According to a population projection based on 2020 Census Data, in 2040, California's population will amount to ***** million inhabitants.

This graph shows population projections for the United States of America. The estimated population of the USA in 2050 is 398 million residents. Population The U.S. Census Bureau presents annual projections for the growth of the U.S. population up to the year 2060. By 2050, it is estimated that the American population will surpass 398 million citizens. The U.S. census also projects a regressing annual growth rate, starting at 0.8 percent in 2015 and decreasing to 0.46 percent by 2060.

The UN population division publishes population projections for the entire world up to the year 2100. The United Nations also projects a regressing annual growth rate of the world population. Between 2015 and 2020, the population is expected to increase by 1.04 percent annually. Around 2060, the annual growth rate will have decreased to 0.34 percent.

United States US: Population Projection: Mid Year: Growth data was reported at 0.450 % in 2050. This stayed constant from the previous number of 0.450 % for 2049. United States US: Population Projection: Mid Year: Growth data is updated yearly, averaging 0.700 % from Jun 2001 (Median) to 2050, with 50 observations. The data reached an all-time high of 0.980 % in 2006 and a record low of 0.450 % in 2050. United States US: Population Projection: Mid Year: Growth data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.US Census Bureau: Demographic Projection.

The I-15 Statewide Tool provides a comprehensive overview of the I-15 corridor across the State of Utah. It helps us understand how well I-15 is performing on important Utah transportation values like mobility, safety, and connectivity. The tool also identifies areas where I-15 should be improved to meet Utah’s needs, and provides standards and guidelines that UDOT and other transportation agencies can use to maintain a consistent I-15 experience throughout the state. This map contains Population Projection 2021 to 2050 data for the I-15 Corridor. It is sourced from the Population Projection TAZ data and is considered static. This intermediate map is not intended to be viewed directly, but through the I-15 Tool.This map is a component of the I-15 Population Projections 2024-2050 app and the broader I-15 ToolFor questions on the data, please contact Andrea Moser at AndreaMoser@utah.gov.

The 2015 to 2040 population projections for towns in the state of Connecticut were developed by the Connecticut State Data Center for planning, analysis, and to inform decision making. The projections are individual population projections for the resident population of each of Connecticut's 169 towns and were published on August 31, 2017. These projections supersede the 2012 edition of the population projections developed by the Connecticut State Data Center.

As a part of DVRPC's long-range planning activities, the Commission is required to maintain forecasts with at least a 20-year horizon. DVRPC has updated forecasts through the horizon year of the 2050 Long-Range Plan. The 2050 Version 2.0 Population and Employment Forecasts (2050 Version 2.0, v2.0) were adopted by the DVRPC Board on October 24, 2024, They update the 2050 v1.0 forecasts with a new county-level age-cohort model and new base data from the 2020 Decennial Census, 2020 Bureau of Economic Analysis (BEA), and 2021 National Establishments Time Series (NETS). The age-cohort model calculates the future population for five-year age-sex cohorts using the 2020 Census base population and anticipated birth, death, and migration rates. These anticipated rates were developed using historic birth and death records from New Jersey and Pennsylvania state health department data, as well as historic net migration data, calculated from decennial census data. Employment forecasts were developed by multiplying population forecasts by a ratio of working age population to jobs, calculated from 2022 ACS and BEA data.

The municipal and TAZ forecasts use the growth factors from the v1.0 forecasts, scaled to the new county and regional population totals from the age-cohort model. While the forecast is not adopted at the transportation analysis zone (TAZ) level, it is allocated to these zones for use in DVRPC's travel demand model, and conforms to municipal/district level adopted totals. This data provides TAZ-level population and employment. Other travel model attributes are available upon request.

DVRPC has prepared regional- and county-level population and employment forecasts in five-year increments for years 2020-2050. 2019 land use model results are also available. A forthcoming Analytical Data Report will document the forecasting process and methodologies.

Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.

These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2023-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2019 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.

Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below. Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.

As these projections may be a valuable input to other analyses, this dataset is made available here as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.

Wasatch Front Real Estate Market Model (REMM) Projections

WFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:

Demographic data from the decennial census
County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature
Current employment locational patterns derived from the Utah Department of Workforce Services
Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff
Current land use and valuation GIS-based parcel data stewarded by County Assessors
Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations
Calibration of model variables to balance the fit of current conditions and dynamics at the county and regional level

‘Traffic Analysis Zone’ Projections

The annual projections are forecasted for each of the Wasatch Front’s 3,546 Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).

