When analyzing the ratio of homelessness to state population, New York, Vermont, and Oregon had the highest rates in 2023. However, Washington, D.C. had an estimated ** homeless individuals per 10,000 people, which was significantly higher than any of the 50 states. Homeless people by race The U.S. Department of Housing and Urban Development performs homeless counts at the end of January each year, which includes people in both sheltered and unsheltered locations. The estimated number of homeless people increased to ******* in 2023 – the highest level since 2007. However, the true figure is likely to be much higher, as some individuals prefer to stay with family or friends - making it challenging to count the actual number of homeless people living in the country. In 2023, nearly half of the people experiencing homelessness were white, while the number of Black homeless people exceeded *******. How many veterans are homeless in America? The  number of homeless veterans in the United States has halved since 2010. The state of California, which is currently suffering a homeless crisis, accounted for the highest number of homeless veterans in 2022. There are many causes of homelessness among veterans of the U.S. military, including post-traumatic stress disorder (PTSD), substance abuse problems, and a lack of affordable housing.

This statistic depicts the rate of homeless individuals in the United States in 2017, by metropolitan area. In 2017, the rate of homelessness per 10,000 individuals was highest in New York City, at ****.

The graph displays the top 15 states by an estimated number of homeless people in the United States for the year 2025. The x-axis represents U.S. states, while the y-axis shows the number of homeless individuals in each state. California has the highest homeless population with 187,084 individuals, followed by New York with 158,019, while Hawaii places last in this dataset with 11,637. This bar graph highlights significant differences across states, with some states like California and New York showing notably higher counts compared to others, indicating regional disparities in homelessness levels across the country.

The graph displays the estimated number of homeless people in the United States from 2007 to 2024. The x-axis represents the years, ranging from 2007 to 2023, while the y-axis indicates the number of homeless individuals. The estimated homeless population varies over this period, ranging from a low of 57,645 in 2014 to a high of 771,000 in 2024. From 2007 to 2013, there is a general decline in numbers from 647,258 to 590,364. In 2014, the number drops significantly to 57,645, followed by an increase to 564,708 in 2015. The data shows fluctuations in subsequent years, with another notable low of 55,283 in 2018. From 2019 onwards, the estimated number of homeless people generally increases, reaching its peak in 2024. This data highlights fluctuations in homelessness estimates over the years, with a recent upward trend in the homeless population.

In the United States in 2023, **** percent of the homeless population living in El Dorado County, California were unsheltered.

In 2023, there were about ******* homeless people estimated to be living in the United States, the highest number of homeless people recorded within the provided time period. In comparison, the second-highest number of homeless people living in the U.S. within this time period was in 2007, at *******. How is homelessness calculated? Calculating homelessness is complicated for several different reasons. For one, it is challenging to determine how many people are homeless as there is no direct definition for homelessness. Additionally, it is difficult to try and find every single homeless person that exists. Sometimes they cannot be reached, leaving people unaccounted for. In the United States, the Department of Housing and Urban Development calculates the homeless population by counting the number of people on the streets and the number of people in homeless shelters on one night each year. According to this count, Los Angeles City and New York City are the cities with the most homeless people in the United States. Homelessness in the United States Between 2022 and 2023, New Hampshire saw the highest increase in the number of homeless people. However, California was the state with the highest number of homeless people, followed by New York and Florida. The vast amount of homelessness in California is a result of multiple factors, one of them being the extreme high cost of living, as well as opposition to mandatory mental health counseling and drug addiction. However, the District of Columbia had the highest estimated rate of homelessness per 10,000 people in 2023. This was followed by New York, Vermont, and Oregon.

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.

