Abstract

Research ICT Africa (RIA) is a non-profit, public interest, research entity which undertakes research on how information and communication technologies are being accessed and used in African countries. The aim is to measure the impact on lifestyles and livelihoods of people and households and to understand how informal businesses can prosper through the use of ICTs. This research can facilitate informed policy-making for improved access, use and application of ICT for social development and economic growth. RIA collects both supply-side and demand-side data. On the demand-side nationally representative surveys are conducted on ICT use and demand in African countries. This survey dataset consists of data collected by household and business surveys conducted in 9 African countries in 2017 and 2018.

Geographic coverage

National coverage, the survey was conducted in Botswana, Cameroon, Ethiopia, Ghana, Kenya, Mozambique, Namibia, Nigeria, Rwanda, South Africa, Tanzania, Uganda, and Tunisia.

Analysis unit

Households and individuals

Universe

The data is nationally representative on a household and individual level for individuals 16 years of age or older.

Kind of data

Sample survey data [ssd]

Sampling procedure

The random sampling was performed in four steps for households and businesses, and five steps for individuals. • Step 1: The national census sample frames was split into urban and rural Enumerator areas (EAs). • Step 2: EAs were sampled for each stratum using probability proportional to size (PPS). • Step 3: For each EA two listings were compiled, one for households and one for businesses. The listings serve as sample frame for the simple random sections. • Step 4: 24 Households and 10 businesses were sampled using simple random sample for each selected EA. • Step 5: From all household members 15 years or older or visitors staying the night at the house one was randomly selected based on simple random sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consisted of 16 modules. - Admin (enumerator completes it before Interviewing the Household) - Household Roster, list all household members 15 years or older - Household Roster, list all household members 14 years or younger - Household Attributes - Demographic Information - Income and Expenditure - Social Activities - Mobile Phone - No Mobile Phone - Mobile Money - Internet - No Internet Use - Social Media - No Social Media - Micro work - Household Attributes of Visitor

Abstract

Financial inclusion is critical in reducing poverty and achieving inclusive economic growth. When people can participate in the financial system, they are better able to start and expand businesses, invest in their children’s education, and absorb financial shocks. Yet prior to 2011, little was known about the extent of financial inclusion and the degree to which such groups as the poor, women, and rural residents were excluded from formal financial systems.

By collecting detailed indicators about how adults around the world manage their day-to-day finances, the Global Findex allows policy makers, researchers, businesses, and development practitioners to track how the use of financial services has changed over time. The database can also be used to identify gaps in access to the formal financial system and design policies to expand financial inclusion.

Geographic coverage

National Coverage

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Frequency of data collection

Triennial

Sampling procedure

As in the first edition, the indicators in the 2014 Global Findex are drawn from survey data covering almost 150,000 people in more than 140 economies-representing more than 97 percent of the world's population. The survey was carried out over the 2014 calendar year by Gallup, Inc. as part of its Gallup World Poll, which since 2005 has continually conducted surveys of approximately 1,000 people in each of more than 160 economies and in over 140 languages, using randomly selected, nationally representative samples. The target population is the entire civilian, noninstitutionalized population age 15 and above. The set of indicators will be collected again in 2017.

Surveys are conducted face to face in economies where telephone coverage represents less than 80 percent of the population or is the customary methodology. In most economies the fieldwork is completed in two to four weeks. 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 by means of the Kish grid. In economies where cultural restrictions dictate gender matching, respondents are randomly selected through the Kish grid from among all eligible adults of the interviewer's gender.

In economies where telephone interviewing is employed, random digit dialing or a nationally representative list of phone numbers is used. In most economies where cell phone penetration is high, a dual sampling frame is used. Random selection of respondents is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to reach a person in each household, spread over different days and times of day.

The sample size in Namibia was 1,000 individuals.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup Inc. also provided valuable input. The questionnaire was piloted in multiple countries, using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on cash withdrawals, saving using an informal savings club or person outside the family, domestic remittances, school fees, and agricultural payments are only asked in developing economies and few other selected countries. The question on mobile money accounts was only asked in economies that were part of the Mobile Money for the Unbanked (MMU) database of the GSMA at the time the interviews were being held.

