Comprehensive demographic dataset for South L.A., Los Angeles, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for Historic South-Central, Los Angeles, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Comprehensive demographic dataset for South Park, Los Angeles, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

Census 2010 population/demographic data approximated from block groups to LA Neighborhood Councils using Esri software.

About this dataset

Context

This list ranks the 86 cities in the Los Angeles County, CA by South African population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

The Los Angeles County Social Survey (LACSS) continues the Los Angeles Metropolitan Area Studies (LAMAS) and the Southern California Social Surveys (SCSS). The Log Angeles County Social Survey (LACSS) is part of a continuing annual research project supported by the Institute for Social Science Research at the University of California, Los Angeles (UCLA). Each year a University of California researcher is given an opportunity to be principal investigator and to use a segment of the LACSS for his or her own research. The 1992 principal investigator was Dr. Lawrence Bobo, who was an Associate Professor of Sociology at UCLA. The LACSS 1992 was conducted between February and July 1992. Los Angeles County residents were asked questions concerning ethnic relations, social dominance, social distance, immigration, affirmative action, employment, and government. A split ballot methodology was utilized concerning the topics of immigration and affirmative action. Respondents were randomly selected to answer a series of questions from one of three ballots. In addition, a different series of social distance questions were asked depending on the respondent's ethnicity. Questionnaires were provided in both English and Spanish languages. Demographic information collected includes race, gender, religion, age, education level, occupation, birth place, political party affiliation and ideology, and origin of ancestry.

FBI National Incident-Based Reporting System (FBI NIBRS) crime data for Highway Patrol: South Los Angeles Area Office (State Police) in California, including incidents, statistics, demographics, and detailed incident information.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the city of Los Angeles. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore Los Angeles's Data using Kaggle and all of the data sources available through the city of Los Angeles organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Markus Spiske on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under the following licenses: Creative Commons 1.0 Universal (Public Domain Dedication)

The Census Designated Places 2020 (CDP 2020) boundary usually is defined by the Census Bureau in cooperation with state, local or tribal officials. The boundaries are updated prior to each decennial census. These boundaries, which usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity boundary, have no legal status, nor do these places have officials elected to serve traditional municipal functions. CDP boundaries may change from one decennial census to the next with changes in the settlement pattern; a CDP with the same name as in an earlier census does not necessarily have the same boundary. CDPs must be contained within a single state and may not extend into an incorporated place. There are no population size requirements for CDPs. incorporatedCDP data is download from Census Bureau's TIGER 2020 website (https://www2.census.gov/geo/tiger/TIGER2020/PLACE/) and extracted for Los Angeles County. This data includes LA County 88 incorporated cities and 54 CDPs.

Comprehensive demographic dataset for Beverly Hills South East, Beverly Hills, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

The map illustrates the 2019 Areawide Source emissions for the AB 617 South Los Angeles (LA) community. Emissions in tons per year are based on the latest CARB State Implementation Plan emission inventory with a base year of 2017, and are projected to 2019 using the most up-to-date growth and control factors at the regional scale. Areawide source emissions for each source categories are distributed to more specific locations using the latest spatial surrogates resulting in high-resolution 1x1km emission grids for the community. Examples of spatial surrogates include population, housing, employment, land cover type etc.

Graph and download economic data for Bachelor's Degree or Higher (5-year estimate) in Los Angeles County, CA (HC01ESTVC1706037) from 2010 to 2023 about Los Angeles County, CA; Los Angeles; educational attainment; tertiary schooling; education; CA; 5-year; and USA.

The map illustrates the 2019 emissions by major sources: stationary, areawide, and mobile (on-road and off-road) in the AB 617 South Los Angeles (LA) community.Emissions in tons per year are based on the latest CARB State Implementation Plan emission inventory with a base year of 2017 (CEPAM 2019SIP v1.01) and are projected to 2019 using the most up-to-date growth and control factors at the regional scale. Stationary point emission data is based on the 2018 reported data within the California Emission Inventory Development and Reporting System (CEIDARS) where Air Districts report annual emissions for facilities. Source emissions are distributed to more specific locations using the latest spatial surrogates resulting in high-resolution 1x1km emission grids for the community. Examples of spatial surrogates include population, housing, employment, land cover type, etc.

