Population 16 years and over Poverty Rate Statistics for 2023. This is part of a larger dataset covering poverty in Los Angeles, California by age, education, race, gender, work experience and more.

In 2023, the metropolitan area of New York-Newark-Jersey City had the biggest population in the United States. Based on annual estimates from the census, the metropolitan area had around 19.5 million inhabitants, which was a slight decrease from the previous year. The Los Angeles and Chicago metro areas rounded out the top three. What is a metropolitan statistical area? In general, a metropolitan statistical area (MSA) is a core urbanized area with a population of at least 50,000 inhabitants – the smallest MSA is Carson City, with an estimated population of nearly 56,000. The urban area is made bigger by adjacent communities that are socially and economically linked to the center. MSAs are particularly helpful in tracking demographic change over time in large communities and allow officials to see where the largest pockets of inhabitants are in the country. How many MSAs are in the United States? There were 421 metropolitan statistical areas across the U.S. as of July 2021. The largest city in each MSA is designated the principal city and will be the first name in the title. An additional two cities can be added to the title, and these will be listed in population order based on the most recent census. So, in the example of New York-Newark-Jersey City, New York has the highest population, while Jersey City has the lowest. The U.S. Census Bureau conducts an official population count every ten years, and the new count is expected to be announced by the end of 2030.

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

Total household population Health Insurance Coverage Statistics for 2023. This is part of a larger dataset covering consumer health insurance coverage rates in East Los Angeles, California by age, education, race, gender, work experience and more.

Los Angeles County includes 88 incorporated cities and over 2,600 square miles of unincorporated area. The majority of the County’s 10 million residents live inincorporated cities, and about 1 million residents live in unincorporated areas. To ensure that communities across the County received equal representation in the Parks Needs Assessment, the County was divided into individual Study Areas. These geographic boundaries were developed using a GIS-based process that considered existing jurisdictional boundaries such as supervisorial districts, city borders, and County planning areas alongside information about population.The initial Study Area boundaries were reviewed by the Steering Committee at their first meeting. Revised Study Area boundaries incorporated Steering Committeecomments and resulted in a total of 189 Study Areas. However, due to its annexation into the City of Santa Clarita, one unincorporated community was later eliminated, bringing the final total number of Study Areasto 188. The process of establishing Study Area boundaries is illustrated in Figure 5. Each incorporated city was initially assigned a single Study Area. Cities with population over 150,000 were split into two or more Study Areas, to create a more even distribution of population among Study Areas. Each of these larger cities was allocated a number of Study Areas based on their total population:»» City of Los Angeles: 43 Study Areas»» City of Long Beach: 5 Study Areas»» City of Glendale: 2 Study Areas»» City of Santa Clarita: 2 Study Areas»» City of Lancaster: 2 Study Areas»» City of Palmdale: 2 Study Areas»» City of Pomona: 2 Study Areas»» City of Torrance: 2 Study Areas»» City of Pasadena: 2 Study AreasFor each of these cities, project consultants suggested internal Study Area boundaries based on input from city staff, geographic barriers such as major roadways, Citydeveloped boundaries such as council districts or planning areas, and population distribution. Final determination of the internal boundaries of the Study Areas was at the discretion of city staff.Unincorporated communities in the County were evaluated based on population size and geographic location. Each of the 187 incorporated communities was addressed as follows:»» Geographically isolated communities with small populations were added to the Study Area of the adjacent, like-named city. A total of 18 cities agreed toinclude an adjacent unincorporated community within their Study Area boundaries.»» Distinct and/or geographically isolated communities with larger populations each became an individual Study Area. Any of these communities with more than150,000 people was split into two Study Areas, similar to what was done for large cities.»» Geographically adjacent communities with small populations were grouped according to community name and geography, population distribution, andstatistical areas.»» Each Study Area was assigned a unique identification number, illustrated in Figure 6, Figure 7, and Table 1.

