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 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.

Historical chart and dataset showing U.S. life expectancy by year from 1950 to 2025.

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.

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

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 ...).

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

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.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

Historical chart and dataset showing U.S. birth rate by year from 1950 to 2025.

Inflation Rate in the United States increased to 2.40 percent in May from 2.30 percent in April of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100. Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports. In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the g

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20

NOTE: As of 2/16/2023, this page is no longer being updated. This table shows the number and percent of people that have initiated COVID-19 vaccination and are fully vaccinated by race / ethnicity and town. It includes people of all ages. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. A person who has received at least one dose of any vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if they have completed a primary series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. Race and ethnicity data may be self-reported or taken from an existing electronic health care record. Reported race and ethnicity information is used to create a single race/ethnicity variable. People with Hispanic ethnicity are classified as Hispanic regardless of reported race. People with a missing ethnicity are classified as non-Hispanic. People with more than one race are classified as multiple race. A vaccine coverage percentage cannot be calculated for people classified as NH Other race or NH Unknown race since there are not population size estimates for these groups. Data quality assurance activities suggest that NH Other may represent a missing value. Vaccine coverage estimates in specific race/ethnicity groups may be underestimated as result of the exclusion of records classified as NH Unknown Race or NH Other Race. Town of residence is verified by geocoding the reported address and then mapping it a town using municipal boundaries. If an address cannot be geocoded, the reported town is used. Town-level coverage estimates have been capped at 100%. Observed coverage may be greater than 100% for multiple reasons, including census denominator data not including all individuals that currently reside in the town (e.g., part time residents, change in population size since the census) or potential data reporting errors. The population denominators for these town- and age-specific coverage estimates are based on 2014 census estimates. This is the most recent year for which reliable town- and age-specific estimates are available. (https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Town-Population-with-Demographics). Changes in the size and composition of the population between 2014 and 2021 may results in inaccuracy in vaccine coverage estimates. For example, the size of the Hispanic population may be underestimated in a town given the reported increase in the size of the Hispanic population between the 2010 and 2020 censuses resulting in inflated vaccine coverage estimates. The 2014 census data are grouped in 5-year age bands. For vaccine coverage age groupings not consistent with a standard 5-year age band, each age was assumed to be 20% of the total within a 5-year age band. However, given the large deviation from this assumption for Mansfield because of the presence of the University of Connecticut, the age distribution observed in the 2010 census for the age bands 15 to 19 and 20 to 24 was used to estimate the population denominators. This table does not included doses administered to CT residents by out-of-state providers or by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) because they are not yet reported to CT WiZ (the CT immunization Information System). It is expected that these data will be added in the future. Caution should be used when interpreting coverage estimates for towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while study

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

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).

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

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.