This viewer contains data directly from the U.S. Census Bureau. Use this map viewer to identify 2020 Census tract, block group, or block at a location. Map is centered on the City of Long Beach and shows the City boundary as recorded in the Census incorporated places layer. Data source: https://www.census.gov/data/developers/data-sets/TIGERweb-map-service.htmlAbout Census Tracts: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_13About Census Block Groups: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_4About Census Blocks: https://www.census.gov/programs-surveys/geography/about/glossary.html#par_textimage_5

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Long Beach: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 0 households where the householder is under 25 years old, 22(16.06%) households with a householder aged between 25 and 44 years, 46(33.58%) households with a householder aged between 45 and 64 years, and 69(50.36%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of Long Beach, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 Long Beach median household income by age. You can refer the same here

Long Beach 2040 General Plan serves as a comprehensive guide on urban form and land use for the long-term development of the City to meet the needs of the City’s evolving demographics, foster neighborhood enhancement, and plan for diverse open spaces, promote employment and revitalize commercial centers and corridors, and address land use, circulation, housing, conservation, open space, noise and safety. PlaceTypes is a new approach to land use planning that de-emphasizes specific uses and focuses on the form and character of Long Beach’s unique neighborhoods and districts and allows for a wide variety of compatible and complementary uses to create district and “complete” residential neighborhoods, employment centers, open spaces and other areas. Eleven PlaceTypes provide a comprehensive and more flexible way of planning for the future of Long Beach.DEPARTMENT RESPONSIBLE: Community DevelopmentDATA SOURCES: RES-19-0189 (Dec 3rd, 2019)MAINTENANCE: Updated as resolutions are approved by the Long Beach City CouncilREFERENCE: https://www.longbeach.gov/lbcd/planning/advance/general-plan/ RELATED DATA: Long Beach 2040 Height USED FOR: Zoning and Land Use Public Web Application

Graph and download economic data for Resident Population in Los Angeles-Long Beach-Anaheim, CA (MSA) (LNAPOP) from 2010 to 2024 about Los Angeles, residents, CA, population, and USA.

U.S. Census Bureau QuickFacts statistics for Long Beach city, California. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Long Beach. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Long Beach, the median income for all workers aged 15 years and older, regardless of work hours, was $57,813 for males and $36,875 for females.

These income figures highlight a substantial gender-based income gap in Long Beach. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the city of Long Beach.

- Full-time workers, aged 15 years and older: In Long Beach, among full-time, year-round workers aged 15 years and older, males earned a median income of $68,333, while females earned $63,125, resulting in a 8% gender pay gap among full-time workers. This illustrates that women earn 92 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Long Beach.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Long Beach.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Long Beach median household income by race. You can refer the same here

In compliance with the 2015 Racial Identity Profiling Act, the Long Beach Police Department was one of seven law enforcement agencies required to begin collecting stop data on January 1, 2019, for individuals stopped by police and consensual encounters that resulted in a search. The Department will collect data for each calendar year and will submit the data to the California Department of Justice on an annual basis.

Data elements collected include demographic information of the stopped individuals that is perceived by the officer. This demographic information consists of race/ethnicity, gender, LGBT identity, age, English fluency, and perceived or known disability. The date, time, location, reason for stop, actions taken, contraband/evidence discovered, property seized, and result of stop are also included in the data collected.

Long Beach is a city. It is in the United States and has a population of 35,203 people.

U.S. Census Bureau QuickFacts statistics for Long Branch city, New Jersey. QuickFacts data are derived from: Population Estimates, American Community Survey, Census of Population and Housing, Current Population Survey, Small Area Health Insurance Estimates, Small Area Income and Poverty Estimates, State and County Housing Unit Estimates, County Business Patterns, Nonemployer Statistics, Economic Census, Survey of Business Owners, Building Permits.