California was the state with the highest resident population in the United States in 2024, with 39.43 million people. Wyoming had the lowest population with about 590,000 residents. Living the American Dream Ever since the opening of the West in the United States, California has represented the American Dream for both Americans and immigrants to the U.S. The warm weather, appeal of Hollywood and Silicon Valley, as well as cities that stick in the imagination such as San Francisco and Los Angeles, help to encourage people to move to California. Californian demographics California is an extremely diverse state, as no one ethnicity is in the majority. Additionally, it has the highest percentage of foreign-born residents in the United States. By 2040, the population of California is expected to increase by almost 10 million residents, which goes to show that its appeal, both in reality and the imagination, is going nowhere fast.

A dataset listing California counties by population for 2024.

Population 26 years and over Health Insurance Coverage Statistics for 2022. This is part of a larger dataset covering consumer health insurance coverage rates in Lake Los Angeles, California by age, education, race, gender, work experience and more.

Link Function: information

How many incorporated places are registered in the U.S.?

There were 19,502 incorporated places registered in the United States as of July 31, 2019. 16,410 had a population under 10,000 while, in contrast, only 10 cities had a population of one million or more.

Small-town America

Suffice it to say, almost nothing is more idealized in the American imagination than small-town America. When asked where they would prefer to live, 30 percent of Americans reported that they would prefer to live in a small town. Americans tend to prefer small-town living due to a perceived slower pace of life, close-knit communities, and a more affordable cost of living when compared to large cities.

An increasing population

Despite a preference for small-town life, metropolitan areas in the U.S. still see high population figures, with the New York, Los Angeles, and Chicago metro areas being the most populous in the country. Metro and state populations are projected to increase by 2040, so while some may move to small towns to escape city living, those small towns may become more crowded in the upcoming decades.

As the field of phylogeography has matured, it has become clear that analyses of one or a few genes may reveal more about the history of those genes than the populations and species that are the targets of study. To alleviate these concerns, the discipline has moved towards larger analyses of more individuals and more genes, although little attention has been paid to the qualitative or quantitative gains that such increases in scale and scope may yield. Here, we increase the number of individuals and markers by an order of magnitude over previously published work to comprehensively assess the phylogeographical history of a well‐studied declining species, the western pond turtle (Emys marmorata). We present a new analysis of 89 independent nuclear SNP markers and one mitochondrial gene sequence scored for rangewide sampling of >900 individuals, and compare these to smaller‐scale, rangewide genetic and morphological analyses. Our enlarged SNP data fundamentally revise our understanding of evolutionary history for this lineage. Our results indicate that the gains from greatly increasing both the number of markers and individuals are substantial and worth the effort, particularly for species of high conservation concern such as the pond turtle, where accurate assessments of population history are a prerequisite for effective management.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In terms of population size, the sex ratio in the United States favors females, although the gender gap is remaining stable. In 2010, there were around 5.17 million more women, with the difference projected to decrease to around 3 million by 2027.

Gender ratios by U.S. state In the United States, the resident population was estimated to be around 331.89 million in 2021. The gender distribution of the nation has remained steady for several years, with women accounting for approximately 51.1 percent of the population since 2013. Females outnumbered males in the majority of states across the country in 2020, and there were eleven states where the gender ratio favored men.

Metro areas by population National differences between male and female populations can also be analyzed by metropolitan areas. In general, a metropolitan area is a region with a main city at its center and adjacent communities that are all connected by social and economic factors. The largest metro areas in the U.S. are New York, Los Angeles, and Chicago. In 2019, there were more women than men in all three of those areas, but Jackson, Missouri was the metro area with the highest share of female population.