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 Asli Demirguc-Kunt, Leora Klapper, Dorothe Singer, and Peter Van Oudheusden, “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, D.C.

Techsalerator offers an extensive dataset of End-of-Day Pricing Data for all 44 companies listed on the Namibian Stock Exchange (XNAM) in Namibia. This dataset includes the closing prices of equities (stocks), bonds, and indices at the end of each trading session. End-of-day prices are vital pieces of market data that are widely used by investors, traders, and financial institutions to monitor the performance and value of these assets over time.

Top 5 used data fields in the End-of-Day Pricing Dataset for Namibia:

1. Equity Closing Price :The closing price of individual company stocks at the end of the trading day.This field provides insights into the final price at which market participants were willing to buy or sell shares of a specific company.

2. Bond Closing Price: The closing price of various fixed-income securities, including government bonds, corporate bonds, and municipal bonds. Bond investors use this field to assess the current market value of their bond holdings.

3. Index Closing Price: The closing value of market indices, such as the Botswana stock market index, at the end of the trading day. These indices track the overall market performance and direction.

4. Equity Ticker Symbol: The unique symbol used to identify individual company stocks. Ticker symbols facilitate efficient trading and data retrieval.

5. Date of Closing Price: The specific trading day for which the closing price is provided. This date is essential for historical analysis and trend monitoring.

‍

Top 5 financial instruments with End-of-Day Pricing Data in Namibia:

Namibia Stock Exchange (NSX) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Namibia Stock Exchange. This index provides an overview of the overall market performance in Namibia.

Namibia Stock Exchange (NSX) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Namibia Stock Exchange. This index reflects the performance of international companies operating in Namibia.

Company X: A prominent Namibian company with diversified operations across various sectors, such as telecommunications, energy, or banking. This company's stock is widely traded on the Namibia Stock Exchange.

Company Y: A leading financial institution in Namibia, offering banking, insurance, or investment services. This company's stock is actively traded on the Namibia Stock Exchange.

Company Z: A major player in the Namibian agricultural sector, involved in the production and distribution of agricultural products. This company's stock is listed and actively traded on the Namibia Stock Exchange.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Namibia, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Equity Ticker Symbol Equity Closing Price Bond Ticker Symbol Bond Closing Price Index Ticker Symbol Index Closing Price Date of Closing Price Equity Name Equity Volume Equity High Price Equity Low Price Equity Open Price Bond Name Bond Coupon Rate Bond Maturity Index Name Index Change Index Percent Change Exchange Currency Total Market Capitalization Dividend Yield Price-to-Earnings Ratio (P/E) ‍

Q&A:

1. How much does the End-of-Day Pricing Data cost in Namibia ?

The cost of this dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

1. How complete is the End-of-Day Pricing Data coverage in Namibia?

Techsalerator provides comprehensive coverage of End-of-Day Pricing Data for various financial instruments, including equities, bonds, and indices. Thedataset encompasses major companies and securities traded on Namibia exchanges.

1. How does Techsalerator collect this data?

Techsalerator collects End-of-Day Pricing Data from reliable sources, including stock exchanges, financial news outlets, and other market data providers. Data is carefully curated to ensure accuracy and reliability.

1. Can I select specific financial instruments or multiple countries with Techsalerator's End-of-Day Pricing Data?

Techsalerator offers the flexibility to select specific financial instruments, such as equities, bonds, or indices, depending on your needs. While the dataset focuses on Botswana, Techsalerator also provides data for other countries and international markets.

1. How do I pay for this dataset?

Techsalerator accepts various payment methods, including credit cards, direct transfers, ACH, and wire transfers, facilitating a convenient and secure payment process.

1. How do I receive the data?

‍Techsalerator provides the End-of-Day Pricing Data through multiple delivery methods, such as FTP, SFTP, S3 bucket, or email, ensuring easy access and integration into your systems. The dataset is available in formats like JSON, CSV, TXT, or XLS, allowing seamless data processing.

