Graph and download economic data for Resident Population in the Midwest Census Region (CMWRPOP) from 1900 to 2024 about Midwest Census Region, residents, population, and USA.

Resident Population in the Midwest Census Region was 69596.58400 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, Resident Population in the Midwest Census Region reached a record high of 69596.58400 in January of 2024 and a record low of 26359.00000 in January of 1900. Trading Economics provides the current actual value, an historical data chart and related indicators for Resident Population in the Midwest Census Region - last updated from the United States Federal Reserve on September of 2025.

Graph and download economic data for Unemployed Persons in Midwest Census Region (LAURD920000000000004) from Jan 1976 to Jul 2025 about Midwest Census Region, persons, household survey, unemployment, and USA.

Graph and download economic data for Employed Persons in Midwest Census Region (LASRD920000000000005) from Jan 1976 to Jul 2025 about Midwest Census Region, persons, household survey, employment, and USA.

This statistic shows the change in the regional distribution of the U.S. population each decade from 1790 to 2021. In 2021, 17.2 percent of the population in the United States lived in the Northeast.

Graph and download economic data for Civilian Labor Force in Midwest Census Region (LAURD920000000000006A) from 1976 to 2024 about Midwest Census Region, civilian, labor force, labor, household survey, and USA.

ABSTRACT: Objective: To estimate the trends of self-rated health in relation to overweight in the adult population of the capitals of the Brazilian Midwest region and the Federal District. Methods: Cross-sectional study with a population aged 20 to 59 years, using data from the Telephone-based Surveillance of Risk and Protective Factors for Chronic Diseases (VIGITEL), performed between 2008 and 2014. The estimates using the complex sampling design were made using simple linear regression, trend graphs and Boxplot. Results: The categories “poor” and “very poor” didn’t increase in the analyzed period. There was an average increase of 0.5 percentage point per year in the categories “fair” and “good” and an average decrease of 1.0 percentage point in the category “very good”. The trend analysis of mean body mass index found there was a progressive growth in all cities. The worst health perceptions showed higher values of body mass index in both sexes. We observed the existence of obese people assessing their health positively. Conclusion: Self-rated health remained relatively constant whereas the body mass index continued to grow between 2008 and 2014. The self-rated health of individuals with high body mass index (>30 kg/m2) does not seem to be directly related to their weight. Therefore, it is important to analyze the association of these two variables controlling for morbidity, health behaviors (smoking and alcohol consumption, physical activity and diet), and sociodemographic factors.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2019-2023 American Community Survey 5-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Occupation titles and their 4-digit codes are based on the 2018 Standard Occupational Classification..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2023 and later years are based on the 2022 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2023) were recoded to the 2022 Census industry codes. We recommend using caution when comparing data coded using 2022 Census industry codes with data coded using Census industry codes prior to data year 2023. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2019. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..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 beca...

This data set provides a bottom-up CO2 emissions inventory for the mid-continent region of the United States for the year 2007. The study was undertaken as part of the North American Carbon Program (NACP) Mid-Continent Intensive (MCI) campaign.

Emissions for the MCI region were compiled from these resources into nine inventory sources (Table 1):(1) forest biomass and soil carbon, harvested woody products carbon, and agricultural soil carbon from the U.S. Greenhouse Gas (GHG) Inventory (EPA, 2010; Heath et al., 2011); (2) high resolution data on fossil and biofuel CO2 emissions from Vulcan (Gurney et al,. 2009); (3) CO2 uptake by agricultural crops, lateral transport in crop biomass harvest, and livestock CO2 emissions using USDA statistics (West et al., 2011); (4) agricultural residue burning (McCarty et al., 2011); (5) CO2 emissions from landfills (EPA, 2012); (6) and CO2 losses from human respiration using U.S. Census data (West et al., 2009).

The CO2 inventory in the MCI region was dominated by fossil fuel combustion, carbon uptake during crop production, carbon export in biomass (commodities) from the region, and to a lesser extent, carbon sinks in forest growth and incorporation of carbon into timber products.