Colonization of a novel environment by a small group of individuals can lead to rapid evolutionary change, yet evidence of the relative contributions of neutral and selective factors in promoting divergence during the early stages of colonization remain scarce. Here, we use genome-wide SNP data to test the role of neutral and selective forces in driving the divergence of a unique urban population of the Oregon junco (Junco hyemalis oreganus), which became established on the campus of the University of California at San Diego (UCSD) in the early 1980s. Previous studies based on microsatellite loci documented significant genetic differentiation of the urban population as well as divergence in sexual signaling and life-history traits relative to nearby montane populations. However, the geographic origin of the colonization and the factors involved in the onset of the differentiation process remained uncertain. Our genome-wide SNP dataset confirmed the marked genetic differentiation of the UCSD population, and phylogenomic analysis identified the coastal subspecies pinosus from central California as its sister group instead of the neighboring mountain population. Demographic inference based on site frequency spectra recovered a time of separation from pinosus as recent as 20 to 32 generations, and a strong bottleneck at the time of colonization, suggesting a relevant role of founder effects and drift in the genetic differentiation of the UCSD population. However, we also found significant associations between environmental parameters characterizing the urban habitat of UCSD and genome-wide variants linked to functional genes. Some of the identified gene functions, like heavy metal detoxification and high-pitched hearing, have been reported as potentially adaptive in birds inhabiting urban environments. These results suggest that the interplay between founder events and directional selection may result in rapid shifts in both neutral and adaptive loci across the genome, and reveal the UCSD population of juncos as an ongoing case of divergence following the colonization of an anthropic environment. Methods All methods and protocols are described in detail in the article.

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

The Community Credit research project explores pathways for trusted collaboration between credit unions and the communities they serve. To understand the experiences of people historically underserved by the consumer financial services industry, we focused in particular on the lived experience of low-income residents in Southern California. As part of a larger, mixed-methods study, in 2022 we conducted an online survey investigating people’s everyday financial practices, evolving perceptions of trust and risk, and their unmet financial needs. The general population survey data was collected between April 15 and April 22, 2022. The credit union data was collected between May 3 and July 18, 2022. This data set contains the responses of the survey participants after excluding any personally identifying data. All study materials and procedures were approved by the University of California, Irvine Office of Human Research Protections and the Institutional Review Board (protocol ID 20216839)...., Survey data was collected via the Qualtrics platform. The survey contains 52 questions. It was distributed to the general population in zip codes within the counties of Los Angeles and Orange. It was also distributed directly to members of a large credit union headquartered in Orange County (“large†according to NCUA asset classes). Participants were eligible to complete the survey if they live in Orange County or Los Angeles County, are older than 18, and have a combined household income of less than $100,000. Incomplete responses have been removed. The survey yielded 1,370 complete responses (1,213 from the general population participants and 157 from members of the large credit union)., Note that the files do not contain all the responses from the survey questions. Responses that provided potentially identifying information were removed. Survey participants’ gender, education status, employment status, and marital status were removed; data on these elements are provided in aggregate in the readme file. Responses are segmented into two files reflecting participants from the general population (“Gen Pop†) and from the credit union (“CU†).

