Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

About this dataset

Context

The dataset tabulates the Lake View 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 Lake View. The dataset can be utilized to understand the population distribution of Lake View by age. For example, using this dataset, we can identify the largest age group in Lake View.

Key observations

The largest age group in Lake View, AR was for the group of age 50 to 54 years years with a population of 38 (8.37%), according to the ACS 2018-2022 5-Year Estimates. At the same time, the smallest age group in Lake View, AR was the 40 to 44 years years with a population of 10 (2.20%). 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 Lake View is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Lake View 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 Lake View Population by Age. You can refer the same here

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Ancestry listed in this table refers to the total number of people who responded with a particular ancestry; for example, the estimate given for German represents the number of people who listed German as either their first or second ancestry. This table lists only the largest ancestry groups; see the Detailed Tables for more categories. Race and Hispanic origin groups are not included in this table because data for those groups come from the Race and Hispanic origin questions rather than the ancestry question (see Demographic Table)..Data for year of entry of the native population reflect the year of entry into the U.S. by people who were born in Puerto Rico or U.S. Island Areas or born outside the U.S. to a U.S. citizen parent and who subsequently moved to the U.S..The category "with a broadband Internet subscription" refers to those who said "Yes" to at least one of the following types of Internet subscriptions: Broadband such as cable, fiber optic, or DSL; a cellular data plan; satellite; a fixed wireless subscription; or other non-dial up subscription types..An Internet "subscription" refers to a type of service that someone pays for to access the Internet such as a cellular data plan, broadband such as cable, fiber optic or DSL, or other type of service. This will normally refer to a service that someone is billed for directly for Internet alone or sometimes as part of a bundle.."With a computer" includes those who said "Yes" to at least one of the following types of computers: Desktop or laptop; smartphone; tablet or other portable wireless computer; or some other type of computer..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- ...

The Consumer Demographic database is comprised of over 80 sources and includes over 400 different data points for each individual in a household with complete PII. The fields provided include demographics, psychographic, lifestyle criteria, buying behavior, and real property identification.

Each record is ranked by confidence and only the highest quality data is used. The database is multi-sourced and contains both compiled and originated U.S. data. Additionally, the data goes through intensive cleansing including deceased processing and NCOA.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Households not paying cash rent are excluded from the calculation of median gross rent..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Age, Sex, Race, Ethnicity, Total Housing Units, and Voting Age Population. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2019-2023. ACS Table(s): DP05. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

A. SUMMARY This dataset contains population and demographic estimates and associated margins of error obtained and derived from the US Census. The data is presented over multiple years and geographies. The data is sourced primarily from the American Community Survey. B. HOW THE DATASET IS CREATED The raw data is obtained from the census API. Some estimates as published as-is and some are derived. C. UPDATE PROCESS New estimates and years of data are appended to this dataset. To request additional census data for San Francisco, email support@datasf.org D. HOW TO USE THIS DATASET The dataset is long and contains multiple estimates, years and geographies. To use this dataset, you can filter by the overall segment which contains information about the source, years, geography, demographic category and reporting segment. For census data used in specific reports, you can filter to the reporting segment. To use a subset of the data, you can create a filtered view. More information of how to filter data and create a view can be found here

About this dataset

Context

The dataset tabulates the Bay View 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 Bay View. The dataset can be utilized to understand the population distribution of Bay View by age. For example, using this dataset, we can identify the largest age group in Bay View.

Key observations

The largest age group in Bay View, OH was for the group of age 60 to 64 years years with a population of 93 (14.88%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Bay View, OH was the 20 to 24 years years with a population of 4 (0.64%). 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 Bay View is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Bay View 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 Bay View Population by Age. You can refer the same here

The demographic data displayed in this theme of Florida’s Roadmap to Living Healthy are quantitative measures that exhibit the socioeconomic state of Florida’s communities. The data sets comprising this themed map include topics such as population, race, income level, age, education, housing, and lifestyle data for all of Florida’s 67 counties, and other basic demographic characteristics. The Florida Department of Agriculture and Consumer Services has utilized the most current demographic statistical data from trusted sources such as the U.S. Census Bureau, U.S. Department of Housing and Urban Development, U.S. Department of Labor Bureau of Labor Statistics, Florida Department of Children and Families, and Esri to craft this custom visualization. Demographics provide profound perspective to your data analytics and will help you recognize the distinctive characteristics of a population based on its location. This demographic-themed mapping tool will simplify your ability to identify the specific socioeconomic needs of every community in Florida.

Population and other demographic information is collected by the US Census Bureau.

