This table contains 2394 series, with data for years 1991 -1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 3;Income adequacy quintile 2 ...), Age (14 items: At 25 years; At 30 years; At 35 years; At 40 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Probability of survival; Low 95% confidence interval; life expectancy; High 95% confidence interval; life expectancy ...).

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Live Oak. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Live Oak, the median income for all workers aged 15 years and older, regardless of work hours, was $55,656 for males and $38,659 for females.

These income figures highlight a substantial gender-based income gap in Live Oak. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the city of Live Oak.

- Full-time workers, aged 15 years and older: In Live Oak, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,760, while females earned $53,843, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Live Oak.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Live Oak.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Live Oak median household income by race. You can refer the same here

This web map displays data from the voter registration database as the percent of registered voters by census tract in King County, Washington. The data for this web map is compiled from King County Elections voter registration data for the years 2013-2019. The total number of registered voters is based on the geo-location of the voter's registered address at the time of the general election for each year. The eligible voting population, age 18 and over, is based on the estimated population increase from the US Census Bureau and the Washington Office of Financial Management and was calculated as a projected 6 percent population increase for the years 2010-2013, 7 percent population increase for the years 2010-2014, 9 percent population increase for the years 2010-2015, 11 percent population increase for the years 2010-2016 & 2017, 14 percent population increase for the years 2010-2018 and 17 percent population increase for the years 2010-2019. The total population 18 and over in 2010 was 1,517,747 in King County, Washington. The percentage of registered voters represents the number of people who are registered to vote as compared to the eligible voting population, age 18 and over. The voter registration data by census tract was grouped into six percentage range estimates: 50% or below, 51-60%, 61-70%, 71-80%, 81-90% and 91% or above with an overall 84 percent registration rate. In the map the lighter colors represent a relatively low percentage range of voter registration and the darker colors represent a relatively high percentage range of voter registration. PDF maps of these data can be viewed at King County Elections downloadable voter registration maps. The 2019 General Election Voter Turnout layer is voter turnout data by historical precinct boundaries for the corresponding year. The data is grouped into six percentage ranges: 0-30%, 31-40%, 41-50% 51-60%, 61-70%, and 71-100%. The lighter colors represent lower turnout and the darker colors represent higher turnout. The King County Demographics Layer is census data for language, income, poverty, race and ethnicity at the census tract level and is based on the 2010-2014 American Community Survey 5 year Average provided by the United States Census Bureau. Since the data is based on a survey, they are considered to be estimates and should be used with that understanding. The demographic data sets were developed and are maintained by King County Staff to support the King County Equity and Social Justice program. Other data for this map is located in the King County GIS Spatial Data Catalog, where data is managed by the King County GIS Center, a multi-department enterprise GIS in King County, Washington. King County has nearly 1.3 million registered voters and is the largest jurisdiction in the United States to conduct all elections by mail. In the map you can view the percent of registered voters by census tract, compare registration within political districts, compare registration and demographic data, verify your voter registration or register to vote through a link to the VoteWA, Washington State Online Voter Registration web page.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

About this dataset

Context

The dataset tabulates the Sumter 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 Sumter 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 Sumter was 42,766, a 0.07% increase year-by-year from 2022. Previously, in 2022, Sumter population was 42,734, a decline of 1.03% compared to a population of 43,180 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Sumter increased by 2,477. In this period, the peak population was 43,420 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 Sumter is shown in this column.
• Year on Year Change: This column displays the change in Sumter 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 Sumter Population by Year. You can refer the same here

