In 2023, there were ********* adults aged 65 and older living in California, the most out of all U.S. states, followed by Florida with over *** million adults aged 65 and older. Both California and Florida have some of the highest resident population figures in the United States.

About this dataset

Context

The dataset tabulates the data for the California, KY population pyramid, which represents the California population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for California, KY, is 5.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for California, KY, is 26.4.
• Total dependency ratio for California, KY is 32.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for California, KY is 3.8.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the California population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the California for the selected age group is shown in the following column.
• Population (Female): The female population in the California for the selected age group is shown in the following column.
• Total Population: The total population of the California for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Age. You can refer the same here

In 2021, about **** million people aged 65 years or older were living in California -- the most out of any state. In that same year, Florida, Texas, New York, and Pennsylvania rounded out the top five states with the most people aged 65 and over living there.

In 2023, about **** percent of the population of California was between the ages of 25 and 34 years old. A further ** percent of the population was between the ages of 35 and 44 years old in that same year.

In 2022, the state with the highest median age of its population was Maine at 45.1 years. Utah had the lowest median age at 32.1 years. View the distribution of the U.S. population by ethnicity here.

Additional information on the aging population in the United States

High birth rates during the so-called baby boom years that followed World War II followed by lower fertility and morality rates have left the United States with a serious challenge in the 21st Century. However, the issue of an aging population is certainly not an issue unique to the United States. The age distribution of the global population shows that other parts of the world face a similar issue.

Within the United States, the uneven distribution of populations aged 65 years and over among states offers both major challenges and potential solutions. On the one hand, federal action over the issue may be contentious as other states are set to harbor the costs of elderly care in states such as California and Florida. That said, domestic migration from comparably younger states may help to fill gaps in the workforce left by retirees in others.

Nonetheless, aging population issues are set to gain further prominence in the political and economic decisions made by policymakers regardless of the eventual distribution of America’s elderly. Analysis of the financial concerns of Americans by age shows many young people still decades from retirement hold strong concern over their eventual financial position.

About this dataset

Context

The dataset tabulates the California City population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of California City. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 9,471 (63.90% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the California City population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in California City is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the California City is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California City Population by Age. You can refer the same here

These national level shapefiles join the geometry and selected attributes from the 2010 Census TIGER/Line Shapefiles and the 2010 Census Summary File 1 Demographic Profile (DP1) for the United States and Puerto Rico. They contain data items such as total population, total housing units, median age, population 16 years and over, population 65 years and over, race, relationship, average household size, and many others.

The estimated population of the U.S. was approximately 334.9 million in 2023, and the largest age group was adults aged 30 to 34. There were 11.88 million males in this age category and around 11.64 million females. Which U.S. state has the largest population? The population of the United States continues to increase, and the country is the third most populous in the world behind China and India. The gender distribution has remained consistent for many years, with the number of females narrowly outnumbering males. In terms of where the residents are located, California was the state with the highest population in 2023. The U.S. population by race and ethnicity The United States is well known the world over for having a diverse population. In 2023, the number of Black or African American individuals was estimated to be 45.76 million, which represented an increase of over four million since the 2010 census. The number of Asian residents has increased at a similar rate during the same time period and the Hispanic population in the U.S. has also continued to grow.

About this dataset

Context

The dataset tabulates the data for the California, MO population pyramid, which represents the California population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for California, MO, is 25.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for California, MO, is 28.2.
• Total dependency ratio for California, MO is 54.1.
• Potential support ratio, which is the number of youth (working age population) per elderly, for California, MO is 3.5.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the California population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the California for the selected age group is shown in the following column.
• Population (Female): The female population in the California for the selected age group is shown in the following column.
• Total Population: The total population of the California for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the California population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of California. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 48 (68.57% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the California population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in California is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the California is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for California Population by Age. You can refer the same here

The Los Angeles County Climate Vulnerability Assessment identified and incorporated 29 social vulnerability indicators. These indicators are listed below alongside their description and data source. Full report: https://ceo.lacounty.gov/cva-report/Note: All indicators are at the census tract level. Census tracts with no population (data) are omitted from this layer. Indicator Description Source Countywide Average

