ACS) 5-year estimates and joined with Tempe census tracts. This layer is symbolized to show the percent of population with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). Layer includes percent of population with a disability categorized as: · an independent living difficulty · a hearing difficulty · an ambulatory difficulty · a vision difficulty · a cognitive difficulty · a selfcare difficulty Data is from US Census American Community Survey (ACS) 5-year estimates. Vintage: 2015-2019 ACS Table(s): S1810 (Not all lines of this ACS table are available in this feature layer.) Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: December 10, 2020 National Figures: data.census.gov

Abstract

The Census provides a great deal of useful information about demographic, social and economic characteristics of the population. The 1991 Census counted all persons who were living in Western Samoa on census night. It excluded Western Samoans living in other countries. The 1991 census was processed in house in the newly established Data Processing Division of the Department of Statistics. Two publications of the 1991 census have already been released. The village directory was released in October 1992 and a publication of selected tables in May 1993. The census process began with a decision by the Statistics Advisory Board late in 1989 to take a census in November 1991. A project document was prepared with the assistance from ESCAP, through its Regional Adviser for Censuses and Surveys, and submitted to UNFPA for support. The document was subsequently approved and UNFPA assistance was secured. The Minister of Statistics also gave assurance of government suport which provided the impetus for preparatory work. The first step was to draw up a detailed work plan complete with timing and duration of each activity its cost and its expected output. Once this plan was approved, some important elements could be into place. The first was to ensure that the department of statistics and cooperating agencies were organised in a way conducive to performing key census tasks.

Geographic coverage

The national coverage was based on four main regions; Apia Urban Area, North West Upolu, Rest of Upolu and Savaii.

Analysis unit

A Census of Population and Housing with community-level questionnaire would have the following units of analysis: individuals and households

Universe

The 1991 Census counted all persons who were living in Western Samoa on census night. The survey covered all household; private households and institutions. The survey covered all household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

Other essential tasks included the design of the questionnaires and the census processing system. Some important developments had occured since 1986, leading to a need for modifications in data collection or processing. Thus while consistency between the 1991 and earlier censuses was important, it was equally vital that improvements be made and proper field tests carried out in time. Among the changes to the questionnaire were included a reintroduction of a question on literacy and a more intensive approach to collecting data on economic activity. This included and additional question to enable responses to be classified to the revised skill based International Classification of Occupations (ISCO 1988) and greater probing on the activities of womendescribed as housewives, in the belief that in earlier censuses may had erroneously been excluded from labour force. In addition, the census was designed to cover housing characteristics, the first time since 1981. Two sets of questionnaires were used in the census:

1) A household questionnaire which was used to collect information on all household members (usual residents), the household, and the dwelling. 2) A housing questionnaire gives information about the building occupied by the household and some details on the household occupying the building

Cleaning operations

The 1986 census was the first to be processed in-house. Many lessons were learned during that processing which were applied in the processing of the 1991 census. The system was based on an integrated software package known as U-SP. The system was designed and tested well before the census, utilising the completed pre-test schedules. The processing of the 1989 Census of Agriculture also helped in providing experience. Processing was entirely interactive. Data entry, editing and amendments were completed by statisticians who worked "hands-on" at the computer terminals. As a result processing was faster and more efficient than in any previous census. There were some problems however. Checking and coding were delayed for at least 2 months when a state of emergency was declared following cyclone Val early in December 1991. Damage to the office building and frequent electricity failures delayed work further.

Data appraisal

Population Pyramid Sex Ratio by Age Group

Graph and download economic data for Quarterly Financial Report: U.S. Corporations: Paper: Provision for Current and Deferred Domestic Income Taxes (QFRD114322USNO) from Q4 2000 to Q1 2025 about deferred, paper, finance, nondurable goods, tax, domestic, corporate, goods, income, manufacturing, industry, and USA.