The 1992 Malawi Demographic and Health Survey (MDHS) was a nationally representative sample survey designed to provide information on levels and trends in fertility, early childhood mortality and morbidity, family planning knowledge and use, and maternal and child health. The survey was implemented by the National Statistical Office during September to November 1992. In 5323 households, 4849 women age 15-49 years and 1151 men age 20-54 years were interviewed. The Malawi Demographic and Health Survey (MDHS) was a national sample survey of women and men of reproductive age designed to provide, among other things, information on fertility, family planning, child survival, and health of mothers and children. Specifically, the main objectives of the survey were to: Collect up-to-date information on fertility, infant and child mortality, and family planning Collect information on health-related matters, including breastleeding, antenatal and maternity services, vaccinations, and childhood diseases and treatment Assess the nutritional status of mothers and children Collect information on knowledge and attitudes regarding AIDS Collect information suitable for the estimation of mortality related to pregnancy and childbearing Assess the availability of health and family planning services. MAIN FINDINGS The findings indicate that fertility in Malawi has been declining over the last decade; at current levels a woman will give birth to an average of 6.7 children during her lifetime. Fertility in rural areas is 6.9 children per woman compared to 5.5 children in urban areas. Fertility is higher in the Central Region (7.4 children per woman) than in the Northem Region (6.7) or Southern Region (6.2). Over the last decade, the average age at which a woman first gives birth has risen slightly over the last decade from 18.3 to 18.9 years. Still, over one third of women currently under 20 years of age have either already given birlh to at least one child or are currently pregnant. Although 58 percent of currently married women would like to have another child, only 19 percent want one within the next two years. Thirty-seven percent would prefer to walt two or more years. Nearly one quarter of married women want no more children than they already have. Thus, a majority of women (61 percent) want either to delay their next birth or end childbearing altogether. This represents the proportion of women who are potentially in need of family planning. Women reported an average ideal family size of 5.7 children (i.e., wanted fertility), one child less than the actual fertility level measured in the surveyfurther evidence of the need for family planning methods. Knowledge of contraceptive methods is high among all age groups and socioeconomic strata of women and men. Most women and men also know of a source to obtain a contraceptive method, although this varies by the type of method. The contraceptive pill is the most commonly cited method known by women; men are most familiar with condoms. Despite widespread knowledge of family planning, current use of contraception remains quite low. Only 7 percent of currently married women were using a modem method and another 6 percent were using a traditional method of family planning at the time of the survey. This does, however, represent an increase in the contraceptive prevalence rate (modem methods) from about 1 percent estimated from data collected in the 1984 Family Formation Survey. The modem methods most commonly used by women are the pill (2.2 percent), female sterilisation (1.7 percent), condoms (1.7 percent), and injections (1.5 percent). Men reported higher rates of contraceptive use (13 percent use of modem methods) than women. However, when comparing method-specific use rates, nearly all of the difference in use between men and women is explained by much higher condom use among men. Early childhood mortality remains high in Malawi; the under-five mortality rate currently stands at 234 deaths per 1000 live births. The infant mortality rate was estimated at 134 per 10130 live births. This means that nearly one in seven children dies before his first birthday, and nearly one in four children does not reach his fifth birthday. The probability of child death is linked to several factors, most strikingly, low levels of maternal education and short intervals between births. Children of uneducated women are twice as likely to die in the first five years of life as children of women with a secondary education. Similarly, the probablity of under-five mortality for children with a previous birth interval of less than 2 years is two times greater than for children with a birth interval of 4 or more years. Children living in rural areas have a higher rate ofunder-fwe mortality than urban children, and children in the Central Region have higher mortality than their counterparts in the Northem and Southem Regions. Data were collected that allow estimation ofmatemalmortality. It is estimated that for every 100,000 live births, 620 women die due to causes related to pregnancy and childbearing. The height and weight of children under five years old and their mothers were collected in the survey. The results show that nearly one half of children under age five are stunted, i.e., too short for their age; about half of these are severely stunted. By age 3, two-thirds of children are stunted. As with childhood mortality, chronic undernutrition is more common in rural areas and among children of uneducated women. The duration of breastfeeding is relatively long in Malawi (median length, 21 months), but supplemental liquids and foods are introduced at an early age. By age 2-3 months, 76 percent of children are already receiving supplements. Mothers were asked to report on recent episodes of illness among their young children. The results indicate that children age 6-23 months are the most vulnerable to fever, acute respiratory infection (ARI), and diarrhea. Over half of the children in this age group were reported to have had a fever, about 40 percent had a bout with diarrhea, and 20 percent had symptoms indicating ARI in the two-week period before the survey. Less than half of recently sick children had been taken to a health facility for treatment. Sixty-three percent of children with diarrhea were given rehydration therapy, using either prepackaged rehydration salts or a home-based preparation. However, one quarter of children with diarrhea received less fluid than normal during the illness, and for 17 percent of children still being breastfed, breastfeeding of the sick child was reduced. Use of basic, preventive maternal and child health services is generally high. For 90 percent of recent births, mothers had received antenatal care from a trained medical person, most commonly a nurse or trained midwife. For 86 percent of births, mothers had received at least one dose of tetanus toxoid during pregnancy. Over half of recent births were delivered in a health facility. Child vaccination coverage is high; 82 percent of children age 12-23 months had received the full complement of recommended vaccines, 67 percent by exact age 12 months. BCG coverage and first dose coverage for DPT and polio vaccine were 97 percent. However, 9 percent of children age 12-23 months who received the first doses of DPT and polio vaccine failed to eventually receive the recommended third doses. Information was collected on knowledge and attitudes regarding AIDS. General knowledge of AIDS is nearly universal in Malawi; 98 percent of men and 95 percent of women said they had heard of AIDS. Further, the vast majority of men and women know that the disease is transmitted through sexual intercourse. Men tended to know more different ways of disease transmission than women, and were more likely to mention condom use as a means to prevent spread of AIDS. Women, especially those living in rural areas, are more likely to hold misconceptions about modes of disease transmission. Thirty percent of rural women believe that AIDS can not be prevented.

Context

There's a story behind every dataset and here's your opportunity to share yours.

This dataset collects characteristics of the population in each region (age distribution, unemployment rate, immigration percent and primary economic sector) and cross it with the votes per each political part.

It has 52 fields:

1) Code [String]: Region code of the different Spanish areas. There are 8126 different regions, but the dataset only contains 8119, because some sources were incomplete.

2) RegionName [String]: Name of the region.

3) Population [Int]: Amount of people living in that area (1st January 2015)

4) TotalCensus [Int]: Number of people over 18 years old, which means that can vote.

5) TotalVotes [Int]: Number of total votes.

