20 datasets found
  1. M

    Kano, Nigeria Metro Area Population (1950-2025)

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). Kano, Nigeria Metro Area Population (1950-2025) [Dataset]. https://www.macrotrends.net/global-metrics/cities/22005/kano/population
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    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 1950 - Jun 19, 2025
    Area covered
    Nigeria
    Description

    Chart and table of population level and growth rate for the Kano, Nigeria metro area from 1950 to 2025.

  2. o

    Kano LGA Population and Uncertainty Estimates - Dataset - openAFRICA

    • open.africa
    Updated Sep 6, 2019
    + more versions
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    (2019). Kano LGA Population and Uncertainty Estimates - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/kano-lga-population-and-uncertainty-estimates
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    Dataset updated
    Sep 6, 2019
    Area covered
    Kano
    Description

    Estimate population figures at local government administrative level and different age groups

  3. Largest cities in Nigeria 2024

    • statista.com
    Updated Aug 16, 2024
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    Statista (2024). Largest cities in Nigeria 2024 [Dataset]. https://www.statista.com/statistics/1121444/largest-cities-in-nigeria/
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    Dataset updated
    Aug 16, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Africa
    Description

    Nigeria is the African country with the largest population, counting over 230 million people. As of 2024, the largest city in Nigeria was Lagos, which is also the largest city in sub-Saharan Africa in terms of population size. The city counts more than nine million inhabitants, whereas Kano, the second most populous city, registers around 3.6 million inhabitants. Lagos is the main financial, cultural, and educational center in the country. Where Africa’s urban population is booming The metropolitan area of Lagos is also among the largest urban agglomerations in the world. Besides Lagos, another most populated citiy in Africa is Cairo, in Egypt. However, Africa’s urban population is booming in other relatively smaller cities. For instance, the population of Bujumbura, in Burundi, could grow by 123 percent between 2020 and 2035, making it the fastest growing city in Africa and likely in the world. Similarly, Zinder, in Niger, could reach over one million inhabitants by 2035, the second fastest growing city. Demographic urban shift More than half of the world’s population lives in urban areas. In the next decades, this will increase, especially in Africa and Asia. In 2020, over 80 percent of the population in Northern America was living in urban areas, the highest share in the world. In Africa, the degree of urbanization was about 40 percent, the lowest among all continents. Meeting the needs of a fast-growing population can be a challenge, especially in low-income countries. Therefore, there will be a growing necessity to implement policies to sustainably improve people’s lives in rural and urban areas.

  4. w

    Migration Household Survey 2009 - Nigeria

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Jun 3, 2019
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    Zibah Consults Limited (2019). Migration Household Survey 2009 - Nigeria [Dataset]. https://microdata.worldbank.org/index.php/catalog/402
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    Dataset updated
    Jun 3, 2019
    Dataset authored and provided by
    Zibah Consults Limited
    Time period covered
    2009
    Area covered
    Nigeria
    Description

    Geographic coverage

    National

    Analysis unit

    • Household
    • Individual

    Universe

    18 of the 37 states in Nigeria were selected using procedures described in the methodology report

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A. Sampling Frame The sampling frame was the 2006 National Population Census. For administrative purposes, Nigeria has 36 states and the Federal Capital Territory. These states are grouped into six geopolitical zones - the North Central, North East, North West, South East, South South and South West. The states in turn are divided into 776 Local Governments. The demographic and political characteristics of the states vary considerably. For example, the number of component local government areas in the states ranges from 8 in Bayelsa State (in the South South) to 44 in Kano State (in the North West). Likewise state populations vary widely from 1.41 million in the Abuja Federal Capital Territory to 9.38 million in Kano State. The National Bureau of Statistics splits the country further into 23, 070 enumeration areas (EAs). While the enumeration areas are equally distributed across the local government areas, with each local government area having 30 enumeration areas, the differences in the number of local government areas across states implies that there are also huge differences in the number of enumeration areas across states. Appendix table 1 summarizes the population according to the 2006 population census (in absolute and proportionate numbers), number of local government areas, and number of enumeration areas in each state .

