Chart and table of population level and growth rate for the Los Angeles metro area from 1950 to 2025.

About this dataset

Context

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

Key observations

In 2023, the population of Los Angeles County was 9.66 million, a 0.58% decrease year-by-year from 2022. Previously, in 2022, Los Angeles County population was 9.72 million, a decline of 0.91% compared to a population of 9.81 million in 2021. Over the last 20 plus years, between 2000 and 2023, population of Los Angeles County increased by 121,245. In this period, the peak population was 10.09 million in the year 2016. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

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

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Los Angeles County is shown in this column.
• Year on Year Change: This column displays the change in Los Angeles County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

In 2023, the real GDP of the Los Angeles metro area amount to around 1.08 trillion U.S. dollars, and increase after 2021. The overall quarterly GDP growth in the United States can be found here. Gross domestic product of Los AngelesWith a population of over 12.8 million inhabitants in 2023, Los Angeles is the second-largest city in America, following only New York. The Los Angeles metro area also ranked second among U.S. metro areas in terms of gross metropolitan product, second again only to New York City metro area, which came in with a GMP of 1.99 trillion U.S. dollars to Los Angeles’ 1.13 trillion U.S. dollars in the fiscal year of 2021. Chicago metro area ranked third with GMP of 757.2 billion U.S. dollars. Additional detailed statistics about GDP in the United States is available here. Despite Los Angeles’ high GDP, L.A. did not do as well as some cities in terms of median household income. Los Angeles ranked 9th with a median household income of 76,135 U.S. dollars annually in 2022. This was slightly higher than the median household income of the United States in 2022, which came in at 74,580 U.S. dollars annually. Located in Southern California, Los Angeles is home to Hollywood, the famous epicenter of the U.S. film and television industries. The United States is one of the leading film markets worldwide, producing 449 films in 2022, many of them produced by Hollywood-based studios. In 2018, movie ticket sales in North America generated over 11.89 billion U.S. dollars in box office revenue. Famous Hollywood actresses earn millions annually, with the best paid, Sofia Vergara, earning 43 million U.S. dollars in 2020. Second on the list was Angelina Jolie with earnings of 35.5 million U.S. dollars.

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.

Historical dataset of population level and growth rate for the Los Angeles metro area from 1950 to 2025.

About this dataset

Context

The dataset tabulates the Los Angeles population by year. The dataset can be utilized to understand the population trend of Los Angeles.

Content

The dataset constitues the following datasets

• Los Angeles, CA Population Dataset: Yearly Figures, Population Change, and Percent Change Analysis

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

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

The median rent for one- and two-bedroom apartments in Los Angeles, California, amounted to about ***** U.S. dollars in January 2025. Rents soared during the COVID-19 pandemic, with rental growth hitting **** percent in March 2022. This trend has since reversed, with growth turning negative in May 2023. Among the different states in the U.S., California ranks as the second most expensive rental market after Hawaii.

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

Graph and download economic data for Real Gross Domestic Product: All Industries in Los Angeles County, CA (REALGDPALL06037) from 2001 to 2023 about Los Angeles County, CA; Los Angeles; CA; real; industry; GDP; and USA.

The Los Angeles Data Center Market is Segmented by DC Size (Small, Medium, Large, Massive, Mega), Tier Type (Tier 1&2, Tier 3, Tier 4), Absorption (Utilized (Colocation Type (Retail, Wholesale, Hyperscale), End User (Cloud & IT, Telecom, Media & Entertainment, Government, BFSI, Manufacturing, E-Commerce)), and Non-Utilized). The Market Sizes and Forecasts are Provided in Terms of Value (MW) for all the Above Segments.

Graph and download economic data for Estimate of Median Household Income for Los Angeles County, CA (MHICA06037A052NCEN) from 1989 to 2023 about Los Angeles County, CA; Los Angeles; CA; households; median; income; and USA.

Graph and download economic data for Total Gross Domestic Product for Los Angeles-Long Beach-Anaheim, CA (MSA) (NGMP31080) from 2001 to 2023 about Los Angeles, CA, industry, GDP, and USA.