View the US Census Bureau's Quick Facts page about Bloomington, Indiana at https://www.census.gov/quickfacts

The Demographic Profile and other data for Bloomington can be viewed or downloaded from the American FactFinder search tool: https://factfinder.census.gov/bkmk/cf/1.0/en/place/Bloomington city, Indiana/POPULATION/DECENNIAL_CNT

The Census Bureau is creating a new platform for data. This site is in a preview stage and some parts are under construction. Here is a link for Bloomington: https://data.census.gov/cedsci/results/all?q=Bloomington%20city,%20Indiana&g=1600000US1805860&ps=app*from@SINGLE_SEARCH

The City webpage for Census data contains other related information: https://bloomington.in.gov/about/census-data

Users can obtain demographic characteristics of the foreign-born population in each state. Topics include: language, education, income, and poverty. Background The American Community Survey and Census Data on the Foreign-Born interactive map was created by the Migration Policy Institute using Census data. This website provides information pertaining to the immigrant population in the United States. Topics include: demographics, language, education, income and poverty of the foreign-born population. User Functionality Users can click on states to generate fact sheets about the demographic, social, language, educ ation, workforce, income, and poverty characteristics of the population in each state. Data can be downloaded into SAS statistical software. Users can view demographic information by race/ethnicity, Hispanic origin, place of origin, citizenship status, sex/gender, and marital status. Data Notes Data are derived from the 1990 and 2000 Decennial Censuses and the 2007 American Community Surveys (ACS). Information is available on national and state levels. The website does not indicate when the data will be updated.

The U.S. Census Bureau TIGER/Line® files in this data collection were originally distributed by the Inter-university Consortium for Political and Social Research (ICPSR) through its TIGER/Line file web site, which was decommissioned in 2018 (archived version: https://web.archive.org/web/20090924181858/http://www.icpsr.umich.edu/TIGER/index.html). There, users could download various versions of the U.S. Census Bureau's TIGER (Topologically Integrated Geographic Encoding and Referencing) database. The TIGER/Line files do not include demographic data, but they do contain geographic information that can be linked to the Census Bureau’s demographic data. Due to file number limitations in openICPSR, the original data collections have been bundled into single zip packages. A single TIGER_directory.txt file listing the original files and the original directory structure is included with the root directory. Documentation files are also included as standalone subdirectories in each collection so users do not need to download entire zip bundles to view documentation. The TIGER/Line data are stored in compressed format in subdirectories by state name. There is one TIGER/Line file (in a compressed format) for each county or county equivalent. The file names consist of TGR + the 2-digit state FIPS (Federal Information Processing Standards) code + the 3-digit county FIPS code (i.e. TGR01031.ZIP for Coffee County, Alabama). Each state folder contains individual county files.The individual county files include one file for each record type included for that county with the following name convention: tgr01031.rt1. The convention follows the order described above with each file having a suffix which includes 'rt' (record type) followed by its designation (in this case record type 1). Each county file also contains its own metadata record.If present, documentation files for the TIGER/Line data are stored in a directory named '0docs' which is located in the 'Parent Directory'. This directory appears at the top of the index of state subdirectories for each edition of the TIGER/Line files. The documentation includes a complete list of FIPS state and county codes.

This table contains 84 series, with data for years 1998 - 2004 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Viewer demographic characteristics (3 items: Total, all persons two years and older;Anglophones, two years and older;Francophones, two years and older), Signal source (7 items: Total, signal sources;Canadian conventional stations;Canadian pay television and specialty services;Videocassette recorder (VCR); ...), Content of programme (4 items: Total television programmes, Canadian, foreign and unknown origin;Canadian television programmes;Foreign television programmes;Unknown origin of television programmes)

These datasets contain measures of socioeconomic and demographic characteristics by U.S. census tract for the years 1990-2022 and ZIP code tabulation area (ZCTA) for the years 2008-2022. Example measures include population density; population distribution by race, ethnicity, age, and income; income inequality by race and ethnicity; and proportion of population living below the poverty level, receiving public assistance, and female-headed or single parent families with kids. The datasets also contain a set of theoretically derived measures capturing neighborhood socioeconomic disadvantage and affluence, as well as a neighborhood index of Hispanic, foreign born, and limited English.

This transformed view of Employee Demographics - Public dataset counts the number of and percentage of city employees by race as self-reported by employee based on EEOC classification. This information is used by "City Employee vs. Community Demographics dataset" at https://citydata.mesaaz.gov/Economic-Development/Chart-Data-for-City-Employee-vs-Community-Demograp/bt2n-zimw

This map is just one of the many data visualizations on the Global Midwives Hub, a digital resource with open data, maps, and mapping applications (among other things), to support advocacy for improved maternal and newborn services, supported by the International Confederation of Midwives (ICM), UNFPA, WHO, and Direct Relief.