    Given the above, a stratified random sampling technique was thought to be needed to select areas according to population and the expected prevalence of migrants. The National Bureau of Statistics (NBS) provided a randomly selected set of enumeration areas and households spread across all states in the Federation from the 2006 sampling frame. Every state in Nigeria has three senatorial zones (often referred to as North, Central and South or East, Central and West). The NBS sample enumeration areas were distributed such that within each state, local government areas from each senatorial zones were included in the sample, with Local Governments in each state nearly evenly distributed between rural and urban areas. In all, a total of 3188 enumeration areas were selected. These enumeration areas were unevenly spread across States; some states in the North West (Kano, Katsina, and Jigawa), and a few in the South South (Akwa Ibom and Delta) had over 100 enumeration areas selected while others such as Imo and Abia in the South East, and Borno, Gombe and Taraba in the North East, had as few as 20 enumeration areas selected. This selection partially reflected the relative population distribution and number of Local Government Areas in the component states. Annex Table B shows details of the states and geopolitical regions, their shares in population of the country, the number of Local Government Areas and enumeration areas in each state and the number of enumeration areas given in the NBS list that formed the frame for the study.

    B. The Sample for the Migration Survey

    a. Sample Selection of States, Local Governments and Enumeration Areas Originally, the intention was to have proportionate allocation across all states, using the population of each state in the 2006 Census to select the number of households to be included in the sample. But it was later recognized that this would not yield enough migrant households, particularly those with international migrants, especially as the total number of households that could likely be covered in the sample to was limited to 2000. Consequently, a disproportionate sampling approach was adopted, with the aim of oversampling areas of the country with more migrants. According to Bilsborrow (2006), this approach becomes necessary because migrants are rare populations for which a distinct disproportionate sampling procedure is needed to ensure they are adequately captured. Given the relative rareness of households with out-migrants to international destinations within the 10 year reference period (selected by the World Bank for all countries) prior to the planned survey, sampling methods appropriate for sampling rare elements were desirable, specifically, stratified sampling with two-phase sampling at the last stage.

    Establishing the strata would require that there be previous work, say from the most recent Census, to determine migration incidence among the states. However, the needed census data could not be obtained from either the National Bureau of Statistics or the National Population Commission. Therefore, the stratification procedure had to rely on available literature, particularly Hernandez-Coss and Bun (2007), Agu (2009) and a few other recent, smaller studies on migration and remittances in Nigeria. Information from this literature was supplemented by expert judgement about migration from team members who had worked on economic surveys in Nigeria in the past. Information from the literature and the expert assessment indicated that migration from households is considerably higher in the South than in the North. Following this understanding, the states were formed into two strata- those with high and those with low incidence of migration. In all, 18 States (16 in the South and 2 in the North) were put into the high migration incidence stratum while 19 states (18 in the North and 1 in the South) were classified l into the low migration incidence stratum (column C of Appendix Table 1).

    The Aggregate population of the 18 states in the high migration incidence stratum was 67.04 million, spread across 10,850 Enumeration areas. Thus, the mean population of an EA in the high migration stratum was 6179. In turn, the aggregate population of the 19 states in the low migration incidence stratum was 72.95 million spread across 12,110 EAs yielding a mean EA population of 6024. These numbers were close enough to assume the mean population of EAs was essentially the same. To oversample states in the high stratum, it was decided to select twice as high a proportion of the states as in the low stratum. To further concentrate the sample and make field work more efficient in being oriented to EAs more likely to have international migrants, we decided to select randomly twice as many LGAs in each state in the high stratum states as in the low stratum states.

