All zip codes within the City of Los Angeles

Analysis of ‘Zip Codes within the City of Los Angeles’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/deb3f636-f0a2-4357-9eae-134637716b1f on 26 January 2022.

--- Dataset description provided by original source is as follows ---

All zip codes within the City of Los Angeles

--- Original source retains full ownership of the source dataset ---

Zip code boundaries from Los Angeles County.

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Culver City, CA, is 27.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Culver City, CA, is 27.6.
• Total dependency ratio for Culver City, CA is 55.3.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Culver City, CA is 3.6.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Official Assessor Parcel Numbers assigned by the County of Los Angeles and entered and maintained by the Bureau of Engineering. Each APN is related to a Parcel by the Parcel Identification Number (PIN). The associated Parcel GIS Layer can be found at: https://data.lacity.org/A-Livable-and-Sustainable-City/Parcels/sa82-xry4

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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

description: FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme, orthographic imagery, is packaged in a separate NFIP Metadata Profile): cadastral, geodetic control, governmental unit, transportation, general structures, hydrography (water areas & lines. These data include an encoding of the geographic extent of the features and a minimal number of attributes needed to identify and describe the features. (Source: Circular A16, p. 13); abstract: FEMA Framework Basemap datasets comprise six of the seven FGDC themes of geospatial data that are used by most GIS applications (Note: the seventh framework theme, orthographic imagery, is packaged in a separate NFIP Metadata Profile): cadastral, geodetic control, governmental unit, transportation, general structures, hydrography (water areas & lines. These data include an encoding of the geographic extent of the features and a minimal number of attributes needed to identify and describe the features. (Source: Circular A16, p. 13)

🇺🇸 미국

This dataset is about artists. It has 1 row and is filtered where the artworks is "Residence B" for Aline Barnsdall, Olive Hill, Los Angeles, California. It features 9 columns including birth date, death date, country, and gender.

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. Face refers to the areal (polygon) topological primitives that make up MTDB. A face is bounded by one or more edges; its boundary includes only the edges that separate it from other faces, not any interior edges contained within the area of the face. The Topological Faces Shapefile contains the attributes of each topological primitive face. Each face has a unique topological face identifier (TFID) value. Each face in the shapefile includes the key geographic area codes for all geographic areas for which the Census Bureau tabulates data for both the 2020 Census and the annual estimates and surveys. The geometries of each of these geographic areas can then be built by dissolving the face geometries on the appropriate key geographic area codes in the Topological Faces Shapefile.

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 filewith no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independentdata set, or they can be combined to cover the entire nation. The Address Range / Feature Name Relationship File (ADDRFN.dbf) contains a record for each address range / linear feature name relationship. The purpose of this relationship file is to identify all street names associated with each address range. An edge can have several feature names; an address range located on an edge can be associated with one or any combination of the available feature names (an address range can be linked to multiple feature names). The address range is identified by the address range identifier (ARID) attribute that can be used to link to the Address Ranges Relationship File (ADDR.dbf). The linear feature name is identified by the linear feature identifier (LINEARID) attribute that can be used to link to the Feature Names Relationship File (FEATNAMES.dbf).

Feature layer generated from running the Overlay layers solution.

