Geographic Coordinate System: GCS_WGS_1984 Datum: D_WGS_1984 Source: Ethiopian Road Authority (ERA)

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the largest cities (>50k habitants) in the country. This 1km resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

Ethiopia ET: Population in Largest City data was reported at 4,215,965.000 Person in 2017. This records an increase from the previous number of 4,039,927.000 Person for 2016. Ethiopia ET: Population in Largest City data is updated yearly, averaging 1,690,413.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 4,215,965.000 Person in 2017 and a record low of 519,177.000 Person in 1960. Ethiopia ET: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ethiopia – Table ET.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

The settlements dataset contains the location of cities, towns and villages in Ethiopia.

Populated places dataset for Ethiopia endorsed by the Inter-Cluster Information Management Working group (ICMWG) after cleaning and processing done by Information Technology Outreach Services (ITOS). Source: Multiple sources

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the largest cities (>50k habitants) in the country.

This 1km resolution raster dataset is part of FAO's Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

Data publication: 2021-02-01

Contact points:

Metadata Contact: Food and Agriculture Organization of the United Nations

Resource Contact: Maribel Elias

Maintainer: Maribel Elias

Data lineage:

Produced using GADM country border; the OpenStreetMap data for roads, railways, waterways, the HydroSHEDS 15' resolution GRID for the DEM, the JRC Big Data Analytics Platform for the urban areas, The Hand-in-Hand Initiative, 2020

Resource constraints:

The datasets are freely available for academic use and other non-commercial use. Redistribution or commercial use is not allowed without prior permission.

Online resources:

Accessibility: Travel time-cost to major cities (Ethiopia - ~ 1Km)

The dataset consists of the annual crop yield data for six major crops grown during the main Meher season by private peasant holdings across nine regions and one administrative city in Ethiopia from 1996 to 2022. features include crop type, year, region, area cultivated (in hectares), production (in kilograms), and yield (in kg/ha). The dataset was compiled from Ethiopian Statistical Agency annual reports and aims to provide well organized, accessible data for agricultural research and data analysis. Missing values are included as they appear in the original reports and Researchers are encouraged to manage these missing values in accordance with the needs of their study.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

This data set is collected from Addis Ababa Sub city police departments. The data set has been prepared from manual records of road traffic accident of the year 2017-20. All the sensitive information has been excluded during data encoding and finally it has 32 features and 12316 instances of the accident. Then it is preprocessed and for identification of major causes of the accident by analyzing it using different machine learning classification algorithms. RTA Dataset.csv is the dataset before preprocessing and cleaned.csv is the preprocessed dataset.

Shapefiles for Ethiopia's Administrative boundaries: Regions, Zones and Woredas

Accessibility to regional cities dataset is modeled as raster-based travel time/cost analysis, computed for the largest cities surrounding the country. The following cities are included: City - Population Addis Ababa, Ethiopia - 5 153 002 Asmara, Eritrea - 1 258 001 Sohag, Egypt - 979 800 Wau, South Sudan - 328 651 Abeche, Chad - 83 155 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

This dataset contains traffic accident records from Addis Ababa City, Ethiopia, spanning the years 2016 to 2022. The dataset includes 13,064 rows and 31 features related to various factors influencing road traffic accident severity. The target variable is categorized into three severity levels: slight, serious, and fatal injuries.The dataset aims to facilitate the analysis and prediction of road traffic accident severity using machine learning algorithms. It was initially collected by the Addis Ababa Police Department and contains a rich set of variables, including weather conditions, collision type, driver demographics, road conditions, and time of accident, among others. This comprehensive dataset serves as a foundation for developing predictive models for accident severity, which can be valuable for urban planning, traffic safety research, and policy development.Key features in the dataset include:Accident Severity (Target Variable): Categorical variable indicating the severity of the accident (slight, serious, fatal).Weather Conditions: Describes the weather at the time of the accident (e.g., clear, rainy, foggy).Collision Type: The type of collision (e.g., rear-end, side-impact).Driver Demographics: Features like driver age, driver sex, and experience that may affect accident outcomes.Location: Various aspects of the accident location, including junction type, road type, and alignment.Temporal Features: Time-related variables such as day of the week, time of day, and seasonal trends.Vehicle Information: Includes vehicle type, vehicle defect, vehicle movement, and the relationship between the vehicle owner and the driver.Casualty Information: Includes age, sex, and fitness of the casualty.