    Thus, 12 states were randomly selected with probabilities of selection proportionate to the population size of each state (so states with larger populations were accordingly more likely to fall in the sample) from the high stratum states. Then two LGAs were randomly selected from each sample state and 2 EAs per sample LGA (one urban, one rural) to yield a total of 12 x 2 x 2 or 48 EAs in the high stratum states. For the low stratum, 6 states were randomly selected. From each of these, 1 LGA was randomly picked and 2 EAs were selected per sample LGA to give a total of 6 x 1 x 2 or 12 EAs in the low stratum. This yielded a total of 60 EAs for both strata. Given the expected range of 2000 households to be sampled, approximately 67 households were to be sampled from each local government area or 34 households from each enumeration area.

    So far, the discussion has assumed two groups of households - migrant and non-migrant households. However, the study was interested in not just lumping all migrants together, but rather in classifying migrants according to whether their destination was within or outside the country. Migrant households were thus subdivided into those with former household members who were international migrants and those with former household members who were internal migrants. Three strata of households were therefore required, namely:

    1. Households with an international migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live in an international destination and has remained abroad;
    2. Households with an internal migrant: at least one person who was a member of the household since Jan. 1, 2000 left to live elsewhere in Nigeria (outside the sample LGA) and has not returned to the LGA; and
    3. Households with no migrant: No member of the household has left to live elsewhere either within or outside the country since Jan. 1, 2000.

    The selection of states to be included in the sample from both strata was based on Probabilities of Selection Proportional to (Estimated) Size or PPES. The population in each stratum was cumulated and systematic sampling was performed, with an interval of 12.16 million for the low stratum (72.95 million divided by 6 States), and 5.59 million for the high stratum (67.04 million divided by 12 States). This yields approximately double the rate of sampling in the high migration stratum, as earlier explained. Using a random start between 0 and 12.16, the following states were sampled in the low stratum: Niger, Bauchi, Yobe, Kano, Katsina, and Zamfara. In the high stratum, states sampled were Abia, Ebonyi, Imo, Akwa Ibom, Delta, Edo, Rivers, Lagos, Ondo, Osun and Oyo. Given its large population size, Lagos fell into the sample twice. The final sample, with LGAs and EAs moving from North to South (i.e. from the low to the high stratum states) is presented in Table 1 below.

    The sample was concentrated in the South since that is where it was expected that more households have international migrants. It was expected that the survey would still also be reasonably representative of the whole country and of both internal migrant and non-migrant households through weighting the data. To this effect, field teams were asked to keep careful track at all stages of the numbers of people and households listed compared to the number in the

  5. n

    Kano (492) Census 2011

    • gramvikas.nskmultiservices.in
    Updated Mar 1, 2011
    + more versions
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    (2011). Kano (492) Census 2011 [Dataset]. https://gramvikas.nskmultiservices.in/india/himachal-pradesh/solan/kandaghat/kano-492
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    Dataset updated
    Mar 1, 2011
    License

    https://data.gov.in/sites/default/files/Gazette_Notification_OGDL.pdfhttps://data.gov.in/sites/default/files/Gazette_Notification_OGDL.pdf

    Time period covered
    2011
    Description

    Comprehensive population and demographic data for Kano (492) Village

  6. Largest cities in Africa 2024, by number of inhabitants

    • statista.com
    • ai-chatbox.pro
    Updated May 24, 2024
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    Statista (2024). Largest cities in Africa 2024, by number of inhabitants [Dataset]. https://www.statista.com/statistics/1218259/largest-cities-in-africa/
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    Dataset updated
    May 24, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    Africa
    Description

