World Bank has a Poverty and Inequality Platform where country data can be downloaded for Poverty, Inequality and Multi-dimensional Poverty. The link https://pip.worldbank.org/country-profiles will take you to the Country Poverty Profile and from this page you can select any country and choose between one of three the Poverty Lines: $1.9, $3.2 or $5.5 (at 2011 international prices) and that Poverty Profile will be called up. Then you can select the Poverty, Inequality and Multi-dimensional Poverty data that you want to download. The Reporting Years are: 2000, 2008 and 2018.

About this dataset

Context

The dataset tabulates the population of Black Earth town by race. It includes the population of Black Earth town across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Black Earth town across relevant racial categories.

Key observations

The percent distribution of Black Earth town population by race (across all racial categories recognized by the U.S. Census Bureau): 95.17% are white, 2.42% are Asian and 2.42% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Black Earth town
• Population: The population of the racial category (excluding ethnicity) in the Black Earth town is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Black Earth town total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth town Population by Race & Ethnicity. You can refer the same here

This dataset contains data provided alongside the paper "An all-Africa dataset of energy model “supply regions” for solar PV and wind power".

It concerns a novel representative subset of attractive sites for solar PV and onshore wind power for the entire African continent. We refer to these sites as “Model Supply Regions” (MSRs). This MSR dataset was created from an in-depth analysis of various existing datasets on resource potential, grid infrastructure, land use, topography and others (see Methods), and achieves hourly temporal resolution and kilometre-scale spatial resolution. This dataset fills an important research need by closing the gap between comprehensive datasets on African VRE potential (such as the Global Solar Atlas and Global Wind Atlas) on the one hand, and the input needed to run cost-optimisation models on the other. It also allows a detailed analysis of the trade-offs involved in exploiting excellent, but far-from-grid resources as compared to mediocre but more accessible resources, which is a crucial component of power systems planning to be elaborated for many African countries.

Five separate datasets are included:

(1) 20220412_country_maps.rar: Country-level visualisations (in the form of maps) of the screened MSRs. We screened the dataset according to the criterion that the total area of screened MSRs should not exceed 5% of an individual country’s surface area. See also (4).

(2) 20220412_excel_files.rar: Excel files containing the screened MSRs suggested for model inclusion alongside various metadata. See also (5).

(3) 20220412_shapefiles.rar: Shapefiles containing the screened MSRs in GIS format.

(4) 20220705_clusters_maps.zip: Same as (1), but showing the clusters (formed from individual MSRs) described in the publication. We use an example of clustering down to 10, 5 or 2 clusters per country, depending on country size. The archive also contains Excel files summarising the clusters, including model-ready hourly profiles.

(5) 20220822_excel_files_prescreen.rar: Same as (2), but containing all identified MSRs prior to screening.

Note: This dataset has been updated with transmission lines for the MENA region. This is the most complete and up-to-date open map of Africa's electricity grid network. This dataset serves as an updated and improved replacement for the Africa Infrastructure Country Diagnostic (AICD) data that was published in 2007. Coverage This dataset includes planned and existing grid lines for all continental African countries and Madagascar, as well as the Middle East region. The lines range in voltage from sub-kV to 700 kV EHV lines, though there is a very large variation in the completeness of data by country. An interactive tool has been created for exploring this data, the Africa Electricity Grids Explorer. Sources The primary sources for this dataset are as follows: Africa Infrastructure Country Diagnostic (AICD) OSM © OpenStreetMap contributors For MENA: Arab Union of Electricity and country utilities. For West Africa: West African Power Pool (WAPP) GIS database World Bank projects archive and IBRD maps There were many additional sources for specific countries and areas. This information is contained in the files of this dataset, and can also be found by browsing the individual country datasets, which contain more extensive information. Limitations Some of the data, notably that from the AICD and from World Bank project archives, may be very out of date. Where possible this has been improved with data from other sources, but in many cases this wasn't possible. This varies significantly from country to country, depending on data availability. Thus, many new lines may exist which aren't shown, and planned lines may have completely changed or already been constructed. The data that comes from World Bank project archives has been digitized from PDF maps. This means that these lines should serve as an indication of extent and general location, but shouldn't be used for precisely location grid lines.

About this dataset

Context

The dataset tabulates the population of Black Earth by race. It includes the population of Black Earth across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Black Earth across relevant racial categories.