This digital dataset contains historical geochemical and other information for 200 samples of produced water from 182 sites in 25 oil fields in Los Angeles and Orange Counties, southern California. Produced water is a term used in the oil industry to describe water that is produced as a byproduct along with the oil and gas. The locations from which these historical samples have been collected include 152 wells. Well depth and (or) perforation depths are available for 114 of these wells. Sample depths are available for two additional wells in lieu of well or perforation depths. Additional sample sites include four storage tanks, and two unidentifiable sample sources. One of the storage tank samples (Dataset ID 57) is associated with a single identifiable well. Historical samples from other storage tanks and unidentifiable sample sources may also represent pre- or post-treated composite samples of produced water from single or multiple wells. Historical sample descriptions provide further insight about the site type associated with some of the samples. Twenty-four sites, including 21 wells, are classified as "injectate" based on the sample description combined with the designated well use at the time of sample collection (WD, water disposal or WF, water flood). Historical samples associated with these sites may represent water that originated from sources other than the wells from which they were collected. For example, samples collected from two wells (Dataset IDs 86 and 98) include as part of their description “blended and treated produced water from across the field”. Historical samples described as formation water (45 samples), including 38 wells with a well type designation of OG (oil/gas), are probably produced water, representing a mixture of formation water and water injected for enhanced recovery. A possible exception may be samples collected from OG wells prior to the onset of production. Historical samples from four wells, including three with a sample description of "formation water", were from wells identified as water source wells which access groundwater for use in the production of oil. The numerical water chemistry data were compiled by the U.S. Geological Survey (USGS) from scanned laboratory analysis reports available from the California Geologic Energy Management Division (CalGEM). Sample site characteristics, such as well construction details, were attributed using a combination of information provided with the scanned laboratory analysis reports and well history files from CalGEM Well Finder. The compiled data are divided into two separate data files described as follows: 1) a summary data file identifying each site by name, the site location, basic construction information, and American petroleum Institute (API) number (for wells), the number of chemistry samples, period of record, sample description, and the geologic formation associated with the origin of the sampled water, or intended destination (formation into which water was to intended to be injected for samples labeled as injectate) of the sample; and 2) a data file of geochemistry analyses for selected water-quality indicators, major and minor ions, nutrients, and trace elements, parameter code and (or) method, reporting level, reporting level type, and supplemental notes. A data dictionary was created to describe the geochemistry data file and is provided with this data release.

US Postal Service ZIP Code boundaries. This layer was created by Los Angeles County eGIS to align with parcel boundaries.ZIP is an acronym for Zone Improvement Plan.Legal vs. Postal Cities: Many users confuse the name the Post Office delivers mail to (e.g. Van Nuys, Hollywood) as a legal city (in this case Los Angeles), when they are a postal city. The County contains 88 legal cities, and over 400 postal names that are tied to the ZIP Codes. To support usability and geocoding, we have attached the first 3 postal cities to each address, based upon its ZIP Code.The US Postal Service is the authoritative source for ZIP Code data. See their website for more information.

This dataset contains records of regulated businesses, referred to as inventory, including restaurants, markets, and other food-related programs overseen by the County of Los Angeles Environmental Health. Each record represents a specific regulated program within a facility and includes details about its location, ownership, program type, and geographic coordinates (latitude and longitude). The inventory covers businesses in Los Angeles County’s unincorporated areas and 85 of its 88 cities, excluding Pasadena, Long Beach, and Vernon, which operate their own health departments. The dataset is updated quarterly and includes records from the past three years to reflect active and newly established facilities. The FACILITY ID serves as the primary key to link each facility to its associated inspections, found in the Environmental Health Restaurant and Market Inspections dataset.Data Dictionary: Field Name Description FACILITY ID Unique identifier for the facility. FACILITY NAME Official name of the facility.RECORD ID Unique identifier for the health program associated with the facility.PROGRAM NAME A unique name for each program. There may be more than one program at a facility. PROGRAM ELEMENT Code representing the specific type of program under which the facility operates. PE DESCRIPTION Description of the program element or category. FACILITY ADDRESS Street address of the facility. FACILITY CITY City where the facility is located. FACILITY STATE State where the facility is located. FACILITY ZIP ZIP code of the facility’s location. FACILITY LATITUDE Geographical latitude coordinate of the facility. FACILITY LONGITUDE Geographical longitude coordinate of the facility. OWNER ID Unique identifier assigned to the business owner. OWNER NAME Legal name of the business owner. OWNER ADDRESS Street address of the business owner. OWNER CITY City where the business owner resides or is headquartered. OWNER STATE State where the business owner resides or is headquartered. OWNER ZIP zip code for the business owner's address.