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter. -----------------------------------------This layer shows age and sex demographics in Tempe. Data is from US Census American Community Survey (ACS) 5-year estimates and joined with Tempe census tracts.This layer is symbolized to the percent of the population ages 18 to 24 years old. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online).Layer includes:Key demographicsTotal populationMale total populationFemale total populationPercent male total population (calculated)Percent female total population (calculated)Age and other indicatorsTotal population by AGE (various ranges)Total population by SELECTED AGE CATEGORIES (various ranges) Total population by SUMMARY INDICATORS (including median age, sex ratio, age dependency ratio, old age dependency ratio, child dependency ratio)Percent total population by AGE (various ranges)Percent total population by SELECTED AGE CATEGORIES (various ranges)Male by ageMale total population by AGE (various ranges)Male total population by SELECTED AGE CATEGORIES (various ranges)Male total population Median age (years)Percent male total population by AGE (various ranges)Percent male total population by SELECTED AGE CATEGORIES (various ranges)Female by ageFemale total population by AGE (various ranges)Female total population by SELECTED AGE CATEGORIES (various ranges)Female total population Median age (years)Percent female total population by AGE (various ranges)Percent female total population by SELECTED AGE CATEGORIES (various ranges)

Data is from US Census American Community Survey (ACS) 5-year estimates.

Current Vintage: 2015-2019

ACS Table(s): S0101 (Not all lines of this ACS table are available in this feature layer.)

Data downloaded from: Census Bureau's API for American Community Survey

Date of Census update: December 10, 2020

National Figures: data.census.gov

All addresses in Self Response enumeration areas (TEA 1) received one of two mailing strategies – Internet First or Internet Choice. This table is the daily and cumulative self-response and return rates by mode as well as undeliverable as addressed (UAA) rates for all addresses in areas receiving the Internet Choice mailing..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..Internet Choice mailings:.Mailing 1 – Letter and Questionnaire.Mailing 2 – Letter.Mailing 3 – Postcard.Mailing 4 – Letter and Questionnaire.Mailing 5 – “It’s Not Too Late Postcard”.Mailing 6 – Pre-NRFU COVID-19 Postcard.Mailing 7 – Letter and Questionnaire.Mailings 3-7 were targeted to nonrespondents.Mailings 6 and 7 were added during the census due to the COVID-19 pandemic.For more information about the impacts of the COVID-19 pandemic on the 2020 Census, click here: COVID-19 Decennial Census..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

Notice: The U.S. Census Bureau is delaying the release of the 2016-2020 ACS 5-year data until March 2022. For more information, please read the Census Bureau statement regarding this matter. -----------------------------------------This layer shows population broken down by race and Hispanic origin. This layer shows Census data from Esri's Living Atlas and is clipped to only show Tempe census tracts. This layer is symbolized to show the predominant race living within an area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). Data is from US Census American Community Survey (ACS) 5-year estimates. Vintage: 2015-2019 ACS Table(s): B03002 (Not all lines of this ACS table are available in this feature layer.) Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: December 10, 2020 National Figures: data.census.gov Additional Census data notes and data processing notes are available at the Esri Living Atlas Layer: https://tempegov.maps.arcgis.com/home/item.html?id=23ab8028f1784de4b0810104cd5d1c8f&view=list&sortOrder=desc&sortField=defaultFSOrder#overview (Esri's Living Atlas always shows latest data)

After the Great Reforms of the 1860s – 1870s the Russian government embarked on the construction of a modern nation-state and was faced with the need to unify all parts ofthe empire administratively, culturally, legally, and socially. The new ethno-confessional policy in Russian historiography is often called Russification because the order establishedafter the Great Reforms in the Great Russian provinces served as a model for the transformation of all parts of the empire. The Russification policy included many aspects, including Russifying [obrusenie] - the introduction of the Russian language as obligatory in the record keeping of public institutions, in court and administration, in education and everyday life. While the policy of Russifying has found ample reflection in the historiography, its results have been insufficiently studied. The purpose of this article is to fill this gap and to try to assess the process of Russifying ethnic minorities at the imperial level, drawing upon the first general census of the Russian Empire in 1897. The analysis has led to the conclusion that the policy of Russifying did notprovide the expected results.

DOI retrieved: 2022

2020 US Census Bureau Decennial Census count data for population, age, sex, and race aggregated to the tract level. Age and sex variables will not be released by the US Census Bureau until late June 2022. This data set will be updated once they are released.