Chart and table of population level and growth rate for the state of California from 1900 to 2024.

About this dataset

Context

The dataset tabulates the California population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of California across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2024, the population of California was 39.43 million, a 0.59% increase year-by-year from 2023. Previously, in 2023, California population was 39.2 million, an increase of 0.14% compared to a population of 39.14 million in 2022. Over the last 20 plus years, between 2000 and 2024, population of California increased by 5.44 million. In this period, the peak population was 39.52 million in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2024

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2024)
• Population: The population for the specific year for the California is shown in this column.
• Year on Year Change: This column displays the change in California population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 California Population by Year. You can refer the same here

The TIGER/Line shapefiles and related database files (.dbf) 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 MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. 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. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). 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.

About this dataset

Context

The dataset tabulates the Thousand Oaks population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Thousand Oaks across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Thousand Oaks was 123,463, a 0.86% decrease year-by-year from 2022. Previously, in 2022, Thousand Oaks population was 124,529, a decline of 1.26% compared to a population of 126,118 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Thousand Oaks increased by 6,084. In this period, the peak population was 128,661 in the year 2015. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Thousand Oaks is shown in this column.
• Year on Year Change: This column displays the change in Thousand Oaks population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Thousand Oaks Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Newport Beach population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Newport Beach across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Newport Beach was 82,637, a 0.97% decrease year-by-year from 2022. Previously, in 2022, Newport Beach population was 83,446, a decline of 1.14% compared to a population of 84,404 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Newport Beach increased by 3,594. In this period, the peak population was 86,921 in the year 2015. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Newport Beach is shown in this column.
• Year on Year Change: This column displays the change in Newport Beach population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 Newport Beach Population by Year. You can refer the same here

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.

To collect this data, an Internet-wide IP address sweep was conducted. Every IP address in the ranges allocated by IANA was pinged once by sending ICMP ECHO_REQUEST (PING) packet. If the response (ICMP_ECHO_REPLY) came, its IP address was recorded in this data-set. In all, over 2.5 billion distinct IP addresses were probed during this experiment.

Statistics Canada's 2016 Census data, by community and age group, on the number and proportion of people whose income is below the low-income line. Keywords: Low-income, LICO-AT Statistics Canada. 2017. Census Profile. 2016 Census. Statistics Canada Catalogue no. 98-316-X2016001. Ottawa. Released November 29, 2017. https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/index.cfm?Lang=E

Statistics Canada's 2016 Census data, by community, on the amount of taxable and non-taxable income earned by individuals over the age of 15 in 2015. Keywords: median, average, after-tax income Statistics Canada. 2017. Census Profile. 2016 Census. Statistics Canada Catalogue no. 98-316-X2016001. Ottawa. Released November 29, 2017. https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/index.cfm?Lang=E

The SCAG_ATDB_Demographics shapefile contains Census tract level population, race, employment, English speaking, income, and elderly data of the SCAG region. Race data includes the percentage of population that is white, black, Asian, Latino, Pacific Islander, Native American, multiple races, or other. Population data includes 2010 population 2015 population, and population density. Employment data includes 2015 employment, unemployment, and employment density. English speaking data includes the percentage of the population that speaks English well. This shapefile also includes median household income and percentage of the population that is 65 years or older. This data was sourced mostly from Census data as well as the Healthy Places Index (HPI). Original data sources are listed in the relevant fields.

