In 2021, the population of the San Diego-Chula Vista-Carlsbad metropolitan area in the United States was about 3.29 million people. This is was a slight decrease compared to the previous year, when the population was about 3.3 million.

About this dataset

Context

The dataset tabulates the data for the San Diego, CA population pyramid, which represents the San Diego 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 San Diego, CA, is 22.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for San Diego, CA, is 20.2.
• Total dependency ratio for San Diego, CA is 42.5.
• Potential support ratio, which is the number of youth (working age population) per elderly, for San Diego, CA is 5.0.

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 San Diego population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the San Diego for the selected age group is shown in the following column.
• Population (Female): The female population in the San Diego for the selected age group is shown in the following column.
• Total Population: The total population of the San Diego 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 San Diego Population by Age. You can refer the same here

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races (5-year estimate) in San Diego County, CA (B03002009E006073) from 2009 to 2023 about San Diego County, CA; San Diego; CA; non-hispanic; estimate; persons; 5-year; population; and USA.

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

In 2023, the population of the San Antonio-New Braunfels metropolitan area in the United States was about 2.7 million people. This was a slight increase from the previous year, when the population was about 2.66 million people.

About this dataset

Context

The dataset presents the median household income across different racial categories in San Diego. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of San Diego population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 50.43% of the total residents in San Diego. Notably, the median household income for White households is $112,041. Interestingly, despite the White population being the most populous, it is worth noting that Asian households actually reports the highest median household income, with a median income of $124,834. This reveals that, while Whites may be the most numerous in San Diego, Asian households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in San Diego.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 San Diego median household income by race. You can refer the same here

Graph and download economic data for Bachelor's Degree or Higher (5-year estimate) in San Diego County, CA (HC01ESTVC1706073) from 2010 to 2023 about San Diego County, CA; San Diego; tertiary schooling; educational attainment; education; CA; 5-year; and USA.

Graph and download economic data for Resident Population in San Antonio-New Braunfels, TX (MSA) (SATPOP) from 2000 to 2024 about San Antonio, residents, TX, population, and USA.

In 2023, there were an estimated ******* white homeless people in the United States, the most out of any ethnicity. In comparison, there were around ******* Black or African American homeless people in the U.S. How homelessness is counted The actual number of homeless individuals in the U.S. is difficult to measure. The Department of Housing and Urban Development uses point-in-time estimates, where employees and volunteers count both sheltered and unsheltered homeless people during the last 10 days of January. However, it is very likely that the actual number of homeless individuals is much higher than the estimates, which makes it difficult to say just how many homeless there are in the United States. Unsheltered homeless in the United States California is well-known in the U.S. for having a high homeless population, and Los Angeles, San Francisco, and San Diego all have high proportions of unsheltered homeless people. While in many states, the Department of Housing and Urban Development says that there are more sheltered homeless people than unsheltered, this estimate is most likely in relation to the method of estimation.