INTRODUCTION: As California’s homeless population continues to grow at an alarming rate, large metropolitan regions like the San Francisco Bay Area face unique challenges in coordinating efforts to track and improve homelessness. As an interconnected region of nine counties with diverse community needs, identifying homeless population trends across San Francisco Bay Area counties can help direct efforts more effectively throughout the region, and inform initiatives to improve homelessness at the city, county, and metropolitan level. OBJECTIVES: The primary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness across San Francisco Bay Area counties between the years 2018-2022. The secondary objective of this research is to compare the annual Point-in-Time (PIT) counts of homelessness among different age groups in each of the nine San Francisco Bay Area counties between the years 2018-2022. METHODS: Two datasets were used to conduct research. The first dataset (Dataset 1) contains Point-in-Time (PIT) homeless counts published by the U.S. Department of Housing and Urban Development. Dataset 1 was cleaned using Microsoft Excel and uploaded to Tableau Desktop Public Edition 2022.4.1 as a CSV file. The second dataset (Dataset 2) was published by Data SF and contains shapefiles of geographic boundaries of San Francisco Bay Area counties. Both datasets were joined in Tableau Desktop Public Edition 2022.4 and all data analysis was conducted using Tableau visualizations in the form of bar charts, highlight tables, and maps. RESULTS: Alameda, San Francisco, and Santa Clara counties consistently reported the highest annual count of people experiencing homelessness across all 5 years between 2018-2022. Alameda, Napa, and San Mateo counties showed the largest increase in homelessness between 2018 and 2022. Alameda County showed a significant increase in homeless individuals under the age of 18. CONCLUSIONS: Results from this research reveal both stark and fluctuating differences in homeless counts among San Francisco Bay Area Counties over time, suggesting that a regional approach that focuses on collaboration across counties and coordination of services could prove beneficial for improving homelessness throughout the region. Results suggest that more immediate efforts to improve homelessness should focus on the counties of Alameda, San Francisco, Santa Clara, and San Mateo. Changes in homelessness during the COVID-19 pandemic years of 2020-2022 point to an urgent need to support Contra Costa County.

This map shows Point in Time counts of the overall homeless populations from 2019. Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

This layer contains detailed Point in Time counts of homeless populations from 2019 by Continuum of Care (CoC) area. This layer includes data for the 11 Texas Continuum of Cares.Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.

This layer contains detailed Point in Time counts of homeless populations from 2018, 2013, and 2008. A 2019 version is now available!Layer is symbolized to show the count of the overall homeless population in 2018, with overall counts from 2008 and 2013 in the pop-up, as well as a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. The 2018, 2013, and 2008 sheets within the "2007 - 2018 PIT Counts within CoCs.xlsx" (downloaded on 2/7/2019) file were combined and joined to the CoC boundaries available from HUD's Open Data site. As noted in the "Mergers" sheet in the PIT Excel file, some CoC Areas have merged over the years. Use caution when comparing numbers in these CoCs across years. Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

This layer contains detailed Point in Time counts of homeless populations from 2019. This layer is modeled after a similar layer that contains data for 2018, 2013, and 2008.Layer is symbolized to show the count of the overall homeless population in 2019, with a pie chart of breakdown of type of shelter. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. The Point-in-Time (PIT) count is a count of sheltered and unsheltered homeless persons on a single night in January. HUD requires that Continuums of Care Areas (CoCs) conduct an annual count of homeless persons who are sheltered in emergency shelter, transitional housing, and Safe Havens on a single night. CoCs also must conduct a count of unsheltered homeless persons every other year (odd numbered years). Each count is planned, coordinated, and carried out locally.The Point-in-Time values were retrieved from HUD's Historical Data site. Original source is the 2019 sheet within the "2007 - 2019 PIT Counts by CoCs.xlsx" (downloaded on 3/10/2020) file. Key fields were kept and joined to the CoC boundaries available from HUD's Open Data site.Data note: MO-604 covers territory in both Missouri and Kansas. The record described in this file represents the CoC's total territory, the sum of the point-in-time estimates the CoC separately reported for the portions of its territory in MO and in KS.For more information and attributes on the CoC Areas themselves, including contact information, see this accompanying layer.Suggested Citation: U.S. Department of Housing and Urban Development (HUD)'s Point in Time (PIT) 2019 counts for Continuum of Care Grantee Areas, accessed via ArcGIS Living Atlas of the World on (date).

In 2022, about ****** veterans living in California were homeless, the most out of all U.S. states.

This statistic presents the estimated rate of homelessness across Australia in 2016, by state or territory. According to the source, there were approximately 599 homeless people per 10,000 people living in the Northern Territory on Census night in 2016.

In the United States in 2023, **** percent of the unaccompanied homeless youth in the Watsonville/Santa Cruz City and County, California were unsheltered.

Abstract

The population and housing census (PHC) is the unique source of reliable and comprehensive data about the size of population and also on major socio-economic & socio-demographic characteristics of the country. It provides data on geographic and administrative distribution of population and household in addition to the demographic and socio-economic characteristics of all the people in the country. Generally, it provides for comparing and projecting demographic data, social and economic characteristics, as well as household and housing conditions at all levels of the country’s administrative units and dimensions: national, regional, districts and localities. The data from the census is classified, tabulated and disseminated so that researchers, administrators, policy makers and development partners can use the information in formulating and implementing various multi-sectorial development programs at the national and community levels. Data on all key variables namely area, household, population, economic activity, literacy and education, fertility and child survival, housing conditions and sanitation are collected and available in the census data. The 2021 PHC in Ghana had an overarching goal of generating updated demographic, social and economic data, housing characteristics and dwelling conditions to support national development planning activities.

