Historical dataset of population level and growth rate for the Addis Ababa, Ethiopia metro area from 1950 to 2025.

Geographic coverage

National coverage

Analysis unit

Household Person Housing unit

Universe

The census has counted people on dejure and defacto basis. The dejure population comprises all the persons who belong to a given area at a given time by virtue of usual residence, while under defacto approach people were counted as the residents of the place where they found. In the census, a person is said to be a usual resident of a household (and hence an area) if he/she has been residing in the household continuously for at least six months before the census day or intends to reside in the household for six months or longer. Thus, visitors are not included with the usual (dejure) population. Homeless persons were enumerated in the place where they spent the night on the enumeration day. The 2007 census counted foreign nationals who were residing in the city administration. On the other hand all Ethiopians living abroad were not counted.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

Two type sof questionnaires were used to collect census data: i) Short questionnaire ii) Long questionnaire

Unlike the previous censuses, the contents of the short and long questionnaires were similar both for the urban and rural areas as well as for the entire city. But the short and the long questionnaires differ by the number of variables they contained. That is, the short questionnaire was used to collect basic data on population characteristics, such as population size, sex, age, language, ethnic group, religion, orphanhood and disability. Whereas the long questionnaire includes information on marital status, education, economic activity, migration, fertility, mortality, as well as housing stocks and conditions in addition to those questions contained in a short questionnaire.

The metropolitan area of Lagos in Nigeria counted over ********** middle-class people as of 2018. This was the highest number in Africa. Addis Ababa in Ethiopia followed with *********** individuals belonging to the middle class. The middle-class population included people who had a disposable income of over ** percent of their salary, were employed, had a business activity, or were in education, and had at least a secondary school degree.

Clinical and pathological characteristics of the study population (subgroup of AACCR*) compared to the AACCR cohort.

Patients characteristics and treatment received in the study cohort.

Abstract

Introduction Dabat Health and Demographic Surveillance System (HDSS), also called the Dabat Research Center (DRC), was established at Dabat District in 1996 after conducting initial census. Later re-census was done in 2008. The surveillance is run by the College of Medicine and Health Sciences which is one of the colleges/faculties of the University of Gondar. Dabat district is one of the 21 districts in North Gondar Administrative Zone of Amhara Region in Ethiopia. According to the report published by the Central Statistical Agency in 2007, the district has an estimated total population of 145,458 living in 27 rural and 3 urban Kebeles (sub-districts). The altitude of the district ranges from about 1000 meters to over 2500 meters above sea level. The district population largely depends on subsistence agriculture economy. There are two health centers, three health stations, and twenty-nine health posts providing health services for the community. An all-weather road runs from Gondar town through Dabat to some towns of Tigray. Dabat town, the capital of Dabat District, is located approximately 821 km northwest of Addis Ababa and 75 kms north of Gondar town. The surveillance is funded by Centers for Disease Control and Prevention (CDC) through Ethiopian Public Health Association.

Objectives Dabat HDSS/ Dabat Research Centre was established to generate longitudinal data on health and population at district level and provide a study base and sampling frame for community-based research.

Methods Dabat district was initially selected purposively as a surveillance site for its unique three climatic conditions, namely Dega (high land and cold), Woina dega (mid land and temperate) and Kolla (low land and hot). The choice was made with the assumption that there would be differences in morbidity and mortality in the different climatic areas. Accordingly, seven kebeles from Dega, one kebele from Woina dega, and two kebeles from Kolla were selected randomly after stratification of the kebeles by climatic zone.

After the re-census, update has been done regularly every 6 months. During each round, data has been collected using a semi-structured questionnaire which included information related to birth and other pregnancy outcomes, death, migration, and marital status change. Interviews are administered to the heads of the household but in the absence of the head, the next elder family member is interviewed. This is only done after repeated trial of getting the head. While the regular update round is every six months, deaths that occur in the surveillance site are reported immediately to the data collectors by the local guides. After the mourning period, usually 45 days, the trained data collectors administer Verbal Autopsy (VA) questionnaire to the close relative of the deceased to get information on the possible cause(s) of death. Three VA questionnaires are prepared for the age groups 0-28 days, 29 days to 15 years, and greater than 15 years. To assign cause(s) of death, the VA data collected by data collectors is given to physicians who have got training on VA. These physicians independently assign causes of death using the standard International Classification of Diseases (ICD-10).