The raster dataset consists of a 1km score grid for coffee storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Coffee. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1)

Urban agriculture is an emerging field, the comprehensive understanding of which is best achieved through an interdisciplinary research approach. Utilising such a strategy, this research will specifically address urban food production and the management of associated health risks. Research objectives include: Identify health risks and health risk pathways; Quantify selected health risks (eg from toxic elements/pathogens); Assess potential health impacts on urban farmers, market workers and consumers; Develop health risk analysis tools (eg contaminant pathway mapping) and; Develop health risk mitigation tools (eg land zoning/crop selection strategies). Research will be constructed around the comparative analysis of three case studies in Addis Ababa, Ethiopia; Hyderabad, India and; Accra, Ghana. Qualitative and quantitative research methods will be combined to identify and map health risks and health risk pathways in urban food production across the three cities. Research tools will include semi-structured interviews; farm, market and consumer surveys; geographical information mapping; water, soil and crop sampling and testing and; health impact survey, assessment and mapping. Through this interdisciplinary approach to the research topics, this project has clear potential for reducing risk in urban food production, having critical relevance for international academics, policymakers, and producer and consumer communities alike.

BackgroundThe risk factors for tuberculosis (TB) disease development in children remained understudied, particularly in low-income countries like Ethiopia. The objective of this study was to identify determinants of TB disease development in general and in relation to BCG vaccination in children in central Ethiopia.MethodsWe employed a 1:1 age-matched case-control design to compare the characteristics of children who developed TB (cases) with those who did not (controls). Data were collected in healthcare facilities in Addis Ababa city, Adama, and Bishoftu towns between September 25, 2021, and June 24, 2022. Two hundred and fifty-six cases were drawn at random from a list of childhood TB patients entered into SPSS software, and 256 controls were selected sequentially at triage from the same healthcare facilities where the cases were treated. A bivariate conditional logistic regression analysis was performed first to select candidate variables with p-values less than or equal to 0.20 for the multivariable model. Finally, variables with a p-value less than 0.05 for a matched adjusted odds ratio (mORadj) were reported as independent determinants of TB disease development.ResultsThe mean age of the cases was nine years, while that of the controls was 10 years. Males comprised 126 cases (49.2%) and 119 controls (46.5%), with the remainder being females. Ninety-nine (38.7%) of the cases were not BCG-vaccinated, compared to 58 (22.7%) of the controls. Household TB contact was experienced by 43 (16.8%) of the cases and 10 (3.9%) of the controls. Twenty-two (8.6%) of the cases and six (2.3%) of the controls were exposed to a cigarette smoker in their household. Twenty-two (8.6%) of the cases and three (1.2%) of the controls were positive for HIV. Children who were not vaccinated with BCG at birth or within two weeks of birth had more than twice the odds (mORadj = 2.11, 95% CI = 1.28–3.48) of developing TB compared to those who were. Children who ever lived with a TB-sick family member (mORadj = 4.28, 95% CI = 1.95–9.39), smoking family members (mORadj = 3.15, 95% CI = 1.07–9.27), and HIV-infected children (mORadj = 8.71, 95% CI = 1.96–38.66) also had higher odds of developing TB disease than their counterparts.ConclusionsBeing BCG-unvaccinated, having household TB contact, having a smoker in the household, and being HIV-infected were found to be independent determinants of TB disease development among children.

This dataset accompanies the publication in City (Taylor & Francis) and contains data collected through ethnographic field research in Addis Ababa, Ethiopia. The research explores how self-organized, informal circular practices operate in the city. All data have been anonymized.

The study focused on three components:

1. Itinerant waste collectors – Shadowing and mapping their routes (GPX), interviews, Photo Voice, and field observations.
2. Minalesh Tera informal recycling center – Participatory mapping, interviews, and observations.
3. Urban resource flows – Simulations of how resources circulate through the city.