Namibia NA: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data was reported at 0.189 % in 2010. This stayed constant from the previous number of 0.189 % for 2000. Namibia NA: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data is updated yearly, averaging 0.189 % from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 0.189 % in 2010 and a record low of 0.189 % in 2010. Namibia NA: Rural Land Area Where Elevation is Below 5 Meters: % of Total Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Namibia – Table NA.World Bank.WDI: Land Use, Protected Areas and National Wealth. Rural land area below 5m is the percentage of total land where the rural land elevation is 5 meters or less.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Weighted average;

The 2006-07 Namibia Demographic and Health Survey (NDHS) is a nationally representative survey of 9,804 women age 15-49 and 3,915 men age 15-49. The 2006-07 NDHS is the third comprehensive survey conducted in Namibia as part of the Demographic and Health Surveys (DHS) programme. The data are intended to provide programme managers and policymakers with detailed information on levels and trends in fertility; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality, adult and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The 2006-07 NDHS is the first NDHS survey to collect information on malaria prevention and treatment. The 2006-07 NDHS has been a large-scale research project. Twenty-eight field teams interviewed about 9,200 households, 9,800 women and 3,900 men age 15-49. The interviews were conducted between November 2006 and March 2007. The survey covered about 500 primary sampling units in all regions. The 2006-07 Namibia Demographic and Health Survey is designed to: Determine key demographic rates, particularly fertility, under-five mortality, and adult mortality rates; Investigate the direct and indirect factors that determine the level and trends of fertility; Measure the level of contraceptive knowledge and practice among women and men by method; Determine immunisation coverage and prevalence and treatment of diarrhoea and acute respiratory diseases among children under five; identify infant and young child feeding practices and assess the nutritional status of children age 6-59 months and women age 15-49 years; Assess knowledge and attitudes of women and men regarding sexually transmitted infections and HIV/AIDS, and evaluate patterns of recent behaviour regarding condom use; Identify behaviours that protect or predispose people to HIV infection and examine social, economic, and cultural determinants of HIV; Determine the proportion of households with orphans and vulnerable children (OVCs); and Determine the proportion of households with sick people taken care of at household level. The 2006-07 NDHS is part of the worldwide Demographic and Health Surveys (DHS) programme funded by the United States Agency for International Development (USAID). DHS surveys are designed to collect data on fertility, family planning, and maternal and child health; assist countries in conducting periodic surveys to monitor changes in population, health, and nutrition; and provide an international database that can be used by researchers investigating topics related to population, health, and nutrition. MAIN RESULTS Fertility : The survey results show that Namibia has experienced a decline in fertility of almost two births over the past 15 years, with the fertility rate falling from 5.4 births per woman in 19901992 to 3.6 births in 2005-07. Family planning : Knowledge of family planning in Namibia has been nearly universal since 1992. In the 2006-07 NDHS, 98 percent of all women reported knowing about a contraceptive method. Male condoms, injectables, and the pill are the most widely known methods. Child health : Data from the 2006-07 NDHS indicate that the under-five mortality rate in Namibia is 69 deaths per 1,000 live births (based on the five-year period preceding the survey). Maternal health : In Namibia, almost all women who had a live birth in the five years preceding the survey received antenatal care from health professionals (95 percent): 16 percent from a doctor and 79 percent from a nurse or midwife. Only 4 percent of mothers did not receive any antenatal care. Breastfeeding and nutrition : Breastfeeding is common in Namibia, with 94 percent of children breastfed at some point during childhood. The median breastfeeding duration in Namibia is 16.8 months. Malaria: One in four households interviewed in the survey has at least one mosquito net, and most of these households have a net that has been treated at some time with an insecticide (20 percent). HIV/AIDS and STIS : Knowledge of HIV and AIDS is universal in Namibia; 99 percent of women age 15-49 and 99 percent of men age 15-49 have heard of AIDS. Orphans and vulnerable children : One-quarter of Namibian children under age 18 in the households sampled for the 2006-07 NDHS live with both parents, while one in three does not live with either parent. Seventeen percent of children under age 18 are orphaned, that is, one or both parents is dead. Access to health facilities : Households interviewed in the 2006-07 NDHS were asked to name the nearest government health facility, the mode of transport they would use to visit the facility, and how long it takes to get to the facility using the transport of choice.

