This dataset provides population estimate trends from 1998 to the current year for each of California’s 58 counties, further disaggregated by Detailed Analysis Units (DAUs) - the smallest geographic units historically used by the California Department of Water Resources for water planning as part of the California Water Plan. DAUs are subdivisions of Planning Areas and often align with county boundaries, although a single DAU may span multiple counties. They have traditionally supported water demand estimates based on crop and land use types.

The population estimates were developed using U.S. Bureau Census 2000, 2010 and 2020 data. Throughout the estimation process, intermediate results were reviewed and adjusted as needed, with professional judgment applied to smooth trends where appropriate.

Since the California Water Plan is retiring DAUs as its planning and analysis framework, future updates to this dataset will transition away from DAU based geography. Instead, population estimates will be provided based on other geographic units, such as the 8-digit Hydrologic Units (HUC8) defined by the U.S. Geological Survey’s Watershed Boundary Dataset.

A dashboard is available for visualizing historical population trends by county and DAU.

About this dataset

Context

The dataset tabulates the Alpine County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Alpine County. The dataset can be utilized to understand the population distribution of Alpine County by age. For example, using this dataset, we can identify the largest age group in Alpine County.

Key observations

The largest age group in Alpine County, CA was for the group of age 70 to 74 years years with a population of 186 (10.97%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Alpine County, CA was the 80 to 84 years years with a population of 3 (0.18%). 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 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 in consideration
• Population: The population for the specific age group in the Alpine County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Alpine County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

The 1998 Dress Rehearsal was conducted as a prelude to the United States Census of Population and Housing, 2000, in the following locations: (1) Columbia, South Carolina, and surrounding areas, including the town of Irmo and the counties of Chester, Chesterfield, Darlington, Fairfield, Kershaw, Lancaster, Lee, Marlboro, Newberry, Richland, and Union, (2) Sacramento, California, and (3) Menominee County, Wisconsin, including the Menominee American Indian Reservation. This collection contains map files showing various levels of geography (in the form of Census Tract Outline Maps, Voting District/State Legislative District Outline Maps, and County Block Maps), TIGER/Line digital files, and Corner Point files for the Census 2000 Dress Rehearsal sites. The Corner Point data files contain the bounding latitude and longitude coordinates for each individual map sheet of the 1998 Dress Rehearsal Public Law (P.L.) 94-171 map products. These files include a sheet identifier, minimum and maximum longitude, minimum and maximum latitude, and the map scale (integer value) for each map sheet. The latitude and longitude coordinates are in decimal degrees and expressed as integer values with six implied decimal places. There is a separate Corner Point File for each of the three map types: County Block Map, Census Tract Outline Map, and Voting District/State Legislative District Outline Map. Each of the three map file types is provided in two formats: Portable Document Format (PDF), for viewing, and Hewlett-Packard Graphics Language (HP-GL) format, for plotting. The County Block Maps show the greatest detail and the most complete set of geographic information of all the maps. These large-scale maps depict the smallest geographic entities for which the Census Bureau presents data -- the census blocks -- by displaying the features that delineate them and the numbers that identify them. These maps show the boundaries, names, and codes for American Indian/Alaska Native areas, county subdivisions, places, census tracts, and, for this series, the geographic entities that the states delineated in Phase 2, Voting District Project, of the Redistricting Data Program. The HP-GL version of the County Block Maps is broken down into index maps and map sheets. The map sheets cover a small area, and the index maps are composed of multiple map sheets, showing the entire area. The intent of the County Block Map series is to provide a map for each county on the smallest possible number of map sheets at the maximum practical scale, dependent on the area size of the county and the density of the block pattern. The latter affects the display of block numbers and feature identifiers. The Census Tract Outline Maps show the boundaries and numbers of census tracts, and name the features underlying the boundaries. These maps also show the boundaries and names of counties, county subdivisions, and places. They identify census tracts in relation to governmental unit boundaries. The mapping unit is the county. These large-format maps are produced to support the P.L. 94-171 program and all other 1998 Dress Rehearsal data tabulations. The Voting District/State Legislative District Outline Maps show the boundaries and codes for voting districts as delineated by the states in Phase 2, Voting District Project, of the Redistricting Data Program. The features underlying the voting district boundaries are shown, as well as the names of these features. Additionally, for states that submit the information, these maps show the boundaries and codes for state legislative districts and their underlying features. These maps also show the boundaries of and names of American Indian/Alaska Native areas, counties, county subdivisions, and places. The scale of the district maps is optimized to keep the number of map sheets for each area to a minimum, but the scale and number of map sheets will vary by the area size of the county and the voting districts and state legislative districts delineated by the states. The Census 2000 Dress Rehearsal TIGER/Line Files consist of line segments representing physical features and governmental and statistical boundaries. The files contain information distributed over a series of record types for the spatial objects of a county. These TIGER/Line Files are an extract of selected geographic and cartographic information from the Census TIGER (Topological

