The Gross Domestic Product (GDP) in South Africa was worth 400.26 billion US dollars in 2024, according to official data from the World Bank. The GDP value of South Africa represents 0.38 percent of the world economy. This dataset provides the latest reported value for - South Africa GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The Gross Domestic Product (GDP) in South Africa expanded 0.10 percent in the first quarter of 2025 over the previous quarter. This dataset provides - South Africa GDP Growth Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Access one of the most comprehensive African economic datasets currently on the market, helping investors to spot trends early and stay informed.

This dataset is about countries per year in Southern Africa. It has 320 rows. It features 4 columns: country, GDP, and tax revenue.

This Swahili News Classification Dataset offers critical insights into media streams across East Africa, allowing for tailored insights related to racial tensions and social shifts. By utilizing the columns of text, label and content, this dataset allows researchers and data scientists to track classified news content from different countries in the region. From political unrest to gender-based violence, this dataset offers a comprehensive portrait of the various news stories from East African nations with practical applications for understanding how culture shapes press reporting and how media outlets portray world events. Alongside direct text information about individual stories, it is important that we study classifications like category and label in order to draw important conclusions about our society; by addressing these research questions with precise categorizations at hand we can ensure alignment between collected data points while also recognizing the unique nuances that characterize each country's media stream. This comprehensive dataset is essential for any project related to understanding communication processes between societies or tracking information flows within an interconnected global system

More Datasets For more datasets, click here.

Featured Notebooks 🚨 Your notebook can be here! 🚨! How to use the dataset This dataset is perfect for anyone looking to build a machine learning model to classify news content across East Africa. With this dataset, you can create a classifier that can automatically identify and categorize news stories into topics such as politics, economics, health, sports, environment and entertainment. This dataset contains labeled text data for training a model to learn how to classify the content of news articles written in Swahili.

Step 1: Understand the Dataset The first step towards building your classifier is getting familiar with the dataset provided. The list below outlines each column in the dataset:

text: The text of the news article

label: The category or topic assigned to the article

content: The text content of the news article

category: The category or topic assigned to the article

This dataset contains all you need for creating your classification model— pre-labeled articles with topics assigned by human annotators. Additionally, there are no date values associated with any of these columns listed. All articles have been labeled already so we won’t need those when creating our classifier!

We also need information about what languages are used in this context– good thing we’re working on classifying Swahili texts! After understanding more about which language these texts use we can move on towards selecting an appropriate algorithm for our task at hand – i.e., applying supervised machine learning algorithms that leverage both labeled and unlabeled data sets within this circumstances such as Language Modeling and Text Classification models like Naive Bayes Classifiers (NBCs), Maximum Entropy (MaxEnt) models among other traditional ML Models too but they most probably won’t be up enough robustness & accuracy merely when predicting unseen texts correctly; deep learning techniques often known as multi-layer perceptron (MLPs) may boost out best reporting performance results as desired from expected predictions from our trained/tested set yet since it sounds kinda costly computation complexity wise regarding its many layers involved nature than just classic linear sequence network ones — something could easily cover most cases am sure– however this tutorial does not focus precisely upon such topics since its part will take us way beyond current bounds so just keep moving along! ^^

Step 2 Preprocess Text Data Once you understand what each column represents we can start preparing our data by preprocessing it so that it is ready to be used by any algorithm chosen

Research Ideas Predicting trend topics of news coverage across East Africa by identifying news categories with the highest frequency of occurrences over given time periods. Identifying and flagging potential bias in news coverage across East Africa by analyzing the prevalence of certain labels or topics to discover potential trends in reporting style. Developing a predictive model to determine which topic or category will have higher visibility based on the amount of related content that is published in each region around East Africa

Columns File: train_v0.2.csv

Column name Description text The full article content of each news item. (String) label Labels that define what subject matter each article covers. (String) File: train.csv

Column name Description content The full article content of each news item. (Text) category Labels that define what subject matter each article covers. (Categorical)