Asian Percent identifying as non-Hispanic Asian US Census Bureau, American Community Survey 2018 5-Year Estimates 14.4%

Asthma Age-adjusted rate of emergency department visits for asthma California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 52.2

Black Percent identifying as non-Hispanic black or African American US Census Bureau, American Community Survey 2018 5-Year Estimates 7.9%

Cardiovascular Age-adjusted rate of emergency department visits for heart attacks per 10,000 California Environmental Health Tracking Program (CEHTP) and Office of Statewide Health Planning and Development (OSHPD) 8.4

Children Percent of people 18 and under US Census Bureau, American Community Survey 2018 5-Year Estimates 24.9%

Disability Percent of persons with either mental or physical disability US Census Bureau, American Community Survey 2018 5-Year Estimates 9.9%

Female Percent female US Census Bureau, American Community Survey 2018 5-Year Estimates 50.7%

Female householder Percent of households that have a female householder with no spouse present US Census Bureau, American Community Survey 2018 5-Year Estimates 16.2%

Foreign born Percent of the total population who was not born in the United States or Puerto Rico US Census Bureau, American Community Survey 2018 5-Year Estimates 35.2%

Hispanic Latinx Percent identifying as Hispanic or Latino US Census Bureau, American Community Survey 2018 5-Year Estimates 48.5%

Households without vehicle access Percent of households without access to a personal vehicle US Census Bureau, American Community Survey 2018 5-Year Estimates 8.8%

Library access Each tract's average block distance to nearest library LA County Internal Services Department 1.14 miles

Limited English Percent limited English speaking households US Census Bureau, American Community Survey 2018 5-Year Estimates 13.6%

Living in group quarters Percent of persons living in (either institutionalized or uninstitiutionalized) group quarters US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

Median income Median household income of census tract US Census Bureau, American Community Survey 2018 5-Year Estimates $69,623

Mobile homes Percent of occupied housing units that are mobile homes US Census Bureau, American Community Survey 2018 5-Year Estimates 1.8%

No health insurance Percent of persons without health insurance US Census Bureau, American Community Survey 2018 5-Year Estimates 0.2%

No high school diploma Percent of persons 25 and older without a high school diploma US Census Bureau, American Community Survey 2018 5-Year Estimates 10.8%

No internet subscription Percent of the population without an internet subscription US Census Bureau, American Community Survey 2018 5-Year Estimates 22.6%

Older adults Percent of people 65 and older US Census Bureau, American Community Survey 2018 5-Year Estimates 18.4%

Older adults living alone Percent of households in which the householder is 65 and over who and living alone US Census Bureau, American Community Survey 2018 5-Year Estimates 12.9%

Outdoor workers Percentage of outdoor workers - agriculture, fishing, mining, extractive, construction occupations US Census Bureau, American Community Survey 2018 5-Year Estimates 8.0%

Poverty Percent of the population living in a family earning below 100% of the federal poverty threshold US Census Bureau, American Community Survey 2018 5-Year Estimates 5.4%

Rent burden Percent of renters paying more than 30 percent of their monthly income on rent and utilities US Census Bureau, American Community Survey 2018 5-Year Estimates 16.1%

Renters Percentage of renters per census tract US Census Bureau, American Community Survey 2018 5-Year Estimates 54.3%

Transit access Percent of population residing within a ½ mile of a major transit stop Healthy Places Index, SCAG 52.8%

Tribal and Indigenous Percent identifying as non-Hispanic American Indian and Alaska native US Census Bureau, American Community Survey 2018 5-Year Estimates 54.9%

Unemployed Percent of the population over the age of 16 that is unemployed and eligible for the labor force US Census Bureau, American Community Survey 2018 5-Year Estimates 6.9%

Voter turnout rate Percentage of registered voters voting in the 2016 general election CA Statewide General Elections Database 2016 63.8%

