Yearly statewide and by-Continuum of Care total counts of individuals receiving homeless response services by age group, race, gender, veteran status, and disability status.

This data comes from the Homelessness Data Integration System (HDIS), a statewide data warehouse which compiles and processes data from all 44 California Continuums of Care (CoC)—regional homelessness service coordination and planning bodies. Each CoC collects data about the people it serves through its programs, such as homelessness prevention services, street outreach services, permanent housing interventions and a range of other strategies aligned with California’s Housing First objectives.

The dataset uploaded reflects the 2024 HUD Data Standard Changes. Previously, Race and Ethnicity were separate files but are now combined.

Information updated as of 7/29/2025.

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.

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.

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.

BackgroundAddressing Citizen’s perspectives on homelessness is crucial for the design of effective and durable policy responses, and available research in Europe is not yet substantive. We aim to explore citizens’ opinions about homelessness and to explain the differences in attitudes within the general population of eight European countries: France, Ireland, Italy, the Netherlands, Poland, Portugal, Spain, and Sweden.MethodsA nationally representative telephone survey of European citizens was conducted in 2017. Three domains were investigated: Knowledge, Attitudes, and Practices about homelessness. Based on a multiple correspondence analysis (MCA), a generalized linear model for clustered and weighted samples was used to probe the associations between groups with opposing attitudes.ResultsResponse rates ranged from 30.4% to 33.5% (N = 5,295). Most respondents (57%) had poor knowledge about homelessness. Respondents who thought the government spent too much on homelessness, people who are homeless should be responsible for housing, people remain homeless by choice, or homelessness keeps capabilities/empowerment intact (regarding meals, family contact, and access to work) clustered together (negative attitudes, 30%). Respondents who were willing to pay taxes, welcomed a shelter, or acknowledged people who are homeless may lack some capabilities (i.e. agreed on discrimination in hiring) made another cluster (positive attitudes, 58%). Respondents living in semi-urban or urban areas (ORs 1.33 and 1.34) and those engaged in practices to support people who are homeless (ORs > 1.4; p

The dataset described testing, infection and complication rates of COVID-19 among people with a recent history of homelessness in Ontario

Homeless Shelter Capacity in Canada, bed and shelter counts by target population and geographical location for emergency shelters, transitional housing, and domestic violence shelters.

This dataset supports measure S.D.4.c of SD23. The Downtown Austin Community Court (DACC) was established to address quality of life and public order offenses occurring in the downtown Austin area utilizing a restorative justice court model. DACC’s priority population consists of individuals experiencing homelessness and the program’s main goal is to permanently stabilize individuals experiencing homelessness. To effectively serve these individuals, DACC created an Intensive Case Management (ICM) Program, which uses a client-centered and housing-focused approach. The ICM Program focuses on rehabilitating and stabilizing individuals using an evidenced-based model of wraparound interventions to help them achieve long-term stability. Because individuals participating in case management are literally homeless, case managers must actively seek their clients in the community through outreach activities and often times work on behalf of the client via collateral engagement with other social service and housing providers. This measure highlights case management activities accomplished via outreach and collateral engagement.

View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/65cb-wtrs

Data source: manually tracked internally on a monthly checkbox report Calculation: Numerator: number of clients served through outreach Denominator: total number of cases filed that are homeless this dataset on the portal covers an annual range based on the city's fiscal year.

Background: Homeless people are a socially excluded group whose health reflects exposures to intersecting social determinants of health. The aim of this study was to describe and compare the demographic composition, certain social determinants of health, and self-reported health among homeless people in Stockholm, Sweden, in 2006 and 2018.Methods: Analysis of data from face-to-face interviews with homeless people in Stockholm 2006 (n = 155) and 2018 (n = 148), based on a public health survey questionnaire adapted to the group, including the EQ-5D-3L instrument. The chi-squared test was employed to test for statistical significance between groups and the independent t-test for comparison of mean scores and values. Ordinary Least Squares (OLS) regression, with Robust Standard Errors (RSE) was performed on merged 2006 and 2018 data with mean observed EQ VAS score as outcome variable.Results: In 2018 more homeless people originated from countries outside Europe, had temporary social assistance than long-term social insurance, compared to in 2006. In 2018 more respondents reported lack of social support, exposure to violence, and refrained from seeking health care because of economic reasons. Daily smoking, binge drinking, and use of narcotic drugs was lower 2018 than 2006. In 2018 a higher proportion reported problems in the EQ-5D-3L dimensions, the mean TTO index value and the VAS index value was significantly lower than in 2006. In the regression analysis of merged data there was no significant difference between the years.Conclusions: Homeless people are an extremely disadvantaged group, have high rates of illness and disease and report poor health in all EQ-5D-3L dimensions. The EQ VAS score among the homeless people in 2018 is comparable to the score among persons aged 95–104 years in the general Swedish population 2017. The EQ-5D-3L instrument was easily administered to this group, its use allows comparison with larger population groups. Efforts are needed regarding housing, but also intensified collaboration by public authorities with responsibilities for homeless people's health and social welfare. Further studies should evaluate the impact of such efforts by health and social care services on the health and well-being of homeless people.