ObjectiveThere is currently inconclusive evidence regarding the relationship between recidivism and mental illness. This retrospective study aimed to use rigorous machine learning methods to understand the unique predictive utility of mental illness for recidivism in a general population (i.e.; not only those with mental illness) prison sample in the United States.MethodParticipants were adult men (n = 322) and women (n = 72) who were recruited from three prisons in the Midwest region of the United States. Three model comparisons using Bayesian correlated t-tests were conducted to understand the incremental predictive utility of mental illness, substance use, and crime and demographic variables for recidivism prediction. Three classification statistical algorithms were considered while evaluating model configurations for the t-tests: elastic net logistic regression (GLMnet), k-nearest neighbors (KNN), and random forests (RF).ResultsRates of substance use disorders were particularly high in our sample (86.29%). Mental illness variables and substance use variables did not add predictive utility for recidivism prediction over and above crime and demographic variables. Exploratory analyses comparing the crime and demographic, substance use, and mental illness feature sets to null models found that only the crime and demographics model had an increased likelihood of improving recidivism prediction accuracy.ConclusionsDespite not finding a direct relationship between mental illness and recidivism, treatment of mental illness in incarcerated populations is still essential due to the high rates of mental illnesses, the legal imperative, the possibility of decreasing institutional disciplinary burden, the opportunity to increase the effectiveness of rehabilitation programs in prison, and the potential to improve meaningful outcomes beyond recidivism following release.

In 2020, about 82.66 percent of the total population in the United States lived in cities and urban areas. As the United States was one of the earliest nations to industrialize, it has had a comparatively high rate of urbanization over the past two centuries. The urban population became larger than the rural population during the 1910s, and by the middle of the century it is expected that almost 90 percent of the population will live in an urban setting. Regional development of urbanization in the U.S. The United States began to urbanize on a larger scale in the 1830s, as technological advancements reduced the labor demand in agriculture, and as European migration began to rise. One major difference between early urbanization in the U.S. and other industrializing economies, such as the UK or Germany, was population distribution. Throughout the 1800s, the Northeastern U.S. became the most industrious and urban region of the country, as this was the main point of arrival for migrants. Disparities in industrialization and urbanization was a key contributor to the Union's victory in the Civil War, not only due to population sizes, but also through production capabilities and transport infrastructure. The Northeast's population reached an urban majority in the 1870s, whereas this did not occur in the South until the 1950s. As more people moved westward in the late 1800s, not only did their population growth increase, but the share of the urban population also rose, with an urban majority established in both the West and Midwest regions in the 1910s. The West would eventually become the most urbanized region in the 1960s, and over 90 percent of the West's population is urbanized today. Urbanization today New York City is the most populous city in the United States, with a population of 8.3 million, while California has the largest urban population of any state. California also has the highest urbanization rate, although the District of Columbia is considered 100 percent urban. Only four U.S. states still have a rural majority, these are Maine, Mississippi, Montana, and West Virginia.

United States CPI U: Midwest: Size Class A data was reported at 236.586 1982-1984=100 in Jun 2018. This records an increase from the previous number of 235.884 1982-1984=100 for May 2018. United States CPI U: Midwest: Size Class A data is updated monthly, averaging 170.000 1982-1984=100 from Dec 1977 (Median) to Jun 2018, with 433 observations. The data reached an all-time high of 236.586 1982-1984=100 in Jun 2018 and a record low of 60.600 1982-1984=100 in Dec 1977. United States CPI U: Midwest: Size Class A data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I014: Consumer Price Index: Urban: By Region. All metropolitan areas with population over 1.5 million