To provide a snapshot of citywide student enrollment and demographic information across multiple years. Data is collected using multiple data sources, including DOE's Audited Register, biographic data from Automate The Schools (ATS) system and the Location Code Generation and Management System (LCGMS). Data can be used to view citywide demographic and enrollment trends over time. Enrollment counts are based on the October 31 Audited Register for each school year. Please note that October 31 enrollment is not audited for charter schools or Pre-K Early Education Centers(NYCEECs). Charter schools are required to submit enrollment as of BEDS Day the first Wednesday in October to the New York State Education Department of Education. Enrollment counts will exceed operational enrollment counts due the fact that long term absence (LTA) students are excluded for funding purposes.

Household Demographics in the 2010 CensusThis feature layer contains demographics about households as reported by the U.S. Census Bureau (USCB) in the 2010 U.S. Census. These attributes cover topics such as household types, household size, nonfamily households, householder age, and households with children. A small subset of attributes from the 2000 Census are also included as reference.Per the Census, "Also known as the Population and Housing Census, the Decennial U.S. Census is designed to count every resident in the United States. It is mandated by Article I, Section 2 of the Constitution and takes place every 10 years. The data collected by the decennial census determine the number of seats each state has in the U.S. House of Representatives and is also used to distribute hundreds of billions of dollars in federal funds to local communities."Four layers are available: state, county, census tract, and census block group. Each layer contains the same set of demographic attributes. Each geography level has a viewing range optimal for the geography size, and the map detail increases when zooming to local areas. Only one geography is in view at any time.Household Demographics 2010 CensusData currency: 2010Data download: Explore Census DataFor more information: Households and Families: 2010For feedback please contact: ArcGIScomNationalMaps@esri.comData Processing notes:State and county boundaries are simplified representations offered from the Census Bureau's 2010 MAF/TIGER databaseTract and block group boundaries are 2010 TIGER boundaries with select water area boundaries erased (coastlines and major water bodies)Field names and aliases are processed by Esri as created for the ArcGIS Platform.For a list of fields and alias names, access the following excel document.U.S. Census BureauPer USCB, "the Census Bureau is the federal government’s largest statistical agency. We are dedicated to providing current facts and figures about America’s people, places, and economy. Federal law protects the confidentiality of all the information the Census Bureau collects."

This dataset contains the geographic data used to create maps for the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt

Demographic data from the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Demographics/q9ix-kfws

Filter by the Indicator column to select data for a particular indicator map.

Export notes: Dataset may not automatically open correctly in Excel due to geospatial data. To export the data for geospatial analysis, select Shapefile or GEOJSON as the file type. To view the data in Excel, export as a CSV but do not open the file. Then, open a blank Excel workbook, go to the Data tab, select “From Text/CSV,” and follow the prompts to import the CSV file into Excel. Alternatively, use the exploration options in "View Data" to hide the geographic column prior to exporting the data.

USER NOTES: 4/7/2025 - The maps and data have been removed for the Health Professional Shortage Areas indicator due to inconsistencies with the data source leading to some missing health professional shortage areas. We are working to fix this issue, including exploring possible alternative data sources.

5/21/2025 - The following changes were made to the 2023 report data (Equity Report Year = 2023). Self-Sufficiency Wage - a typo in the indicator name was fixed (changed sufficienct to sufficient) and the percent for one PUMA corrected from 56.9 to 59.9 (PUMA = San Diego County (Northwest)--Oceanside City & Camp Pendleton). Notes were made consistent for all rows where geography = ZCTA. A note was added to all rows where geography = PUMA. Voter registration - label "92054, 92051" was renamed to be in numerical order and is now "92051, 92054". Removed data from the percentile column because the categories are not true percentiles. Employment - Data was corrected to show the percent of the labor force that are employed (ages 16 and older). Previously, the data was the percent of the population 16 years and older that are in the labor force. 3- and 4-Year-Olds Enrolled in School - percents are now rounded to one decimal place. Poverty - the last two categories/percentiles changed because the 80th percentile cutoff was corrected by 0.01 and one ZCTA was reassigned to a different percentile as a result. Low Birthweight - the 33th percentile label was corrected to be written as the 33rd percentile. Life Expectancy - Corrected the category and percentile assignment for SRA CENTRAL SAN DIEGO. Parks and Community Spaces - corrected the category assignment for six SRAs.

5/21/2025 - Data was uploaded for Equity Report Year 2025. The following changes were made relative to the 2023 report year. Adverse Childhood Experiences - added geographic data for 2025 report. No calculation of bins nor corresponding percentiles due to small number of geographic areas. Low Birthweight - no calculation of bins nor corresponding percentiles due to small number of geographic areas.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).