Chart and table of Mali population density from 1950 to 2025. United Nations projections are also included through the year 2100.

The population density in Mali increased by 0.6 inhabitants per square kilometer (+3.27 percent) compared to the previous year. With 18.91 inhabitants per square kilometer, the population density thereby reached its highest value in the observed period. Notably, the population density continuously increased over the last years.Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Sierra Leone and Niger.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Mali data available from WorldPop here.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata.

DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted.

REGION: Africa

SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator)

PROJECTION: Geographic, WGS84

UNITS: Estimated persons per grid square

MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org)

FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available.

Mali data available from WorldPop here.

WorldPop produces different types of gridded population count datasets, depending on the methods used and end application. Please make sure you have read our Mapping Populations overview page before choosing and downloading a dataset.

Datasets are available to download in Geotiff and ASCII XYZ format at a resolution of 30 arc-seconds (approximately 1km at the equator)

-Unconstrained individual countries 2000-2020: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population count datasets by dividing the number of people in each pixel by the pixel surface area. These are produced using the unconstrained top-down modelling method.
-Unconstrained individual countries 2000-2020 UN adjusted: Population density datasets for all countries of the World for each year 2000-2020 – derived from the corresponding Unconstrained individual countries 2000-2020 population UN adjusted count datasets by dividing the number of people in each pixel, adjusted to match the country total from the official United Nations population estimates (UN 2019), by the pixel surface area. These are produced using the unconstrained top-down modelling method.

Data for earlier dates is available directly from WorldPop.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00674

Mali population density for 400m H3 hexagons.

Built from Kontur Population: Global Population Density for 400m H3 Hexagons Vector H3 hexagons with population counts at 400m resolution.

Fixed up fusion of GHSL, Facebook, Microsoft Buildings, Copernicus Global Land Service Land Cover, Land Information New Zealand, and OpenStreetMap data.

This statistic shows the total population of Mali from 2013 to 2023 by gender. In 2023, Mali's female population amounted to approximately 11.77 million, while the male population amounted to approximately 11.99 million inhabitants.

The population density in Liberia increased by 1.2 inhabitants per square kilometer (+2.2 percent) in 2022 in comparison to the previous year. With 55.79 inhabitants per square kilometer, the population density thereby reached its highest value in the observed period. Notably, the population density continuously increased over the last years.Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Sierra Leone and Mali.

Chart and table of SM.POP.NETM population density from 1950 to 2025. United Nations projections are also included through the year 2100.

Social distancing is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance (also referred to as physical distance), with distances of 6 feet or 2 metres commonly advised. Feasibility of social distancing is dependent on the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission may be needed. To help identify locations where social distancing is difficult, we have developed an ease of social distancing index. By index, we mean a composite measure, intended to highlight variations in ease of social distancing in urban settings, calculated based on the space available around buildings and estimated population density. Index values were calculated for small spatial units (vector polygons), typically bounded by roads, rivers or other features. This dataset provides index values for small spatial units within urban areas in Mali. Measures of population density were calculated from high-resolution gridded population datasets from WorldPop, and the space available around buildings was calculated using building footprint polygons derived from satellite imagery (Ecopia.AI and Maxar Technologies. 2020). These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 project with funding from the Bill and Melinda Gates Foundation and the United Kingdom’s Department for International Development. Project partners included the United Nations Population Fund (UNFPA), Center for International Earth Science Information Network (CIESIN) in the Earth Institute at Columbia University, and the Flowminder Foundation.

Mali administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata.
Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population

In 2022, the population density in Cabo Verde remained nearly unchanged at around 128.97 inhabitants per square kilometer. Nevertheless, 2022 still represents a peak in the population density in Cabo Verde with 128.97 inhabitants per square kilometer. Population density is calculated by dividing the total population by the total land area, to show the average number of people living there per square kilometer of land.Find more key insights for the population density in countries like Mauritania and Mali.

This bar chart displays male population (people) by date using the aggregation sum and is filtered where the country is Mali. The data is about countries per year.

Physical conservation for small dams has been assessed on the basis of population density, water runoff and time of transportations to reach the nearest market. Average time of transport to the market: Criteria selection is based on the fact that the farms targeted for this technology are oriented towards commercialization. Runoff: areas in the band 20-150 mm / year are considered highly relevant, areas with a runoff above 150 are considered to be moderately relevant. Areas where runoff is less than 20 mm / year are assessed as unfit. Population density: a medium and low density of population is considered favorable

This statistic shows the median age of the population in Mali from 1950 to 2100*.The median age is the age that divides a population into two numerically equal groups; that is, half the people are younger than this age and half are older. It is a single index that summarizes the age distribution of a population. In 2020, the median age of the population in Mali was 15.1 years.

Abstract

Mali is a Sahelian country, landlocked and structurally vulnerable to food insecurity and malnutrition. The economy is heavily dependent on the primary sector: agriculture, livestock, fishing and forestry account for 68.0% of the active population1 . This sector is itself dependent on exogenous factors, mainly climatic, such as recurrent droughts. In 2018, the prevalence of food insecurity at the national level was 19.1%, of which 2.6% was severely food insecure. The most affected regions were Kidal, Gao, Timbuktu, Mopti and Kayes. The Global Food Crisis Network Partnership Programme baseline studies are designed to feed into the overall monitoring, evaluation, accountability and learning programme of each project. In this regard, the baseline study has short, medium and long-term objectives.