The Census Bureau released revised delineations for urban areas on December 29, 2022. The new criteria (contained in this Federal Register Notice) is based primarily on housing unit density measured at the census block level. The minimum qualifying threshold for inclusion as an urban area is an area that contains at least 2,000 housing units or has a population of at least 5,000 persons. It also eliminates the classification of areas as “urban clusters/urbanized areas”. This represents a change from 2010, where urban areas were defined as areas consisting of 50,000 people or more and urban clusters consisted of at least 2,500 people but less than 50,000 people with at least 1,500 people living outside of group quarters. Due to the new population thresholds for urban areas, 36 urban clusters in California are no longer considered urban areas, leaving California with 193 urban areas after the new criteria was implemented.

The State of California experienced an increase of 1,885,884 in the total urban population, or 5.3%. However, the total urban area population as a percentage of the California total population went down from 95% to 94.2%. For more information about the mapped data, download the Excel spreadsheet here.

Please note that some of the 2020 urban areas have different names or additional place names as a result of the inclusion of housing unit counts as secondary naming criteria.

Please note there are four urban areas that cross state boundaries in Arizona and Nevada. For 2010, only the parts within California are displayed on the map; however, the population and housing estimates represent the entirety of the urban areas. For 2020, the population and housing unit estimates pertains to the areas within California only.

Data for this web application was derived from the 2010 and 2020 Censuses (2010 and 2020 Census Blocks, 2020 Urban Areas, and Counties) and the 2016-2020 American Community Survey (2010 -Urban Areas) and can be found at data.census.gov.

For more information about the urban area delineations, visit the Census Bureau's Urban and Rural webpage and FAQ.

To view more data from the State of California Department of Finance, visit the Demographic Research Unit Data Hub.

About this dataset

Context

The dataset tabulates the Butte County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Butte County. The dataset can be utilized to understand the population distribution of Butte County by age. For example, using this dataset, we can identify the largest age group in Butte County.

Key observations

The largest age group in Butte County, CA was for the group of age 20 to 24 years years with a population of 22,959 (10.96%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Butte County, CA was the 85 years and over years with a population of 3,777 (1.80%). 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 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 in consideration
• Population: The population for the specific age group in the Butte County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Butte County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset tabulates the Riverside County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Riverside County. The dataset can be utilized to understand the population distribution of Riverside County by age. For example, using this dataset, we can identify the largest age group in Riverside County.

Key observations

The largest age group in Riverside County, CA was for the group of age 10 to 14 years years with a population of 181,182 (7.40%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Riverside County, CA was the 85 years and over years with a population of 41,240 (1.68%). 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 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 in consideration
• Population: The population for the specific age group in the Riverside County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Riverside County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset tabulates the Los Angeles County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Los Angeles County. The dataset can be utilized to understand the population distribution of Los Angeles County by age. For example, using this dataset, we can identify the largest age group in Los Angeles County.

Key observations

The largest age group in Los Angeles County, CA was for the group of age 30 to 34 years years with a population of 794,152 (7.99%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Los Angeles County, CA was the 80 to 84 years years with a population of 166,481 (1.68%). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 in consideration
• Population: The population for the specific age group in the Los Angeles County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Los Angeles County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

The 2015 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

The 2015 TIGER Geodatabases are extracts of selected nation based and state based geographic and cartographic information from the U.S. Census Bureau's Master Address File/Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) database. The geodatabases include feature class layers of information for the fifty states, the District of Columbia, Puerto Rico, and the Island areas (American Samoa, the Commonwealth of the Northern Mariana Islands, Guam, and the United States Virgin Islands). The geodatabases do not contain any sensitive data. The 2015 TIGER Geodatabases are designed for use with Esriâ s ArcGIS.

        The 2015 State Geodatabase for California contains multiple layers. These layers are the Block, Block Group, Census Designated Place, Census Tract, County Subdivision and
        Incorporated Place layers.

        Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered
        within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same
        decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that
        census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and
        Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses
        county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban
        areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. 

        The BG boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the
        2010 Census. 

        The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to
        previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people.
        When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living
        conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by
        highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to
        population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable
        features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to
        allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and
        county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may
        consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities
        that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that
        include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American
        Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little
        or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial
        park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area. 

        An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD),
        which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state,
        but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have
        other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated
        to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state
        in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide
        with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial
        census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily
        have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. 