6) AbstentionPtge [Float]: Percent of the people that have not votes in the election. (TotalCensus-TotalVotes)/TotalCensus*100 %

7) BlankVotesPtge [Float]: Percent of votes that were blank. Calculated as follows: BlankVotes/TotalVotes*100 %

8) NullVotesPtge [Float]: Percent of votes that were null. Calculated as follows: NullVotes/TotalVotes*100 %

9) PP_Ptge [Float]: Percent of the votes given to the political party called “Partido Popular”. (PP_Votes)/TotalVotes*100 %

10) PSOE_Ptge [Float]: Percent of the votes given to the political party called “Partido Socialista Obrero Español” (PSOE_Votes)/TotalVotes*100 %

11) Podemos_Ptge [Float]: Percent of the votes given to the political party called “Podemos” (Podemos_Votes)/TotalVotes*100 %

12) Ciudadanos_Ptge [Float]: Percent of the votes given to the political party called “Ciudadanos” (Ciudadanos_Votes)/TotalVotes*100 %

13) Others_Ptge [Float]: Percent of the votes given to the others political parties (∑▒MinoritaryVotes)/TotalVotes*100 %

14) Age_0-4_Ptge [Float]: Percent of the populations which age is between 0 and 4 years old. It is calculated as follows: (Number of people in (0-4))/TotalPopulation*100 %

15) Age_5-9_Ptge [Float]: Percent of the populations which age is between 5 and 9 year old.

16) Age_10-14_Ptge [Float]: Percent of the populations which age is between 10 and 14 years old

17) Age_15-19_Ptge [Float]: Percent of the populations which age is between 15 and 19 years old

18) Age_20-24_Ptge [Float]: Percent of the populations which age is between 20 and 24 years old

19) Age_25-29_Ptge [Float]: Percent of the populations which age is between 25 and 29 years old

20) Age_30-34_Ptge [Float]: Percent of the populations which age is between 30 and 34 years old

21) Age_35-39_Ptge [Float]: Percent of the populations which age is between 35 and 39 years old

22) Age_40-44_Ptge [Float]: Percent of the populations which age is between 40 and 44 years old

23) Age_45-49_Ptge [Float]: Percent of the populations which age is between 45 and 49 years old

24) Age_50-54_Ptge [Float]: Percent of the populations which age is between 50 and 54 years old

25) Age_55-59_Ptge [Float]: Percent of the populations which age is between 55 and 59 years old

26) Age_60-64_Ptge [Float]: Percent of the populations which age is between 60 and 64 years old

27) Age_65-69_Ptge [Float]: Percent of the populations which age is between 65 and 69 years old

28) Age_70-74_Ptge [Float]: Percent of the populations which age is between 70 and 74 years old

29) Age_75-79_Ptge [Float]: Percent of the populations which age is between 75 and 79 year old

30) Age_80-84_Ptge [Float]: Percent of the populations which age is between 80 and 84 years old

31) Age_85-89_Ptge [Float]: Percent of the populations which age is between 85 and 89 year old

32) Age_90-94_Ptge [Float]: Percent of the populations which age is between 90 and 94 years old

33) Age_95-99_Ptge [Float]: Percent of the populations which age is between 95 and 99 years old

34) Age_100+_Ptge [Float]: Percent of the populations which is older than 100 years old.

35) ManPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: ManPopulation/TotalPopulation*100

36) WomanPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: WomanPopulation/TotalPopulation*100

37) SpanishPtge [Float]: Percentage of people with spanish nationality in a region. Calculated as follows: NativeSpanishPopulation/TotalPopulation*100

38) ForeignersPtge [Float]: Percentage of foreign people in a region. Calculated as follows: ForeignPopulation/TotalPopulation*100

39) SameComAutonPtge [Float]: Percentage of people who live in the same autonomic community (same province) that was born. Calculated as follows: SameComAutonPopulation/TotalPopulation*100

40) SameComAutonDiffProvPtge [Float]: Percentage of people who live in the same autonomic community (different province) that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

41) DifComAutonPtge [Float]: Percentage of people who live in different autonomic community that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

42) UnemployLess25_Ptge [Float]: Percent of unemployed people that are under 25 years and older than 18. It is calculated over the total amount of unemployment. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalUnemployment*100

43) Unemploy25_40_Ptge [Float]: Percent of unemployed people that are 25-40 years over the total amount of unemployment. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalUnemployment*100

44) UnemployMore40_Ptge [Float]: Percent of unemployed people that are older that 40 and younger than 69 years over the total amount of unemployment. (Unemployment(40-69)_Man+Unemployment(40-69)_Woman)/TotalUnemployment*100

45) UnemployLess25_population_Ptge [Float]: Percent of unemployed people younger than 25 and older than 18, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

46) Unemploy25_40_population_Ptge [Float]: Percent of unemployed people (25-40) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalPopulation*100

47) UnemployMore40_population_Ptge [Float]: Percent of unemployed people (40-69) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

48) AgricultureUnemploymentPtge [Float]: Percent of unemployment in the agriculture sector relative to the total amount of unemployment. PeopleUnemployedInAgriculture/TotalUnemployment*100

49) IndustryUnemploymentPtge [Float]: Percent of unemployment in the industry sector relative to the total amount of unemployment. PeopleUnemployedInIndustry/TotalUnemployment*100