‘City Area’ Projections

The TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.

Summary Variables in the Datasets

Annual projection counts are available for the following variables (please read Key Exclusions note below):

Demographics

Household Population Count (excludes persons living in group quarters) 
Household Count (excludes group quarters) 

Employment

Typical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)
Retail Job Count (retail, food service, hotels, etc)
Office Job Count (office, health care, government, education, etc)
Industrial Job Count (manufacturing, wholesale, transport, etc)
Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count 
All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).

• These variables includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.

Key Exclusions from TAZ and ‘City Area’ Projections

As the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

Statewide Projections

Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.

In 2024, Germany was the leading EU country in terms of population, with around 85 million inhabitants. In 2050, approximately 89.2 million people will live in Germany, according to the forecast. See the total EU population figures for more information. The global population The global population is rapidly increasing. Between 1990 and 2015, it increased by around 2 billion people. Furthermore, it is estimated that the global population will have increased by another 1 billion by 2030. Asia is the continent with the largest population, followed by Africa and Europe. In Asia,the two most populous nations worldwide are located, China and India. In 2014, the combined population in China and India alone amounted to more than 2.6 billion people. for comparison, the total population in the whole continent of Europe is at around 741 million people. As of 2014, about 60 percent of the global population was living in Asia, with only approximately 10 percent in Europe and even less in the United States. Europe is the continent with the second-highest life expectancy at birth in the world, only barely surpassed by Northern America. In 2013, the life expectancy at birth in Europe was around 78 years. Stable economies and developing and emerging markets in European countries provide for good living conditions. Seven of the top twenty countries in the world with the largest gross domestic product in 2015 are located in Europe.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Whereas the population is expected to decrease somewhat until 2100 in Asia, Europe, and South America, it is predicted to grow significantly in Africa. While there were 1.55 billion inhabitants on the continent at the beginning of 2025, the number of inhabitants is expected to reach 3.81 billion by 2100. In total, the global population is expected to reach nearly 10.18 billion by 2100. Worldwide population In the United States, the total population is expected to steadily increase over the next couple of years. In 2024, Asia held over half of the global population and is expected to have the highest number of people living in urban areas in 2050. Asia is home to the two most populous countries, India and China, both with a population of over one billion people. However, the small country of Monaco had the highest population density worldwide in 2024. Effects of overpopulation Alongside the growing worldwide population, there are negative effects of overpopulation. The increasing population puts a higher pressure on existing resources and contributes to pollution. As the population grows, the demand for food grows, which requires more water, which in turn takes away from the freshwater available. Concurrently, food needs to be transported through different mechanisms, which contributes to air pollution. Not every resource is renewable, meaning the world is using up limited resources that will eventually run out. Furthermore, more species will become extinct which harms the ecosystem and food chain. Overpopulation was considered to be one of the most important environmental issues worldwide in 2020.

Of the G7 countries, Canada, the United Kingdom, and the United States were forecast to have a constant population ******** until 2050. In Japan, Germany, and Italy, the population is forecast to constantly ******* due to aging populations and falling fertility rates. In France, the population was first expected to decline by 2048.

Data calculated for State of the Tropics 2014 report from original source: United Nations Population Division, Department of Economic and Social Affairs - World Population Prospects: the 2012 Revision. Data was calculated from median population growth and based on the assumption that the proportion of the population living in the tropical regions of large nations that straddle the tropics remains constant.

United States EIA Projection: Population: 16 Years & Over data was reported at 326,696.625 Person th in 2050. This records an increase from the previous number of 325,115.143 Person th for 2049. United States EIA Projection: Population: 16 Years & Over data is updated yearly, averaging 295,935.073 Person th from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 326,696.625 Person th in 2050 and a record low of 256,707.825 Person th in 2015. United States EIA Projection: Population: 16 Years & Over data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.G005: Population: Projection: Energy Information Administration.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

The share of projected population increase in Uttar Pradesh, India from 2011 until 2036 is expected to grow by nearly ** percent. By contrast, the estimated population increase in Uttarakhand is expected to be less than *** percent during the same time period.