Geographic coverage

National Coverage , Region , District

Analysis unit

• Individuals
• Households
• Emigrants
• Absentee population
• Mortality
• Type of residence (households and non household)

Universe

All persons who spent census night (midnight of 27th June 2021) in Ghana

Kind of data

Census/enumeration data [cen]

Sampling procedure

This 10% sample data for the 2021 PHC is representative at the district/subdistrict level and also by the urban rural classification.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

GSS developed two categories of instruments for the 2021 PHC: the listing form and the enumeration instruments. The listing form was only one, while the enumeration instruments comprised six questionnaires, designated as PHC 1A, PHC 1B, PHC 1C, PHC 1D, PHC 1E and PHC 1F. The PHC 1A was the most comprehensive with the others being its subsets.

1. Listing Form: The listing form was developed to collect data on type of structures, level of completion, whether occupied or vacant and use(s) of the structures. It was also used to collect information about the availability, number and types of toilet facilities in the structures. It was also used to capture the number of households in a structure, number of persons in households and the sex of the persons residing in the households if occupied. Finally, the listing form was used to capture data on non-household populations such as the population in institutions, floating population and sex of the non-household populations.

2. PHC 1A: The PHC 1A questionnaire was used to collect data from all households in the country. Primarily, it was used to capture household members and visitors who spent the Census Night in the dwelling of the household, and their relationship with the head of the household. It was also used to collect data on homeless households. Members of the households who were absent were enumerated at the place where they had spent the Census Night. The questionnaire was also used to collect the following household information: emigration; socio-demographic characteristics (sex, age, place of birth and enumeration, survival status of parents, literacy and education; economic activities; difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

3. PHC 1B: The PHC 1B questionnaire was used to collect data from persons in stable institutions comprising boarding houses, hostels and prisons who were present on Census Night. Other information that was captured with this instrument are socio-demographic characteristics, literacy and education, economic activities, difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

4. PHC 1C: The PHC 1C questionnaire was used to collect data from persons in “unstable” institutions such as hospitals and prayer camps who were present at these places on Census Night. The instrument was used to capture only the socio-demographic characteristics of individuals.

5. PHC 1D: The PHC 1D questionnaire was used to collect data from the floating population. This constitutes persons who were found at airports, seaports, lorry stations and similar locations waiting for or embarking on long-distance travel, as well as outdoor sleepers on Census Night. The instrument captured the socio-demographic information of individuals.

6. PHC 1E: All persons who spent the Census Night at hotels, motels and guest houses were enumerated using the PHC 1E. The content of the questionnaire was similar to that of the PHC 1D.

7. PHC 1F: The PHC 1F questionnaire was administered to diplomats in the country.

Cleaning operations

The Census data editing was implemented at three levels: 1. data editing by enumerators and supervisors during data collection 2. data editing was done at the regional level by the regional data quality monitors during data collection 3. Final data editing was done at the national level using the batch edits in CSPro and STATA Data editing and cleaning was mainly digital.

Response rate

100 percent

Data appraisal

A post Enumeration Survey (PES) was conducted to assess the extent of coverage and content error.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from Georgia Department of Education to show public school enrollment and student characteristics, including gifted/special education/English learner status, absences/withdrawal, and Milestones assessment scores, for 2016, by census tract in the Atlanta region.

Attributes:

GEOID10 = 2010 Census tract identifier (combination of FIPS codes for state, county, and tract)

County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

Area_Name = 2010 Census tract number and county name

Total_Population_ACS_2016 = # Total population estimate, 2016 (American Community Survey)

Total_Population_ACS_MOE_2016 = # Total population estimate (Margin of Error), 2016 (American Community Survey)

Planning_Region = Planning region designation for ARC purposes

AcresLand = Land area within the tract (in acres)

AcresWater = Water area within the tract (in acres)

AcresTotal = Total area within the tract (in acres)

SqMi_Land = Land area within the tract (in square miles)

SqMi_Water = Water area within the tract (in square miles)

SqMi_Total = Total area within the tract (in square miles)

TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.