    Lagos, in Nigeria, ranked as the most populated city in Africa as of 2024, with an estimated population of roughly nine million inhabitants living in the city proper. Kinshasa, in Congo, and Cairo, in Egypt, followed with some 7.8 million and 7.7 million dwellers. Among the 15 largest cities in the continent, another two, Kano, and Ibadan, were located in Nigeria, the most populated country in Africa. Population density trends in Africa As of 2022, Africa exhibited a population density of 48.3 individuals per square kilometer. At the beginning of 2000, the population density across the continent has experienced a consistent annual increment. Projections indicated that the average population residing within each square kilometer would rise to approximately 54 by the year 2027. Moreover, Mauritius stood out as the African nation with the most elevated population density, exceeding 640 individuals per square kilometre. Mauritius possesses one of the most compact territories on the continent, a factor that significantly influences its high population density. Urbanization dynamics in Africa The urbanization rate in Africa was anticipated to reach close to 44 percent in 2021. Urbanization across the continent has consistently risen since 2000, with urban areas accommodating 35 percent of the total population. This trajectory is projected to continue its ascent in the years ahead. Nevertheless, the distribution between rural and urban populations shows remarkable diversity throughout the continent. In 2021, Gabon and Libya stood out as Africa’s most urbanized nations, each surpassing 80 percent urbanization. In 2023, Africa's population was estimated to expand by 2.35 percent compared to the preceding year. Since 2000, the population growth rate across the continent has consistently exceeded 2.45 percent, reaching its pinnacle at 2.59 percent between 2012 and 2013. Although the growth rate has experienced a deceleration, Africa's population will persistently grow significantly in the forthcoming years.

  7. o

    Kano Built-Up Areas - Dataset - openAFRICA

    • open.africa
    Updated Sep 6, 2019
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    (2019). Kano Built-Up Areas - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/kano-built-up-areas
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    Dataset updated
    Sep 6, 2019
    Area covered
    Kano
    Description

    Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

  8. g

    Kano Small Settlement Areas

    • grid3.gov.ng
    Updated Jul 21, 2020
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    (2020). Kano Small Settlement Areas [Dataset]. http://grid3.gov.ng/dataset/kano-small-settlement-areas
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    Dataset updated
    Jul 21, 2020
    Area covered
    Kano
    Description

    A populated place consisting of more than 15 houses − place or area with clustered or scattered buildings and a permanent human population (city

  9. d

    Kano State

    • deepfo.com
    csv, excel, html, xml
    Updated May 17, 2018
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    Deepfo.com by Polyolbion SL, Barcelona, Spain (2018). Kano State [Dataset]. https://deepfo.com/en/most/Kano-State
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    html, csv, xml, excelAvailable download formats
    Dataset updated
    May 17, 2018
    Dataset authored and provided by
    Deepfo.com by Polyolbion SL, Barcelona, Spain
    License

    https://deepfo.com/documentacion.php?idioma=enhttps://deepfo.com/documentacion.php?idioma=en

    Area covered
    Kano
    Description

    Kano State. name, type, Area, capital city, Country, continent, population

  10. W

    Kano Small Settlements

    • cloud.csiss.gmu.edu
    geojson
    Updated Jul 15, 2021
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    Open Africa (2021). Kano Small Settlements [Dataset]. https://cloud.csiss.gmu.edu/uddi/dataset/kano-small-settlements
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    geojsonAvailable download formats
    Dataset updated
    Jul 15, 2021
    Dataset provided by
    Open Africa
    Area covered
    Kano
    Description

    Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

  11. Cholera hotspots classification based on statistical method (SaTScan).

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Moise Chi Ngwa; Chikwe Ihekweazu; Tochi Okwor; Sebastian Yennan; Nanpring Williams; Kelly Elimian; Nura Yahaya Karaye; Imam Wada Bello; David A. Sack (2023). Cholera hotspots classification based on statistical method (SaTScan). [Dataset]. http://doi.org/10.1371/journal.pntd.0009046.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Moise Chi Ngwa; Chikwe Ihekweazu; Tochi Okwor; Sebastian Yennan; Nanpring Williams; Kelly Elimian; Nura Yahaya Karaye; Imam Wada Bello; David A. Sack
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cholera hotspots classification based on statistical method (SaTScan).

  12. o

    Kano Small Settlements - Dataset - openAFRICA

    • open.africa
    Updated Sep 6, 2019
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    (2019). Kano Small Settlements - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/kano-small-settlements
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    Dataset updated
    Sep 6, 2019
    Area covered
    Kano
    Description

    Populated place − place or area with clustered or scattered buildings and a permanent human population (city, settlement, town, village) and by definition has no legal boundaries

  13. f

    Cholera clusters.