License

CC0

Original Data Source: East African News Classification

The Gross Domestic Product per capita in South Africa was last recorded at 5708.96 US dollars in 2024. The GDP per Capita in South Africa is equivalent to 45 percent of the world's average. This dataset provides the latest reported value for - South Africa GDP per capita - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This dataset is now superseded by the updated Africa Electricity Transmission and Distribution Grid Map (2017). Please use that if looking for the most up to date data. The Africa Infrastructure Country Diagnostic (AICD) was an unprecedented knowledge program on Africa’s infrastructure that grew out of the pledge by the G8 Summit of 2005 at Gleneagles to substantially increase ODA assistance to Africa, particularly to the infrastructure sector, and the subsequent formation of the Infrastructure Consortium for Africa (ICA). The AICD study was founded on the recognition that sub-Saharan Africa (SSA) suffers from a very weak infrastructural base, and that this is a key factor in the SSA region failing to realize its full potential for economic growth, international trade, and poverty reduction. The study broke new ground, with primary data collection efforts covering network service infrastructures (ICT, power, water & sanitation, road transport, rail transport, sea transport, and air transport) from 2001 to 2006 in 24 selected African countries AICD has been implemented by the World Bank on behalf of a steering committee representing the African Union, the New Partnership for Africa’s Development (NEPAD), Africa’s regional economic communities, the African Development Bank, and major infrastructure donors.

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

The Gross Domestic Product (GDP) in Nigeria was worth 187.76 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Nigeria represents 0.18 percent of the world economy. This dataset provides the latest reported value for - Nigeria GDP - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Money Supply M0 in South Africa increased to 507346 ZAR Million in May from 494191 ZAR Million in April of 2025. This dataset provides the latest reported value for - South Africa Money Supply M0 - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