        The boundaries of most incorporated places in this shapefile are as of January 1, 2013, as reported through the Census Bureau's Boundary and
        Annexation Survey (BAS). Limited updates that occurred after January 1, 2013, such as newly incorporated places, are also included. The boundaries
        of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.

        The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no
        counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The
        latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri,
        Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary
        divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data
        presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data
        presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto
        Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin
        Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. 

        The boundaries for counties and equivalent entities are mostly as of January 1, 2013, primarily as reported through the Census Bureau's Boundary and
        Annexation Survey (BAS). However, some changes made after January 2013, including the addition and deletion of counties, are included.

        County subdivisions are the primary divisions of counties and their equivalent entities for the reporting of Census Bureau data. They include
        legally-recognized minor civil divisions (MCDs) and statistical census county divisions (CCDs), and unorganized territories. For the 2010 Census,
        the MCDs are the primary governmental and/or administrative divisions of counties in 29 States and Puerto Rico; Tennessee changed from having CCDs
        for Census 2000 to having MCDs for the 2010 Census. In MCD States where no MCD exists or is not defined, the Census Bureau creates statistical
        unorganized territories to complete coverage. The entire area of the United States, Puerto Rico, and the Island Areas are covered by county
        subdivisions. The boundaries of most legal MCDs are as of January 1, 2013, as reported through the Census Bureau's Boundary and Annexation Survey
        (BAS). 

        The boundaries of all CCDs, delineated in 21 states, are those as reported as part of the Census Bureau's Participant Statistical Areas Program
        (PSAP) for the 2010 Census.

Ages chart illustrates the age and gender trends across all age and gender groupings. A chart where the the covered area is primarily on the right describes a very young population while a chart where the the covered area is primarily on the left illustrates an aging population.

VITAL SIGNS INDICATOR Daily Miles Traveled (T15)

FULL MEASURE NAME Per-capita vehicle miles traveled

LAST UPDATED July 2017

DESCRIPTION Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for per-capita vehicle miles traveled.

DATA SOURCE California Department of Transportation: California Public Road Data/Highway Performance Monitoring System 2001-2015 http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php

California Department of Finance: Population and Housing Estimates Forms E-8 and E-5 2001-2015 http://www.dof.ca.gov/research/demographic/reports/estimates/e-8/ http://www.dof.ca.gov/research/demographic/reports/estimates/e-5/2011-20/view.php

U.S. Census Bureau: Summary File 1 2010 http://factfinder2.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Vehicle miles traveled reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examine county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas within each metropolitan area (9-nine region for the San Francisco Bay Area and the primary MSA for all others). For the metro analysis, no VMT data is available outside of other urbanized areas; it is only available for intraregional analysis purposes.

VMT per capita is calculated by dividing VMT by an estimate of the traveling population. The traveling population does not include people living in institutionalized facilities, which are defined by the Census. Because institutionalized population is not estimated each year, the proportion of people living in institutionalized facilities from the 2010 Census was applied to the total population estimates for all years.

About this dataset

Context

The dataset tabulates the Nevada County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Nevada County. The dataset can be utilized to understand the population distribution of Nevada County by age. For example, using this dataset, we can identify the largest age group in Nevada County.

Key observations

The largest age group in Nevada County, CA was for the group of age 65 to 69 years years with a population of 9,557 (9.33%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Nevada County, CA was the 85 years and over years with a population of 2,990 (2.92%). 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 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 in consideration
• Population: The population for the specific age group in the Nevada County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Nevada County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

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.

Daily Miles Traveled (T14)

FULL MEASURE NAME

Total vehicle miles traveled

LAST UPDATED

August 2022

DESCRIPTION

Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE

California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php

2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm

2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/

2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html

2020

CONTACT INFORMATION

vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)

Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Vehicle population is updated annually, each April, to reflect the number of vehicles “on the road” during the previous calendar year. Vehicle population counts vehicles whose registration is either current or less than 35 days expired. Sales are higher than population because of vehicle retirements, accidents, owners moving out of state, or other reasons.

Data as of: December 31, 2022

Regional Data Categories Map Filter. Data can be reflected at the county, metropolitan statistical area (MSA), or ZIP code level. Individual registrations were assigned to a region based on mailing address, with the following exceptions:

Out of State: vehicles registered in California with a mailing address in a different state

ZIP Code “99999”: indicates that the ZIP code in the Vehicle Registration database was invalid

Unassigned: remote areas not associated with an MSA

Unknown: Invalid ZIP codes categorized under MSA

This Data contains information from 2010 to 2022.

Please note, that columns 'Manufacturer' and 'Model' include Null values since it was less data in 2010-2012 and more data from 2013.