This raster layer shows the areas of Los Angeles, California, United States which were developed between 1990 and 2000. Categories of new development represented in these data include: infill, extension and leapfrog. Infill represents development within all the open spaces in the urban footprint of the earlier period excluding exterior open space. Extension represents development in contiguous clusters that contained exterior open space in the earlier period and that were not infUnited States Leapfrog represents development entirely outside the exterior open space of the earlier period. These data are part of the Atlas of Urban Expansion.

IntroductionThis metadata is broken up into different sections that provide both a high-level summary of the Housing Element and more detailed information about the data itself with links to other resources. The following is an excerpt from the Executive Summary from the Housing Element 2021 – 2029 document:The County of Los Angeles is required to ensure the availability of residential sites, at adequate densities and appropriate development standards, in the unincorporated Los Angeles County to accommodate its share of the regional housing need--also known as the Regional Housing Needs Allocation (RHNA). Unincorporated Los Angeles County has been assigned a RHNA of 90,052 units for the 2021-2029 Housing Element planning period, which is subdivided by level of affordability as follows:Extremely Low / Very Low (<50% AMI) - 25,648Lower (50 - 80% AMI) - 13,691Moderate (80 - 120% AMI) - 14,180Above Moderate (>120% AMI) - 36,533Total - 90,052NOTES - Pursuant to State law, the projected need of extremely low income households can be estimated at 50% of the very low income RHNA. Therefore, the County’s projected extremely low income can be estimated at 12,824 units. However, for the purpose of identifying adequate sites for RHNA, no separate accounting of sites for extremely low income households is required. AMI = Area Median IncomeDescriptionThe Sites Inventory (Appendix A) is comprised of vacant and underutilized sites within unincorporated Los Angeles County that are zoned at appropriate densities and development standards to facilitate housing development. The Sites Inventory was developed specifically for the County of Los Angeles, and has built-in features that filter sites based on specific criteria, including access to transit, protection from environmental hazards, and other criteria unique to unincorporated Los Angeles County. Other strategies used within the Sites Inventory analysis to accommodate the County’s assigned RHNA of 90,052 units include projected growth of ADUs, specific plan capacity, selected entitled projects, and capacity or planned development on County-owned sites within cities. This accounts for approximately 38 percent of the RHNA. The remaining 62 percent of the RHNA is accommodated by sites to be rezoned to accommodate higher density housing development (Appendix B).Caveats:This data is a snapshot in time, generally from the year 2021. It contains information about parcels, zoning and land use policy that may be outdated. The Department of Regional Planning will be keeping an internal tally of sites that get developed or rezoned to meet our RHNA goals, and we may, in the future, develop some public facing web applications or dashboards to show the progress. There may even be periodic updates to this GIS dataset as well, throughout this 8-year planning cycle.Update History:1/7/25 - Following the completion of the annexation to the City of Whittier on 11/12/24, 27 parcels were removed along Whittier Blvd which contained 315 Very Low Income units and 590 Above Moderate units. Following a joint County-City resolution of the RHNA transfer to the city, 247 Very Low Income units and 503 Above Moderate units were taken on by Whittier. 10/16/24 - Modifications were made to this layer during the updates to the South Bay and Westside Area Plans following outreach in these communities. In the Westside Planning area, 29 parcels were removed and no change in zoning / land use policy was proposed; 9 Mixed Use sites were added. In the South Bay, 23 sites were removed as they no longer count towards the RHNA, but still partially changing to Mixed Use.5/31/22 – Los Angeles County Board of Supervisors adopted the Housing Element on 5/17/22, and it received final certification from the State of California Department of Housing and Community Development (HCD) on 5/27/22. Data layer published on 5/31/22.Links to other resources:Department of Regional Planning Housing Page - Contains Housing Element and it's AppendicesHousing Element Update - Rezoning Program Story Map (English, and Spanish)Southern California Association of Governments (SCAG) - Regional Housing Needs AssessmentCalifornia Department of Housing and Community Development Housing Element pageField Descriptions:OBJECTID - Internal GIS IDAIN - Assessor Identification Number*SitusAddress - Site Address (Street and Number) from Assessor Data*Use Code - Existing