50) ConstructionUnemploymentPtge [Float]: Percent of unemployment in the construction sector relative to the total amount of unemployment. PeopleUnemployedInConstruction/TotalUnemployment*100

51) ServicesUnemploymentPtge [Float]: Percent of unemployment in the services sector relative to the total amount of unemployment. PeopleUnemployedInServices/TotalUnemployment*100

52) NotJobBeforeUnemploymentPtge [Float]: Percent of unemployment of people that didn’t have an employ before, over the total amount of unemployment. PeopleUnemployedWithoutEmployBefore/TotalUnemployment*100

References:

[1] Unemployment: www.datos.gob.es/es/catalogo/e00142804-paro-registrado-por-municipios

[2] Age distribution per region Relation between Spanish and foreigners Relation between woman and man Relation between people born in the same area or different areas of Spain http://www.ine.es/dynt3/inebase/index.htm?type=pcaxis&file=pcaxis&path=%2Ft20%2Fe245%2Fp05%2F%2Fa2015

[3] Congress elections result of Spanish election (June 2016) http://www.infoelectoral.interior.es/min/areaDescarga.html?method=inicio

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Osceola by race. It includes the distribution of the Non-Hispanic population of Osceola across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Osceola across relevant racial categories.

Key observations

Of the Non-Hispanic population in Osceola, the largest racial group is White alone with a population of 3,647 (90% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Osceola
• Population: The population of the racial category (for Non-Hispanic) in the Osceola is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Osceola total Non-Hispanic population. 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 Osceola Population by Race & Ethnicity. You can refer the same here

Overview The Human Vital Signs Dataset is a comprehensive collection of key physiological parameters recorded from patients. This dataset is designed to support research in medical diagnostics, patient monitoring, and predictive analytics. It includes both original attributes and derived features to provide a holistic view of patient health.

Attributes Patient ID

Description: A unique identifier assigned to each patient. Type: Integer Example: 1, 2, 3, ... Heart Rate

Description: The number of heartbeats per minute. Type: Integer Range: 60-100 bpm (for this dataset) Example: 72, 85, 90 Respiratory Rate

Description: The number of breaths taken per minute. Type: Integer Range: 12-20 breaths per minute (for this dataset) Example: 16, 18, 15 Timestamp

Description: The exact time at which the vital signs were recorded. Type: Datetime Format: YYYY-MM-DD HH:MM Example: 2023-07-19 10:15:30 Body Temperature

Description: The body temperature measured in degrees Celsius. Type: Float Range: 36.0-37.5°C (for this dataset) Example: 36.7, 37.0, 36.5 Oxygen Saturation

Description: The percentage of oxygen-bound hemoglobin in the blood. Type: Float Range: 95-100% (for this dataset) Example: 98.5, 97.2, 99.1 Systolic Blood Pressure

Description: The pressure in the arteries when the heart beats (systolic pressure). Type: Integer Range: 110-140 mmHg (for this dataset) Example: 120, 130, 115 Diastolic Blood Pressure

Description: The pressure in the arteries when the heart rests between beats (diastolic pressure). Type: Integer Range: 70-90 mmHg (for this dataset) Example: 80, 75, 85 Age

Description: The age of the patient. Type: Integer Range: 18-90 years (for this dataset) Example: 25, 45, 60 Gender

Description: The gender of the patient. Type: Categorical Categories: Male, Female Example: Male, Female Weight (kg)

Description: The weight of the patient in kilograms. Type: Float Range: 50-100 kg (for this dataset) Example: 70.5, 80.3, 65.2 Height (m)

Description: The height of the patient in meters. Type: Float Range: 1.5-2.0 m (for this dataset) Example: 1.75, 1.68, 1.82 Derived Features Derived_HRV (Heart Rate Variability)

Description: A measure of the variation in time between heartbeats. Type: Float Formula: 𝐻 𝑅

𝑉

Standard Deviation of Heart Rate over a Period Mean Heart Rate over the Same Period HRV= Mean Heart Rate over the Same Period Standard Deviation of Heart Rate over a Period ​

Example: 0.10, 0.12, 0.08 Derived_Pulse_Pressure (Pulse Pressure)

Description: The difference between systolic and diastolic blood pressure. Type: Integer Formula: 𝑃

𝑃

Systolic Blood Pressure − Diastolic Blood Pressure PP=Systolic Blood Pressure−Diastolic Blood Pressure Example: 40, 45, 30 Derived_BMI (Body Mass Index)

Description: A measure of body fat based on weight and height. Type: Float Formula: 𝐵 𝑀

𝐼

Weight (kg) ( Height (m) ) 2 BMI= (Height (m)) 2

Weight (kg) ​

Example: 22.8, 25.4, 20.3 Derived_MAP (Mean Arterial Pressure)

Description: An average blood pressure in an individual during a single cardiac cycle. Type: Float Formula: 𝑀 𝐴

