VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

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 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

GapMinder collects data from a handful of sources, including the Institute for Health
Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

Variable Name , Description of Indicator & Sources Unique Identifier: Country

1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2 As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well. With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county). This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity). A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case. These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities. These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020. Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve.

The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj.

The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 .

The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 .

The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed.

This dataset includes a count and rate per 100,000 population for COVID-19 cases, a count of COVID-19 molecular diagnostic tests, and a percent positivity rate for tests among people living in community settings for the previous two-week period. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

Percent positivity is calculated as the number of positive tests among community residents conducted during the 14 days divided by the total number of positive and negative tests among community residents during the same period. If someone was tested more than once during that 14 day period, then those multiple test results (regardless of whether they were positive or negative) are included in the calculation.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly and reflect the previous two full Sunday-Saturday (MMWR) weeks (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf).

DPH note about change from 7-day to 14-day metrics: Prior to 10/15/2020, these metrics were calculated using a 7-day average rather than a 14-day average. The 7-day metrics are no longer being updated as of 10/15/2020 but the archived dataset can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/s22x-83rd

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

Additional notes: As of 11/5/2020, CT DPH has added antigen testing for SARS-CoV-2 to reported test counts in this dataset. The tests included in this dataset include both molecular and antigen datasets. Molecular tests reported include polymerase chain reaction (PCR) and nucleic acid amplicfication (NAAT) tests.

The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used.

Data suppression is applied when the rate is <5 cases per 100,000 or if there are <5 cases within the town. Information on why data suppression rules are applied can be found online here: https://www.cdc.gov/cancer/uscs/technical_notes/stat_methods/suppression.htm

Dataset Description

This dataset serves as a comprehensive repository of global development metrics, consolidating data from multiple international organizations into a single, unified structure. It provides a granular view of the state of health, economy, and nutrition across 193 countries over a 30-year period (1990–2019).

The data is organized by Country, Year, and Gender (Male, Female, and Both Sexes), making it a valuable resource for longitudinal studies, demographic analysis, and socio-economic research. It combines high-level economic indicators (like GDP) with granular health metrics (specific mortality rates) and detailed nutritional breakdowns (diet composition by food group).

Content Overview

The dataset covers a wide spectrum of categories:

• Demographics & Economy: Population stats, GNI, GDP, and poverty rates.
• Mortality & Life Expectancy: Survival rates at various ages, maternal mortality, and life expectancy.
• Public Health: Incidence of infectious diseases (Malaria, Tuberculosis, Hepatitis B) and prevalence of health risks (Tobacco, road traffic accidents).
• Environmental Health: Mortality attributed to air pollution, sanitation access, and clean fuel availability.
• Nutrition: Detailed caloric and quantity breakdown of food consumption (fruits, vegetables, cereals, meats, etc.).
• Healthcare Infrastructure: Coverage of essential health services and density of medical professionals.

Sources

The data was extracted and unified via an ETL process from the following organizations:

• World Bank: World Development Indicators
• WHO: Global Health Observatory Data
• UNICEF: Data Warehouse
• Our World In Data: Statistics and Research
• FAOSTAT: Food and Agriculture Data
• Kaggle: WHO World Health Statistics 2020 Complete

Data Dictionary

Index Columns

• Country: Name of the country.
• Year: The calendar year of the recorded data.
• Gender: The gender category for the data (Female, Male, or Both sexes).

Demographics & Health Metrics

• Life Expectancy: The average number of years a newborn is expected to live.
• Infant Mortality Rate: Number of infants dying before reaching one year of age, per 1,000 live births.
  • Includes Low/High Confidence Interval (CI) columns.
• Under 5 Mortality Rate: Probability of a child dying before reaching age 5, per 1,000 live births.
  • Includes Low/High CI columns.
• Neonatal Mortality Rate: Number of deaths during the first 28 days of life per 1,000 live births.
  • Includes Low/High CI columns.
• Maternal Mortality Ratio: Number of maternal deaths due to childbirth per 100,000 live births.
  • Includes Low/High CI columns.
• Birth Rate: Number of births per 1,000 inhabitants.
• Death Rate: Number of deaths per 1,000 inhabitants.
• Adolescent Birth Rate: Number of births by women aged 15 to 19 per 1,000 women in that age range.
• % Population Aged 0-14 / 15-64 / 65+: Percentage of the total population falling into these specific age brackets.
• % Population Aged 65-69 / 70-74 / 75-79 / 80+: Granular breakdown of the elderly population percentages.
• Total Population: Total number of inhabitants.