A growing body of evidence suggests that spatial population structure can develop in marine species despite large population sizes and high gene flow. Characterizing population structure is important for the effective management of exploited species, as it can be used to identify appropriate scales of management in fishery and aquaculture contexts. The California sea cucumber, Apostichopus californicus, is one such exploited species whose management could benefit from further characterization of population structure. Using restriction site-associated DNA (RAD) sequencing, we developed 2,075 single nucleotide polymorphisms (SNPs) to quantify genetic structure over a broad section of the species’ range along the North American west coast and within the Salish Sea, a region supporting the Washington State A. californicus fishery and developing aquaculture production of the species. We found evidence for population structure (global fixation index (FST) = 0.0068) with limited dispersal driving two patterns of differentiation: isolation-by-distance and a latitudinal gradient of differentiation. Notably, we found detectable population differences among collection sites within the Salish Sea (pairwise FST = 0.001–0.006). Using FST outlier detection and gene-environment association, we identified 10.2% of total SNPs as putatively adaptive. Environmental variables (e.g., temperature, salinity) from the sea surface were more correlated with genetic variation than those same variables measured near the benthos, suggesting that selection on pelagic larvae may drive adaptive differentiation to a greater degree than selection on adults. Our results were consistent with previous estimates of, and patterns in, population structure for this species in other extents of the range. Additionally, we found that patterns of neutral and adaptive differentiation co-varied, suggesting that adaptive barriers may limit dispersal. Our study provides guidance to decision-makers regarding the designation of management units for A. californicus and adds to the growing body of literature identifying genetic population differentiation in marine species despite large, nominally connected populations. Methods Approximately 50 adult A. californicus were collected by scuba divers from nine collection sites along the Pacific Coast of North America, ranging from Alaska to Oregon, including four collection sites within the southern Salish Sea. For each animal, a tissue sample was excised from a radial muscle band and stored in 100% ethanol. DNA was extracted from tissue samples using the EZNA Mollusc DNA Kit (OMEGA Bio-tek, Norcross, GA, USA) and the Qiagen DNeasy Kit (Qiagen, Germantown, MD, USA). DNA was quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) and DNA quality was checked by gel electrophoresis. DNA concentration was normalized to 500 ng in 20 μL of PCR-grade water. We selected samples with high DNA quality for restriction site associated DNA (RAD) sequencing and RAD libraries were prepared following standard protocols1. Briefly, DNA samples were barcoded with an individual six-base identifier sequence attached to an Illumina P1 adapter. Samples were then pooled into sub-libraries, containing approximately 12 individuals. Sub-libraries were sheared using a Bioruptor sonicator and size selected to 200-400 bp using a MinElute Gel Extraction Kit (Qiagen, Germantown, MD, USA). P2 adapters were ligated to DNA in sub-libraries and amplified with PCR using 12–18 cycles as in Etter et al. (2011). Finally, amplified sub-libraries were combined into pools of approximately 72 individuals. Paired-end 2 x 150-base pair sequencing was performed on an Illumina HiSeq4000 (San Diego, California, USA) at the Beijing Genomics Institute and the University of Oregon Genomics and Cell Characterization Core Facility. Only forward reads were used for analysis. To estimate genotyping error, 14 individuals were sequenced twice. Raw RAD sequencing data were demultiplexed using the process_radtags module in the pipeline STACKS v.1.442. A threshold of 800,000 reads was used to exclude poorly sequenced individuals. Because a genome was not available for A. californicus, we aligned individual sequences to the genome of a closely related species, A. parvimensis (GenBank accession number = GCA_000934455.1). The A. parvimensis genome was 760,654,621 bp, with 21,559 scaffolds and an N50 size of 9,587. We retained reads with a minimum mapping quality score of 20. Then, we used dDocent v.2.7.8 to perform a reference-guided locus assembly using the filtered reads and default parameters3. Additionally, a parallel de novo assembly was performed, which produced nearly identical results for population structure and 1.8–2.8% lower mean expected heterozygosity, 0.9–1.8% higher mean observed heterozygosity, and 1.2–3.3% higher proportions of polymorphic SNPs than in the with-reference assembly, although with similar patterns across collection sites. The reference-guided assembly was retained for further analyses due to decreased confidence in identifying genotyping errors in the de novo assembly4. We used vcftools v.0.1.165 to remove indels and to retain only single nucleotide polymorphisms (SNPs) with a minimum quality score of 20, minimum minor allele frequency of 0.05 and maximum missing data per locus of 30% across collection sites. Individuals with more than 30% missing data across SNPs were removed. In cases of multiple SNPs per RAD tag, we retained the SNP with the highest minor allele frequency6. SNPs that were not in Hardy Weinberg Equilibrium (HWE) were considered sequencing errors or poorly assembled loci and were removed from our data set, as selection and inbreeding are unlikely to cause significant deviations from HWE equilibrium at biallelic loci7. We tested SNPs for deviations from HWE using the R package genepop v.1.1.4 (Rousset, 2008). SNPs were identified as being out of HWE if they had a q-value below 0.05 in at least 2 of the collection sites after correcting for false discovery rate, following Waples (2015). We used a suite of R packages, stand-alone software, and custom scripts in the programming language R v.3.5.0 9 to quantify genetic diversity and population structure. Mean expected heterozygosity, observed heterozygosity, and the inbreeding coefficient (FIS) per SNP were calculated using the R package genepop. The proportion of polymorphic SNPs per collection site was calculated using a custom R script. To investigate population structure, we first calculated Weir-Cockerham fixation index (FST)10 to quantify population differentiation using the R packages genepop and hierfstat v.0.5.7. Exact G-tests11 were used to test for significant genic differentiation using the R package genepop. To investigate patterns of spatial differentiation among collection sites, the R package adegenet v.2.1.112 was used to conduct discriminant analyses of principal components (DAPC), a multivariate method that summarizes the between-group variation (i.e., population structure), while minimizing within-group variation13. The built-in optimization algorithm was used to retain the number of principal components that minimized over-fitting and under-fitting of the model. To determine the potential number of underlying populations, the program ADMIXTURE v.1.3.0 was used to conduct a clustering analysis14. Specifically, ADMIXTURE uses a maximum likelihood-based approach to estimate individual ancestries across different assumed numbers of populations, with the best fit selected using cross-validation. To examine the presence of hierarchical population structure, we conducted analyses of molecular variance (AMOVA) using the ade4 method of the R package poppr v.2.8.115. Significance of AMOVAs was determined using permutation tests with 1,000 iterations. Using AMOVA, we investigated whether the following oceanographic barriers limit dispersal: 1) the Victoria Sill (Victoria Sill grouping), 2) Admiralty Inlet (Admiralty Inlet grouping), and 3) the North Pacific Current (NPC grouping). Because AMOVAs for each oceanographic barrier include sites in an area with other potential oceanographic barriers, we added a fourth grouping of all three oceanographic barriers (All Barriers grouping) to investigate the relative role of oceanographic barriers compared to other factors. Additionally, we conducted an AMOVA by state or province (State grouping). Although not biologically meaningful, we included the State grouping to determine how much genetic variation is captured by regional management boundaries. Isolation-by-distance (IBD) was tested with Mantel tests16 in R as linear correlation between linearized FST17 using all SNPs and shortest Euclidean distance through water (in-water distance hereafter) approximated in Google Maps18 Following Xuereb et al (2018), we also tested IBD in the northern and southern population section separately. Following Buonaccorsi et al. (2005), we estimated mean dispersal distance from the slope of the regression of linearized FST and in-water distance. We used this 1-dimensional model because it is an appropriate approximation for coastal species with dispersal dimensions greater than one dimension of the habitat (e.g., dispersal distance likely greater than water depth for A. californicus). We estimated mean dispersal distance from a set of potential population density estimates as population density estimates were unavailable. We used two approaches to investigate putatively adaptive SNPs: FST outlier detection and gene-environment association. FST outlier detection is used to identify loci potentially under spatial selection20,21, although this method does not identify the potential cause of selection. Although gene-environment association does not explicitly test whether such associations are adaptive, this method is used to identify locus-environment associations as evidence for potential local

