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 July of 2025.

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

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

About this dataset

Context

The dataset tabulates the Midwest City population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Midwest City across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Midwest City was 58,086, a 0.15% increase year-by-year from 2022. Previously, in 2022, Midwest City population was 57,997, a decline of 0.29% compared to a population of 58,164 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Midwest City increased by 4,319. In this period, the peak population was 58,464 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Midwest City is shown in this column.
• Year on Year Change: This column displays the change in Midwest City population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Midwest City Population by Year. You can refer the same here

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.

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.

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.

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 [UNITED STATES]: SUMMARY FILE 1, STATES (ICPSR 3194). Summary File 1 data contain information compiled from the questions asked of all people and of every housing unit enumerated in Census 2000: sex, age, race, Hispanic or Latino origin, type of living quarters (household/group quarters), household relationship, housing unit vacancy status, and housing unit tenure (owner/renter). The information is presented in 286 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. Each subset comprises all of the cases in one of two summary levels: whole census tracts (summary level 140) and census tracts in places (summary level 158). The latter covers whole tracts completely within places and portions of tracts that cross place boundaries. Five files are provided for each subset. There is a file for each of the four census regions (East, Midwest, South, and West) and a combined national file. Puerto Rico is included in the national and South files.

This dataset includes microsatellite genotypes for 8,454 brook trout from 188 wild Midwestern populations and 26 hatchery strains of both Midwest and eastern (Atlantic seaboard) origin. Each individual was genotyped at either 5 or 7 loci.

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.

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.

U.S. Census Bureau QuickFacts statistics for Midwest City city, Oklahoma. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

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

Population aged 5 years and over by journey time to work, school or college by NUTS3. (Census 2022 Theme 11 Table 3 )Census 2022 table 11.3 is population aged 5+ by journey time to work, school or college. Attributes include a breakdown of population by time taken to 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

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

Milwaukee is the largest city in the state of Wisconsin and the fifth-largest city in the Midwestern United States. The county seat of Milwaukee County, it is on Lake Michigan's western shore. Ranked by estimated 2014 population, Milwaukee was the 31st largest city in the United States.[7] The city's estimated population in 2015 was 600,155.[8] Milwaukee is the main cultural and economic center of the Milwaukee metropolitan area. It is also part of the larger Milwaukee-Racine-Waukesha combined statistical area, which had an estimated population of 2,026,243 in the 2010 census. Milwaukee is also the second most densely populated metropolitan area in the Midwest, surpassed only by Chicago. View our Open Data Policy by selecting the link below, https://city.milwaukee.gov/ImageLibrary/Groups/cityOpenData/MilwaukeeOpenDataPolicy.pdf

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