Objective: The homeless population experiences inequality in health compared with the general population, which may have widened during the COVID-19 pandemic. However, the impact of being homeless on the outcomes of COVID-19 is uncertain. This systematic review aimed to analyse the impact of experiencing homelessness on the clinical outcomes of COVID-19, including the effects on health inequalities.Methods: A review protocol was developed and registered in PROSPERO (PROSPERO registration 2022 CRD42022304941). Nine databases were searched in November 2022 to identify studies on homeless populations which contained primary research on the following outcomes of COVID-19: incidence, hospitalisation, mortality, long COVID, mental wellbeing, and evidence of inequalities. Included studies were summarised with narrative synthesis.Results: The searches yielded 8,233 initial hits; after screening, 41 studies were included. Overall, evidence showed that those in crowded living settings had a higher risk of COVID-19 infection compared to rough sleepers and the general population. The homeless population had higher rates of hospitalisation and mortality than the general population, lower vaccination rates, and suffered negative mental health impacts.Conclusion: This systematic review shows the homeless population is more susceptible to COVID-19 outcomes. Further research is needed to determine the actual impact of the pandemic on this population, and of interventions to mitigate overall risk, given the low certainty of findings from some of the low-quality evidence available. In addition, further research is required to ascertain the impact of long COVID on those experiencing homelessness, since the present review yielded no studies on this topic.

The Downtown Austin Community Court (DACC) was established to address quality of life and public order offenses occurring in the downtown Austin area utilizing a restorative justice court model. DACC’s priority population consists of individuals experiencing homelessness and the program’s main goal is to permanently stabilize individuals experiencing homelessness. To effectively serve these individuals, DACC created an Intensive Case Management (ICM) Program, which uses a client-centered and housing-focused approach. The ICM Program focuses on rehabilitating and stabilizing individuals using an evidenced-based model of wraparound interventions to help them achieve long-term stability. Because individuals participating in case management are literally homeless, case managers must actively seek their clients in the community through outreach activities and often times work on behalf of the client via collateral engagement with other social service and housing providers. This measure highlights case management activities accomplished via outreach and collateral engagement.

The Veterans' Employment and Training Service (VETS) tracks HVRP participant outcomes using data collected from grant recipients. VETS shares HVRP outcomes with the public. These data show the national level targets and outcomes for eleven (11) measures by Program Year (PY), including breakouts by sex, ethnicity, race, age, and grant population. The 11 measures are: Number of Participants Served Percentage of Total Participants Served Number of Exiters Percentage of Total Number of Exiters Number of Participants Co-Enrolled at American Job Centers (AJCs) Average Hourly Wage at Placement Placement Rate (exit-based) Placement Rate – Episodically Homeless (exit-based) Employment Rate 2nd Quarter After Exit Employment Rate 4th Quarter After Exit Median Earnings 2nd Quarter After Exit"

Percent distribution of homeless individuals by duration of homelessness, according to selected characteristics, Nipissing District, Ontario 2021.

Percent distribution of homeless individuals by barrier to housing, according to selected characteristics, Nipissing District, Ontario 2021.

Percent distribution of homeless individuals by sleeping location, according to selected characteristics, Nipissing District, Ontario 2021.

This dataset supports measure S.D.4.c of SD23.

The Downtown Austin Community Court (DACC) was established to address quality of life and public order offenses occurring in the downtown Austin area utilizing a restorative justice court model. DACC’s priority population consists of individuals experiencing homelessness and the program’s main goal is to permanently stabilize individuals experiencing homelessness. To effectively serve these individuals, DACC created an Intensive Case Management (ICM) Program, which uses a client-centered and housing-focused approach. The ICM Program focuses on rehabilitating and stabilizing individuals using an evidenced-based model of wraparound interventions to help them achieve long-term stability. Because individuals participating in case management are literally homeless, case managers must actively seek their clients in the community through outreach activities and often times work on behalf of the client via collateral engagement with other social service and housing providers. This measure highlights case management activities accomplished via outreach and collateral engagement.

View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/65cb-wtrs

Data source: manually tracked internally on a monthly checkbox report

Calculation: Numerator: number of clients served through outreach Denominator: total number of cases filed that are homeless

this dataset on the portal covers an annual range based on the city's fiscal year.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The index is constructed using socioeconomic and demographic, exposure, health, and housing indicators and is intended to serve as a planning tool for health and climate adaptation. Steps for calculating the index can be found in in the "An Assessment of San Francisco’s Vulnerability to Flooding & Extreme Storms" located at https://sfclimatehealth.org/wp-content/uploads/2018/12/FloodVulnerabilityReport_v5.pdf.pdfData Dictionary: (see attachment here also: https://data.sfgov.org/Health-and-Social-Services/San-Francisco-Flood-Health-Vulnerability/cne3-h93g)

Field Name Data Type Definition Notes (optional)

Census Blockgroup Text San Francisco Census Block Groups

Children Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014.