SAFI (Studying African Farmer-Led Irrigation) is a currently running project which is looking at farming and irrigation methods. This is survey data relating to households and agriculture in Tanzania and Mozambique. The survey data was collected through interviews conducted between November 2016 and June 2017. The survey covered such things as; household features (e.g. construction materials used, number of household members), agricultural practices (e.g. water usage), assets (e.g. number and types of livestock) and details about the household members.This is a teaching version of the collected data, it is not the full dataset. The survey is split into several sections:A – General questions about when and where the survey was conducted.B - Information about the household and how long they have been living in the areaC – Details about the accommodation and other buildings on the farmD – Details about the different plots of land they grow crops onE – Details about how they irrigate the land and availability of waterF – Financial details including assets owned and sources of incomeG – Details of Financial hardshipsX – Information collected directly from the smartphone (GPS) or automatically included in the form (instanceID)key_id Added to provide a unique Id for each observation. (The InstanceID field does this as well but it is not as convenient to use)A01_interview_date, Date of InterviewA03_quest_no, Questionnaire numberA04_start, Timestamp of start of InterviewA05_end, Timestamp of end of InterviewA06_province, Province nameA07_district, District nameA08_ward, Ward nameA09_village, Village nameA11_years_farm, Number of years the household have been farming in this areaA12_agr_assoc, Does the head of the household belong to an agricultural association_note2 Possible form comment relating to the sectionB_no_membrs, How many members of the household?_members_count Internal count of membersB11_remittance_money, Is there any financial assistance from family members not living on the farmB16_years_liv, How many years have you been living in this village or neighbouring village?B17_parents_liv, Did your parents live in this village or neighbouring village?B18_sp_parents_liv, Did your spouse's parents live in this village or neighbouring village?B19_grand_liv, Did your grandparents live in this village or neighbouring village?B20_sp_grand_liv, Did your spouse's grandparents live in this village or neighbouring village?C01_respondent_roof_type, What type of roof does their house have?C02_respondent_wall_type, What type of walls does their house have (from list)C02_respondent_wall_type_other, What type of walls does their house have (not on list)C03_respondent_floor_type, What type of floor does their house have C04_window_type, Does the house have glass in at least one window?C05_buildings_in_compound, How many buildings are in the compound? Do not include stores, toilets or temporary structures.C06_rooms, How many rooms in the main house are used for sleeping?C07_other_buildings, Does the DU own any other buildings other than those on this plotD_no_plots, How many plots were cultivated in the last 12 months?D_plots_count, Internal count of plotsE01_water_use, Do you bring water to your fields, stop water leaving your fields or drain water out of any of your fields?E_no_group_count, How many plots are irrigated?E_yes_group_count, How many plots are not irrigated?E17_no_enough_water, Are there months when you cannot get enough water for your crops? Indicate which months.E18_months_no_water, Please select the monthsE19_period_use, For how long have you been using these methods of watering crops? (years)E20_exper_other, Do you have experience of such methods on other farms?E21_other_meth, Have you used other methods before?E22_res_change, Why did you change the way of watering your crops?E23_memb_assoc, Are you a member of an irrigation association?E24_resp_assoc, Do you have responsibilities in that association?E25_fees_water, Do you pay fees to use water?E26_affect_conflicts, Have you been affected by conflicts with other irrigators in the area ?_note Form comment for sectionF04_need_money, If you started or changed the way you water your crops recently, did you need any money for it?F05_money_source, Where did the money came from? (list)F05_money_source_other, Where did the money came from? (not on list)F06_crops_contr, Considering fields where you have applied water, how much do those crops contribute to your overall income?F08_emply_lab, In the most recent cultivation season, did you employ day labourers on fields?F09_du_labour, In the most recent cultivation season, did anyone in the household undertake day labour work on other farm?F10_liv_owned, What types of livestock do you own? (list)F10_liv_owned_other, What types of livestock do you own? (not on list)F_liv_count, Livestock countF12_poultry, Own poultry?F13_du_look_aftr_cows, At the present time, does the household look after cows for someone else in return for milk or money?F14_items_owned, Which of the following items are owned by the household? (list)F14_items_owned_other, Which of the following items are owned by the household? (not on list)G01_no_meals, How many meals do people in your household normally eat in a day?G02_months_lack_food, Indicate which months, In the last 12 months have you faced a situation when you did not have enough food to feed the household?G03_no_food_mitigation, When you have faced such a situation what do you do?gps:Latitude, Location latitude (provided by smartphone)gps:Longitude, Location Longitude (provided by smartphone)gps:Altitude, Location Altitude (provided by smartphone)gps:Accuracy, Location accuracy (provided by smartphone)instanceID, Unique identifier for the form data submission

The number of Youtube users in Africa was forecast to continuously increase between 2024 and 2029 by in total 0.03 million users (+3.95 percent). The Youtube user base is estimated to amount to 0.79 million users in 2029. User figures, shown here regarding the platform youtube, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Youtube users in countries like Worldwide and the Americas.

The number of Twitter users in Africa was forecast to continuously increase between 2024 and 2028 by in total 28.1 million users (+100.75 percent). After the ninth consecutive increasing year, the Twitter user base is estimated to reach 55.96 million users and therefore a new peak in 2028. Notably, the number of Twitter users of was continuously increasing over the past years.User figures, shown here regarding the platform twitter, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Twitter users in countries like Australia & Oceania and North America.

Abstract

The Afrobarometer project assesses attitudes and public opinion on democracy, markets, and civil society in several sub-Saharan African.This dataset was compiled from the studies in Round 2 of the Afrobarometer, conducted from 2002-2004 in 16 countries, including Botswana, Cape Verde, Ghana, Kenya, Lesotho, Malawi, Mali, Mozambique, Namibia, Nigeria, Senegal, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe

Geographic coverage

The Round 2 Afrobarometer surveys have national coverage for the following countries: Botswana, Ghana, Kenya, Lesotho, Malawi, Mali, Mozambique, Namibia, Nigeria, Republic of Cabo Verde, Senegal, South Africa, Tanzania, Uganda, Zambia, Zimbabwe.