Land Use Code (corresponds to Use Type and Use Description) from Assessor Data*Use Type - Existing Land Use Type from Assessor Data*Use Description - Existing Land Use Description from Assessor Data*Vacant / Nonvacant – Parcels that are vacant or non-vacant per the Use Code from the Assessor Data*Units Total - Total Existing Units from Assessor Data*Max Year - Maximum Year Built from Assessor Data*Supervisorial District (2021) - LA County Board of Supervisor DistrictSubmarket Area - Inclusionary Housing Submarket AreaPlanning Area - Planning Areas from the LA County Department of Regional Planning General Plan 2035Community Name - Unincorporated Community NamePlan Name - Land Use Plan Name from the LA County Department of Regional Planning (General Plan and Area / Community Plans)LUP - 1 - Land Use Policy from Dept. of Regional Planning - Primary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 1 (% area) - Land Use Policy from Dept. of Regional Planning - Primary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*LUP - 2 - Land Use Policy from Dept. of Regional Planning - Secondary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 2 (% area) - Land Use Policy from Dept. of Regional Planning - Secondary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*LUP - 3 - Land Use Policy from Dept. of Regional Planning - Tertiary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 3 (% area) - Land Use Policy from Dept. of Regional Planning - Tertiary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*Current LUP (Description) – This is a brief description of the land use category. In the case of multiple land uses, this would be the land use category that covers the majority of the parcel*Current LUP (Min Density - net or gross) - Minimum density for this category (as net or gross) per the Land Use Plan for this areaCurrent LUP (Max Density - net or gross) - Maximum density for this category (as net or gross) per the Land Use Plan for this areaProposed LUP – Final – The proposed land use category to increase density.Proposed LUP (Description) – Brief description of the proposed land use policy.Prop. LUP – Final (Min Density) – Minimum density for the proposed land use category.Prop. LUP – Final (Max Density) – Maximum density for the proposed land use category.Zoning - 1 - Zoning from Dept. of Regional Planning - Primary Zone (in cases where there are more than one zone category present)*Zoning - 1 (% area) - Zoning from Dept. of Regional Planning - Primary Zone (% of parcel covered in cases where there are more than one zone category present)*Zoning - 2 - Zoning from Dept. of Regional Planning - Secondary Zone (in cases where there are more than one zone category present)*Zoning - 2 (% area) - Zoning from Dept. of Regional Planning - Secondary Zone (% of parcel covered in cases where there are more than one zone category present)*Zoning - 3 - Zoning from Dept. of Regional Planning - Tertiary Zone (in cases where there are more than one zone category present)*Zoning - 3 (% area) - Zoning from Dept. of Regional Planning - Tertiary Zone (% of parcel covered in cases where there are more than one zone category present)*Current Zoning (Description) - This is a brief description of the zoning category. In the case of multiple zoning categories, this would be the zoning that covers the majority of the parcel*Proposed Zoning – Final – The proposed zoning category to increase density.Proposed Zoning (Description) – Brief description of the proposed zoning.Acres - Acreage of parcelMax Units Allowed - Total Proposed Land Use Policy UnitsRHNA Eligible? – Indicates whether the site is RHNA Eligible or not. NOTE: This layer only shows those that are RHNA Eligible, but internal versions of this layer also show sites that were not-RHNA eligible, or removed during the development of this layer in 2020 – 2022.Very Low Income Capacity - Total capacity for the Very Low Income level as defined in the Housing ElementLow Income Capacity - Total capacity for the Low Income level as defined in the Housing ElementModerate Income Capacity - Total capacity for the Moderate Income level as defined in the Housing ElementAbove Moderate Income Capacity - Total capacity for the Above Moderate Income level as defined in the Housing ElementRealistic Capacity - Total Realistic Capacity of parcel (totaling all income levels). Several factors went into this final calculation. See the Housing Element (Links to Other Resources above) in the following locations - "Sites Inventory - Lower Income RHNA" (p. 223), and "Rezoning - Very Low / Low Income RHNA" (p231).Income Categories - Income Categories assigned to the parcel (relates to income capacity units)Lot Consolidation ID - Parcels with a unique identfier for consolidation potential (based on parcel ownership)Lot Consolidation Notes - Specific notes for consolidationConsolidation - Adjacent Parcels - All adjacent parcels that are tied to each lot consolidation IDsShape_Length - Perimeter (feet)Shape_Area - Area (sq feet)*As it existed in 2021