The GIS layer "Census_sum_15" provides a standardized tool for examining spatial patterns in abundance and demographic trends of the southern sea otter (Enhydra lutris nereis), based on data collected during the spring 2015 range-wide census. The USGS range-wide sea otter census has been undertaken twice a year since 1982, once in May and once in October, using consistent methodology involving both ground-based and aerial-based counts. The spring census is considered more accurate than the fall count, and provides the primary basis for gauging population trends by State and Federal management agencies. This Shape file includes a series of summary statistics derived from the raw census data, including sea otter density (otters per square km of habitat), linear density (otters per km of coastline), relative pup abundance (ratio of pups to independent animals) and 5-year population trend (calculated as exponential rate of change). All statistics are calculated and plotted for small sections of habitat in order to illustrate local variation in these statistics across the entire mainland distribution of sea otters in California (as of 2015). Sea otter habitat is considered to extend offshore from the mean low tide line and out to the 60m isobath: this depth range includes over 99% of sea otter feeding dives, based on dive-depth data from radio tagged sea otters (Tinker et al 2006, 2007). Sea otter distribution in California (the mainland range) is considered to comprise this band of potential habitat stretching along the coast of California, and bounded to the north and south by range limits defined as "the points farthest from the range center at which 5 or more otters are counted within a 10km contiguous stretch of coastline (as measured along the 10m bathymetric contour) during the two most recent spring censuses, or at which these same criteria were met in the previous year". The polygon corresponding to the range definition was then sub-divided into onshore/offshore strips roughly 500 meters in width. The boundaries between these strips correspond to ATOS (As-The-Otter-Swims) points, which are arbitrary locations established approximately every 500 meters along a smoothed 5 fathom bathymetric contour (line) offshore of the State of California.

About this dataset

Context

This list ranks the 55 counties in the California by Hispanic White population, as estimated by the United States Census Bureau. It also highlights population changes in each counties over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Hispanic White Population: This column displays the rank of counties in the California by their Hispanic White population, using the most recent ACS data available.
• counties: The counties for which the rank is shown in the previous column.
• Hispanic White Population: The Hispanic White population of the counties is shown in this column.
• % of Total counties Population: This shows what percentage of the total counties population identifies as Hispanic White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total California Hispanic White Population: This tells us how much of the entire California Hispanic White population lives in that counties. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

Graph and download economic data for Population Estimate, Total (5-year estimate) in Orange County, CA (B03002001E006059) from 2009 to 2023 about Orange County, CA; Los Angeles; CA; estimate; persons; 5-year; population; and USA.

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.

US Census American Community Survey (ACS) 2015, 5-year estimates of the key demographic characteristics of Census Tracts geographic level in Orange County, California. The data contains 105 fields for the variable groups D01: Sex and age (universe: total population, table X1, 49 fields); D02: Median age by sex and race (universe: total population, table X1, 12 fields); D03: Race (universe: total population, table X2, 8 fields); D04: Race alone or in combination with one or more other races (universe: total population, table X2, 7 fields); D05: Hispanic or Latino and race (universe: total population, table X3, 21 fields), and; D06: Citizen voting age population (universe: citizen, 18 and over, table X5, 8 fields). The US Census geodemographic data are based on the 2015 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

Data included in this dataset include: 1) population estimate data; 2) microhabitat use data; and 3) microhabitat availability data for the Santa Ana Sucker (Catostomus santaanae) and the Arroyo Chub (Gila orcutti) in the Santa Ana River.

US Census American Community Survey (ACS) 2015, 5-year estimates of the key economic characteristics of Census Tracts geographic level in Orange County, California. The data contains 397 fields for the variable groups E01: Employment status (universe: population 16 years and over, table X23, 7 fields); E02: Work status by age of worker (universe: population 16 years and over, table X23, 36 fields); E03: Commuting to work (universe: workers 16 years and over, table X8, 8 fields); E04: Travel time to work (universe: workers 16 years and over who did not work at home, table X8, 14 fields); E05: Number of vehicles available for workers (universe: workers 16 years and over in households, table X8, 8 fields); E06: Median age by means of transportation to work (universe: median age, workers 16 years and over, table X8, 7 fields); E07: Means of transportation to work by race (universe: workers 16 years and over, table X8, 64 fields); E08: Occupation (universe: civilian employed population 16 years and over, table X24, 53 fields); E09: Industry (universe: civilian employed population 16 years and over, table X24, 43 fields); E10: Class of worker (universe: civilian employed population 16 years and over, table X24, 19 fields); E11: Household income and earnings in the past 12 months (universe: total households, table X19, 37 fields); E12: Income and earnings in dollars (universe: inflation-adjusted dollars, tables X19-X20, 31 fields); E13: Family income in dollars (universe: total families, table X19, 17 fields); E14: Health insurance coverage (universe: total families, table X19, 17 fields); E15: Ratio of income to Poverty level (universe: total population for whom Poverty level is determined, table X17, 8 fields); E16: Poverty in population in the past 12 months (universe: total population for whom Poverty level is determined, table X17, 7 fields); E17: Poverty in households in the past 12 months (universe: total households, table X17, 9 fields); E18: Percentage of families and people whose income in the past 12 months is below the poverty level (universe: families, population, table X17, 8 fields), and; X19: Poverty and income deficit (dollars) in the past 12 months for families (universe: families with income below Poverty level in the past 12 months, table X17, 4 fields). The US Census geodemographic data are based on the 2015 TigerLines across multiple geographies. The spatial geographies were merged with ACS data tables. See full documentation at the OCACS project github page (https://github.com/ktalexan/OCACS-Geodemographics).