VariableDescriptionTime FrameChildren in foster carePoint in time counts of children placed in Foster Care.Point in time data as of 12/31/2021If an estimated child population for a CSA for any of the above categories (denominator) was 50 or less, the number of referrals or cases (numerator) for that ethnicity was reduced to zero. CSAs range in size and population, from the City of Long Beach to small unincorporated enclaves encompassing a few city blocks. Communities that had low overall populations might have a “Gossip Factor”, in which counts for all ethnicities were reduced to zero.

This dataset comprises of the intake and outcome record from Long Beach Animal Shelter.

The Census Bureau (https://www.census.gov/) maintains geographic boundaries for the analysis and mapping of demographic information across the United States. Every 10 years the Census Bureau counts the population of the United States as mandated by Constitution. The Census Bureau releases the results of this county as demographic data with geographic identifiers so that maps and analysis can be performed on the US population. There are little more Census Tracts within Los Angeles County in 2020 Census TIGER/Line Shapefiles, compared to 2010.Created/Updated: Updated on September 2023, to merged Long Beach Breakwater land-based tracts silver polygons into bigger tract 990300 as per 2022 TIGER Line Shapefiles, and to update Santa Catalina Islands and San Clemente Islands tract boundary based on DPW City boundaries (except 599000 tract in Avalon). Updated on Sep 2022 and Dec 2022, to align tract boundary along city boundaries. Created on March 2021. How was this data created? This geographic file was downloaded from Census Bureau website: https://www2.census.gov/geo/tiger/TIGER2020PL/STATE/06_CALIFORNIA/06037/on February, 2021 and customized for LA County. Data Fields:1. CT20 (TRACTCE20): 6-digit census tract number, 2. Label (NAME20): Decimal point census tract number.

In compliance with the 2015 Racial Identity Profiling Act, the Long Beach Police Department was one of seven law enforcement agencies required to begin collecting stop data on January 1, 2019, for individuals stopped by police and consensual encounters that resulted in a search. The Department will collect data for each calendar year and will submit the data to the California Department of Justice on an annual basis.

Data elements collected include demographic information of the stopped individuals that is perceived by the officer. This demographic information consists of race/ethnicity, gender, LGBT identity, age, English fluency, and perceived or known disability. The date, time, location, reason for stop, actions taken, contraband/evidence discovered, property seized, and result of stop are also included in the data collected.

In compliance with the 2015 Racial Identity Profiling Act, the Long Beach Police Department was one of seven law enforcement agencies required to begin collecting stop data on January 1, 2019, for individuals stopped by police and consensual encounters that resulted in a search. The Department will collect data for each calendar year and will submit the data to the California Department of Justice on an annual basis.

Data elements collected include demographic information of the stopped individuals that is perceived by the officer. This demographic information consists of race/ethnicity, gender, LGBT identity, age, English fluency, and perceived or known disability. The date, time, location, reason for stop, actions taken, contraband/evidence discovered, property seized, and result of stop are also included in the data collected.

VariableDescriptionTime FrameSubstantiated ReferralsAnnual counts of children with substantiated referrals. Referral received dates from 1/1/2021 - 12/31/2021If an estimated child population for a CSA for any of the above categories (denominator) was 50 or less, the number of referrals or cases (numerator) for that ethnicity was reduced to zero. CSAs range in size and population, from the City of Long Beach to small unincorporated enclaves encompassing a few city blocks. Communities that had low overall populations might have a “Gossip Factor”, in which counts for all ethnicities were reduced to zero.