Geographic coverage

Dabat Health and Demographic Surveillance System (HDSS) included seven rural kebeles (sub districts) and three urban kebeles in Dabat district which is located 75 km North of Gondar town in Ethiopia. There are highlands, midlands and few low land households in the HDSS site.

Analysis unit

Individual

Universe

All individuals residing in Dabat HDSS site.

Kind of data

Event history data

Frequency of data collection

Two rounds per year

Mode of data collection

Face-to-face [f2f]

Research instrument

All questionnaires are prepared in Amharic language. The surveillance questionnaires are related to birth and other pregnancy outcomes, death, and migration.

Cleaning operations

The filled questionnaire is checked by filled supervisors, document clerk, data entry clerks for missings and other violations. In addition, DRC Software, a software developed from Microsoft Access and Visual Basic, checks violations against set of rules for data quality during data entry.

Response rate

100% response rate

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric  Illegal   Lega  Total  Metric RunDate 
ET051 MicroDataCleaned Starts  0  59082  0  0.0 2014-06-27 19:33 
ET051 MicroDataCleaned Transitions 0  129938 129938 0.0 2014-06-27 19:33 
ET051 MicroDataCleaned Ends 0  59082  0  0.0 2014-06-27 19:33

Abstract

The 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) is a nationwide survey with a nationally representative sample of 9,150 selected households. All women age 15-49 who were usual members of the selected households and those who spent the night before the survey in the selected households were eligible to be interviewed in the survey. In the selected households, all children under age 5 were eligible for height and weight measurements. The survey was designed to produce reliable estimates of key indicators at the national level as well as for urban and rural areas and each of the 11 regions in Ethiopia.

The primary objective of the 2019 EMDHS is to provide up-to-date estimates of key demographic and health indicators. Specifically, the main objectives of the survey are: ▪ To collect high-quality data on contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; child nutrition; and other health issues relevant to achievement of the Sustainable Development Goals (SDGs) ▪ To collect information on health-related matters such as breastfeeding, maternal and child care (antenatal, delivery, and postnatal), children’s immunizations, and childhood diseases ▪ To assess the nutritional status of children under age 5 by measuring weight and height

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Health facility

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019 EMDHS is a frame of all census enumeration areas (EAs) created for the 2019 Ethiopia Population and Housing Census (EPHC) and conducted by the Central Statistical Agency (CSA). The census frame is a complete list of the 149,093 EAs created for the 2019 EPHC. An EA is a geographic area covering an average of 131 households. The sampling frame contains information about EA location, type of residence (urban or rural), and estimated number of residential households.

Administratively, Ethiopia is divided into nine geographical regions and two administrative cities. The sample for the 2019 EMDHS was designed to provide estimates of key indicators for the country as a whole, for urban and rural areas separately, and for each of the nine regions and the two administrative cities.

The 2019 EMDHS sample was stratified and selected in two stages. Each region was stratified into urban and rural areas, yielding 21 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before sample selection, according to administrative units in different levels, and by using a probability proportional to size selection at the first stage of sampling.

To ensure that survey precision was comparable across regions, sample allocation was done through an equal allocation wherein 25 EAs were selected from eight regions. However, 35 EAs were selected from each of the three larger regions: Amhara, Oromia, and the Southern Nations, Nationalities, and Peoples’ Region (SNNPR).

In the first stage, a total of 305 EAs (93 in urban areas and 212 in rural areas) were selected with probability proportional to EA size (based on the 2019 EPHC frame) and with independent selection in each sampling stratum. A household listing operation was carried out in all selected EAs from January through April 2019. The resulting lists of households served as a sampling frame for the selection of households in the second stage. Some of the selected EAs for the 2019 EMDHS were large, with more than 300 households. To minimise the task of household listing, each large EA selected for the 2019 EMDHS was segmented. Only one segment was selected for the survey, with probability proportional to segment size. Household listing was conducted only in the selected segment; that is, a 2019 EMDHS cluster is either an EA or a segment of an EA.