Causes of Death & Disease

• % Death Cardiovascular: Probability of dying from cardiovascular diseases, cancer, diabetes, or chronic respiratory diseases between ages 30 and 70.
  • Includes Low/High CI columns.
• Incidence of Malaria: Number of malaria cases per 1,000 inhabitants at risk per year.
• Incidence of Tuberculosis: Estimated cases of tuberculosis per 100,000 inhabitants.
  • Includes Low/High CI columns.
• Hepatitis B Surface Antigen: Prevalence of hepatitis B surface antigen.
  • Includes Low/High CI columns.
• Road Traffic Deaths: Number of deaths due to traffic accidents per 100,000 people.
• Poisoning Mortality Rate: Deaths attributed to unintentional poisoning per 100,000 people.
• Conflict and Terrorism Deaths: Number of deaths due to armed conflicts and terrorism.
• Battle Related Deaths: Number of deaths related to battles in an armed conflict.
• % Injury Deaths: Percentage of deaths caused by injuries.
• Suicides Rate: Number of deliberate deaths per 100,000 inhabitants.
• Homicide Rate: Number of homicides per 100,000 inhabitants.

Air Pollution Mortality

• Air Pollution Death Rate Total: Probability of dying fr...

California Infectious Disease Cases

Rates and Counts By County, Disease, Sex, and Year (2001-2014)

By Health [source]

About this dataset

This dataset provides comprehensive information on the number and rate of infectious diseases in California. Focusing on counties, sexes, and various diseases between 2001-2014, it offers powerful insights into the health status of its citizens. Its data also reveals trends in the spread of common illnesses in this state. Whether you are an epidemiologist looking to inform public health policy or a researcher seeking to investigate particular illnesses within certain populations, this dataset contains all the necessary information to answer your questions. Explore it today and discover hidden stories waiting to be uncovered!

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How to use the dataset

This dataset contains counts and rates of infectious diseases in California by county, disease, sex, and year. This dataset can be used to generate trends to understand the changes in incidence of different types of diseases over time and across counties or between sexes.

To use this dataset: - Select the columns you are interested in exploring - these could include Disease, County, Sex or Year. - Filter out the rows that do not relate to your question - for example filtering by a specific county or disease. - Examine the average rate per 100000 people for each group you selected as well as its lower and upper confidence intervals (CI). - Use Rate as your dependent variable for analysis; Population is likely also important determining factors. Make sure to check if any Rates have 'unstable' flags.
- Visualise or statistically analyse your data using suitable methods such as descriptive statistics (means/medians/mode etc.)for comparison between 2+ groups or correlation/regression based models when comparing one variable to another over time etc.

Research Ideas

• Analyzing the geographic spread of infectious diseases over time to identify areas in need of increased education, resources, and care.
• Comparing rates of disease by sex to identify and understand any gender-based differences in infectious disease cases.
• Using the Unstable column to determine whether a particular county or region needs further study of a certain type of infectious disease due to unusual spikes or drops in rate or count during a specific year

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Infectious_Disease_Cases_by_County_Year_and_Sex_2001-2014.csv | Column name | Description | |:---------------|:---------------------------------------------------------------------------------------------------------------| | Disease | The type of infectious disease reported. (String) | | County | The county in California where the cases were reported. (String) | | Year | The year in which the cases were reported. (Integer) | | Sex | The gender of the individuals who contracted the disease. (String) | | Population | The population size of the county in which the cases were reported. (Integer) | | Rate | The rate of infection per 100 thousand people living in the county. (Float) | | CI.lower | The lower confidence interval associated with the rate of infection. (Float) | | CI.upper | The upper confidence interval associated with the rate of infection. (Float) ...