IntroductionThis metadata is broken up into different sections that provide both a high-level summary of the Housing Element and more detailed information about the data itself with links to other resources. The following is an excerpt from the Executive Summary from the Housing Element 2021 – 2029 document:The County of Los Angeles is required to ensure the availability of residential sites, at adequate densities and appropriate development standards, in the unincorporated Los Angeles County to accommodate its share of the regional housing need--also known as the Regional Housing Needs Allocation (RHNA). Unincorporated Los Angeles County has been assigned a RHNA of 90,052 units for the 2021-2029 Housing Element planning period, which is subdivided by level of affordability as follows:Extremely Low / Very Low (<50% AMI) - 25,648Lower (50 - 80% AMI) - 13,691Moderate (80 - 120% AMI) - 14,180Above Moderate (>120% AMI) - 36,533Total - 90,052NOTES - Pursuant to State law, the projected need of extremely low income households can be estimated at 50% of the very low income RHNA. Therefore, the County’s projected extremely low income can be estimated at 12,824 units. However, for the purpose of identifying adequate sites for RHNA, no separate accounting of sites for extremely low income households is required. AMI = Area Median IncomeDescriptionThe Sites Inventory (Appendix A) is comprised of vacant and underutilized sites within unincorporated Los Angeles County that are zoned at appropriate densities and development standards to facilitate housing development. The Sites Inventory was developed specifically for the County of Los Angeles, and has built-in features that filter sites based on specific criteria, including access to transit, protection from environmental hazards, and other criteria unique to unincorporated Los Angeles County. Other strategies used within the Sites Inventory analysis to accommodate the County’s assigned RHNA of 90,052 units include projected growth of ADUs, specific plan capacity, selected entitled projects, and capacity or planned development on County-owned sites within cities. This accounts for approximately 38 percent of the RHNA. The remaining 62 percent of the RHNA is accommodated by sites to be rezoned to accommodate higher density housing development (Appendix B).Caveats:This data is a snapshot in time, generally from the year 2021. It contains information about parcels, zoning and land use policy that may be outdated. The Department of Regional Planning will be keeping an internal tally of sites that get developed or rezoned to meet our RHNA goals, and we may, in the future, develop some public facing web applications or dashboards to show the progress. There may even be periodic updates to this GIS dataset as well, throughout this 8-year planning cycle.Update History:5/31/22– Los Angeles County Board of Supervisors adopted the Housing Element on 5/17/22, and it received final certification from the State of California Department of Housing and Community Development (HCD) on 5/27/22. Data layer published on 5/31/22.Links to other resources:Department of Regional Planning Housing Page - Contains Housing Element and it's AppendicesHousing Element Update - Rezoning Program Story Map (English, and Spanish)Southern California Association of Governments (SCAG) - Regional Housing Needs AssessmentCalifornia Department of Housing and Community Development Housing Element pageField Descriptions:OBJECTID - Internal GIS IDAIN - Assessor Identification Number*ASI Status - Sites Inventory Status (Nonvacant or Vacant)SitusAddress- Site Address (Street and Number) from Assessor Data*SitusCity - Site Address (City) from Assessor Data*SitusZIP - Site Address (ZIP) from Assessor Data*LV_IV_Ratio - Land Value to Improvement Value Ratio from Assessor Data*YearBuiltMax- Maximum Year Built from Assessor Data*Use Code - Existing Land Use Code (corresponds to Use Type and Use Description) from Assessor Data*Use Type - Existing Land Use Type from Assessor Data*Use Description - Existing Land Use Description from Assessor Data*Publicly Owned - If publicly owned, indicates whether it's Federal, State, County, or Special DistrictUnits Total - Total Existing Units from Assessor Data*Supervisorial District (2021) - LA County Board of Supervisor DistrictSubmarket Area - Inclusionary Housing Submarket AreaPlanning Area - Planning Areas from the LA County Department of Regional Planning General Plan 2035Community Name - Unincorporated Community NamePlan Name - Land Use Plan Name from the LA County Department of Regional Planning (General Plan and Area / Community Plans)Zoning - 1 - Zoning from Dept. of Regional Planning - Primary Zone (in cases where there are more than one zone category present)*Zoning - 1 (% area) - Zoning from Dept. of Regional Planning - Primary Zone (% of parcel covered