Analysis unit

Individuals

Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Kind of data

Sample survey data [ssd]

Sampling procedure

Afrobarometer uses national probability samples designed to meet the following criteria. Samples are designed to generate a sample that is a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of being selected for an interview. They achieve this by:

• using random selection methods at every stage of sampling; • sampling at all stages with probability proportionate to population size wherever possible to ensure that larger (i.e., more populated) geographic units have a proportionally greater probability of being chosen into the sample.

The sampling universe normally includes all citizens age 18 and older. As a standard practice, we exclude people living in institutionalized settings, such as students in dormitories, patients in hospitals, and persons in prisons or nursing homes. Occasionally, we must also exclude people living in areas determined to be inaccessible due to conflict or insecurity. Any such exclusion is noted in the technical information report (TIR) that accompanies each data set.

Sample size and design Samples usually include either 1,200 or 2,400 cases. A randomly selected sample of n=1200 cases allows inferences to national adult populations with a margin of sampling error of no more than +/-2.8% with a confidence level of 95 percent. With a sample size of n=2400, the margin of error decreases to +/-2.0% at 95 percent confidence level.

The sample design is a clustered, stratified, multi-stage, area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location.

Area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analysed. Any oversamples is noted in the TIR.

Sample stages Samples are drawn in either four or five stages:

Stage 1: In rural areas only, the first stage is to draw secondary sampling units (SSUs). SSUs are not used in urban areas, and in some countries they are not used in rural areas. See the TIR that accompanies each data set for specific details on the sample in any given country. Stage 2: We randomly select primary sampling units (PSU). Stage 3: We then randomly select sampling start points. Stage 4: Interviewers then randomly select households. Stage 5: Within the household, the interviewer randomly selects an individual respondent. Each interviewer alternates in each household between interviewing a man and interviewing a woman to ensure gender balance in the sample.

To keep the costs and logistics of fieldwork within manageable limits, eight interviews are clustered within each selected PSU.

Data weights For some national surveys, data are weighted to correct for over or under-sampling or for household size. "Withinwt" should be turned on for all national -level descriptive statistics in countries that contain this weighting variable. It is included as the last variable in the data set, with details described in the codebook. For merged data sets, "Combinwt" should be turned on for cross-national comparisons of descriptive statistics. Note: this weighting variable standardizes each national sample as if it were equal in size.

Further information on sampling protocols, including full details of the methodologies used for each stage of sample selection, can be found at https://afrobarometer.org/surveys-and-methods/sampling-principles

Mode of data collection

Face-to-face [f2f]

Research instrument

Certain questions in the questionnaires for the Afrobarometer 2 survey addressed country-specific issues, but many of the same questions were asked across surveys. Citizens of the 16 countries were asked questions about their economic and social situations, and their opinions were elicited on recent political and economic changes within their country.

The Africa Population Distribution Database provides decadal population density data for African administrative units for the period 1960-1990. The databsae was prepared for the United Nations Environment Programme / Global Resource Information Database (UNEP/GRID) project as part of an ongoing effort to improve global, spatially referenced demographic data holdings. The database is useful for a variety of applications including strategic-level agricultural research and applications in the analysis of the human dimensions of global change.

This documentation describes the third version of a database of administrative units and associated population density data for Africa. The first version was compiled for UNEP's Global Desertification Atlas (UNEP, 1997; Deichmann and Eklundh, 1991), while the second version represented an update and expansion of this first product (Deichmann, 1994; WRI, 1995). The current work is also related to National Center for Geographic Information and Analysis (NCGIA) activities to produce a global database of subnational population estimates (Tobler et al., 1995), and an improved database for the Asian continent (Deichmann, 1996). The new version for Africa provides considerably more detail: more than 4700 administrative units, compared to about 800 in the first and 2200 in the second version. In addition, for each of these units a population estimate was compiled for 1960, 70, 80 and 90 which provides an indication of past population dynamics in Africa. Forthcoming are population count data files as download options.