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races (5-year estimate) in Kendall County, TX (B03002019E048259) from 2009 to 2023 about Kendall County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Comal County, TX (B03002021E048091) from 2009 to 2023 about Comal County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races (5-year estimate) in Bexar County, TX (B03002019E048029) from 2009 to 2023 about Bexar County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Medina County, TX (B03002020E048325) from 2009 to 2023 about Medina County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races (5-year estimate) in Atascosa County, TX (B03002019E048013) from 2009 to 2023 about Atascosa County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Bandera County, TX (B03002021E048019) from 2009 to 2023 about Bandera County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Hispanic or Latino, Two or More Races, Two Races Excluding Some Other Race, and Three or More Races (5-year estimate) in Guadalupe County, TX (B03002021E048187) from 2009 to 2023 about Guadalupe County, TX; San Antonio; latino; hispanic; TX; estimate; persons; 5-year; population; and USA.

Graph and download economic data for Population Estimate, Total, Not Hispanic or Latino, Two or More Races, Two Races Including Some Other Race (5-year estimate) in Wilson County, TX (B03002010E048493) from 2009 to 2023 about Wilson County, TX; San Antonio; TX; non-hispanic; estimate; persons; 5-year; population; and USA.

About this dataset

Context

The dataset presents the median household income across different racial categories in San Antonio. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of San Antonio population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 87.72% of the total residents in San Antonio. Notably, the median household income for White households is $88,487. Interestingly, despite the White population being the most populous, it is worth noting that Two or More Races households actually reports the highest median household income, with a median income of $152,857. This reveals that, while Whites may be the most numerous in San Antonio, Two or More Races households experience greater economic prosperity in terms of median household income.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in San Antonio.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 San Antonio median household income by race. You can refer the same here