This collection contains a cumulative datafile for The Los Angeles County Social Survey (LACSS) comprised of participants from years 1992 and 1994-1998. The LACSS continues the Los Angeles Metropolitan Area Studies (LAMAS) and the Southern California Social Surveys (SCSS). The Los Angeles County Social Survey (LACSS) is part of a continuing annual research project supported by the Institute for Social Science Research at the University of California, Los Angeles (UCLA). Each year a University of California researcher is given an opportunity to be principal investigator and to use a segment of the LACSS for his or her own research. Data for this collection represents the LACSS conducted between February 1992 and June 1998. No data was included for the year 1993. Each year, Los Angeles County residents were asked questions concerning ethnic relations, social dominance, social distance, immigration, affirmative action, employment, and government. A split ballot methodology was utilized concerning the topics of immigration and affirmative action. Respondents were randomly selected to answer a series of questions from one of three ballots. In addition, a different series of social distance questions were asked depending on the respondent's ethnicity. Demographic information collected includes race, gender, religion, age, education level, occupation, birth place, political party affiliation and ideology, and origin of ancestry.

Most of the United States (U.S.) population live together in a few densely populated areas. While this is a well known fact, visual explanations of this characteristic can be quite striking. These four maps illustrate in different ways where we live, and how we actually inhabit so little of our country's space.Map 1 shows the coastal shoreline counties of the U.S., which are the counties that are directly adjacent to an open ocean, a major estuary, or the Great Lakes. According to 2014 Census data, 39.1 percent of the U.S. population lived in those counties, often within miles of the coast.Map 2 highlights the largest and smallest counties in the U.S. Roughly fifty percent of the U.S. population lives in the country's 144 largest counties, while the roughly other 50 percent lives in 2,998 counties.Map 3 compares America's two largest counties (Los Angeles and Downtown Chicago) with the 14 smallest states.Map 4 compares the population of these two counties with 1,437 of the country's smallest counties. Nearly five percent of America's population lives in the counties covering downtown Los Angeles and downtown Chicago, which is the same proportion as those that live in the country's 1,437 smallest counties.Source: Ana Swanson, Washington Post Wonkblog. September 3, 2015

20 year Projected Urban Growth scenarios. Base year is 2000. Projected year in this dataset is 2020.

By 2020, most forecasters agree, California will be home to between 43 and 46 million residents-up from 35 million today. Beyond 2020 the size of California's population is less certain. Depending on the composition of the population, and future fertility and migration rates, California's 2050 population could be as little as 50 million or as much as 70 million. One hundred years from now, if present trends continue, California could conceivably have as many as 90 million residents.

Where these future residents will live and work is unclear. For most of the 20th Century, two-thirds of Californians have lived south of the Tehachapi Mountains and west of the San Jacinto Mountains-in that part of the state commonly referred to as Southern California. Yet most of coastal Southern California is already highly urbanized, and there is relatively little vacant land available for new development. More recently, slow-growth policies in Northern California and declining developable land supplies in Southern California are squeezing ever more of the state's population growth into the San Joaquin Valley.

How future Californians will occupy the landscape is also unclear. Over the last fifty years, the state's population has grown increasingly urban. Today, nearly 95 percent of Californians live in metropolitan areas, mostly at densities less than ten persons per acre. Recent growth patterns have strongly favored locations near freeways, most of which where built in the 1950s and 1960s. With few new freeways on the planning horizon, how will California's future growth organize itself in space? By national standards, California's large urban areas are already reasonably dense, and economic theory suggests that densities should increase further as California's urban regions continue to grow. In practice, densities have been rising in some urban counties, but falling in others.

These are important issues as California plans its long-term future. Will California have enough land of the appropriate types and in the right locations to accommodate its projected population growth? Will future population growth consume ever-greater amounts of irreplaceable resource lands and habitat? Will jobs continue decentralizing, pushing out the boundaries of metropolitan areas? Will development densities be sufficient to support mass transit, or will future Californians be stuck in perpetual gridlock? Will urban and resort and recreational growth in the Sierra Nevada and Trinity Mountain regions lead to the over-fragmentation of precious natural habitat? How much water will be needed by California's future industries, farms, and residents, and where will that water be stored? Where should future highway, transit, and high-speed rail facilities and rights-of-way be located? Most of all, how much will all this growth cost, both economically, and in terms of changes in California's quality of life?

Clearly, the more precise our current understanding of how and where California is likely to grow, the sooner and more inexpensively appropriate lands can be acquired for purposes of conservation, recreation, and future facility siting. Similarly, the more clearly future urbanization patterns can be anticipated, the greater our collective ability to undertake sound city, metropolitan, rural, and bioregional planning.

Consider two scenarios for the year 2100. In the first, California's population would grow to 80 million persons and would occupy the landscape at an average density of eight persons per acre, the current statewide urban average. Under this scenario, and assuming that 10% percent of California's future population growth would occur through infill-that is, on existing urban land-California's expanding urban population would consume an additional 5.06 million acres of currently undeveloped land. As an alternative, assume the share of infill development were increased to 30%, and that new population were accommodated at a density of about 12 persons per acre-which is the current average density of the City of Los Angeles. Under this second scenario, California's urban population would consume an additional 2.6 million acres of currently undeveloped land. While both scenarios accommodate the same amount of population growth and generate large increments of additional urban development-indeed, some might say even the second scenario allows far too much growth and development-the second scenario is far kinder to California's unique natural landscape.

