Demographic variables of 7481 Polling Booth Catchments (PBCs) in Australia. The CCDs at the 2006 Census of Population and Housing were spatially allocated to a nearest polling booth location to form polling booth catchments within each of the 150 Electoral Divisions. The 150 booth catchments layers were then merged into one Australia booth catchments layer. The demographic variables were derived from 2006 census.

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center// Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. For the United States, total population available from 1950-2060, and other demographic variables available from 1980-2060. See methodology at https://www.census.gov/programs-surveys/international-programs/about/idb.html

This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

P values represent statistical significance of the different tungsten concentrations for each demographic variable. The P value presented in () includes an adjustment for urinary creatinine. Statistical comparisons excluded individuals in the unknown categoriesTables.

The Scanian Economic Demographic Database (SEDD) is based on family reconstitutions for nine rural parishes (Ekeby, Frillestad, Halmstad, Hässlunda, Hög, Kågeröd, Kävlinge, Sireköpinge and Stenestad) and one city (Helsingborg) in Scania, the southernmost county of Sweden, in which information in church records on births, deaths and marriages are linked together to form families. The database is the result of project collaborations between the Centre for Economic Demography (CED) and the Regional Archives in Lund. The co-operation has produced both an event database, in which all basic source material is registered, and an applied research database. The event database is accessible through the Regional Archives in Lund.

The description that follows primarily relates to the research database which contains information on five parishes (Halmstad, Hög, Kågeröd, Kävlinge and Sireköpinge). From the late 19th century onwards, one of the rural parishes was transformed from a minor rural village to a small industrial town (Kävlinge), while the others preserved their rural characters.

At its present stage, the research database includes data for 104 000 individuals from 1646 up to 1968, to which data from central registers for the period up to 2011 has been added. Data for the city of Landskrona are presently digitized. The database contains a variety of information on individual as well as household/family level and each individual in the database is under observation from birth/ in-migration and throughout the life span/ until an out-migration occurs. The fact that the database covers almost four centuries and that it combines economic and demographic data in one database has made it unique by Swedish comparisons.

Demographic variables include:

Births
Deaths
Cause of death
Marriages
In-migration
Out-migration
Birth-order

Socioeconomic and health variables include:

Occupation
Land holdings (farm size etc.)
Type of residence (farm, croft, cottage etc.)
Property ownership (freehold, crown, noble)
Income
Height
Health at birth
Health at mustering

Household variables include:

House-hold size
Typology of house-hold members (servants, nuclear family, lodgers etc.)

Purpose:

The purpose of the Scanian Economic Demographic Database (SEDD) is to function as a research infrastructure for economic as well as demographic research.

2016-2020 ACS 5-Year estimates of demographic variables (see below) compiled at the tract level.The American Community Survey (ACS) 5 Year 2016-2020 demographic information is a subset of information available for download from the U.S. Census. Tables used in the development of this dataset include: B01001 - Sex By Age; B03002 - Hispanic Or Latino Origin By Race; B11001 - Household Type (Including Living Alone); B11005 - Households By Presence Of People Under 18 Years By Household Type; B11006 - Households By Presence Of People 60 Years And Over By Household Type; B16005 - Nativity By Language Spoken At Home By Ability To Speak English For The Population 5 Years And Over; B25010 - Average Household Size Of Occupied Housing Units By Tenure, and; B15001 - Sex by Educational Attainment for the Population 18 Years and Over; To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_ACS 5-Year Demographic Estimate Data by TractDate of Coverage: 2016-2020

Dataset Description

This dataset contains a simulated collection of 1,00000 patient records designed to explore hypertension management in resource-constrained settings. It provides comprehensive data for analyzing blood pressure control rates, associated risk factors, and complications. The dataset is ideal for predictive modelling, risk analysis, and treatment optimization, offering insights into demographic, clinical, and treatment-related variables.

Dataset Structure

1. Dataset Volume

   • Size: 10,000 records. • Features: 19 variables, categorized into Sociodemographic, Clinical, Complications, and Treatment/Control groups.

2. Variables and Categories

A. Sociodemographic Variables

1. Age:
•  Continuous variable in years.
•  Range: 18–80 years.
•  Mean ± SD: 49.37 ± 12.81.
2. Sex:
•  Categorical variable.
•  Values: Male, Female.
3. Education:
•  Categorical variable.
•  Values: No Education, Primary, Secondary, Higher Secondary, Graduate, Post-Graduate, Madrasa.
4. Occupation:
•  Categorical variable.
•  Values: Service, Business, Agriculture, Retired, Unemployed, Housewife.
5. Monthly Income:
•  Categorical variable in Bangladeshi Taka.
•  Values: <5000, 5001–10000, 10001–15000, >15000.
6. Residence:
•  Categorical variable.
•  Values: Urban, Sub-urban, Rural.

B. Clinical Variables

7. Systolic BP:
•  Continuous variable in mmHg.
•  Range: 100–200 mmHg.
•  Mean ± SD: 140 ± 15 mmHg.
8. Diastolic BP:
•  Continuous variable in mmHg.
•  Range: 60–120 mmHg.
•  Mean ± SD: 90 ± 10 mmHg.
9. Elevated Creatinine:
•  Binary variable (\geq 1.4 \, \text{mg/dL}).
•  Values: Yes, No.
10. Diabetes Mellitus:
•  Binary variable.
•  Values: Yes, No.
11. Family History of CVD:
•  Binary variable.
•  Values: Yes, No.
12. Elevated Cholesterol:
•  Binary variable (\geq 200 \, \text{mg/dL}).
•  Values: Yes, No.
13. Smoking:
•  Binary variable.
•  Values: Yes, No.

C. Complications

14. LVH (Left Ventricular Hypertrophy):
•  Binary variable (ECG diagnosis).
•  Values: Yes, No.
15. IHD (Ischemic Heart Disease):
•  Binary variable.
•  Values: Yes, No.
16. CVD (Cerebrovascular Disease):
•  Binary variable.
•  Values: Yes, No.
17. Retinopathy:
•  Binary variable.
•  Values: Yes, No.

D. Treatment and Control

18. Treatment:
•  Categorical variable indicating therapy type.
•  Values: Single Drug, Combination Drugs.
19. Control Status:
•  Binary variable.
•  Values: Controlled, Uncontrolled.

Dataset Applications

1. Predictive Modeling:
•  Develop models to predict blood pressure control status using demographic and clinical data.
2. Risk Analysis:
•  Identify significant factors influencing hypertension control and complications.
3. Severity Scoring:
•  Quantify hypertension severity for patient risk stratification.
4. Complications Prediction:
•  Forecast complications like IHD, LVH, and CVD for early intervention.
5. Treatment Guidance:
•  Analyze therapy efficacy to recommend optimal treatment strategies.