CountyName = County Name

TOT_STUDENTS_ENROLLED_SCHOOL_YR = Total count of students enrolled at any time during the school year

SUBSET_STUDENTS_GRADES_PK_5 = Subset of total students - any student in grades PK-5

SUBSET_STUDENTS_GRADES_6_8 = Subset of total students - any student in grades 6-8

SUBSET_STUDENTS_GRADES_9_12 = Subset of total students - any student in grades 9-12

PCT_GRADES_PK_5 = Percent in grades PK-5

PCT_GRADES_6_8 = Percent in grades 6-8

PCT_GRADES_9_12 = Percent in grades 9-12

STUDENT_SERVED_BY_SPECIAL_ED = Student served by special education program

PCT_SERVED_BY_SPECIAL_ED = Percent served by special ed program

STUDENT_SERVED_BY_GIFTED = Student served by Gifted program

PCT_SERVED_BY_GIFTED = Percent served by gifted program

STUDENT_IS_ENGLISH_LEARNER = Student is a member of the English Learner student group (EL=Y or EL=Monitored Status)

PCT_ENGLISH_LEARNER = Percent in English Learner Student group

CT_RETAINED_STUDTS = Retained Student Count

PCT_RETAINED_STUDTS = Percent of Retained Students

CT_HOMELESS_UNACCOMP_STUDTS = Count of Homeless Students (Marked either "Homeless" or "Unaccompanied Youth" in SR)

PCT_HOMELESS = Percent homeless

CT_STUDTS_PARENT_ACTV_MILITARY = Count of students with parent(s) in Active Military

PCT_STUDTS_PARENT_ACTV_MILITARY = Percent students with parents in Active Military

CT_MID_STUDENTS_WITHDRAW_HOME = Grade 6-8 students withdrawn during school year, reason "H" (Withdrawn to Homeschool)

PCT_MID_STUDENTS_WITHDRAW_HOME = Percent of Middle School students withdrawn for homeschool

CT_HS_STUDENTS_WITHDRAW_HOME = Grade 9-12 students withdrawn during school year, reason "H" (Withdrawn to Homeschool)

PCT_HS_STUDENTS_WITHDRAW_HOME = Percent of High School students withdrawn for homeschool

CT_MID_STUDENTS_WITHDRAW_DJJ = Grade 6-8 students withdrawn during school year, reason "4" (Withdrawn to DJJ)

PCT_MID_STUDENTS_WITHDRAW_DJJ = Percent of Middle School students withdrawn to Department of Juvenile Justice

CT_HS_STUDENTS_WITHDRAW_DJJ = Grade 9-12 students withdrawing during school year with reason "4" (Withdrawn to DJJ)

PCT_HS_STUDENTS_WITHDRAW_DJJ = Percent of High School students withdrawn to Department of Juvenile Justice

CT_STUDENTS_WITHDRAW_ANY = Students withdrawn, any reason, 1 mo. after beginning school yr., 1 mo. before end school yr.

PCT_STUDENTS_WITHDRAW_ANY = Percent withdrawn, any reason, 1 mo. after beginning school yr., 1 mo. before end school yr.

STUDENTS_ABSENT_0_5_days = Absence Bracket A Student Count - Students absent 0-5 days

PCT_STUDENTS_ABSENT_0_5_days = Percent students absent 0-5 days

STUDENTS_ABSENT_6_15_days = Absence Bracket B Student Count - Students absent 6-15 days

PCT_STUDENTS_ABSENT_6_15_days = Percent students absent 6-15 days

STUDENTS_ABSENT_16_MORE_DAYS = Absence Bracket C Student Count - Students absent 16 or More days

PCT_STUDTS_ABSENT_16_MORE_DAYS = Percent students absent more than 15 days

CT_STUDTS_REC_DISCIPLINE = Count of students receiving any discipline event records during school year

PCT_STUDTS_ABS_REC_DISCIPLINE = Percent students absent receiving any discipline event

CT_STUDTS_OSS_MORE_10_days = Students assigned to Out of School Suspension for more than 10 days during school year

PCT_STUDTS_OSS_MORE_10_days = Percent students assigned to Out of School Suspension for more than 10 days

CT_STUDTS_ISS_MORE_10_days = Students assigned to In School Suspension for more than 10 days during school year

PCT_STUDTS_ISS_MORE_10_days = Percent students assigned to In School Suspension for more than 10 days

CT_GRD3_MILES_EOG_ELA_PRO_DIS = Count of Grade 3 Milestones EOG ELA Test Takers Scoring PRO or DIS