Why project population?
Population projections for a country are becoming increasingly important now than ever before. They are used primarily by government policy makers and planners to better understand and gauge future demand for basic services that predominantly include water, food and energy. In addition, they also support in indicating major movements that may affect economic development and in turn, employment and labour productivity. Consequently, this leads to amending policies in order to better adapt to the needs of society and to various circumstances.

Demographic projections and health interventions Demographic figures serve the foremost purpose of improving health and health related services among the population. Some of the government interventions include antenatal and neonatal care with the aim of reducing maternal and neonatal mortality and morbidity rates. In addition, it also focuses on improving immunization coverage across the country. Further, demographic estimates help in better preempting the needs of growing populations, such as the geriatric population within a country.

United States EIA Projection: Population: incl Armed Forces Overseas data was reported at 397,524.506 Person th in 2050. This records an increase from the previous number of 395,735.260 Person th for 2049. United States EIA Projection: Population: incl Armed Forces Overseas data is updated yearly, averaging 364,229.645 Person th from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 397,524.506 Person th in 2050 and a record low of 321,977.692 Person th in 2015. United States EIA Projection: Population: incl Armed Forces Overseas data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.G005: Population: Projection: Energy Information Administration.

Important Dataset Update 6/24/2020:Summit and Wasatch Counties updated.Important Dataset Update 6/12/2020:MAG area updated.Important Dataset Update 7/15/2019:This dataset now includes projections for all populated statewide traffic analysis zones (TAZs).Projections within the Wasatch Front urban area ( SUBAREAID = 1) were produced with using the Real Estate Market Model as described below.Socioeconomic forecasts produced for Cache MPO (Cache County, SUBAREAID = 2), Dixie MPO (Washington County, SUBAREAID = 3), Summit County (SUBAREAID = 4), and UDOT (other areas of the state, SUBAREAID = 0) all adhere to the University of Utah Gardner Policy Institute's county-level projection controls, but other modeling methods are used to arrive at the TAZ-level forecasts for these areas.As with any dataset that presents projections into the future, it is important to have a full understanding of the data before using it. Before using this data, you are strongly encouraged to read the metadata description below and direct any questions or feedback about this data to analytics@wfrc.org.Every four years, the Wasatch Front’s two metropolitan planning organizations (MPOs), Wasatch Front Regional Council (WFRC) and Mountainland Association of Governments (MAG), collaborate to update a set of annual small area -- traffic analysis zone and ‘city area’, see descriptions below) -- population and employment projections for the Salt Lake City-West Valley City (WFRC), Ogden-Layton (WFRC), and Provo-Orem (MAG) urbanized areas.These projections are primarily developed for the purpose of informing long-range transportation infrastructure and services planning done as part of the 4 year Regional Transportation Plan update cycle, as well as Utah’s Unified Transportation Plan, 2019-2050. Accordingly, the foundation for these projections is largely data describing existing conditions for a 2015 base year, the first year of the latest RTP process. The projections are included in the official travel models, which are publicly released at the conclusion of the RTP process.As these projections may be a valuable input to other analyses, this dataset is made available at http://data.wfrc.org/search?q=projections as a public service for informational purposes only. It is solely the responsibility of the end user to determine the appropriate use of this dataset for other purposes.Wasatch Front Real Estate Market Model (REMM) ProjectionsWFRC and MAG have developed a spatial statistical model using the UrbanSim modeling platform to assist in producing these annual projections. This model is called the Real Estate Market Model, or REMM for short. REMM is used for the urban portion of Weber, Davis, Salt Lake, and Utah counties. REMM relies on extensive inputs to simulate future development activity across the greater urbanized region. Key inputs to REMM include:Demographic data from the decennial census;County-level population and employment projections -- used as REMM control totals -- are produced by the University of Utah’s Kem C. Gardner Policy Institute (GPI) funded by the Utah State Legislature;Current employment locational patterns derived from the Utah Department of Workforce Services;Land use visioning exercises and feedback, especially in regard to planned urban and local center development, with city and county elected officials and staff;Current land use and valuation GIS-based parcel data stewarded by County Assessors;Traffic patterns and transit service from the regional Travel Demand Model that together form the landscape of regional accessibility to workplaces and other destinations; andCalibration of model variables to balance the fit of current conditions and dynamics at the county and regional level.‘Traffic Analysis Zone’ ProjectionsThe annual projections are forecasted for each of the Wasatch Front’s 2,800+ Traffic Analysis Zone (TAZ) geographic units. TAZ boundaries are set along roads, streams, and other physical features and average about 600 acres (0.94 square miles). TAZ sizes vary, with some TAZs in the densest areas representing only a single city block (25 acres).‘City Area’ ProjectionsThe TAZ-level output from the model is also available for ‘city areas’ that sum the projections for the TAZ geographies that roughly align with each city’s current boundary. As TAZs do not align perfectly with current city boundaries, the ‘city area’ summaries are not projections specific to a current or future city boundary, but the ‘city area’ summaries may be suitable surrogates or starting points upon which to base city-specific projections.Summary Variables in the DatasetsAnnual projection counts are available for the following variables (please read Key Exclusions note below):DemographicsHousehold Population Count (excludes persons living in group quarters)Household Count (excludes group quarters)EmploymentTypical Job Count (includes job types that exhibit typical commuting and other travel/vehicle use patterns)Retail Job Count (retail, food service, hotels, etc)Office Job Count (office, health care, government, education, etc)Industrial Job Count (manufacturing, wholesale, transport, etc)Non-Typical Job Count* (includes agriculture, construction, mining, and home-based jobs) This can be calculated by subtracting Typical Job Count from All Employment Count.All Employment Count* (all jobs, this sums jobs from typical and non-typical sectors).* These variable includes REMM’s attempt to estimate construction jobs in areas that experience new and re-development activity. Areas may see short-term fluctuations in Non-Typical and All Employment counts due to the temporary location of construction jobs.Population and employment projections for the Wasatch Front area can be combined with those developed by Dixie MPO (St. George area), Cache MPO (Logan area), and the Utah Department of Transportation (for the remainder of the state) into one database for use in the Utah Statewide Travel Model (USTM). While projections for the areas outside of the Wasatch Front use different forecasting methods, they contain the same summary-level population and employment projections making similar TAZ and ‘City Area’ data available statewide. WFRC plans, in the near future, to add additional areas to these projections datasets by including the projections from the USTM model.Key Exclusions from TAZ and ‘City Area’ ProjectionsAs the primary purpose for the development of these population and employment projections is to model future travel in the region, REMM-based projections do not include population or households that reside in group quarters (prisons, senior centers, dormitories, etc), as residents of these facilities typically have a very low impact on regional travel. USTM-based projections also excludes group quarter populations. Group quarters population estimates are available at the county-level from GPI and at various sub-county geographies from the Census Bureau.