in cases where there are more than one zone category present)*Zoning - 2 - Zoning from Dept. of Regional Planning - Secondary Zone (in cases where there are more than one zone category present)*Zoning - 2 (% area)- Zoning from Dept. of Regional Planning - Secondary Zone (% of parcel covered in cases where there are more than one zone category present)*Zoning - 3 - Zoning from Dept. of Regional Planning - Tertiary Zone (in cases where there are more than one zone category present)*Zoning - 3 (% area) - Zoning from Dept. of Regional Planning - Tertiary Zone (% of parcel covered in cases where there are more than one zone category present)*LUP - 1 - Land Use Policy from Dept. of Regional Planning - Primary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 1 (% area) - Land Use Policy from Dept. of Regional Planning - Primary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*LUP - 2 - Land Use Policy from Dept. of Regional Planning - Secondary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 2 (% area) - Land Use Policy from Dept. of Regional Planning - Secondary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*LUP - 3 - Land Use Policy from Dept. of Regional Planning - Tertiary Land Use Policy (in cases where there are more than one Land Use Policy category present)*LUP - 3 (% area) - Land Use Policy from Dept. of Regional Planning - Tertiary Land Use Policy (% of parcel covered in cases where there are more than one Land Use Policy category present)*SP - 1 - Specific Plan from Dept. of Regional Planning - Primary Specific Plan (in cases where there are more than one Specific Plan category present)*SP - 1 (desc) - Specific Plan from Dept. of Regional Planning - Primary Specific Plan Category Description (in cases where there are more than one Specific Plan category present)*SP - 1 (% area) - Specific Plan from Dept. of Regional Planning - Primary Specific Plan (% of parcel covered in cases where there are more than one Specific Plan category present)*SP - 2 - Specific Plan from Dept. of Regional Planning - Secondary Specific Plan (in cases where there are more than one Specific Plan category present)*SP - 2 (desc) - Specific Plan from Dept. of Regional Planning - Secondary Specific Plan Category Description (in cases where there are more than one Specific Plan category present)*SP - 2 (% area) - Specific Plan from Dept. of Regional Planning - Secondary Specific Plan (% of parcel covered in cases where there are more than one Specific Plan category present)*SP - 3 - Specific Plan from Dept. of Regional Planning - Tertiary Specific Plan (in cases where there are more than one Specific Plan category present)*SP - 3 (desc) - Specific Plan from Dept. of Regional Planning - Tertiary Specific Plan Category Description (in cases where there are more than one Specific Plan category present)*SP - 3 (% area) - Specific Plan from Dept. of Regional Planning - Tertiary Specific Plan (% of parcel covered in cases where there are more than one Specific Plan category present)*Acres - Acreage of parcelLUP Units - Total - Total Land Use Policy Units (note - takes into account different densities and % area covered if there are multiple categories)Current LUP (Min Density - net or gross)- Minimum density for this category (as net or gross) per the Land Use Plan for this areaCurrent LUP (Max Density - net or gross) - Maximum density for this category (as net or gross) per the Land Use Plan for this areaSite Status - Status of the site - mostly shows as 'available', but some are flagged as 'Pending Project'Very Low Income Capacity - Total capacity for the Very Low Income level as defined in the Housing ElementLow Income Capacity - Total capacity for the Low Income level as defined in the Housing ElementModerate Income Capacity - Total capacity for the Moderate Income level as defined in the Housing ElementAbove Moderate Income Capacity - Total capacity for the Above Moderate Income level as defined in the Housing ElementRealistic Capacity - Total Realistic Capacity of parcel (totaling all income levels). Several factors went into this final calculation. See the Housing Element (Links to Other Resources above) in the following locations - "Sites Inventory - Lower Income RHNA" (p. 223), and "Rezoning - Very Low / Low Income RHNA" (p231).Income Categories - Income Categories assigned to the parcel (relates to income capacity units)Lot Consolidation ID - Parcels with a unique identfier for consolidation potential (based on parcel ownership)Lot Consolidation Notes - Specific notes for consolidationConsolidation - Adjacent Parcels - All adjacent parcels that are tied to each lot consolidation IDsUsed in Previous Housing Elements? - These are the Very Low and Low Income level parcels that showed up in previous Housing ElementsShape_Length - Perimeter (feet)Shape_Area - Area (sq feet)*As it existed in 2021