PCT_GRD3_MILES_EOG_ELA_PRO_DIS = Percent of Grade 3 Milestones EOG ELA Test Takers Scoring PRO or DIS

CT_GRD5_MILES_EOG_ELA_PRO_DIS = Count of Grade 5 Milestones EOG ELA Test Takers Scoring PRO or DIS

PCT_GRD5_MILES_EOG_ELA_PRO_DIS = Percent of Grade 5 Milestones EOG ELA Test Takers Scoring PRO or DIS

CT_GRD8_MILES_EOG_ELA_PRO_DIS = Count of Grade 8 Milestones EOG ELA Test Takers Scoring PRO or DIS

PCT_GRD8_MILES_EOG_ELA_PRO_DIS = Percent of Grade 8 Milestones EOG ELA Test Takers Scoring PRO or DIS

CT_GRD3_MILES_EOG_MATH_PRO_DIS = Count of Grade 3 Milestones EOG Math Test Takers Scoring PRO or DIS

PCT_GRD3_MILES_EOG_MATH_PRO_DIS = Percent of Grade 3 Milestones EOG Math Test Takers Scoring PRO or DIS

CT_GRD5_MILES_EOG_MATH_PRO_DIS = Count of Grade 5 Milestones EOG Math Test Takers Scoring PRO or DIS

PCT_GRD5_MILES_EOG_MATH_PRO_DIS = Percent of Grade 5 Milestones EOG Math Test Takers Scoring PRO or DIS

CT_GRD8_MILES_EOG_MATH_PRO_DIS = Count of Grade 8 Milestones EOG Math Test Takers Scoring PRO or DIS

PCT_GRD8_MILES_EOG_Math_PRO_DIS = Percent of Grade 8 Milestones EOG Math Test Takers Scoring PRO or DIS

CT_MILES_EOC_ALGEBRA_PRO_or_DIS = Count of Milestones EOC Algebra Test Takers Scoring PRO or DIS

PCT_MILES_EOC_ALGEBRA_PRO_DIS = Percent of Milestones EOC Algebra Test Takers Scoring PRO or DIS

DENOM_TOT_GRD3_MILES_EOG_ELA = Denominator - Total Count of Grade 3 Milestones EOG ELA Test Takers

DENOM_TOT_GRD5_MILES_EOG_ELA = Denominator - Total Count of Grade 5 Milestones EOG ELA Test Takers

DENOM_TOT_GRD8_MILES_EOG_ELA = Denominator - Total Count of Grade 8 Milestones EOG ELA Test Takers

DENOM_TOT_GRD3_MILES_EOG_MATH = Denominator - Total Count of Grade 3 Milestones EOG Math Test Takers

DENOM_TOT_GRD5_MILES_EOG_MATH = Denominator - Total Count of Grade 5 Milestones EOG Math Test Takers

DENOM_TOT_GRD8_MILES_EOG_MATH = Denominator - Total Count of Grade 8 Milestones EOG Math Test Takers

DENOM_TOT_MILES_EOC_ALG_TAKERS = Denominator - Total Count of Milestones EOC Algebra Test Takers

last_edited_date = Last date the feature was edited by ARC

Source: Georgia Department of Education, Atlanta Regional Commission

Date: 2016

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

In 2023, the resident population of California was ***** million. This is a slight decrease from the previous year, with ***** million people in 2022. This makes it the most populous state in the U.S. Californian demographics Along with an increase in population, California’s gross domestic product (GDP) has also been increasing, from *** trillion U.S. dollars in 2000 to **** trillion U.S. dollars in 2023. In the same time period, the per-capita personal income has almost doubled, from ****** U.S. dollars in 2000 to ****** U.S. dollars in 2022. In 2023, the majority of California’s resident population was Hispanic or Latino, although the number of white residents followed as a close second, with Asian residents making up the third-largest demographic in the state. The dark side of the Golden State While California is one of the most well-known states in the U.S., is home to Silicon Valley, and one of the states where personal income has been increasing over the past 20 years, not everyone in California is so lucky: In 2023, the poverty rate in California was about ** percent, and the state had the fifth-highest rate of homelessness in the country during that same year, with an estimated ** homeless people per 10,000 of the population.

Nearly 11.3 thousand people in Russia were homeless, based on the population census data from 2021. The number of homeless residents decreased by 82 percent compared to 2010. The largest share of homeless people in the country lived in urban areas, at around 95 percent in 2021.