EIA Projection: Population: 65 Years & Over data was reported at 87,861.542 Person th in 2050. This records an increase from the previous number of 87,119.713 Person th for 2049. EIA Projection: Population: 65 Years & Over data is updated yearly, averaging 76,691.349 Person th from Dec 2015 (Median) to 2050, with 36 observations. The data reached an all-time high of 87,861.542 Person th in 2050 and a record low of 48,022.327 Person th in 2015. EIA Projection: Population: 65 Years & Over data remains active status in CEIC and is reported by Energy Information Administration. The data is categorized under Global Database’s USA – Table US.G005: Population: Projection: Energy Information Administration.

VITAL SIGNS INDICATOR Population (LU1)

FULL MEASURE NAME
Population estimates

LAST UPDATED
February 2023

DESCRIPTION
Population is a measurement of the number of residents that live in a given geographical area, be it a neighborhood, city, county or region.

DATA SOURCE
California Department of Finance: Population and Housing Estimates - http://www.dof.ca.gov/Forecasting/Demographics/Estimates/
Table E-6: County Population Estimates (1960-1970)
Table E-4: Population Estimates for Counties and State (1970-2021)
Table E-8: Historical Population and Housing Estimates (1990-2010)
Table E-5: Population and Housing Estimates (2010-2021)

Bay Area Jurisdiction Centroids (2020) - https://data.bayareametro.gov/Boundaries/Bay-Area-Jurisdiction-Centroids-2020-/56ar-t6bs
Computed using 2020 US Census TIGER boundaries

U.S. Census Bureau: Decennial Census Population Estimates - http://www.s4.brown.edu/us2010/index.htm- via Longitudinal Tract Database Spatial Structures in the Social Sciences, Brown University
1970-2020

U.S. Census Bureau: American Community Survey (5-year rolling average; tract) - https://data.census.gov/
2011-2021
Form B01003

Priority Development Areas (Plan Bay Area 2050) - https://opendata.mtc.ca.gov/datasets/MTC::priority-development-areas-plan-bay-area-2050/about

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
All historical data reported for Census geographies (metropolitan areas, county, city and tract) use current legal boundaries and names. A Priority Development Area (PDA) is a locally-designated area with frequent transit service, where a jurisdiction has decided to concentrate most of its housing and jobs growth for development in the foreseeable future. PDA boundaries are current as of December 2022.