Population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2020 census tracts split by 2023 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries as of July 1, 2023. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/)released 2020 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Fields:CT20: 2020 Census tractFIP22: 2023 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2023) CSA: Countywide Statistical Area (CSA) - Unincorporated area community names and LA City neighborhood names.CT20FIP23CSA: 2020 census tract with 2023 city FIPs for incorporated cities and unincorporated areas and LA neighborhoods. SPA22: 2022 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD22: 2022 Health District (HD) number: HD_NAME: Health District name.POP23_AGE_0_4: 2023 population 0 to 4 years oldPOP23_AGE_5_9: 2023 population 5 to 9 years old POP23_AGE_10_14: 2023 population 10 to 14 years old POP23_AGE_15_17: 2022 population 15 to 17 years old POP23_AGE_18_19: 2023 population 18 to 19 years old POP23_AGE_20_44: 2023 population 20 to 24 years old POP23_AGE_25_29: 2023 population 25 to 29 years old POP23_AGE_30_34: 2023 population 30 to 34 years old POP23_AGE_35_44: 2023 population 35 to 44 years old POP23_AGE_45_54: 2023 population 45 to 54 years old POP23_AGE_55_64: 2023 population 55 to 64 years old POP23_AGE_65_74: 2023 population 65 to 74 years old POP23_AGE_75_84: 2023 population 75 to 84 years old POP23_AGE_85_100: 2023 population 85 years and older POP23_WHITE: 2023 Non-Hispanic White POP23_BLACK: 2023 Non-Hispanic African AmericanPOP23_AIAN: 2023 Non-Hispanic American Indian or Alaska NativePOP23_ASIAN: 2023 Non-Hispanic Asian POP23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific IslanderPOP23_HISPANIC: 2023 HispanicPOP23_MALE: 2023 Male POP23_FEMALE: 2023 Female POV23_WHITE: 2023 Non-Hispanic White below 100% Federal Poverty Level POV23_BLACK: 2023 Non-Hispanic African American below 100% Federal Poverty Level POV23_AIAN: 2023 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV23_ASIAN: 2023 Non-Hispanic Asian below 100% Federal Poverty Level POV23_HNPI: 2023 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV23_HISPANIC: 2023 Hispanic below 100% Federal Poverty Level POV23_TOTAL: 2023 Total population below 100% Federal Poverty Level POP23_TOTAL: 2023 Total PopulationAREA_SQMil: Area in square mile.POP23_DENSITY: 2023 Population per square mile.POV23_PERCENT: 2023 Poverty rate/percentage.How this data created?Population by age groups, ethnic groups and gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2020 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Notes:1. Population and poverty data estimated as of July 1, 2023. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundaries are as of July 1, 2023.

9,6 (Percent) in 2021. The number of people aged 65 years and over per hundred economically productive population (15-64 years). The calculation excludes unspecified age group. Figures for the following census dates: February 14 (2000), 17 October (2005) and June 12 (2010).

This dataset contains non-seasonally adjusted California Labor Force Participation rate By Age Group from the Current Population Survey (CPS). The age group ranges are as follows; 16-19 ; 20 - 24; 25 - 34; 35 - 44; 45 - 54; 55 -64; 65+. Note: this data is based on a 12-month moving average.

COVID-19 Vaccine Rates of Population younger than 65 yrs of age classified by Race & Ethnicity per Zip CodeVaccine Source: CAIR2 (California Immunization Registry)Population Source: Claritas 2021

This dataset contains non-seasonally adjusted California Unemployment Rate by age groups, from the Current Population Survey (CPS). The age group ranges are as follows: 16-19; 20-24; 25-34; 35-44; 45-54; 55-64; 65+. This data is based on a 12-month moving average.