This report presents the results of a series of baseline population and urban growth projections for California's 38 urban counties through the year 2100. Presented in map and table form, these projections are based on extrapolations of current population trends and recent urban development trends. The next section, titled Approach, outlines the methodology and data used to develop the various projections. The following section, Baseline Scenario, reviews the projections themselves. A final section, entitled Baseline Impacts, quantitatively assesses the impacts of the baseline projections on wetland, hillside, farmland and habitat loss.

In 2019, the health care and social assistance industry in the Southern California region employed nearly **** million workers, with this forecasted to grow to over *** million in the year 2024. Southern California has a population of about ** million and includes, for example, Los Angeles County, Orange County, and San Diego County.

Comprehensive demographic dataset for Beverly Hills Burton South, Beverly Hills, CA, US including population statistics, household income, housing units, education levels, employment data, and transportation with year-over-year changes.

This dataset contains customer satisfaction scores collected from a survey, alongside key demographic and behavioral data. It includes variables such as customer age, gender, location, purchase history, support contact status, loyalty level, and satisfaction factors. The dataset is designed to help analyze customer satisfaction, identify trends, and develop insights that can drive business decisions.

File Information: File Name: customer_satisfaction_data.csv (or your specific file name)

File Type: CSV (or the actual file format you are using)

Number of Rows: 120

Number of Columns: 10

Column Names:

Customer_ID – Unique identifier for each customer (e.g., 81-237-4704)

Group – The group to which the customer belongs (A or B)

Satisfaction_Score – Customer's satisfaction score on a scale of 1-10

Age – Age of the customer

Gender – Gender of the customer (Male, Female)

Location – Customer's location (e.g., Phoenix.AZ, Los Angeles.CA)

Purchase_History – Whether the customer has made a purchase (Yes or No)

Support_Contacted – Whether the customer has contacted support (Yes or No)

Loyalty_Level – Customer's loyalty level (Low, Medium, High)

Satisfaction_Factor – Primary factor contributing to customer satisfaction (e.g., Price, Product Quality)

Statistical Analyses:

Descriptive Statistics:

Calculate mean, median, mode, standard deviation, and range for key numerical variables (e.g., Satisfaction Score, Age).

Summarize categorical variables (e.g., Gender, Loyalty Level, Purchase History) with frequency distributions and percentages.

Two-Sample t-Test (Independent t-test):

Compare the mean satisfaction scores between two independent groups (e.g., Group A vs. Group B) to determine if there is a significant difference in their average satisfaction scores.

Paired t-Test:

If there are two related measurements (e.g., satisfaction scores before and after a certain event), you can compare the means using a paired t-test.

One-Way ANOVA (Analysis of Variance):

Test if there are significant differences in mean satisfaction scores across more than two groups (e.g., comparing the mean satisfaction score across different Loyalty Levels).

Chi-Square Test for Independence:

Examine the relationship between two categorical variables (e.g., Gender vs. Purchase History or Loyalty Level vs. Support Contacted) to determine if there’s a significant association.

Mann-Whitney U Test:

For non-normally distributed data, use this test to compare satisfaction scores between two independent groups (e.g., Group A vs. Group B) to see if their distributions differ significantly.

Kruskal-Wallis Test:

Similar to ANOVA, but used for non-normally distributed data. This test can compare the median satisfaction scores across multiple groups (e.g., comparing satisfaction scores across Loyalty Levels or Satisfaction Factors).

Spearman’s Rank Correlation:

Test for a monotonic relationship between two ordinal or continuous variables (e.g., Age vs. Satisfaction Score or Satisfaction Score vs. Loyalty Level).

Regression Analysis:

Linear Regression: Model the relationship between a continuous dependent variable (e.g., Satisfaction Score) and independent variables (e.g., Age, Gender, Loyalty Level).

Logistic Regression: If analyzing binary outcomes (e.g., Purchase History or Support Contacted), you could model the probability of an outcome based on predictors.

Factor Analysis:

To identify underlying patterns or groups in customer behavior or satisfaction factors, you can apply Factor Analysis to reduce the dimensionality of the dataset and group similar variables.

Cluster Analysis:

Use K-Means Clustering or Hierarchical Clustering to group customers based on similarity in their satisfaction scores and other features (e.g., Loyalty Level, Purchase History).

Confidence Intervals:

Calculate confidence intervals for the mean of satisfaction scores or any other metric to estimate the range in which the true population mean might lie.

The average regular season attendance at Los Angeles Dodgers games in Major League Baseball remained relatively consistent from 2009 to 2025. In 2025, the average attendance was 49,536, up from the previous year's value of 48,657 people.