OverviewThese are the Homeless Counts for 2020 as provided by the Los Angeles Homeless Services Authority (LAHSA), and the cities of Glendale, Pasadena, and Long Beach. The majority of this data comes from LAHSA using tract-level counts; the cities of Glendale, Pasadena, and Long Beach did not have tract-level counts available. The purpose of this layer is to depict homeless density at a community scale. Please read the note from LAHSA below regarding the tract level counts. In this layer LAHSA's tract-level population count was rounded to the nearest whole number, and density was determined per square mile of each community. It should be noted that not all of the sub-populations captured from LAHSA (eg. people living in vans, unaccompanied minors, etc.) are not captured here; only sheltered, unsheltered, and total population. Data generated on 12/2/20.Countywide Statistical AreasLos Angeles County's 'Countywide Statistical Areas' layer was used to classify the city / community names. Since this is tract-level data there are several times where a tract is in more than one city/community. Whatever the majority of the coverage of a tract is, that is the community that got coded. The boundaries of these communities follow aggregated tract boundaries and will therefore often deviate from the 'Countywide Statistical Area' boundaries.Note from LAHSALAHSA does not recommend aggregating census tract-level data to calculate numbers for other geographic levels. Due to rounding, the census tract-level data may not add up to the total for Los Angeles City Council District, Supervisorial District, Service Planning Area, or the Los Angeles Continuum of Care.The Los Angeles Continuum of Care does not include the Cities of Long Beach, Glendale, and Pasadena and will not equal the countywide Homeless Count Total.Street Count Data include persons found outside, including persons found living in cars, vans, campers/RVs, tents, and makeshift shelters. A conversion factor list can be found at https://www.lahsa.org/homeless-count/Please visit https://www.lahsa.org/homeless-count/home to view and download data.Last updated 07/16/2020

Abstract (en): Data on labor force activity for the week prior to the survey are supplied in this collection. Information is available on the employment status, occupation, and industry of persons 15 years old and over. Demographic variables such as age, sex, race, marital status, veteran status, household relationship, educational background, and Hispanic origin are included. In addition to providing these core data, the October survey also contains a special supplement on school enrollment for all persons surveyed aged 3 years old or older. This supplement includes the following items: current grade attending at public or private school, whether attending college full- or part-time at a two- or four-year institution, year last attended a regular school, and year graduated from high school. All persons in the civilian noninstitutional population of the United States living in households. The probability sample selected to represent the universe consists of approximately 57,000 households. The sample was located in 729 primary sampling units comprising 1,297 counties and independent cities with coverage in every state, the District of Columbia, and the sub-state areas of New York City and the Los Angeles-Long Beach metropolitan area. The codebook is provided as a Portable Document Format (PDF) file. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided through the ICPSR Website on the Internet.

VariableDescriptionTime FrameAll children with open casesPoint in time counts of children with open cases. This includes children served while with their families and those who were placed in foster care. Point in time data as of 12/31/2021If an estimated child population for a CSA for any of the above categories (denominator) was 50 or less, the number of referrals or cases (numerator) for that ethnicity was reduced to zero. CSAs range in size and population, from the City of Long Beach to small unincorporated enclaves encompassing a few city blocks. Communities that had low overall populations might have a “Gossip Factor”, in which counts for all ethnicities were reduced to zero.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Long Branch. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Long Branch, the median income for all workers aged 15 years and older, regardless of work hours, was $43,292 for males and $30,165 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 30% between the median incomes of males and females in Long Branch. With women, regardless of work hours, earning 70 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Long Branch.

- Full-time workers, aged 15 years and older: In Long Branch, among full-time, year-round workers aged 15 years and older, males earned a median income of $59,565, while females earned $53,326, resulting in a 10% gender pay gap among full-time workers. This illustrates that women earn 90 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Long Branch.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Long Branch.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Long Branch median household income by race. You can refer the same here

VariableDescriptionTime FrameInvestigationsAnnual counts of children with child maltreatment allegations. If a child is referred multiple times in a specific year, they are counted more than once.Referral received dates from 1/1/2021 - 12/31/2021If an estimated child population for a CSA for any of the above categories (denominator) was 50 or less, the number of referrals or cases (numerator) for that ethnicity was reduced to zero. CSAs range in size and population, from the City of Long Beach to small unincorporated enclaves encompassing a few city blocks. Communities that had low overall populations might have a “Gossip Factor”, in which counts for all ethnicities were reduced to zero.