African population density data were compiled from a large number of heterogeneous sources, including official government censuses and estimates/projections derived from yearbooks, gazetteers, area handbooks, and other country studies. The political boundaries template (PONET) of the Digital Chart of the World (DCW) was used delineate national boundaries and coastlines for African countries.

For more information on African population density and administrative boundary data sets, see metadata files at [http://na.unep.net/datasets/datalist.php3] which provide information on file identification, format, spatial data organization, distribution, and metadata reference.

References:

Deichmann, U. 1994. A medium resolution population database for Africa, Database documentation and digital database, National Center for Geographic Information and Analysis, University of California, Santa Barbara.

Deichmann, U. and L. Eklundh. 1991. Global digital datasets for land degradation studies: A GIS approach, GRID Case Study Series No. 4, Global Resource Information Database, United Nations Environment Programme, Nairobi.

UNEP. 1997. World Atlas of Desertification, 2nd Ed., United Nations Environment Programme, Edward Arnold Publishers, London.

WRI. 1995. Africa data sampler, Digital database and documentation, World Resources Institute, Washington, D.C.

Swahili is spoken by 100-150 million people across East Africa. In Tanzania, it is one of two national languages (the other is English) and it is the official language of instruction in all schools. News in Swahili is an important part of the media sphere in Tanzania.

News contributes to education, technology, and the economic growth of a country, and news in local languages plays an important cultural role in many Africa countries. In the modern age, African languages in news and other spheres are at risk of being lost as English becomes the dominant language in online spaces.

The Swahili news dataset was created to reduce the gap of using the Swahili language to create NLP technologies and help AI practitioners in Tanzania and across the Africa continent to practice their NLP skills to solve different problems in organizations or societies related to the Swahili language. Swahili News were collected from different websites that provide news in the Swahili language. I was able to find some websites that provide news in Swahili only and others in different languages including Swahili.

The dataset was created for a specific task of text classification, this means each news content can be categorized into six different topics (Local News, International News, Finance News, Health News, Sports News, and Entertainment news). The dataset comes with a specified train/test split. The train set contains 75% of the dataset.

Acknowledgment: This project was supported by the AI4D language dataset fellowship through K4All and Zindi Africa.

The Power Africa Enabling Environment Tracker 2020 data in the DDL are used in Power Africa's Enabling Environment Tracker dashboard. The Enabling Environment Tracker is an interactive data aggregation and visualization tool that pulls together publicly available data on policy and regulatory trends across Africa’s energy sector into one, easily accessible location. The dataset includes publicly available, third-party data points, which were selected based off Power Africa's Enabling Environment Principles. The principles lay out key elements for increasing private sector investment and doubling electricity access in Sub-Saharan Africa. The hope is that Power Africa's African government partners, private sector partners, development partners, and other stakeholders use the tool to assess enabling environment progress, inform technical assistance interventions, and guide advocacy for needed reforms.

The number of Facebook users in Africa was forecast to continuously increase between 2024 and 2028 by in total 141.6 million users (+56.79 percent). After the ninth consecutive increasing year, the Facebook user base is estimated to reach 390.94 million users and therefore a new peak in 2028. Notably, the number of Facebook users of was continuously increasing over the past years.User figures, shown here regarding the platform facebook, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Facebook users in countries like Europe and Asia.

The number of WhatsApp users in Africa was forecast to continuously increase between 2024 and 2029 by in total 43.8 million users (+47.79 percent). After the ninth consecutive increasing year, the WhatsApp user base is estimated to reach 135.44 million users and therefore a new peak in 2029. Notably, the number of WhatsApp users of was continuously increasing over the past years.User figures, shown here regarding the platform whatsapp, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of WhatsApp users in countries like Asia and the Americas.