𝑃

Diastolic Blood Pressure + 1 3 ( Systolic Blood Pressure − Diastolic Blood Pressure ) MAP=Diastolic Blood Pressure+ 3 1 ​ (Systolic Blood Pressure−Diastolic Blood Pressure) Example: 93.3, 100.0, 88.7 Target Feature Risk Category Description: Classification of patients into "High Risk" or "Low Risk" based on their vital signs. Type: Categorical Categories: High Risk, Low Risk Criteria: High Risk: Any of the following conditions Heart Rate: > 90 bpm or < 60 bpm Respiratory Rate: > 20 breaths per minute or < 12 breaths per minute Body Temperature: > 37.5°C or < 36.0°C Oxygen Saturation: < 95% Systolic Blood Pressure: > 140 mmHg or < 110 mmHg Diastolic Blood Pressure: > 90 mmHg or < 70 mmHg BMI: > 30 or < 18.5 Low Risk: None of the above conditions Example: High Risk, Low Risk This dataset, with a total of 200,000 samples, provides a robust foundation for various machine learning and statistical analysis tasks aimed at understanding and predicting patient health outcomes based on vital signs. The inclusion of both original attributes and derived features enhances the richness and utility of the dataset.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Morrow by race. It includes the distribution of the Non-Hispanic population of Morrow across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Morrow across relevant racial categories.

Key observations

Of the Non-Hispanic population in Morrow, the largest racial group is Black or African American alone with a population of 1,941 (43.38% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Morrow
• Population: The population of the racial category (for Non-Hispanic) in the Morrow is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Morrow total Non-Hispanic population. 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 Morrow Population by Race & Ethnicity. You can refer the same here

Coronavirus (COVID-19) vaccination rates for people aged 18 years and over in England. Estimates by socio-demographic characteristic, region and local authority.

National and subnational mid-year population estimates for the UK and its constituent countries by administrative area, age and sex (including components of population change, median age and population density).

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

The 1993 Turkish Demographic and Health Survey (TDHS) is a nationally representative survey of ever-married women less than 50 years old. The survey was designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. The TDHS was conducted by the Hacettepe University Institute of Population Studies under a subcontract through an agreement between the General Directorate of Mother and Child Health and Family Planning, Ministry of Health and Macro International Inc. of Calverton, Maryland. Fieldwork was conducted from August to October 1993. Interviews were carried out in 8,619 households and with 6,519 women. The Turkish Demographic and Health Survey (TDHS) is a national sample survey of ever-married women of reproductive ages, designed to collect data on fertility, marriage patterns, family planning, early age mortality, socioeconomic characteristics, breastfeeding, immunisation of children, treatment of children during episodes of illness, and nutritional status of women and children. The TDHS, as part of the international DHS project, is also the latest survey in a series of national-level population and health surveys in Turkey, which have been conducted by the Institute of Population Studies, Haeettepe University (HIPS). More specifically, the objectives of the TDHS are to: Collect data at the national level that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyse the direct and indirect factors that determine levels and trends in fertility and childhood mortality; Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; Collect data on mother and child health, including immunisations, prevalence and treatment of diarrhoea, acute respiratory infections among children under five, antenatal care, assistance at delivery, and breastfeeding; Measure the nutritional status of children under five and of their mothers using anthropometric measurements. The TDHS information is intended to assist policy makers and administrators in evaluating existing programs and in designing new strategies for improving family planning and health services in Turkey. MAIN RESULTS Fertility in Turkey is continuing to decline. If Turkish women maintain current fertility rates during their reproductive years, they can expect to have all average of 2.7 children by the end of their reproductive years. The highest fertility rate is observed for the age group 20-24. There are marked regional differences in fertility rates, ranging from 4.4 children per woman in the East to 2.0 children per woman in the West. Fertility also varies widely by urban-rural residence and by education level. A woman living in rural areas will have almost one child more than a woman living in an urban area. Women who have no education have almost one child more than women who have a primary-level education and 2.5 children more than women with secondary-level education. The first requirement of success ill family planning is the knowledge of family planning methods. Knowledge of any method is almost universal among Turkish women and almost all those who know a method also know the source of the method. Eighty percent of currently married women have used a method sometime in their life. One third of currently married women report ever using the IUD. Overall, 63 percent of currently married women are currently using a method. The majority of these women are modern method users (35 percent), but a very substantial proportion use traditional methods (28 percent). the IUD is the most commonly used modern method (I 9 percent), allowed by the condom (7 percent) and the pill (5 percent). Regional differences are substantial. The level of current use is 42 percent in tile East, 72 percent in tile West and more than 60 percent in tile other three regions. "File common complaints about tile methods are side effects and health concerns; these are especially prevalent for the pill and IUD. One of the major child health indicators is immunisation coverage. Among children age 12-23 months, the coverage rates for BCG and the first two doses of DPT and polio were about 90 percent, with most of the children receiving those vaccines before age one. The results indicate that 65 percent of the children had received all vaccinations at some time before the survey. On a regional basis, coverage is significantly lower in the Eastern region (41 percent), followed by the Northern and Central regions (61 percent and 65 percent, respectively). Acute respiratory infections (ARI) and diarrhea are the two most prevalent diseases of children under age five in Turkey. In the two weeks preceding the survey, the prevalence of ARI was 12 percent and the prevalence of diarrhea was 25 percent for children under age five. Among children with diarrhea 56 percent were given more fluids than usual. Breastfeeding in Turkey is widespread. Almost all Turkish children (95 percent) are breastfed for some period of time. The median duration of breastfeeding is 12 months, but supplementary foods and liquids are introduced at an early age. One-third of children are being given supplementary food as early as one month of age and by the age of 2-3 months, half of the children are already being given supplementary foods or liquids. By age five, almost one-filth of children arc stunted (short for their age), compared to an international reference population. Stunting is more prevalent in rural areas, in the East, among children of mothers with little or no education, among children who are of higher birth order, and among those born less than 24 months after a prior birth. Overall, wasting is not a problem. Two percent of children are wasted (thin for their height), and I I percent of children under five are underweight for their age. The survey results show that obesity is d problem among mothers. According to Body Mass Index (BMI) calculations, 51 percent of mothers are overweight, of which 19 percent are obese.