Tabular data of population by age groups, race and gender, and the poverty by race is attached to the split tract geography to create this split tract with population and poverty data. Split tract data is the product of 2010 census tracts split by 2015 incorporated city boundaries and unincorporated community/countywide statistical areas (CSA) boundaries. The census tract boundaries have been altered and aligned where necessary with legal city boundaries and unincorporated areas, including shoreline/coastal areas. Census Tract:Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau (https://www.census.gov/) released 2010 geographic boundaries data including census tracts for the analysis and mapping of demographic information across the United States. City Boundary:City Boundary data is the base map information for the County of Los Angeles. These City Boundaries are based on the Los Angeles County Seamless Cadastral Landbase. The Landbase is jointly maintained by the Los Angeles County Assessor and the Los Angeles County Department of Public Works (DPW). This layer represents current city boundaries within Los Angeles County. The DPW provides the most current shapefiles representing city boundaries and city annexations. True, legal boundaries are only determined on the ground by surveyors licensed in the State of California.Countywide Statistical Areas (CSA): The countywide Statistical Area (CSA) was defined to provide a common geographic boundary for reporting departmental statistics for unincorporated areas and incorporated Los Angeles city to the Board of Supervisors. The CSA boundary and CSA names are established by the CIO and the LA County Enterprise GIS group worked with the Los Angeles County Board of Supervisors Unincorporated Area and Field Deputies that reflect as best as possible the general name preferences of residents and historical names of areas. This data is primarily focused on broad statistics and reporting, not mapping of communities. This data is not designed to perfectly represent communities, nor jurisdictional boundaries such as Angeles National Forest. CSA represent board approved geographies comprised of Census block groups split by cities.Data Field:CT10: 2010 Census tractFIP15: 2015 City FIP CodeCITY: City name for incorporated cities and “Unincorporated” for unincorporated areas (as of July 1, 2015) CT10FIP15: 2010 census tract with 2015 city FIPs for incorporated cities and unincorporated areas. SPA12: 2012 Service Planning Area (SPA) number.SPA_NAME: Service Planning Area name.HD12: 2012 Health District (HD) number: HD_NAME: Health District name.POP15_AGE_0_4: 2015 population 0 to 4 years oldPOP15_AGE_5_9: 2015 population 5 to 9 years old POP15_AGE_10_14: 2015 population 10 to 14 years old POP15_AGE_15_17: 2015 population 15 to 17 years old POP15_AGE_18_19: 2015 population 18 to 19 years old POP15_AGE_20_44: 2015 population 20 to 24 years old POP15_AGE_25_29: 2015 population 25 to 29 years old POP15_AGE_30_34: 2015 population 30 to 34 years old POP15_AGE_35_44: 2015 population 35 to 44 years old POP15_AGE_45_54: 2015 population 45 to 54 years old POP15_AGE_55_64: 2015 population 55 to 64 years old POP15_AGE_65_74: 2015 population 65 to 74 years old POP15_AGE_75_84: 2015 population 75 to 84 years old POP15_AGE_85_100: 2015 population 85 years and older POP15_WHITE: 2015 Non-Hispanic White POP15_BLACK: 2015 Non-Hispanic African AmericanPOP15_AIAN: 2015 Non-Hispanic American Indian or Alaska NativePOP15_ASIAN: 2015 Non-Hispanic Asian POP15_HNPI: 2015 Non-Hispanic Hawaiian Native or Pacific IslanderPOP15_HISPANIC: 2015 HispanicPOP15_MALE: 2015 Male POP15_FEMALE: 2015 Female POV15_WHITE: 2015 Non-Hispanic White below 100% Federal Poverty Level POV15_BLACK: 2015 Non-Hispanic African American below 100% Federal Poverty Level POV15_AIAN: 2015 Non-Hispanic American Indian or Alaska Native below 100% Federal Poverty Level POV15_ASIAN: 2015 Non-Hispanic Asian below 100% Federal Poverty Level POV15_HNPI: 2015 Non-Hispanic Hawaiian Native or Pacific Islander below 100% Federal Poverty Level POV15_HISPANIC: 2015 Hispanic below 100% Federal Poverty Level POV15_TOTAL: 2015 Total population below 100% Federal Poverty Level POP15_TOTAL: 2015 Total PopulationAREA_SQMIL: Area in square milePOP15_DENSITY: Population per square mile.POV15_PERCENT: Poverty rate/percentage.How this data created?The tabular data of population by age groups, by ethnic groups and by gender, and the poverty by ethnic groups is attributed to the split tract geography to create this data. Split tract polygon data is created by intersecting 2010 census tract polygons, LA Country City Boundary polygons and Countywide Statistical Areas (CSA) polygon data. The resulting polygon boundary aligned and matched with the legal city boundary whenever possible. Note:1. Population and poverty data estimated as of July 1, 2015. 2. 2010 Census tract and 2020 census tracts are not the same. Similarly, city and community boundary are not the same because boundary is reviewed and updated annually.