This graph shows the GDP of the Los Angeles metro area in 2022, by industry. In 2022, its GDP amounted to about **** trillion U.S. dollars. About **** billion U.S. dollars were generated by the manufacturing industry. The overall quarterly GDP growth in the United States can be found here.

Gross domestic product of Los Angeles

With a population of over *** million inhabitants in 2011, Los Angeles is the second largest city in America, following only New York. The Los Angeles metro area also ranked second among U.S. metro areas in terms of gross metropolitan product, second again only to New York City metro area, which came in with a GMP of USD ***** trillion to Los Angeles’ *** billion USD in 2011. Chicago metro area ranked third with GMP of *** billion U.S. dollars. Washington metro area ranked fourth with *** billion U.S. dollars in 2011. Additional detailed statistics about GDP and GMP in the United States is available here.

Despite Los Angeles’ high GDP, L.A. did not do as well as some cities in terms of median household income. Los Angeles ranked 11th with a median household income of ****** U.S. dollars annually in 2013. This was lower than the median household income of the United States in 2013, which came in at ****** U.S. dollars annually.

Located in Southern California, Los Angeles is home to Hollywood, the famous epicenter of the U.S. film and television industries. The United States is one of the leading film markets worldwide, producing *** films in 2011, many of them produced by Hollywood-based studios. In 2012, movie ticket sales in North America generated over **** billion U.S. dollars in box office revenue. Famous Hollywood actresses earn millions annually, with the best paid, Angelina Jolie, earning ** million U.S. dollars between ********* and *********. Second on the list was Jennifer Lawrence with earnings of ** million U.S. dollars.