Contents of the Dataset

1. Shadowing Maps Waste Collectors (gpx)
2. Participatory Mapping Minalesh Tera (pdf)
3. Documentation Artisan Spaces in Minalesh Tera (sketches) (pdf)
4. Photo Voice Images (jpg)
5. Resource Journey Simulations (pdf)

IntroductionThe largest risk of child mortality occurs within the first week after birth. Early neonatal mortality remains a global public health concern, especially in sub-Saharan African countries. More than 75% of neonatal death occurs within the first seven days of birth, but there are limited prospective follow- up studies to determine time to death, incidence and predictors of death in Ethiopia particularly in the study area. The study aimed to determine incidence and predictors of early neonatal mortality among neonates admitted to the neonatal intensive care unit of Addis Ababa public hospitals, Ethiopia 2021.MethodsInstitutional prospective cohort study was conducted in four public hospitals found in Addis Ababa City, Ethiopia from June 7th, 2021 to July 13th, 2021. All early neonates consecutively admitted to the corresponding neonatal intensive care unit of selected hospitals were included in the study and followed until 7 days-old. Data were coded, cleaned, edited, and entered into Epi data version 3.1 and then exported to STATA software version 14.0 for analysis. The Kaplan Meier survival curve with log- rank test was used to compare survival time between groups. Moreover, both bi-variable and multivariable Cox proportional hazard regression model was used to identify the predictors of early neonatal mortality. All variables having P-value ≤0.2 in the bi-variable analysis model were further fitted to the multivariable model. The assumption of the model was checked graphically and using a global test. The goodness of fit of the model was performed using the Cox-Snell residual test and it was adequate.ResultsA total of 391 early neonates with their mothers were involved in this study. The incidence rate among admitted early neonates was 33.25 per 1000 neonate day’s observation [95% confidence interval (CI): 26.22, 42.17]. Being preterm birth [adjusted hazard ratio (AHR): 6.0 (95% CI 2.02, 17.50)], having low fifth minute Apgar score [AHR: 3.93 (95% CI; 1.5, 6.77)], low temperatures [AHR: 2.67 (95%CI; 1.41, 5.02)] and, resuscitating of early neonate [AHR: 2.80 (95% CI; 1.51,5.10)] were associated with increased hazard of early neonatal death. However, early neonatal crying at birth [AHR: 0.48 (95%CI; 0.26, 0.87)] was associated with reduced hazard of death.ConclusionsEarly neonatal mortality is high in Addis Ababa public Hospitals. Preterm birth, low five-minute Apgar score, hypothermia and crying at birth were found to be independent predictors of early neonatal death. Good care and attention to neonate with low Apgar scores, premature, and hypothermic neonates.

The raster dataset consists of a 1km score grid for cereal storage location, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The location score is achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Cereal. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + (”Dry ports accessibility” * 0.3) + ("Major Cities Accessibility" * 0.2) + ("Human Population Density" * 0.1)

BackgroundMaternal near-miss is a serious public health concern in impoverished countries such as Ethiopia. Despite its huge burden, the prognostic predictive model of maternal near-miss has received little attention in research in the Ethiopian context. As a result, this study aimed to build and validate (internally) a clinical prediction model of maternal near-miss in Bahir Dar City, Northwest Ethiopia, in 2024.MethodsA prospective follow-up study was conducted among 2110 randomly selected pregnant women in Bahir Dar city between May 1, 2023, and March 6, 2024. Pregnant women with gestational age less than 20 weeks were included in the cohort and followed up to 42 days after delivery. Data were extracted from antenatal care records and collected by an interview-administered questionnaire. The model was developed using the standard Cox regression model, and model fitness was checked using the Schoenfeld assumption test. After applying a stepwise elimination, a p-value of less than 0.15 was used to fit the reduced model. Both discrimination and calibration were used to assess the model’s performance. The model was internally validated through the bootstrapping method. The clinical usefulness of the model was checked using decision curve analysis. A nomogram was used for the model presentation.ResultsMaternal near-miss incidence density rate was 1.94 per 1,000 woman-weeks. Maternal age, residence, decision-making power, intention to pregnancy, time of antenatal initiation, genital mutilation, history of cesarean section, middle upper arm circumference, systolic blood pressure, hemoglobin, and history of obstetric morbidity were identified as important predictors to predict maternal near-miss. The model demonstrated good discriminatory performance with a C-index of 0.82(95%CI: 0.80–0.85), and good calibration with close alignment with 45 degrees. A simplified risk score of 40 maximum points was developed. The model was presented using a nomogram.ConclusionThe maternal near-miss incidence density rate was high in the present study. Socio-demographic and clinical factors were key variables for predicting maternal near-miss. The model has good discrimination and calibration. The researchers recommend external validation in different settings to assess the model’s generalizability before applying it to clinical settings.

Clinical-related factors of participants in a public health facility, Bahir Dar City, Northwest Ethiopia, 2024.

Sociodemographic data of participants.