2010 Rural-Urban Commuting Area Codes (revised 7/3/2019) , joined to SD, SPA, and CSA as of Dec. 2023.Data from https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/. Downloaded 1/9/2024.Primary RUCA Codes, 20101 Metropolitan area core: primary flow within an urbanized area (UA)2 Metropolitan area high commuting: primary flow 30% or more to a UA3 Metropolitan area low commuting: primary flow 10% to 30% to a UA4 Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC)5 Micropolitan high commuting: primary flow 30% or more to a large UC6 Micropolitan low commuting: primary flow 10% to 30% to a large UC7 Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC)8 Small town high commuting: primary flow 30% or more to a small UC9 Small town low commuting: primary flow 10% to 30% to a small UC10 Rural areas: primary flow to a tract outside a UA or UC99 Not coded: Census tract has zero population and no rural-urban identifier informationSecondary RUCA Codes, 20101 Metropolitan area core: primary flow within an urbanized area (UA)1No additional code1.1Secondary flow 30% to 50% to a larger UA2 Metropolitan area high commuting: primary flow 30% or more to a UA2No additional code2.1Secondary flow 30% to 50% to a larger UA3 Metropolitan area low commuting: primary flow 10% to 30% to a UA3No additional code4 Micropolitan area core: primary flow within an Urban Cluster of 10,000 to 49,999 (large UC)4No additional code4.1Secondary flow 30% to 50% to a UA5 Micropolitan high commuting: primary flow 30% or more to a large UC5No additional code5.1Secondary flow 30% to 50% to a UA6 Micropolitan low commuting: primary flow 10% to 30% to a large UC6No additional code7 Small town core: primary flow within an Urban Cluster of 2,500 to 9,999 (small UC)7No additional code7.1Secondary flow 30% to 50% to a UA7.2Secondary flow 30% to 50% to a large UC8 Small town high commuting: primary flow 30% or more to a small UC8No additional code8.1Secondary flow 30% to 50% to a UA8.2Secondary flow 30% to 50% to a large UC9 Small town low commuting: primary flow 10% to 30% to a small UC9No additional code10 Rural areas: primary flow to a tract outside a UA or UC10No additional code10.1Secondary flow 30% to 50% to a UA10.2Secondary flow 30% to 50% to a large UC10.3Secondary flow 30% to 50% to a small UC99 Not coded: Census tract has zero population and no rural-urban identifier informationData Sources:Population data for census tracts, by urban-rural components, 2010:U.S. Census Bureau, Census of Population and Housing, 2010. Summary File 1, FTP download: https://www.census.gov/census2000/sumfile1.htmlAssignment of census tracts to specific urban areas or to rural status was completed using ESRI's ArcMap software and Census Bureau shape files:U.S. Census Bureau. Tiger/Line Shapefiles, Census Tracts and Urban Areas, 2010: https://www.census.gov/programs-surveys/geography.htmlCensus tract commuting flows, 2006-2010:U.S. Census Bureau, American Community Survey 2006-2010 Five-year estimates. Special Tabulation: Census Transportation Planning Products, Part 3, Worker Home-to-Work Flow Tables. https://www.fhwa.dot.gov/planning/census_issues/ctpp/data_products/2006-2010_table_list/sheet04.cfmTract-to-tract commuting flow files were constructed from ACS data as part of a special tabulation for the Department of Transportation—the Census Transportation Planning Package. To derive estimates for small geographic units such as census tracts, information collected annually from over 3.5 million housing units was combined across 5 years (2006-2010). As with all survey data, ACS estimates are not exact because they are based on a sample. In general, the smaller the estimate, the larger the degree of uncertainty associated with it.

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Recent advances in quantitative tools for examining urban morphology enable the development of morphometrics that can characterize the size, shape, and placement of buildings; the relationships between them; and their association with broader patterns of development. Although these methods have the potential to provide substantial insight into the ways in which neighborhood morphology shapes the socioeconomic and demographic characteristics of neighborhoods and communities, this question is largely unexplored. Using building footprints in five of the ten largest U.S. metropolitan areas (Atlanta, Boston, Chicago, Houston, and Los Angeles) and the open-source R package, foot, we examine how neighborhood morphology differs across U.S. metropolitan areas and across the urban-exurban landscape. Principal components analysis, unsupervised classification (K-means), and Ordinary Least Squares regression analysis are used to develop a morphological typology of neighborhoods and to examine its association with the spatial, socioeconomic, and demographic characteristics of census tracts. Our findings illustrate substantial variation in the morphology of neighborhoods, both across the five metropolitan areas as well as between central cities, suburbs, and the urban fringe within each metropolitan area. We identify five different types of neighborhoods indicative of different stages of development and distributed unevenly across the urban landscape: these include low-density neighborhoods on the urban fringe; mixed use and high-density residential areas in central cities; and uniform residential neighborhoods in suburban cities. Results from regression analysis illustrate that the prevalence of each of these forms is closely associated with variation in socioeconomic and demographic characteristics such as population density, the prevalence of multifamily housing, and income, race/ethnicity, homeownership, and commuting by car. We conclude by discussing the implications of our findings and suggesting avenues for future research on neighborhood morphology, including ways that it might provide insight into issues such as zoning and land use, housing policy, and residential segregation.