The report looks into the various drivers of social exclusion amongst older people (although many of these indicators are equally relevant amongst all age groups) and attempts to identify areas in London where susceptibility is particularly high. Six key drivers have been included with various indicators used in an attempt to measure these. The majority of these indicators are at Lower Super Output Area (LSOA) level in an effort to identify areas at as small a geography as possible. Key Driver Indicator Description Economic Situation Income deprivation Income Deprivation Affecting Older People Score from the 2015 Indices of Deprivation Transport Accessibility Public Transport Average Public Transport Accessibility Score Car access Percentage aged 65 and over with no cars or vans in household Household Ties One person households Percentage aged 65+ living alone Providing unpaid care Percentage aged 65+ providing 50 or more hours of unpaid care a week Neighbourhood Ties Proficiency in English Percent aged 65+ who cannot speak English well Churn Rate Churn Rate: (inflow+outflow) per 100 population Health Mental health Estimated prevalence of dementia amongst population aged 65 and over (%) General health Percentage aged 65+ with a limiting long-term health problem or disability Safety Fear of crime Percentage in borough worried about anti-social behaviour in area Percentage in borough who feel unsafe walking alone after dark Crime rates Total offences per 100 population

The dataset contains the following indicators :

Table 1a - Population size Population, total and by sex (in thousands) Sex ratio (women/100 men) Table 1b - Composition of the population Percentage of total population under 15 years Percentage of total population aged 60 years and above, by sex Sex ratio in 60+ age group (men/100 women) Table 1c - Population growth and distribution Annual population growth rate Percentage urban population Sex ratio (women/100 men) of international migrants

Table 2a - Life expectancy Life expectancy at birth, by sex Life expectancy at age 60, by sex Table 2b - Maternal mortality and infant mortality Maternal mortality ratio Infant mortality rate Under 5 mortality rate Table 2c - Child-bearing Adolescent fertility rate Total fertility rate Table 2d - Contraceptive prevalence Contraceptive prevalence among married women of childbearing age, any method and modern method Table 2e - HIV/AIDS Estimated number of adults living with HIV/AIDS Women's share of adults living with HIV/AIDS

Table 3a – Persons per room Average number of persons per room by urban/rural area Table 3b – Human settlements Population distribution (%) by urban/rural area Annual rate of population change (%) by urban/rural area Table 3c– Water supply and sanitation Improved Drinking Water Coverage (%) by urban/rural area Improved Sanitation Coverage (%) y urban/rural area

Table 4a - Literacy Adult (15+) literacy rate, by sex Youth (15-24) literacy rate, by sex Table 4b - Primary education Primary net enrolment ratio, by sex Girl's share of primary enrolment Table 4c - Secondary education Secondary net enrolment ratio, by sex Girl's share of secondary enrolment Table 4d - Tertiary education Tertiary gross enrolment ratio, by sex Women's share of tertiary enrolment Table 4e – School life expectancy School life expectancy (primary to tertiary education) by sex

Table 5a – Income and economic activity Adult (15+) economic activity rate, by sex Per capita GDP (US dollars) Table 5b - Part-time employment Percentage of adult employment that is part-time, by sex Women's share of part-time employment Table 5c - Distribution of labour force by status in employment Percentage employees, by sex Percentage employers, by sex Percentage own-account workers, by sex Percentage contributing family workers, by sex Table 5d - Adult unemployment Unemployment rate, by sex

This dataset simulates a set of key economic, social, and environmental indicators for 20 countries over the period from 2010 to 2019. The dataset is designed to reflect typical World Bank metrics, which are used for analysis, policy-making, and forecasting. It includes the following variables:

Country Name: The country for which the data is recorded. Year: The specific year of the observation (from 2010 to 2019). GDP (USD): Gross Domestic Product in billions of US dollars, indicating the economic output of a country. Population: The total population of the country in millions. Life Expectancy (in years): The average life expectancy at birth for the country’s population. Unemployment Rate (%): The percentage of the total labor force that is unemployed but actively seeking employment. CO2 Emissions (metric tons per capita): The per capita carbon dioxide emissions, reflecting environmental impact. Access to Electricity (% of population): The percentage of the population with access to electricity, representing infrastructure development. Country:

Description: Name of the country for which the data is recorded. Data Type: String Example: "United States", "India", "Brazil" Year:

Description: The year in which the data is observed. Data Type: Integer Range: 2010 to 2019 Example: 2012, 2015 GDP (USD):