Detailed description of the dataset sources used in this study, the experimental workflow, and plotting for the paper figures provided at the associated GitHub Meta Repo: https://github.com/IMMM-SFA/Ferencz_et_al_2024_ERL The future water demand projections from this study are hypothetical future water demands that reflect the population and urban land cover changes represented by the scenarios considered. The intent and emphasis of this work is investigating the interactions between population change, evolution of urban morphology, and water demand. These projections are not meant to be likely future demands for specific water providers or the LA region and should not be interpreted as such. The folders contain input and output data for each step of the "Recreate my Experiment" workflow described in the associated GitHub meta-repository as well as data used for plotting Figures for the paper that this dataset supports. Description of each folder's contents and use: Step_1a: Inputs to the associated python script provided on the GitHub repo. Step_1b: Inputs (downscaled population rasters) used by the associated python script provided on the GitHub repo. Original 1-km squared rasters that were downscaled also provided. Step_1c: Urban growth projection rasters corresponding to SSP3 and SSP5 population scenarios are provided in separate subfolders as well as the water provider boundaries used for analysis. Outputs of data processing also provided. Associated python script provided on GitHub. Step_1d: Description of Inputs used by the QGIS Model Builder GUI that automates geospatial processing and clipping the of the high-resolution 60 cm land cover data for each urban land class footprint within a defined polygon boundary. The Model Builder is provided on the GitHub repo and can be used by QGIS. The outputs of this step are in "Clipped Provider Hi Res Landcover". If the user wants to use The Model Builder for different regions of LA or to test our outputs, they will need to download the hi resolution landcover raster listed in the Readme and in Ref [2] of the GitHub Page. Step_1e: All necessary inputs to generate average monthly demand over the 2017-2021 period and the minimum and maximum demands over the 2014-2021 for each water provider. Associated python scripts are on GitHub. Step 2: Output data about land cover metrics (areas and fractions) for each urban land class for each water provider. Associated python script on GitHub. Uses outputs from Step 1d "Clipped Provider Hi Res Landcover" Step 3: Both the Inputs for and Outputs from the urban projection raster analysis Python script on GitHub. The inputs are urban land class rasters for specific SSP and zoning scenarios (low, medium, high) from Step 1c. The outputs are rasters of urban pixels that were converted to a higher land class and the number of land class units that changed (Values of 1, 2, or 3). For example, a value of 2 could be LC 21 -> 23 or LC 22 -> 24. These maps are label "intensification." The other outputs are "urban growth" rasters showing the conversion of non urban to urban land, which are indicated by pixel values of 1. These are used for the urban growth change maps in Figure 3. Step 4: Output projections of indoor and outdoor annual and monthly demands for each water provider for the average, minimum, and maximum monthly demand scenarios for each of the four urban growth scenarios (SSP3 med, SSP5 low, SSP5 med, and SSP5 high). The outputs also include metrics on each water provider used for the demand sensitivity analysis presented in Figure 8. Outputs from Step 4 are used for Figures 4 - 8 of the paper. Figures: This folder has data used for plotting Figures 1 through 5, and 8. Data for Figures 6 and 7 are sourced directly from folders associated with the Processing and Analysis Steps 1 - 4. The GitHub meta repository provides descriptions of how each figure was made and the associated plotting scripts used.

The size of the Los Angeles Data Center market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 4.50% during the forecast period.The Los Angeles Data Center Market is one of the most critical centers for data storage and processing in the United States. This is because of growing demand for cloud computing, artificial intelligence, and other data-intensive technologies.A data center is a place or building, which is used for computer systems and their elements, such as telecom and storage facilities. Data centers help firms and organizations to store and manage their huge amounts of data fully securely and effectively. Data centers play a very important role in many applications, including e-commerce, financial transactions, social media, and scientific researches.Such factors as strategic location, robust infrastructure, and skilled talent availability make the Los Angeles market highly valuable for investment. It acts as a gateway between North America and Asia, thereby creating a perfect geographical match for companies seeking to expand their global reach. The further growth of this market will be fuelled by the increasing adoption of cloud computing and the growing need for data storage and processing. Recent developments include: December 2022: The floating data centers of Nautilus Data Technologies will be brought to Los Angeles. The additions show that its novel strategy of utilizing a water-based platform that taps seas, bays, and rivers to reduce the cost of cooling servers is gaining traction. The new data centers will be housed in port facilities on custom-built barges, with backup equipment on land. Both locations will host a 7.5-megawatt data center, with power and water agreements already in place., June 2022: Prime Data Centres is expanding into the Los Angeles market. Prime will construct a 261,000-square-foot facility and a 49.5MVA electric substation to serve the new site, offering its tenants up to 33 megawatts (MW) of vital power once completed. Vernon's municipally owned and run electric utility (VPU) provides power supply and has some of the lowest retail utility prices in the region. Prime intends to construct the facility as soon as the fourth quarter of 2023.. Key drivers for this market are: Growing Adoption of Cloud Services is expected to flourish the market, Increasing Growth in Wholesale Datacenter Multi-tenant Spaces to propel demand (albeit from a lower base); Increased Emphasis on Compliance with Data Regulations and Cost-Effective Nature of Multi-tenant Facilities to Drive Adoption among SME's. Potential restraints include: Dependence on Regulatory Landscape & Stringent Security Requirements. Notable trends are: Growing Cloud Applications, AI, and Big Data.

Graph and download economic data for S&P CoreLogic Case-Shiller CA-Los Angeles Home Price Index (LXXRSA) from Jan 1987 to Mar 2025 about Los Angeles, CA, HPI, housing, price index, indexes, price, and USA.