The second National Family Health Survey (NFHS-2), conducted in 1998-99, provides information on fertility, mortality, family planning, and important aspects of nutrition, health, and health care. The International Institute for Population Sciences (IIPS) coordinated the survey, which collected information from a nationally representative sample of more than 90,000 ever-married women age 15-49. The NFHS-2 sample covers 99 percent of India's population living in all 26 states. This report is based on the survey data for 25 of the 26 states, however, since data collection in Tripura was delayed due to local problems in the state. IIPS also coordinated the first National Family Health Survey (NFHS-1) in 1992-93. Most of the types of information collected in NFHS-2 were also collected in the earlier survey, making it possible to identify trends over the intervening period of six and one-half years. In addition, the NFHS-2 questionnaire covered a number of new or expanded topics with important policy implications, such as reproductive health, women's autonomy, domestic violence, women's nutrition, anaemia, and salt iodization. The NFHS-2 survey was carried out in two phases. Ten states were surveyed in the first phase which began in November 1998 and the remaining states (except Tripura) were surveyed in the second phase which began in March 1999. The field staff collected information from 91,196 households in these 25 states and interviewed 89,199 eligible women in these households. In addition, the survey collected information on 32,393 children born in the three years preceding the survey. One health investigator on each survey team measured the height and weight of eligible women and children and took blood samples to assess the prevalence of anaemia. SUMMARY OF FINDINGS POPULATION CHARACTERISTICS Three-quarters (73 percent) of the population lives in rural areas. The age distribution is typical of populations that have recently experienced a fertility decline, with relatively low proportions in the younger and older age groups. Thirty-six percent of the population is below age 15, and 5 percent is age 65 and above. The sex ratio is 957 females for every 1,000 males in rural areas but only 928 females for every 1,000 males in urban areas, suggesting that more men than women have migrated to urban areas. The survey provides a variety of demographic and socioeconomic background information. In the country as a whole, 82 percent of household heads are Hindu, 12 percent are Muslim, 3 percent are Christian, and 2 percent are Sikh. Muslims live disproportionately in urban areas, where they comprise 15 percent of household heads. Nineteen percent of household heads belong to scheduled castes, 9 percent belong to scheduled tribes, and 32 percent belong to other backward classes (OBCs). Two-fifths of household heads do not belong to any of these groups. Questions about housing conditions and the standard of living of households indicate some improvements since the time of NFHS-1. Sixty percent of households in India now have electricity and 39 percent have piped drinking water compared with 51 percent and 33 percent, respectively, at the time of NFHS-1. Sixty-four percent of households have no toilet facility compared with 70 percent at the time of NFHS-1. About three-fourths (75 percent) of males and half (51 percent) of females age six and above are literate, an increase of 6-8 percentage points from literacy rates at the time of NFHS-1. The percentage of illiterate males varies from 6-7 percent in Mizoram and Kerala to 37 percent in Bihar and the percentage of illiterate females varies from 11 percent in Mizoram and 15 percent in Kerala to 65 percent in Bihar. Seventy-nine percent of children age 6-14 are attending school, up from 68 percent in NFHS-1. The proportion of children attending school has increased for all ages, particularly for girls, but girls continue to lag behind boys in school attendance. Moreover, the disparity in school attendance by sex grows with increasing age of children. At age 6-10, 85 percent of boys attend school compared with 78 percent of girls. By age 15-17, 58 percent of boys attend school compared with 40 percent of girls. The percentage of girls 6-17 attending school varies from 51 percent in Bihar and 56 percent in Rajasthan to over 90 percent in Himachal Pradesh and Kerala. Women in India tend to marry at an early age. Thirty-four percent of women age 15-19 are already married including 4 percent who are married but gauna has yet to be performed. These proportions are even higher in the rural areas. Older women are more likely than younger women to have married at an early age: 39 percent of women currently age 45-49 married before age 15 compared with 14 percent of women currently age 15-19. Although this indicates that the proportion of women who marry young is declining rapidly, half the women even in the age group 20-24 have married before reaching the legal minimum age of 