Chidlren_wNULLvalues Numeric Percentage of residents under 18 years old. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elderly Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014.

Elderly_wNULLvalues Numeric Percentage of residents aged 65 and older. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

NonWhite Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014.

NonWhite_wNULLvalues Numeric Percentage of residents that do not identify as white (not Hispanic or Latino). American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Poverty Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014.

Poverty_wNULLvalues Numeric Percentage of all individuals below 200% of the poverty level. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Education Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014.

Education_wNULLvalues Numeric Percent of individuals over 25 with at least a high school degree. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

English Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014.

English_wNULLvalues Numeric Percentage of households with no one age 14 and over who speaks English only or speaks English "very well". American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

Elevation Numeric Minimum elevation in feet. United States Geologic Survey 2011.

SeaLevelRise Numeric Percent of land area in the 100-year flood plain with 36-inches of sea level rise. San Francisco Sea Level Rise Committee, AECOM 77inch flood inundation layer, 2014.

Precipitation Numeric Percent of land area with over 6-inches of projected precipitation-related flood inundation during an 100-year storm. San Francisco Public Utilities Commission, AECOM, 2015.

Diabetes Numeric Age-adjusted hospitalization rate due to diabetes; adults 18+. California Office of Statewide Health Planning and Development, 2004-2015.

MentalHealth Numeric Age-adjusted hospitalization rate due to schizophrenia and other psychotic disorders. California Office of Statewide Health Planning and Development, 2004-2015.

Asthma Numeric Age-adjusted hospitalization rate due to asthma; adults 18+. California Office of Statewide Health Planning and Development, 2004 - 2015.

Disability Numeric Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014.

Disability_wNULLvalues

Percentage of total civilian noninstitutionalized population with a disability. American Community Survey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

HousingQuality Numeric Annual housing violations, per 1000 residents. San Francisco Department of Public Health, San Francisco Department of Building Inspections, San Francisco Fire Department, 2010 - 2012.

Homeless Numeric Homeless population, per 1000 residents. San Francisco Homeless Count 2015.

LivAlone Numeric Households with a householder living alone. American Community Surevey 2009 - 2014.

LivAlone_wNULLvalues Numeric Households with a householder living alone. American Community Surevey 2009 - 2014. Because the American Community Survey uses survey estimates, all data is attached to a margin of error. When the coefficient of variation is over .3, the SFDPH considers this data unstable and gives it a NULL value. However, because principal component analysis and the final development of the flood health index could not use NULL values, SFDPH used this unstable data for these limited purposes. For the purpose of transparency, SFDPH has included both datasets with NULL values and without NULL values.

FloodHealthIndex Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood Vulnerability Assessment Methodology Section.

FloodHealthIndex_Quintiles Numeric Comparative ranking of flood health vulnerability, by block group. The Flood Health Index weights the six socioeconomic and demographic indicators (Children, Elderly, NonWhite, Poverty, Education, English) as 20% of the final score, the three exposure indicators (Sea Level Rise, Precipitation, Elevation) as 40% of the final score, the four health indicators (Diabetes, MentalHealth, Asthma, Disability) as 20% of the final score, and the three housing indicators (HousingQuality, Homeless, LivAlone) as 20% of the final score. For methodology used to develop the final Flood Health Index, please read the San Francisco Flood

Objectives: Homeless people lack a secure, stable place to live, and experience higher rates of serious illness than the housed population. Studies, mainly from the US, have reported increased use of unscheduled health care by homeless individuals. We compared the use of unscheduled ED and inpatient care between housed and homeless hospital patients in a high-income European setting. Setting: A large university teaching hospital serving the south inner city in Dublin, Ireland. Patient data is collected on an electronic patient record within the hospital. Participants: We carried out an observational cross-sectional study using data on all ED visits (n=47,174) and all unscheduled admissions under the general medical take (n=7,031) in 2015. Primary and Secondary Outcome Measures: The address field of the hospital’s electronic patient record was used to identify patients living in emergency accommodation or rough sleeping (hereafter referred to as homeless). Data on demographic details, length of stay and diagnoses was extracted. Results: In comparison to housed individuals in the hospital catchment area, homeless individuals had higher rates of ED attendance (0.16 attendances per person/annum vs 3.0 attendances per person/annum respectively) and inpatient bed days (0.3 bed days per person/annum vs 4.4 bed days per person/annum. The rate of leaving ED before assessment was higher in homeless individuals (40% of ED attendances vs 15% of ED attendances in housed individuals). The mean age of homeless medical inpatients was 44.19 (95% CI 42.98-45.40), whereas that of housed patients was 61.20 (95% CI 60.72-61.68). Homeless patients were more likely to terminate an inpatient admission against medical advice (15% of admissions vs 2% of admissions in homeless individuals). Conclusion: Homeless patients represent a significant proportion of ED attendees and medical inpatients. In contrast to housed patients, the bulk of usage of unscheduled care by homeless people occurs in individuals younger than 65.

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.