    • figshare.com
    • plos.figshare.com
    xls
    Updated Jun 5, 2023
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    Moise Chi Ngwa; Chikwe Ihekweazu; Tochi Okwor; Sebastian Yennan; Nanpring Williams; Kelly Elimian; Nura Yahaya Karaye; Imam Wada Bello; David A. Sack (2023). Cholera clusters. [Dataset]. http://doi.org/10.1371/journal.pntd.0009046.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 5, 2023
    Dataset provided by
    PLOS Neglected Tropical Diseases
    Authors
    Moise Chi Ngwa; Chikwe Ihekweazu; Tochi Okwor; Sebastian Yennan; Nanpring Williams; Kelly Elimian; Nura Yahaya Karaye; Imam Wada Bello; David A. Sack
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Cholera clusters.

  14. Malaria Indicator Survey 2021 - Nigeria

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Mar 1, 2023
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    National Malaria Elimination Programme (NMEP) (2023). Malaria Indicator Survey 2021 - Nigeria [Dataset]. https://microdata.worldbank.org/index.php/catalog/5763
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    Dataset updated
    Mar 1, 2023
    Dataset provided by
    National Malaria Eradication Program
    Authors
    National Malaria Elimination Programme (NMEP)
    Time period covered
    2021
    Area covered
    Nigeria
    Description

    Abstract

    The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

    The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

    Geographic coverage

    National coverage

    Analysis unit

    • Household
    • Individual
    • Woman age 15-49

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe • North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara • South East: Abia, Anambra, Ebonyi, Enugu, and Imo • South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

    The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

    A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

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

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    Three questionnaires were used in the 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

    Cleaning operations

    The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

    Response rate

    A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, yielding a response rate of 99%.

    Sampling error estimates

    The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, or incorrect data entry. Although numerous efforts were made during the implementation of the 2021 Nigeria Malaria Indicator Survey (NMIS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

    Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2021 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

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

    If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2021 NMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

    Sampling errors tables are presented in Appendix B of the final report.

    Data appraisal

    Data Quality Tables

    • Household age distribution
    • Age distribution of eligible and interviewed women
    • Age displacement at ages 14/15
    • Age displacement at ages 49/50
    • Live births by years preceding the survey
    • Completeness of reporting
    • Observation of mosquito nets
    • Number of enumeration areas completed by month of fieldwork and zone
    • Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
    • Concordance and discordance between RDT and microscopy results
    • Concordance and discordance between national and external quality control laboratories

    See details of the data quality tables in Appendix C of the final report.

  15. Poverty headcount rate in Nigeria 2019, by state

    • statista.com
    Updated Dec 5, 2022
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    Statista (2022). Poverty headcount rate in Nigeria 2019, by state [Dataset]. https://www.statista.com/statistics/1121438/poverty-headcount-rate-in-nigeria-by-state/
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    Dataset updated
    Dec 5, 2022
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2019
    Area covered
    Nigeria
    Description

    The Nigerian states of Sokoto and Taraba had the largest percentage of people living below the poverty line as of 2019. The lowest poverty rates were recorded in the South and South-Western states. In Lagos, this figure equaled 4.5 percent, the lowest rate in Nigeria.

    A large population in poverty

    In Nigeria, an individual is considered poor when they have an availability of less than 137.4 thousand Nigerian Naira (roughly 334 U.S. dollars) per year. Similarly, a person having under 87.8 thousand Naira (about 213 U.S. dollars) in a year available for food was living below the poverty line according to Nigerian national standards. In total, 40.1 percent of the population in Nigeria lived in poverty.

    Food insecurity on the rise

    On average, 21.4 percent of the population in Nigeria experienced hunger between 2018 and 2020. People in severe food insecurity would go for entire days without food due to lack of money or other resources. Over the last years, the prevalence with severe food among Nigerians has been increasing, as the demand for food is rising together with a fast-growing population.