Key observations

The percent distribution of Black Earth population by race (across all racial categories recognized by the U.S. Census Bureau): 93.91% are white, 0.81% are some other race and 5.28% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Black Earth
• Population: The population of the racial category (excluding ethnicity) in the Black Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Black Earth total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth Population by Race & Ethnicity. You can refer the same here

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for South Africa is 1014.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

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 Instagram users in Africa was forecast to continuously increase between 2024 and 2028 by in total 39.1 million users (+57.16 percent). After the sixth consecutive increasing year, the Instagram user base is estimated to reach 107.54 million users and therefore a new peak in 2028. User figures, shown here with regards to the platform instagram, 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 Instagram users in countries like Europe and Caribbean.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Population subjected to robbery in the previous 12 months (%)





  Dataset Description

This dataset provides country-level data for the indicator "16.1.3 Population subjected to robbery in the previous 12 months (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/population-subjected-to-robbery-in-the-previous-12-months-for-african-countries.

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.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Completion rate (%)





  Dataset Description

This dataset provides country-level data for the indicator "4.1.2 Completion rate (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format, where each row represents a date (yearly) and each column… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/completion-rate-for-african-countries.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Average proportion of deprivations for people multidimensionally poor (%)





  Dataset Description

This dataset provides country-level data for the indicator "1.2.2 Average proportion of deprivations for people multidimensionally poor (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/average-proportion-of-deprivations-for-people-multidimension-for-african-countries.

license: apache-2.0 tags: - africa - sustainable-development-goals - world-health-organization - development

  Population using basic drinking water services (%)





  Dataset Description

This dataset provides country-level data for the indicator "1.4.1 Population using basic drinking water services (%)" across African nations, sourced from the World Health Organization's (WHO) data portal on Sustainable Development Goals (SDGs). The data is presented in a wide format… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/population-using-basic-drinking-water-services-for-african-countries.

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for longer-term time series analysis, cloudless imagery and statistical accuracy.

GeoMAD has two main components: Geomedian and Median Absolute Deviations (MADs)

The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms.

For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas.

Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface and understand change over time.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 2017 – 2022*Spatial Resolution: 10 x 10 meterUpdate Frequency: Annual from 2017 - 2022Product Type: Surface Reflectance (SR)Product Level: Analysis Ready (ARD)Number of Bands: 14 BandsParent Dataset: Sentinel-2 Level-2A Surface ReflectanceSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone.ApplicationsGeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:Longer-term time series analysisCloud-free imageryStatistical accuracyAvailable BandsBand IDDescriptionValue rangeData typeNo data valueB02Geomedian B02 (Blue)1 - 10000uint160B03Geomedian B03 (Green)1 - 10000uint160B04Geomedian B04 (Red)1 - 10000uint160B05Geomedian B05 (Red edge 1)1 - 10000uint160B06Geomedian B06 (Red edge 2)1 - 10000uint160B07Geomedian B07 (Red edge 3)1 - 10000uint160B08Geomedian B08 (Near infrared (NIR) 1)1 - 10000uint160B8AGeomedian B8A (NIR 2)1 - 10000uint160B11Geomedian B11 (Short-wave infrared (SWIR) 1)1 - 10000uint160B12Geomedian B12 (SWIR 2)1 - 10000uint160SMADSpectral Median Absolute Deviation0 - 1float32NaNEMADEuclidean Median Absolute Deviation0 - 31623float32NaNBCMADBray-Curtis Median Absolute Deviation0 - 1float32NaNCOUNTNumber of clear observations1 - 65535uint160Bands can be subdivided as follows:

Geomedian — 10 bands: The geomedian is calculated using the spectral bands of data collected during the specified time period. Surface reflectance values have been scaled between 1 and 10000 to allow for more efficient data storage as unsigned 16-bit integers (uint16). Note parent datasets often contain more bands, some of which are not used in GeoMAD. The geomedian band IDs correspond to bands in the parent Sentinel-2 Level-2A data. For example, the Annual GeoMAD band B02 contains the annual geomedian of the Sentinel-2 B02 band. Median Absolute Deviations (MADs) — 3 bands: Deviations from the geomedian are quantified through median absolute deviation calculations. The GeoMAD service utilises three MADs, each stored in a separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and Bray-Curtis MAD (BCMAD). Each MAD is calculated using the same ten bands as in the geomedian. SMAD and BCMAD are normalised ratios, therefore they are unitless and their values always fall between 0 and 1. EMAD is a function of surface reflectance but is neither a ratio nor normalised, therefore its valid value range depends on the number of bands used in the geomedian calculation.Count — 1 band: The number of clear satellite measurements of a pixel for that calendar year. This is around 60 annually, but doubles at areas of overlap between scenes. “Count” is not incorporated in either the geomedian or MADs calculations. It is intended for metadata analysis and data validation.ProcessingAll clear observations for the given time period are collated from the parent dataset. Cloudy pixels are identified and excluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version 0.2.More details on this dataset can be found here.