In the second stage of selection, a fixed number of 30 households per cluster were selected with an equal probability systematic selection from the newly created household listing. All women age 15-49 who were either permanent residents of the selected households or visitors who slept in the household the night before the survey were eligible to be interviewed. In all selected households, height and weight measurements were collected from children age 0-59 months, and women age 15-49 were interviewed using the Woman’s Questionnaire.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019 EMDHS: (1) the Household Questionnaire, (2) the Woman’s Questionnaire, (3) the Anthropometry Questionnaire, (4) the Health Facility Questionnaire, and (5) the Fieldworker’s Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to Ethiopia. They were shortened substantially to collect data on indicators of particular relevance to Ethiopia and donors to child health programmes.

Cleaning operations

All electronic data files were transferred via the secure internet file streaming system (IFSS) to the EPHI central office in Addis Ababa, where they were stored on a password-protected computer. The data processing operation included secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by EPHI staff members and an ICF consultant who took part in the main fieldwork training. They were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro System software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing, double data entry from both the anthropometry and health facility questionnaires, and data processing were initiated in April 2019 and completed in July 2019.

Response rate

A total of 9,150 households were selected for the sample, of which 8,794 were occupied. Of the occupied households, 8,663 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 9,012 eligible women were identified for individual interviews; interviews were completed with 8,885 women, yielding a response rate of 99%. Overall, there was little variation in response rates according to residence; however, rates were slightly higher in rural than in urban areas.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019 Ethiopia Mini Demographic and Health Survey (EMDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019 EMDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2019 EMDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution

- Age distribution of eligible and interviewed women

These raster files show the land cover classification around Harare in 2006 and 2010. The classification results were based on Spot 5 imagery. Land cover classes in the attribute table are as follows:

Class 1 - Regular Residential (small planned buildings)
Class 2- Regular Residential (small unplanned buildings)
Class 3 - Commercial/Industrial (large buildings)
Class 4 - Natural (Vegetation/Soil/non built-up

This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost.

When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394

This raster dataset contains land cover classification in Brazzaville in 2005 and 2010 derived from SPOT5 imagery.

Land cover classes in the attribute table are as follows:

Class 1 - Regular Residential (small planned buildings)
Class 2- Regular Residential (small unplanned buildings)
Class 3 - Commercial/Industrial (large buildings)
Class 4 - Natural (Vegetation/Soil/non built-up

This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost.

When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394

These raster layers are land cover classifications of Freetown in Sierra Leone. They are derived from SPOT5 imagery captured in 2005, 2006, and 2015

This dataset is part of a paper which illustrates how the capabilities of GIS and satellite imagery can be harnessed to explore and better understand the urban form of several large African cities (Addis Ababa, Nairobi, Kigali, Dar es Salaam, and Dakar). To allow for comparability across very diverse cities, this work looks at the above mentioned cities through the lens of several spatial indicators and relies heavily on data derived from satellite imagery. First, it focuses on understanding the distribution of population across the city, and more specifically how the variations in population density could be linked to transportation. Second, it takes a closer look at the land cover in each city using a semi-automated texture based land cover classification that identifies neighborhoods that appear more regular or irregularly planned. Lastly, for the higher resolution images, this work studies the changes in the land cover classes as one moves from the city core to the periphery. This work also explored the classification of slightly coarser resolution imagery which allowed analysis of a broader number of cities, sixteen, provided the lower cost.

Class 1 - Regular Residential (small planned buildings)
Class 2- Regular Residential (small unplanned buildings)
Class 3 - Commercial/Industrial (large buildings)
Class 4 - Natural (Vegetation/Soil/non built-up)

When using this dataset keep in mind: Accuracy is higher in closer to the City center, and the distinction between class 1 and class 2 has not been validated, so use with caution. To learn more about the methodology please refer to https://ssrn.com/abstract=2883394