18 years. On average, women are five years younger than the men they marry. The median age at marriage varies from about 15 years in Madhya Pradesh, Bihar, Uttar Pradesh, Rajasthan, and Andhra Pradesh to 23 years in Goa. As part of an increasing emphasis on gender issues, NFHS-2 asked women about their participation in household decisionmaking. In India, 91 percent of women are involved in decision-making on at least one of four selected topics. A much lower proportion (52 percent), however, are involved in making decisions about their own health care. There are large variations among states in India with regard to women's involvement in household decisionmaking. More than three out of four women are involved in decisions about their own health care in Himachal Pradesh, Meghalaya, and Punjab compared with about two out of five or less in Madhya Pradesh, Orissa, and Rajasthan. Thirty-nine percent of women do work other than housework, and more than two-thirds of these women work for cash. Only 41 percent of women who earn cash can decide independently how to spend the money that they earn. Forty-three percent of working women report that their earnings constitute at least half of total family earnings, including 18 percent who report that the family is entirely dependent on their earnings. Women's work-participation rates vary from 9 percent in Punjab and 13 percent in Haryana to 60-70 percent in Manipur, Nagaland, and Arunachal Pradesh. FERTILITY AND FAMILY PLANNING Fertility continues to decline in India. At current fertility levels, women will have an average of 2.9 children each throughout their childbearing years. The total fertility rate (TFR) is down from 3.4 children per woman at the time of NFHS-1, but is still well above the replacement level of just over two children per woman. There are large variations in fertility among the states in India. Goa and Kerala have attained below replacement level fertility and Karnataka, Himachal Pradesh, Tamil Nadu, and Punjab are at or close to replacement level fertility. By contrast, fertility is 3.3 or more children per woman in Meghalaya, Uttar Pradesh, Rajasthan, Nagaland, Bihar, and Madhya Pradesh. More than one-third to less than half of all births in these latter states are fourth or higher-order births compared with 7-9 percent of births in Kerala, Goa, and Tamil Nadu. Efforts to encourage the trend towards lower fertility might usefully focus on groups within the population that have higher fertility than average. In India, rural women and women from scheduled tribes and scheduled castes have somewhat higher fertility than other women, but fertility is particularly high for illiterate women, poor women, and Muslim women. Another striking feature is the high level of childbearing among young women. More than half of women age 20-49 had their first birth before reaching age 20, and women age 15-19 account for almost one-fifth of total fertility. Studies in India and elsewhere have shown that health and mortality risks increase when women give birth at such young ages?both for the women themselves and for their children. Family planning programmes focusing on women in this age group could make a significant impact on maternal and child health and help to reduce fertility. INFANT AND CHILD MORTALITY NFHS-2 provides estimates of infant and child mortality and examines factors associated with the survival of young children. During the five years preceding the survey, the infant mortality rate was 68 deaths at age 0-11 months per 1,000 live births, substantially lower than 79 per 1,000 in the five years preceding the NFHS-1 survey. The child mortality rate, 29 deaths at age 1-4 years per 1,000 children reaching age one, also declined from the corresponding rate of 33 per 1,000 in NFHS-1. Ninety-five children out of 1,000 born do not live to age five years. Expressed differently, 1 in 15 children die in the first year of life, and 1 in 11 die before reaching age five. Child-survival programmes might usefully focus on specific groups of children with particularly high infant and child mortality rates, such as children who live in rural areas, children whose mothers are illiterate, children belonging to scheduled castes or scheduled tribes, and children from poor households. Infant mortality rates are more than two and one-half times as high for women who did not receive any of the recommended types of maternity related medical care than for mothers who did receive all recommended types of care. HEALTH, HEALTH CARE, AND NUTRITION Promotion of maternal and child health has been one of the most important components of the Family Welfare Programme of the Government of India. One goal is for each pregnant woman to receive at least three antenatal check-ups plus two tetanus toxoid injections and a full course of iron and folic acid supplementation. In India, mothers of 65 percent of the children born in the three years preceding NFHS-2 received at least one antenatal