Prepared by the Inter-university Consortium for Political and Social Research, the block group subset was extracted from the Census of Population and Housing, 2000, Summary File 3 (SF3). The SF3 data contain information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, household relationship, marital status, caregiving by grandparents, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry to the United States, migration, place of work, journey to work, school enrollment, educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include housing unit vacancy status, housing unit tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, rent, and shelter costs. The information in SF3 is presented in 813 tables, one variable per table cell, plus additional variables with geographic information. However, only 409 of these tables are shown for the block group and higher levels of geography. The remaining 404 tables, which are shown for the census tract and higher levels of geography, were excluded from the block group subset. Cases in the summary file data are classified by levels of observation, known as "summary levels" in the Census Bureau's nomenclature. The block group subset comprises all of the cases in the SF3 data for summary level 150. Five data files are provided with this collection. There is a block group subset for each of the four census regions (Northeast, Midwest, South, and West), plus a national subset that covers all of the regions. (Source: downloaded from ICPSR 7/13/10)

United States CPI U: Midwest: Size Class B/C data was reported at 150.338 Dec1996=100 in Jun 2018. This records an increase from the previous number of 150.217 Dec1996=100 for May 2018. United States CPI U: Midwest: Size Class B/C data is updated monthly, averaging 127.372 Dec1996=100 from Dec 1996 (Median) to Jun 2018, with 259 observations. The data reached an all-time high of 150.338 Dec1996=100 in Jun 2018 and a record low of 100.000 Dec1996=100 in Jan 1997. United States CPI U: Midwest: Size Class B/C data remains active status in CEIC and is reported by Bureau of Labor Statistics. The data is categorized under Global Database’s USA – Table US.I014: Consumer Price Index: Urban: By Region. All metropolitan areas with population smaller than 1.5 million

Prepared by the Inter-university Consortium for Political and Social Research, this data collection consists of selected subsets extracted from the Census of Population and Housing, 2000, Summary File 3 (SF3). The SF3 data contain information compiled from the questions asked of a sample of persons and housing units enumerated in Census 2000. Population items include sex, age, race, Hispanic or Latino origin, household relationship, marital status, caregiving by grandparents, language and ability to speak English, ancestry, place of birth, citizenship status and year of entry to the United States, migration, place of work, journey to work, school enrollment, educational attainment, veteran status, disability, employment status, industry, occupation, class of worker, income, and poverty status. Housing items include housing unit vacancy status, housing unit tenure (owner/renter), number of rooms, number of bedrooms, year moved into unit, occupants per room, units in structure, year structure built, heating fuel, telephone service, plumbing and kitchen facilities, vehicles available, value of home, rent, and shelter costs. The information in SF3 is presented in 813 tables, one variable per table cell, plus additional variables with geographic information. Cases in the summary file data are classified by levels of observation, known as "summary levels" in the Census Bureau's nomenclature, which served as the selection criteria for the subsets generated by ICPSR. Each subset comprises all of the cases in one of 10 summary levels: the nation (summary level 010), states (summary level 040), Metropolitan Statistical Areas (MSA)/Consolidated Metropolitan Statistical Areas (CMSA) (summary level 380), Primary Metropolitan Statistical Areas (PMSA) (summary level 385), places (summary level 160), counties (summary level 050), county subdivisions (summary level 060), whole census tracts (summary level 140), census tracts in places (summary level 158), and 5-Digit ZIP Code Tabulation Areas (ZCTA) (summary level 860). Four files are supplied for the summary level 860 subset: a single file that contains all of the SF3 tables, plus three smaller files, each of which contains about one third of the tables. Five files are supplied for each of the summary level 010, 040, 380, 385, 160, and 050 subsets: a single file that contains all of the SF3 tables, plus four smaller files, each of which contains approximately one quarter of the tables. Fifteen files are provided for each of the summary level 140 and 158 subsets. There is a national file with all of the SF3 tables, plus two smaller national files, each of which contains approximately one half of the tables. Additionally, there are three files for each of the four census regions (Northeast, Midwest, South, and West): a file with all tables and two smaller files each containing about one half of the tables. Twenty files are supplied for summary level 060. There is a national file with all tables, plus three smaller national files, each of which contains approximately one third of the tables. In addition, there are four files for each of the four census regions: a file with all tables and three smaller files each containing about one third of the tables. (Source: downloaded from ICPSR 7/13/10)

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at ICPSR at https://doi.org/10.3886/ICPSR13402.v2. We highly recommend using the ICPSR version as they may make this dataset available in multiple data formats in the future.