Population estimates for Bay Area counties and cities are from the California Department of Finance, which are as of January 1st of each year. Population estimates for non-Bay Area regions are from the U.S. Census Bureau. Decennial Census years reflect population as of April 1st of each year whereas population estimates for intercensal estimates are as of July 1st of each year. Population estimates for Bay Area tracts are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Estimates of population density for tracts use gross acres as the denominator.

Population estimates for Bay Area tracts and PDAs are from the decennial Census (1970-2020) and the American Community Survey (2011-2021 5-year rolling average). Population estimates for PDAs are allocated from tract-level Census population counts using an area ratio. For example, if a quarter of a Census tract lies with in a PDA, a quarter of its population will be allocated to that PDA. Estimates of population density for PDAs use gross acres as the denominator. Note that the population densities between PDAs reported in previous iterations of Vital Signs are mostly not comparable due to minor differences and an updated set of PDAs (previous iterations reported Plan Bay Area 2040 PDAs, whereas current iterations report Plan Bay Area 2050 PDAs).

The following is a list of cities and towns by geographical area:

Big Three: San Jose, San Francisco, Oakland

Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside

Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville

Unincorporated: all unincorporated towns

These data are to supplement the following in-press publication:

Zoraghein, H., and O'Neill B. (2020). U.S. state-level projections of the spatial distribution of population consistent with Shared Socioeconomic Pathways. Sustainability.

The data herein were generated using the population_gravity model which can be found here: https://github.com/IMMM-SFA/population_gravity

CONTENTS:

zoraghein-oneill_population_gravity_inputs_outputs.zip

contains a directory for each U.S. state for inputs and outputs

inputs contain the following:

_1km.tif: Urban and Rural population GeoTIF rasters at a 1km resolution

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

_mask_short_term.tif: Mask GeoTIF rasters at a 1km resolution that contain values from 0.0 to 1.0 for each 1 km grid cell to help calculate suitability depending on topographic and land use and land cover characteristics

value per grid cell: values from 0.0 to 1.0 (float) that are generated from topographic and land use and land cover characteristics to inform suitability as outlined in the companion publication

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

_popproj.csv: Population projection CSV files for urban, rural, and total population (number of humans; float) for SSPs 2, 3, and 5 for years 2010-2100

_coordinates.csv: CSV file containing the coordinates for each 1 km grid cell within the target state. File includes a header with the fields XCoord, YCoord, FID.,Where data types and field descriptions are as follows: (XCoord, float, X coordinate in meters),(YCoord, float, Y coordinate in meters),(FID, int, Unique feature id)

_within_indices.txt: text file containing a file structured as a Python list (e.g. [0, 1]) that contains the index of each grid cell when flattened from a 2D array to a 1D array for the target state.

_params.csv: CSV file containing the calibration parameters (alpha_rural, beta_rural, alpha_urban, beta_urban; float) for the population_gravity model for each year from 2010-2100 in 10-year time-steps as described in the companion publication

outputs contain the following:

jones_oneill directory; these are the comparison datasets used to build Figures 7 and 8 in the companion publication

contains three directories: SSP2, SSP3, and SSP5 that each contain a GeoTIF representing total population (number of humans; float) at 1km resolution for years 2050 and 2100.

1kmtotal_jones_oneill.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

model directory; these are the model outputs from population_gravity for SSP2, SSP3, and SSP5 that each contain a GeoTIF representing urban, rural, and total population (number of humans; float) at 1km resolution for years 2020-2100 in 10-year time-steps.

1km_jones_oneill.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

zoraghein-oneill_population_gravity_national-ssp-maps.zip

Results of the population_gravity model mosaicked to the National scale at a 1km resolution and the comparison Jones and O'Neill research. These are used to generate Figure 6 of the companion paper

National_1km_jones_oneill.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38

National_1km_.tif:

value per grid cell: number of humans (float)

crs: EPSG:102003 - USA_Contiguous_Albers_Equal_Area_Conic - Projected

nodata value: -3.40282e+38