Adult respondents 18+ who walked for transportation or leisure for at least 150 minutes in the past week. Years covered are from 2013-2014 by zip code. Data taken from the California Health Interview Survey Neighborhood Edition (AskCHIS NE) (http://askchisne.ucla.edu/), downloaded February 2018.AskCHIS Neighborhood Edition is an online data dissemination and visualization platform that provides health estimates at sub-county geographic regions. Estimates are powered by data from The California Health Interview Survey (CHIS). CHIS is conducted by The UCLA Center for Health Policy Research, an affiliate of UCLA Fielding School of Public Health.Health estimates available in AskCHIS NE (Neighborhood Edition) are model-based small area estimates (SAEs).SAEs are not direct estimates (estimates produced directly from survey data, such as those provided through AskCHIS).CHIS data and analytic results are used extensively in California in policy development, service planning and research, and is recognized and valued nationally as a model population-based health survey.Before using estimates from AskCHIS NE, it is recommended that you read more about the methodology and data limitations at: http://healthpolicy.ucla.edu/Lists/AskCHIS%20NE%20Page%20Content/AllItems.aspx. You can go to http://askchisne.ucla.edu/ to create your own account.Produced by The California Health Interview Survey and The UCLA Center for Health Policy Research and compiled by the Los Angeles County Department of Public Health. "Field Name = Field Definition"Zipcode" = postal zip code in the City of Los Angeles “Percent” = adults ages 18+ who walked for transportation or leisure for at least 150 minutes in the past week"LowerCL" = the lower 95% confidence limit represents the lower margin of error that occurs with statistical sampling"UpperCL" = the upper 95% confidence limit represents the upper margin of error that occurs in statistical sampling "Population" = estimated population 18 and older (denominator) residing in the zip code Notes: 1) Zip codes are based on the Los Angeles Housing Department Zip Codes Within the City of Los Angeles map (https://media.metro.net/about_us/pla/images/lazipcodes.pdf).2) Zip codes that did not have data available (i.e., null values) are not included in the dataset; there are additional zip codes that fall within the City of Los Angeles.3) Zip code boundaries do not align with political boundaries. These data are best viewed with a City of Los Angeles political boundary file (i.e., City of Los Angeles jurisdiction boundary, City Council boundary, etc.) FAQS: 1. Which cycle of CHIS does AskCHIS Neighborhood Edition provide estimates for?All health estimates in this version of AskCHIS Neighborhood Edition are based on data from the 2013-2014 California Health Interview Survey. 2. Why do your population estimates differ from other sources like ACS? The population estimates in AskCHIS NE represent the CHIS 2013-2014 population sample, which excludes Californians living in group quarters (such as prisons, nursing homes, and dormitories). 3. Why isn't there data available for all ZIP codes in Los Angeles?While AskCHIS NE has data on all ZCTAs (Zip Code Tabulation Areas), two factors may influence our ability to display the estimates:A small population (under 15,000): currently, the application only shows estimates for geographic entities with populations above 15,000. If your ZCTA has a population below this threshold, the easiest way to obtain data is to combine it with a neighboring ZCTA and obtain a pooled estimate.A high coefficient of variation: high coefficients of variation denote statistical instability.

This dataset is about artists. It has 1 row and is filtered where the artworks is Plaster Works, Los Angeles. It features 9 columns including birth date, death date, country, and gender.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Culver City. The dataset can be utilized to gain insights into gender-based income distribution within the Culver City population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Culver City, among individuals aged 15 years and older with income, there were 14,063 men and 15,581 women in the workforce. Among them, 8,758 men were engaged in full-time, year-round employment, while 7,330 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 3.85% fell within the income range of under $24,999, while 3.40% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 61.64% of men in full-time roles earned incomes exceeding $100,000, while 49.51% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

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:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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 Culver City median household income by race. You can refer the same here

About this dataset

Context

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

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Los Angeles County, CA, is 25.2.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Los Angeles County, CA, is 21.6.
• Total dependency ratio for Los Angeles County, CA is 46.8.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Los Angeles County, CA is 4.6.

Content

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

Age groups:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Los Angeles. The dataset can be utilized to gain insights into gender-based income distribution within the Los Angeles population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Los Angeles, among individuals aged 15 years and older with income, there were 1.40 million men and 1.32 million women in the workforce. Among them, 759.51 thousand men were engaged in full-time, year-round employment, while 579.13 thousand women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 9.66% fell within the income range of under $24,999, while 11.29% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 29.23% of men in full-time roles earned incomes exceeding $100,000, while 25.02% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

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:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Los Angeles median household income by race. You can refer the same here