Population aged 5 years and over by means of travel to work, school or college by NUTS3. (Census 2022 Theme 11 Table 1 )Census 2022 table 11.1 is population aged 5+ by means of travel to work, school or college. Attributes include a breakdown of population by means of travel to work, school or college. Census 2022 theme 11 is Commuting, Working from Home and Childcare. The Nomenclature of Territorial Units for Statistics (NUTS) were created by Eurostat in order to define territorial units for the production of regional statistics across the European Union. In 2003 the NUTS classification was established within a legal framework (Regulation (EC) No 1059/2003).Changes made under the 2014 Local Government Act prompted a revision of the Irish NUTS 2 and NUTS 3 Regions. The main changes at NUTS 3 level were the transfer of South Tipperary from the South-East into the Mid-West NUTS 3 region and the movement of Louth from the Border to the Mid-East NUTS 3 Region. NUTS 3 Regions are grouped into three NUTS 2 Regions (Northern and Western, Southern, Eastern and Midland) which correspond to the Regional Assemblies established in the 2014 Local Government Act. The revisions made to the NUTS boundaries have been given legal status under Commission Regulation (EU) 2016/2066.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. NUTS3 Regions 2015This dataset is provided by Tailte Éireann

Usually resident population aged 1 year and over by usual residence 1 year before Census Day by NUTS3. (Census 2022 Theme 2 Table 3 )Census 2022 table 2.3 is the population usually resident in Ireland by usual residence 1 year before Census Day. Details include population by usual residence 1 year before Census Day. Census 2022 theme 2 is Migration, Ethnicity, Religion and Foreign Languages. The Nomenclature of Territorial Units for Statistics (NUTS) were created by Eurostat in order to define territorial units for the production of regional statistics across the European Union. In 2003 the NUTS classification was established within a legal framework (Regulation (EC) No 1059/2003).Changes made under the 2014 Local Government Act prompted a revision of the Irish NUTS 2 and NUTS 3 Regions. The main changes at NUTS 3 level were the transfer of South Tipperary from the South-East into the Mid-West NUTS 3 region and the movement of Louth from the Border to the Mid-East NUTS 3 Region. NUTS 3 Regions are grouped into three NUTS 2 Regions (Northern and Western, Southern, Eastern and Midland) which correspond to the Regional Assemblies established in the 2014 Local Government Act. The revisions made to the NUTS boundaries have been given legal status under Commission Regulation (EU) 2016/2066.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. NUTS3 Regions 2015This dataset is provided by Tailte Éireann

ABSTRACT Purpose: To observe the frequency of occurrence of affections involving the adnexa or the external eye, as well as to describe the demographic profile of patients. Methods: A prospective, population based, randomized study was done in the Midwest region of the state of São Paulo, in the years 2004/2005. Using a Mobile Ophthalmic Unit we evaluated 11,000 people. A comprehensive eye exam was performed. Data were transferred to excel table and for this study we used information relating to annexes and external eye diseases. The frequency of occurrence of the problems detected was statistically analyzed. Results: We identified 1,581(14.4%) disorders in the adnexa or in the external eye. The most common disorders were pterygium (9.4%), hordeolum (0.8%) and changes in eyelid position (1.7%) (ectropion, ptosis and trichiasis). Trauma, ectropion and pterygium were statistically more frequent in the male population. Conclusion: Of the surveyed disorders the most frequent in the population was pterygium, followed by inflammatory changes and alterations in the eyelid position.

Mental health disorders and symptoms by recidivism status.

This release covers the entire study region (Midwest) and includes data used for retrospective population modeling effort as well as climate projection data for each county on the summer breeding grounds for three future time periods (2025-2045, 2060-2080, 2080-2100) under three climate scenarios (RCP 2.6, 4.5, 8.5) of key thermal climate profiles (based on a range of typical thermal thresholds) relevant to butterfly (and other insect growth) including growing degree days and precipitation (Apr through Aug). Values were generated from an ensemble of GCMs.