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

This data set was produced in complement to GLW published by Gilbert et al. (2018) to contain the latest subnational pig distribution data to available to date (November 2018) in support of the risk assessment of the ongoing African Swine Fever epidemics. GLW v3 dataset are organised around a pivot year, 2010 for GLW v3, which correspond to the median year of the subnational data set. In this release, the latest sub-national data sets have been integrated with a particular focus on Asia, with, for example, new data from China (2015) and Indonesia (2017), and much higher or more recent data for other countries such as Thailand or Vietnam. All country totals have been standardized to match the 2015 FAOSTAT numbers, in order to be as close a possible to the present pig stock. Please go through the 1_Pg_2015_Metadata.html file for more information about this dataset.

Context

In April of 2019, The Economist published an article which is titled "How to Predict a Coup", which can be accessed here: https://www.economist.com/graphic-detail/2019/04/23/how-to-predict-a-coup?fsrc=scn%2Ftw%2Fte%2Fbl%2Fed%2Fauto

The article discussed One Earth Future's Coup risk modelling project: https://oefresearch.org/activities/coup-cast. When I looked at the coup dataset which underpins the project, I felt inspired to filter-out the non-African countries, blend it to commodities and policy uncertainty data; and, to create an Africa specific coup model. The product of this inspiration is the dataset I have shared.

Content

The dataset blends data on the political systems of 47 African countries, commodity price indices and global uncertainty. The time-frame it covers: January 1997 to April 2019.

Acknowledgements: The Original Authors of the Data

One Earth Future Research

Reference: Bell, C. (2016. The Rulers, Elections, and Irregular Governance Dataset (REIGN). Broomfield, CO: OEF Research. Available at oefresearch.org

Bank of Canada

Reference: Bank of Canada (2019). Commodity Price Index. [online] Bankofcanada.ca. Available at: https://www.bankofcanada.ca/rates/price-indexes/bcpi/ [Accessed 2 May 2019].

Economic Policy Uncertainty

Reference: Baker, S.R., Bloom, N. and Davis, S.J. (2019). Global Economic Policy Uncertainty Index. [online] policyuncertainty.com. Available at: http://www.policyuncertainty.com/global_monthly.html [Accessed 2 May 2019].

Inspiration

The question I would like to be answered is: Which variables correlate highly with the probability of coups in Africa?

The population of the world, allocated to 1 arcsecond blocks. This refines CIESIN’s Gridded Population of the World project, using machine learning models on high-resolution worldwide Digital Globe satellite imagery. More information.

There is also a tiled version of this dataset that may be easier to use if you are interested in many countries.

About this dataset

Context

The dataset tabulates the population of Black Earth by race. It includes the population of Black Earth across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Black Earth across relevant racial categories.

Key observations

The percent distribution of Black Earth population by race (across all racial categories recognized by the U.S. Census Bureau): 94.39% are white, 0.18% are Black or African American, 0.53% are Asian and 4.90% are multiracial.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Black Earth
• Population: The population of the racial category (excluding ethnicity) in the Black Earth is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Black Earth total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Black Earth Population by Race & Ethnicity. You can refer the same here

The databases contain all the technical, financial, and tariff data collected through the study "Making power affordable in Africa and viable for its utilities." The final study and background papers are available at http://www.worldbank.org/affordableviablepowerforafrica. The objective of making the database public is to make data collected through the study available to utility companies, regulators, and practitioners to provide benchmarks and help inform analysis. The databases will be updated from time to time to make corrections or updates for latest data available and therefore may differ from data that appears in the reports. This database is a publication of the African Renewable Energy Access Program (AFREA), a World Bank Trust Fund Grant Program funded by the Kingdom of the Netherlands through ESMAP. It was prepared by staff of the International Bank for Reconstruction and Development / The World Bank.