Description: The Gross Domestic Product of the country in billions of US dollars, indicating the economic output. Data Type: Float (billions of USD) Example: 14200.56 (represents 14,200.56 billion USD) Population:

Description: The total population of the country in millions. Data Type: Float (millions of people) Example: 331.42 (represents 331.42 million people) Life Expectancy (in years):

Description: The average number of years a newborn is expected to live, assuming that current mortality rates remain constant throughout their life. Data Type: Float (years) Range: Typically between 50 and 85 years Example: 78.5 years Unemployment Rate (%):

Description: The percentage of the total labor force that is unemployed but actively seeking employment. Data Type: Float (percentage) Range: Typically between 2% and 25% Example: 6.25% CO2 Emissions (metric tons per capita):

Description: The amount of carbon dioxide emissions per person in the country, measured in metric tons. Data Type: Float (metric tons) Range: Typically between 0.5 and 20 metric tons per capita Example: 4.32 metric tons per capita Access to Electricity (%):

Description: The percentage of the population with access to electricity. Data Type: Float (percentage) Range: Typically between 50% and 100% Example: 95.7%

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

If this was helpful, a vote is appreciated ❤️ Thank you 🙂

Proportion of population in Pacific Island Countries and Territories (PICTs) living in Low Elevation Coastal Zones (LECZ) of 0-10 and 0-20 meters above sea level. LECZ were delineated using the bathub method overlaid on the Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (AW3D30). Populations within the LECZs were estimated using the Pacific Community (SPC) Statistics for Development Division’s 100m2 population grids.

Find more Pacific data on PDH.stat.

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

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).

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

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).

Content

Police Residence

This folder contains data behind the story Most Police Don’t Live In The Cities They Serve.

Includes the cities with the 75 largest police forces, with the exception of Honolulu for which data is not available. All calculations are based on data from the U.S. Census.

The Census Bureau numbers are potentially going to differ from other counts for three reasons:

1. The census category for police officers also includes sheriffs, transit police and others who might not be under the same jurisdiction as a city’s police department proper. The census category won’t include private security officers.
2. The census data is estimated from 2006 to 2010; police forces may have changed in size since then.
3. There is always a margin of error in census numbers; they are estimates, not complete counts.

How to read police-locals.csv

Note: When a cell contains ** it means that there are fewer than 100 police officers of that race serving that city.

Context

This is a dataset from FiveThirtyEight hosted on their GitHub. Explore FiveThirtyEight data using Kaggle and all of the data sources available through the FiveThirtyEight organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using GitHub's API and Kaggle's API.

This dataset is distributed under the Attribution 4.0 International (CC BY 4.0) license.

Life expectancy at birth and at age 65, by sex, on a three-year average basis.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The infant mortality rate is defined as the number of deaths of children under one year of age, expressed per 1 000 live births. Some of the international variation in infant mortality rates is due to variations among countries in registering practices for premature infants. The United States and Canada are two countries which register a much higher proportion of babies weighing less than 500g, with low odds of survival, resulting in higher reported infant mortality. In Europe, several countries apply a minimum gestational age of 22 weeks (or a birth weight threshold of 500g) for babies to be registered as live births. This indicator is measured in terms of deaths per 1 000 live births.

This indicator is a summary measure of premature mortality, providing an explicit way of weighting deaths occurring at younger ages, which may be preventable. The calculation of Potential Years of Life Lost (PYLL) involves summing up deaths occurring at each age and multiplying this with the number of remaining years to live up to a selected age limit (age 75 is used in OECD Health Statistics). In order to assure cross-country and trend comparison, the PYLL are standardised, for each country and each year. The total OECD population in 2010 is taken as the reference population for age standardisation. This indicator is presented as a total and per gender. It is measured in years lost per 100 000 inhabitants (total), per 100 000 men and per 100 000 women, aged 0-69.

Life expectancy at birth is defined as how long, on average, a newborn can expect to live, if current death rates do not change. However, the actual age-specific death rate of any particular birth cohort cannot be known in advance. If rates are falling, actual life spans will be higher than life expectancy calculated using current death rates. Life expectancy at birth is one of the most frequently used health status indicators. Gains in life expectancy at birth can be attributed to a number of factors, including rising living standards, improved lifestyle and better education, as well as greater access to quality health services. This indicator is presented as a total and per gender and is measured in years.