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

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.2016 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0197 and 0.1094 (in million kms), corressponding to 9.7506% and 54.2725% respectively of the total road length in the dataset region. 0.0725 million km or 35.9769% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0001 million km of information (corressponding to 0.0915% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

These data document a series of video interviews relating to the politics of deep sea mining in PNG and Australia. Recorded between 2016-2018, they focus on the various communities most proximate to the world's first proposed commercial deep sea mining site in Papua New Guinea (Solwara 1 held by Nautilus Minerals). Semi structured interviews were used and a topic guide is attached in the files along with the consent form used.Despite the fact that the deep-sea is by degrees, imperceptible and a boundary to human knowledge, the race to mine it is on. This project will analyse the emergent political challenges of sustainable deep-sea mining taking into account conflicting social interests, the ways in which the deep sea bed is sensed and imagined, and the physical properties of the deep ocean itself. Increasing global demand for economically and strategically important resources such as gold, copper, rare-earth metals and phosphates, coupled with advances in mining technology, has meant that the deep-sea has emerged for the first time as a key site of resource extraction. The industry, which already explores over 1 million sq.km of deep-sea bed, is expected to be worth £40 billion over the next 30 years to the UK alone. To this end, debates have opened up which have focused on the economic and environmental impacts of the deep-sea mining (DSM) industry. Thus the deep sea is increasingly considered in terms of profitability and safety rather than in terms of ownership, ethics and competing politics. In failing to sufficiently take into account the deep sea's political dimensions, current research misses out in explaining how DSM governance comes into being. How is deep-sea mining imagined, visualised and mapped by different stakeholders? Can DSM be part of international development strategies in the global South? What role do the resources, properties and dynamics of the deep-sea play in political issue formation? By addressing these questions through a critical social science reading of DSM, this project has a key role to play in understanding competing global, national and local geopolitical 'imaginaries' - or ways of understanding the deep-sea - and their profound conceptual and substantive implications for DSM research and policy. It offers a comparative study of the two key nation-states in the global South that are engaging actively, but differently, with the prospect of DSM: Papua New Guinea (PNG), which has embraced its economic potential in the country's development plan; and Namibia, which has issued a temporary moratorium on all DSM activity prior to its inclusion in any national development strategy. More radically, how might taking the physical properties of the deep sea itself seriously change the way we think about the politics and policies of resource extraction? Conceptually, it will be the first to bridge emergent contemporary work on both ocean and subsurface geographies and to consider how an engagement with the physical properties of the ocean itself changes the way we think about the politics and policies of DSM. It will build a new conceptual understanding of deep-sea geopolitics by generating new qualitative data through interviews and focus groups with senior government officials in Namibia and PNG, affected communities, global activists, lawyers and oceanographers. Working with communities affected by potential DSM in Namibia and PNG, it will produce new types of 'participatory' maps, that will offer alternative political futures for DSM. It will also comprehensively analyse texts relating to DSM legal treaties and political speeches in order to show how DSM politics is contemporarily made and to influence how it is shaped in the future. Among many other activities, the research will do this at a national scale by producing public policy briefings for UK parliamentary use and globally by working with the International Seabed Authority (the key global regulator of the deep-sea) to create new, critical ways of mapping and governing DSM. Both a physical and online exhibition will show the ways in which alternative ways of imagining the deep-sea can challenge and shape the emergent policy agendas that regulate the mining of it. These outcomes will provide an invaluable resource for a range of current and future users from activists to policy makers, researchers to communities affected by the changing landscapes of DSM activity.

Namibia NA: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data was reported at 55.000 NA in 2023. This records an increase from the previous number of 50.000 NA for 2022. Namibia NA: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data is updated yearly, averaging 30.000 NA from Mar 2017 (Median) to 2023, with 7 observations. The data reached an all-time high of 55.000 NA in 2023 and a record low of 30.000 NA in 2021. Namibia NA: SPI: Pillar 5 Data Infrastructure Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Namibia – Table NA.World Bank.WDI: Governance: Policy and Institutions. The data infrastructure pillar overall score measures the hard and soft infrastructure segments, itemizing essential cross cutting requirements for an effective statistical system. The segments are: (i) legislation and governance covering the existence of laws and a functioning institutional framework for the statistical system; (ii) standards and methods addressing compliance with recognized frameworks and concepts; (iii) skills including level of skills within the statistical system and among users (statistical literacy); (iv) partnerships reflecting the need for the statistical system to be inclusive and coherent; and (v) finance mobilized both domestically and from donors.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;