California leads the nation in the number of older adults with Alzheimer's disease, with nearly 720,000 individuals affected in 2020. This sobering statistic highlights the significant impact of Alzheimer's across the United States, with populous states bearing the heaviest burden. Florida and Texas follow closely behind, each with over 450,000 older residents living with the disease. Projected growth The prevalence of Alzheimer's among older Americans is expected to rise dramatically in the coming decades. By 2030, an estimated 8.5 million people aged 65 and over will be living with Alzheimer's in the United States. This projection underscores the urgent need for improved prevention, treatment, and care strategies to address the growing impact of the disease on individuals, families, and healthcare systems. Mortality and healthcare burden Alzheimer's disease remains a significant cause of mortality in the United States, with 36 deaths per 100,000 people recorded in 2022. The disease's impact on the healthcare system is substantial and growing. Costs to Medicare and Medicaid are projected to reach 637 billion U.S. dollars by 2050, highlighting the economic challenges associated with caring for an aging population increasingly affected by Alzheimer's. Early detection and intervention strategies are crucial in managing the disease's progression and potentially reducing its long-term burden on individuals and society.

The Mendocino mule deer herd complex is comprised of three overlapping black-tailed deer (Odocoileus hemionus columbianus) administrative herds, including Mendocino, Clear Lake, and Alder Springs. Mendocino black-tailed deer exhibit variable movement patterns and strategies, including traditional seasonal migrants, full-time residents, and multi-range migrants. Migrants move between seasonal ranges from a multitude of lower elevation areas within the North Coast Range in winter to higher elevation summer ranges (fig. XXX). Local biologists predict high-use winter ranges throughout both foothill slopes and valley bottoms. Female deer of the Mendocino herd complex exhibit both short-term (seasonal/annual) and long-term (multi-generational) fidelity to their summer ranges (Bose and others, 2017). Population density estimates in 2011 and 2012 based on fecal DNA pellets indicated exceptionally high deer densities on productive summer ranges (50.75 deer/km2; Lounsberry and others, 2015). However, the population was declining strongly at the time of study due to low adult survival, including of prime-aged females (Marescot and others, 2015). Survival rates were lower than typically observed for mule deer populations across their range (Forrester and Wittmer, 2013). Predation from black bears (Ursus americanus) and coyotes (Canis latrans) was the primary cause for low annual survival of fawns (Forrester and Wittmer, 2019) while predation from pumas (Puma concolor) was the foremost cause of adult female mortality (Marescot and others, 2015), often in areas deemed less familiar to the individual mule deer (Forrester and others, 2015). Puma kill rates of mule deer in the study area were the highest reported across their range (Cristescu and others, 2022), likely due to high rates of kleptoparasitism from black bears (Elbroch and others, 2015; Allen and others, 2021). More recent research from CDFW was directed at the collection of DNA from fecal pellets to update population density estimates (CDFW, 2015) and to determine population response to catastrophic wildfire (CDFW, 2019; CAL FIRE, 2021). Habitat use of mule deer (Bose and others, 2018) and their predators (Cristescu and others, 2019) in the area is well understood and most of the habitats occupied by the Mendocino herd complex are protected and not at risk of development or fragmentation. However, the risk of catastrophic wildfire and climate changed induced landscape scale changes exist with the consequences unknown. These mapping layers show the location of the winter range for mule deer (Odocoileus hemionus) in the Mendocino population in California. They were developed from 65 winter sequences collected from a sample size of 45 animals comprising GPS locations collected every 1-13 hours.

The purpose of this survey was to explore the influence of health practices and social relationships on the physical and mental health of a typical sample of the population in Alameda County, California. The information obtained for the 6,928 respondents (including approximately 500 women aged 65 years and older) covers chronic health conditions, health behaviors, social involvements, and psychological characteristics. Questions were asked about marital and life satisfaction, parenting, physical activities, employment, and childhood experiences. Demographic variables include data on respondetns' age, race, height, weight, education, income, and religion.