The 2015 cartographic boundary KMLs 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 cartographic boundary files include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). 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.

VITAL SIGNS INDICATOR Migration (EQ4)

FULL MEASURE NAME Migration flows

LAST UPDATED December 2018

DESCRIPTION Migration refers to the movement of people from one location to another, typically crossing a county or regional boundary. Migration captures both voluntary relocation – for example, moving to another region for a better job or lower home prices – and involuntary relocation as a result of displacement. The dataset includes metropolitan area, regional, and county tables.

DATA SOURCE American Community Survey County-to-County Migration Flows 2012-2015 5-year rolling average http://www.census.gov/topics/population/migration/data/tables.All.html

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Data for migration comes from the American Community Survey; county-to-county flow datasets experience a longer lag time than other standard datasets available in FactFinder. 5-year rolling average data was used for migration for all geographies, as the Census Bureau does not release 1-year annual data. Data is not available at any geography below the county level; note that flows that are relatively small on the county level are often within the margin of error. The metropolitan area comparison was performed for the nine-county San Francisco Bay Area, in addition to the primary MSAs for the nine other major metropolitan areas, by aggregating county data based on current metropolitan area boundaries. Data prior to 2011 is not available on Vital Signs due to inconsistent Census formats and a lack of net migration statistics for prior years. Only counties with a non-negligible flow are shown in the data; all other pairs can be assumed to have zero migration.

Given that the vast majority of migration out of the region was to other counties in California, California counties were bundled into the following regions for simplicity: Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano, Sonoma Central Coast: Monterey, San Benito, San Luis Obispo, Santa Barbara, Santa Cruz Central Valley: Fresno, Kern, Kings, Madera, Merced, Tulare Los Angeles + Inland Empire: Imperial, Los Angeles, Orange, Riverside, San Bernardino, Ventura Sacramento: El Dorado, Placer, Sacramento, Sutter, Yolo, Yuba San Diego: San Diego San Joaquin Valley: San Joaquin, Stanislaus Rural: all other counties (23)

One key limitation of the American Community Survey migration data is that it is not able to track emigration (movement of current U.S. residents to other countries). This is despite the fact that it is able to quantify immigration (movement of foreign residents to the U.S.), generally by continent of origin. Thus the Vital Signs analysis focuses primarily on net domestic migration, while still specifically citing in-migration flows from countries abroad based on data availability.

The municipal census was conducted in the spring of 2015. The census gathers information such as the number of people who live in the home, their ages and gender; the type of dwelling; and length of residency in Strathcona County. An up-to-date census count ensures the County receives the maximum grant dollars based on population. These dollars go toward municipal programs such as roads, transit, and community and family services. Census information is also used to plan for future services and programs.The GIS Branch may use the household data for analysis; however, areas containing less than 50 people will not be reported. This practice is in line with Statistics Canada reporting. The data is not available directly to Strathcona County staff but is available upon request and permission from LLS. The data is not shared publicly at the household level.For mapping purposes, this table can be joined by EA number to the polygon data set (https://data.strathcona.ca/Demographics/Municipal-Census-2015-EA/ns2u-t8q8). Additionally, the centroid (point) of the EA polygon is included."