Health adjusted life expectancy and life expectancy rates, at birth and at age 65, by sex, three-year average, by income quintiles.

The 2006 Uganda Demographic and Health Survey (UDHS) is a nationally representative survey of 8,531 women age 15-49 and 2,503 men age 15-54. The UDHS is the fourth comprehensive survey conducted in Uganda as part of the worldwide Demographic and Health Surveys (DHS) project. The primary purpose of the UDHS is to furnish policymakers and planners with detailed information on fertility; family planning; infant, child, adult, and maternal mortality; maternal and child health; nutrition; and knowledge of HIV/AIDS and other sexually transmitted infections. In addition, in one in three households selected for the survey, women age 15-49, men age 15-54, and children under age 5 years were weighed and their height was measured. Women, men, and children age 6-59 months in this subset of households were tested for anaemia, and women and children were tested for vitamin A deficiency. The 2006 UDHS is the first DHS survey in Uganda to cover the entire country. The 2006 Uganda Demographic and Health Survey (UDHS) was designed to provide information on demographic, health, and family planning status and trends in the country. Specifically, the UDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, and breastfeeding practices. In addition, data were collected on the nutritional status of mothers and young children; infant, child, adult, and maternal mortality; maternal and child health; awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections; and levels of anaemia and vitamin A deficiency. The 2006 UDHS is a follow-up to the 1988-1989, 1995, and 2000-2001 UDHS surveys, which were also implemented by the Uganda Bureau of Statistics (UBOS). The specific objectives of the 2006 UDHS are as follows: To collect data at the national level that will allow the calculation of demographic rates, particularly the fertility and infant mortality rates To analyse the direct and indirect factors that determine the level and trends in fertility and mortality To measure the level of contraceptive knowledge and practice of women and men by method, by urban-rural residence, and by region To collect data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS, and to evaluate patterns of recent behaviour regarding condom use To assess the nutritional status of children under age five and women by means of anthropometric measurements (weight and height), and to assess child feeding practices To collect data on family health, including immunizations, prevalence and treatment of diarrhoea and other diseases among children under five, antenatal visits, assistance at delivery, and breastfeeding To measure vitamin A deficiency in women and children, and to measure anaemia in women, men, and children To measure key education indicators including school attendance ratios and primary school grade repetition and dropout rates To collect information on the extent of disability To collect information on the extent of gender-based violence. MAIN RESULTS Fertility : Survey results indicate that the total fertility rate (TFR) for the country is 6.7 births per woman. The TFR in urban areas is much lower than in the rural areas (4.4 and 7.1 children, respectively). Kampala, whose TFR is 3.7, has the lowest fertility. Fertility rates in Central 1, Central 2, and Southwest regions are also lower than the national level. Removing four districts from the 2006 data that were not covered in the 20002001 UDHS, the 2006 TFR is 6.5 births per woman, compared with 6.9 from the 2000-2001 UDHS. Education and wealth have a marked effect on fertility, with uneducated mothers having about three more children on average than women with at least some secondary education and women in the lowest wealth quintile having almost twice as many children as women in the highest wealth quintile. Family planning : Overall, knowledge of family planning has remained consistently high in Uganda over the past five years, with 97 percent of all women and 98 percent of all men age 15-49 having heard of at least one method of contraception. Pills, injectables, and condoms are the most widely known modern methods among both women and men. Maternal health : Ninety-four percent of women who had a live birth in the five years preceding the survey received antenatal care from a skilled health professional for their last birth. These results are comparable to the 2000-2001 UDHS. Only 47 percent of women make four or more antenatal care visits during their entire pregnancy, an improvement from 42 percent in the 2000-2001 UDHS. The median duration of pregnancy for the first antenatal visit is 5.5 months, indicating that Ugandan women start antenatal care at a relatively late stage in pregnancy. Child health : Forty-six percent of children age 12-23 months have been fully vaccinated. Over nine in ten (91 percent) have received the BCG vaccination, and 68 percent have been vaccinated against measles. The coverage for the first doses of DPT and polio is relatively high (90 percent for each). However, only 64 percent go on to receive the third dose of DPT, and only 59 percent receive their third dose of polio vaccine. There are notable improvements in vaccination coverage since the 2000-2001 UDHS. The percentage of children age 12-23 months fully vaccinated at the time of the survey increased from 37 percent in 2000-2001 to 44 percent in 2006. The percentage who had received none of the six basic vaccinations decreased from 13 percent in 2000-2001 to 8 percent in 2006. Malaria : The 2006 UDHS gathered information on the use of mosquito nets, both treated and untreated. The data show that only 34 percent of households in Uganda own a mosquito net, with 16 percent of households owning an insecticide-treated net (ITN). Only 22 percent of children under five slept under a mosquito net on the night before the interview, while a mere 10 percent slept under an ITN. Breastfeeding and nutrition : In Uganda, almost all children are breastfed at some point. However, only six in ten children under the age of 6 months are exclusively breast-fed. HIV/AIDS AND stis : Knowledge of AIDS is very high and widespread in Uganda. In terms of HIV prevention strategies, women and men are most aware that the chances of getting the AIDS virus can be reduced by limiting sex to one uninfected partner who has no other partners (89 percent of women and 95 percent of men) or by abstaining from sexual intercourse (86 percent of women and 93 percent of men). Knowledge of condoms and the role they can play in preventing transmission of the AIDS virus is not quite as high (70 percent of women and 84 percent of men). Orphanhood and vulnerability : Almost one in seven children under age 18 is orphaned (15 percent), that is, one or both parents are dead. Only 3 percent of children under the age of 18 have lost both biological parents. Women's status and gender violence : Data for the 2006 UDHS show that women in Uganda are generally less educated than men. Although the gender gap has narrowed in recent years, 19 percent of women age 15-49 have never been to school, compared with only 5 percent of men in the same age group. Mortality : At current mortality levels, one in every 13 Ugandan children dies before reaching age one, while one in every seven does not survive to the fifth birthday. After removing districts not covered in the 2000-2001 UDHS from the 2006 data, findings show that infant mortality has declined from 89 deaths per 1,000 live births in the 2000-2001 UDHS to 75 in the 2006 UDHS. Under-five mortality has declined from 158 deaths per 1,000 live births to 137.

About this dataset

Context

The dataset tabulates the Richland County Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Richland County, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Richland County.

Key observations

Among the Hispanic population in Richland County, regardless of the race, the largest group is of Mexican origin, with a population of 1,403 (54.07% of the total Hispanic population).

https://i.neilsberg.com/ch/richland-county-oh-population-by-race-and-ethnicity.jpeg" alt="Richland County Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Origin for Hispanic or Latino population include:

• Mexican
• Black or African American
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Richland County
• Population: The population of the specific origin for Hispanic or Latino population in the Richland County is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Richland County total Hispanic or Latino population. 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 Richland County Population by Race & Ethnicity. You can refer the same here