Examples of Questions you may answer with this Data:

• What is the trend in the number of Battery Electric Vehicles (BEVs) in California from 2010 to 2022?
• How has the adoption of Gasoline vehicles changed over the years in California, specifically for Flex Fuel?
• Which county in California had the highest number of Diesel vehicles in 2010?
• What is the distribution of Gasoline Hybrid vehicles across California counties in 2020?
• How many Natural Gas vehicles were registered in California in 2010, and did this number change significantly by 2020?
• Which car manufacturer produced the most Battery Electric Vehicles (BEVs) in California in 2015?
• What is the overall trend in the number of Zero Emission Vehicles (ZEVs) in California over the past decade?
• What is the average number of vehicles per make and model for all Fuel Types in 2019 in California?
• How has the market share of Battery Electric Vehicles (BEVs) compared to Gasoline vehicles evolved over the years in California?

Columns: Data Year
County
Dashboard Fuel Type Group
Fuel Type
Manufacturer
Model
Number of Vehicles

Daily Miles Traveled (T14)

FULL MEASURE NAME
Total vehicle miles traveled

LAST UPDATED
August 2022

DESCRIPTION
Daily miles traveled, commonly referred to as vehicle miles traveled (VMT), reflects the total and per-person number of miles traveled in personal vehicles on a typical weekday. The dataset includes metropolitan area, regional and county tables for total vehicle miles traveled.

DATA SOURCE
California Department of Transportation: California Public Road Data/Highway Performance Monitoring System - http://www.dot.ca.gov/hq/tsip/hpms/datalibrary.php
2001-2020

Federal Highway Administration: Highway Statistics - https://www.fhwa.dot.gov/policyinformation/statistics/2020/hm71.cfm
2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
2001-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
2020

CONTACT INFORMATION
vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Vehicle miles traveled (VMT) reflects the mileage accrued within the county and not necessarily the residents of that county; even though most trips are due to local residents, additional VMT can be accrued by through-trips. City data was thus discarded due to this limitation and the analysis only examines county and regional data, where through-trips are generally less common.

The metropolitan area comparison was performed by summing all of the urbanized areas for which the majority of its population falls within a given metropolitan area (9-county region for the San Francisco Bay Area and the primary metropolitan statistical area (MSA) for all others). For the metro analysis, no VMT data is available in rural areas; it is only available for intraregional analysis purposes. VMT per capita is calculated by dividing VMT by an estimate of the traveling population.

1. Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.
2. Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.
3. Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.

About this dataset

Context

The dataset tabulates the Orange County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Orange County. The dataset can be utilized to understand the population distribution of Orange County by age. For example, using this dataset, we can identify the largest age group in Orange County.

Key observations

The largest age group in Orange County, CA was for the group of age 25 to 29 years years with a population of 224,994 (7.11%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Orange County, CA was the 80 to 84 years years with a population of 57,972 (1.83%). 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 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 in consideration
• Population: The population for the specific age group in the Orange County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Orange County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The purpose of this file is to provide the geography for the 2010 Census Blocks along with their 2010 housing unit count and population. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

Medical Service Study Areas - Census Detail, 2010California Health & Human Services Agency Open Data Portal DescriptionMedical Service Study Areas (MSSAs) are sub-city and sub-county geographical units used to organize and display population, demographic and physician data. MSSAs were developed in 1976 by the California Healthcare Workforce Policy Commission (formerly California Health Manpower Policy Commission) to respond to legislative mandates requiring it to determine "areas of unmet priority need for primary care family physicians" (Song-Brown Act of 1973) and "geographical rural areas where unmet priority need for medical services exist" (Garamendi Rural Health Services Act of 1976).MSSAs are recognized by the U.S. Health Resources and Services Administration, Bureau of Health Professions' Office of Shortage Designation as rational service areas for purposes of designating Health Professional Shortage Areas (HPSAs), and Medically Underserved Areas and Medically Underserved Populations (MUAs/MUPs).The MSSAs incorporate the U.S. Census total population, socioeconomic and demographic data and are updated with each decadal census. Office of Statewide Health Planning and Development provides updated data for each County's MSSAs to the County and Communities, and will schedule meetings for areas of significant population change. Community meetings will be scheduled throughout the State as needed.Adopted by the California Healthcare Workforce Policy Commission on May 15, 2002.Each MSSA is composed of one or more complete census tracts. MSSAs will not cross county lines. All population centers within the MSSA are within 30 minutes travel time to the largest population center.Urban MSSA - Population range 75,000 to 125,000. Reflect recognized community and neighborhood boundaries. Similar demographic and socio-economic characteristics.Rural MSSA - Population density of less than 250 persons per square mile. No population center exceeds 50,000.Frontier MSSA - Population density of less than 11 persons per square mile.