  16. f

    ICT results for three urban LGAs of Kano state.

    • plos.figshare.com
    • figshare.com
    xls
    Updated Jun 3, 2023
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    Dung D. Pam; Dziedzom K. de Souza; Susan D'Souza; Millicent Opoku; Safiya Sanda; Ibrahim Nazaradden; Ifeoma N. Anagbogu; Chukwu Okoronkwo; Emmanuel Davies; Elisabeth Elhassan; David H. Molyneux; Moses J. Bockarie; Benjamin G. Koudou (2023). ICT results for three urban LGAs of Kano state. [Dataset]. http://doi.org/10.1371/journal.pntd.0006004.t002
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    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS Neglected Tropical Diseases
    Authors
    Dung D. Pam; Dziedzom K. de Souza; Susan D'Souza; Millicent Opoku; Safiya Sanda; Ibrahim Nazaradden; Ifeoma N. Anagbogu; Chukwu Okoronkwo; Emmanuel Davies; Elisabeth Elhassan; David H. Molyneux; Moses J. Bockarie; Benjamin G. Koudou
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kano
    Description

    ICT results for three urban LGAs of Kano state.

  17. Enterprise Survey 2007 - Nigeria

    • catalog.ihsn.org
    • datacatalog.ihsn.org
    • +2more
    Updated Mar 29, 2019
    + more versions
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    World Bank (2019). Enterprise Survey 2007 - Nigeria [Dataset]. https://catalog.ihsn.org/catalog/713
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    Dataset updated
    Mar 29, 2019
    Dataset provided by
    World Bankhttp://worldbank.org/
    UK Department for International Development
    Time period covered
    2007 - 2008
    Area covered
    Nigeria
    Description

    Abstract

    The 2007 Nigeria Enterprise Survey was part of the UK Department for International Development/World Bank Group Investment Climate Program (ICP) that was launched by the Minister of Finance in August 2007. This program was a response to the request from the Nigeria Federal Minister of Finance to the World Bank Group and UK Department for International Development (DFID) to assist in the development of a diagnostic base on which enterprise and investment climate constraints could be measured and benchmarked internally across the 36 states and the Federal Capital Territory of Nigeria and internationally against key comparator countries, particularly the "BRIC" countries (Brazil, Russia, India and China).

    The survey was conducted between September 2007 and February 2008. Data from 2387 establishments was analyzed. The survey was administered across 11 states (Abia, Anambra, Abuja, Bauchi, Cross Rivers, Enugu, Kaduna, Kano, Lagos, Ogun and Sokoto) and included manufacturing and services firms of different sizes.

    The objective of the Enterprise Surveys is to obtain feedback from companies in client countries on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through face-to-face interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

    The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90% of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

    Geographic coverage

    National

    Analysis unit

    The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

    Universe

    The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors.

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    The sample includes 2387 establishments: 1891 enterprises have at least 5 full-time employees and 496 are micro establishments with less than 5 full-time workers.

    The sample for enterprises with more than four employees was designed using stratified random sampling with strata defined by region, sector and firm size.

    Establishments located in 11 states - Abia, Anambra, Abuja, Bauchi, Cross Rivers, Enugu, Kaduna, Kano, Lagos, Ogun and Sokoto - were interviewed.

    Following the ISIC (revision 3.1) classification, the following industries were targeted: all manufacturing sectors (group D), construction (group F), retail and wholesale services (subgroups 52 and 51 of group G), hotels and restaurants (group H), transport, storage, and communications (group I), and computer and related activities (sub-group 72 of group K). For establishments with five or more full-time permanent paid employees, this universe was stratified according to the following categories of industry: 1. Manufacturing: Food and Beverages (Group D, sub-group 15); 2. Manufacturing: Garments (Group D, sub group 18); 3. Manufacturing: Other Manufacturing (Group D excluding sub-groups 15 and 18); 4. Retail Trade: (Group G, sub-group 52); 5. Rest of the universe, including: • Construction (Group F); • Wholesale trade (Group G, sub-group 51); • Hotels, bars and restaurants (Group H); • Transportation, storage and communications (Group I); • Computer related activities (Group K, sub-group 72).

    Size stratification was defined following the standardized definition used for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers.

    The sampling frame of establishments with 5 employees and more was built with lists sourced from the Nigeria Manufacturer Association, the National Bureau of Statistics in Abia, Anambra, Abuja, Cross River, Enugu, Kaduna, Lagos, the ministry of commerce and industry in Ogun, Kano, Bauchi, and from the Abuja Business Directory, the Sokoto Business Directory. This master list was used to set the target sample size for each stratum. During the survey period, the list was updated as new information regarding establishments that had closed or were out-of-scope was gathered. The final population size in all strata and locations was 771018 with the vast majority of establishments operating in the micro and manufacturing strata. The sample (including the entire rest of universe and retail sample in each state) was selected at random from the master list by a computer program.

    In this survey, the micro establishment stratum covers all establishments of the targeted categories of economic activity with less than 5 employees. The implementing agency (EEC Canada) selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum for all states of the survey.

    First, to randomly select individual micro establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there was a high concentration of micro establishments; ii) count all micro establishments in these specific zones; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

    The districts and the specific zones were selected at first according to local sources. The EEC team then went in the field to verify the sources and to count micro establishments. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

    At the head office, the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select micro establishments to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

    In Nigeria, enumerators were sent to each zone with instructions how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

    Mode of data collection

    Face-to-face [f2f]

    Research instrument

    The current survey instruments are available: - Core Questionnaire + Manufacturing Module [ISIC Rev.3.1: 15-37] - Core Questionnaire + Retail Module [ISIC Rev.3.1: 52] - Core Questionnaire [ISIC Rev.3.1: 45, 50, 51, 55, 60-64, 72] - Micro Establishments Questionnaire (for establishments with 1 to 4 employees).

    The "Core Questionnaire" is the heart of the Enterprise Survey and contains the survey questions asked of all firms across the world. There are also two other survey instruments - the "Core Questionnaire + Manufacturing Module" and the "Core Questionnaire + Retail Module." The survey is fielded via three instruments in order to not ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

    The survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs/labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, registration, and performance measures. The questionnaire also assesses the survey respondents' opinions on

  18. Monthly government funds in Nigeria in 2022-2023, by state

    • statista.com
    Updated May 23, 2025
    + more versions
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    Statista (2025). Monthly government funds in Nigeria in 2022-2023, by state [Dataset]. https://www.statista.com/statistics/1413450/monthly-government-funds-in-nigeria-by-state/
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    Dataset updated
    May 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 2022 - Feb 2023
    Area covered
    Nigeria
    Description

    From January 2022 and February 2023, the Nigerian government allocated the highest disbursements to Delta, Akwa Ibom, Rivers, Bayelsa, Lagos, Kano, and Enugu. These states received over 100 billion Naira each, with Delta receiving almost 424.2 billion Naira. The FAAC (Federal Account Allocation Committee) disbursements represent the funds given to states and regions. They are allocated in proportion to each state population and, consequently, to the number of local governments in the state.

  19. f

    Characteristics of study population and univariate analysis of mortality...

    • plos.figshare.com
    xls
    Updated Jun 21, 2023
    + more versions
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    Farouq Muhammad Dayyab; Hussain Abdullahi Bashir; Abdulwahab Kabir Sulaiman; Garba Iliyasu; Muhammad Hamza; Ahmad Maifada Yakasai; Ibrahim Nashabaru; Hadiza Saidu; Bashir Garba Ahmad; Bashir Dabo; Aminu Yusuf Abubakar; Ibrahim Musa Idris; Abdulrauf Sani Yahaya; Mustapha Ado; Ibrahim Sabo Abdurrahman; Hafizu Musa Usman; Mohammed Kabiru Bello; Jaafar Suleiman Jaafar; Anifowose Abdullahi; Abubakar Muhammad Alhassan; Abdulmalik Ahmad; Alika Ehima Allen; Medu Oghenekevwe Ezekiel; Muhammad Abdullahi Umar; Muhammad B. Abdullahi; Sahabi Kabir Sulaiman; Tijjani Hussaini; Amina Abdullahi Umar; Aminu Ibrahim Tsanyawa; Sabitu Y. Shuaibu; Nasir Alhassan Kabo; Basheer Lawan Muhammad; Mohammed Nura Yahaya; Imam Wada Bello; Ashiru Rajab; Abdulhakim Muhammad Daiyab; Aminu Faruk Kabara; Muhammad Sule Garko; Abdulrazaq Garba Habib (2023). Characteristics of study population and univariate analysis of mortality risk factors of COVID-19 patients (N = 195) at the Kwanar Dawaki isolation center, Kano, Nigeria. [Dataset]. http://doi.org/10.1371/journal.pone.0281455.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 21, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Farouq Muhammad Dayyab; Hussain Abdullahi Bashir; Abdulwahab Kabir Sulaiman; Garba Iliyasu; Muhammad Hamza; Ahmad Maifada Yakasai; Ibrahim Nashabaru; Hadiza Saidu; Bashir Garba Ahmad; Bashir Dabo; Aminu Yusuf Abubakar; Ibrahim Musa Idris; Abdulrauf Sani Yahaya; Mustapha Ado; Ibrahim Sabo Abdurrahman; Hafizu Musa Usman; Mohammed Kabiru Bello; Jaafar Suleiman Jaafar; Anifowose Abdullahi; Abubakar Muhammad Alhassan; Abdulmalik Ahmad; Alika Ehima Allen; Medu Oghenekevwe Ezekiel; Muhammad Abdullahi Umar; Muhammad B. Abdullahi; Sahabi Kabir Sulaiman; Tijjani Hussaini; Amina Abdullahi Umar; Aminu Ibrahim Tsanyawa; Sabitu Y. Shuaibu; Nasir Alhassan Kabo; Basheer Lawan Muhammad; Mohammed Nura Yahaya; Imam Wada Bello; Ashiru Rajab; Abdulhakim Muhammad Daiyab; Aminu Faruk Kabara; Muhammad Sule Garko; Abdulrazaq Garba Habib
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Kano, Nigeria
    Description

    Characteristics of study population and univariate analysis of mortality risk factors of COVID-19 patients (N = 195) at the Kwanar Dawaki isolation center, Kano, Nigeria.

  20. f

    Locality data for Shinkailepas tollmanni used in population genetic...

    • plos.figshare.com
    xls
    Updated Jun 3, 2023
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    Takuya Yahagi; Andrew David Thaler; Cindy Lee Van Dover; Yasunori Kano (2023). Locality data for Shinkailepas tollmanni used in population genetic analysis. [Dataset]. http://doi.org/10.1371/journal.pone.0239784.t001
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 3, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Takuya Yahagi; Andrew David Thaler; Cindy Lee Van Dover; Yasunori Kano
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Locality data for Shinkailepas tollmanni used in population genetic analysis.

  21. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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MACROTRENDS (2025). Kano, Nigeria Metro Area Population (1950-2025) [Dataset]. https://www.macrotrends.net/global-metrics/cities/22005/kano/population

Kano, Nigeria Metro Area Population (1950-2025)

Kano, Nigeria Metro Area Population (1950-2025)

Explore at:
2 scholarly articles cite this dataset (View in Google Scholar)
csvAvailable download formats
Dataset updated
May 31, 2025
Dataset authored and provided by
MACROTRENDS
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
Dec 1, 1950 - Jun 19, 2025
Area covered
Nigeria
Description

Chart and table of population level and growth rate for the Kano, Nigeria metro area from 1950 to 2025.

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