Geographic coverage

Regional coverage

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

The EAC-I 2014 has been designed to have national coverage, including both urban and rural areas in all the regions of the country except Kidal. The domains were defined as the entire country, district of Bamako, other urban areas, and rural areas; and in the rural areas: agricultural zones, agro-pastoral zones and pastoral zones. Taking this into account, 51 explicit sample strata were selected. The target population was drawn from households in all regions of Mali except Kidal which was not accessible for security reasons. Kidal also has very low population density.The sample was chosen through a random two stage process: - In the first stage, 1070 enumeration areas (EAs) were selected with Probability Proportional to Size (PPS) using the 2009 Census of Population as the base for the sample, and the number of households as a measure of size. - In the second stage o 3 households were selected with equal probability in each of the rural EAs o 9 households were selected with equal probability in each of the urban EAs The total estimated size of the sample for the survey was 4,218.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Please refer to the Questionnaires for the value labels of the variables.

Cleaning operations

Data Entry & Data cleaning Data entry was performed at the CPS/SDR using a CSPro application designed by an international consultant recruited by the LSMS team. The data entry program allows three types of data checks: (1) range checks; (2) intra-record check to verify inconsistencies pertinent to the particular module of the questionnaire; and (3) inter-record checks to determine inconsistencies pertinent between the different modules of the questionnaire. Data entry for the first visit was done from August 11th, 2014 to November 30th, 2014 and, from February 9th 2015 to March 27th, 2015 for the second visit. Data cleaning was done from May 2015 to July 2015. Data cleaning was done in a CSPro application. The data cleaning focused on more intra-record and inter-record checks.

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Mali.

Analysis unit

• Household
• Individual

Universe

The WVS for Mali covers national population aged 16 years and over, for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling is the process of selecting certain members of a group in such a way that they will represent the universe. Selection of respondents for the project followed a stratified Multi-Stage

Random Selection Procedure as follows:

Selection of Sectors/EAs: Sectors are defined as sampling blocks of equal geographical dimensions with identifiable boundaries, encompassing a substantial number of people. Sectors were divided into high, medium and low density areas. Each of the sectors was thereafter randomly selected from each area using the available street maps already sectorised into different density areas. Where maps are not available, especially for rural areas, an exhaustive list of all sectors was considered. The sample allocated to each density area i.e. high/medium and low was proportionate to the number of sectors in each group. The overall sample for the urban and rural locations determined the number of sectors selected. However, a maximum of five (05) interviews were conducted in each randomly selected sector. All sectors were selected by simple random method via a random numbered table. Group interviewing techniques were adopted for the study across all the study locations. By this design, a team of interviewers under the leadership of a supervisor moved as a group to each selected sector, and then completed the assigned quota for that sector before moving to another sector. This afforded the supervisors the opportunity to closely monitor the interviewers under them. The questionnaire was precoded using the alphabet letters A to K excluding letter I.

Selection of Sampling/Entering Points within each sector: Immediately after the selection of the sectors, the supervisors surveyed each of the selected sectors to determine the sampling/entering points of the sector. These are points where the team started their days interviewing. These can be prominent structures such as churches, mosques, schools, hospitals, etc.

Selection of Dwelling Structure within each sector: In each of the randomly selected sectors, the Days Code was used to determine each interviewers starting point, i.e. [The first house/dwelling structure to enter/approach]. A dwelling structure is defined as a floor of a distinct residential building within a sector of a town/village; where only one household occupied a multi-storey building, the entire building [and not the floor] constituted a dwelling structure. Where it is a multi-storey building with multiple occupants, counting of floors was carried out consistently from the upper floor to the ground floor in an unbroken chain from floor to floor. A fixed sampling gap of one in three (1:3) and one in five (1:5) respectively was observed after each successful call in low, medium and high density areas.

Selection of Household: On entering a selected dwelling structure, each interviewer determined the number of households within the structure. Having done that, the interviewer then used the household selection grid to determine the household where the interview would take place. A household is defined as the collective individuals living under the same roof and having a common feeding arrangement and also with a recognised person in the household as the head of household. Only residents who have stayed in the selected household for at least six [6] months were interviewed. Visiting relations who have stayed for less than six months were not regarded as household members.

Substitution of Households: In the case where the selected room was unoccupied, interviewers were instructed to replace with the next household. Only one substitution was allowed per dwelling structure.

Selection of Respondents: Respondents were randomly selected among the male and female household members. In order to select the final person to interview within the selected household, all the male and female residents of Mali, aged 16 years and above in the selected household were listed by names and ages on the respondents selection grid on the questionnaires. The listing was done from the eldest to the youngest (males and females combined) and then one respondent was selected using the Kish grid a table of randomly generated numbers.

Call Backs/Substitution Criteria: In the case where the selected adult in the household was not available at the time of the call, interviewers were instructed to make up to two additional recalls on different times of the day including evenings when the selected respondent was said to be at home. However, where the selected adult was not available for interviewing within the days of selection, interviewers were asked to regard such a case as a non-response situation or ineffective call. No substitution of respondents within the same household/dwelling structure was allowed.

Coverage/Achievement: The training was organised in five central locations to cover the regions as follows: - The central briefing in Bamako centre to cover: Bamako and the region of Koulikoro (15 interviewers, 3 supervisors and 2 quality controllers) - Sikasso center to cover the region of Sikasso (16 interviewers, 3 supervisors and 1 quality controller) - Kayes center to cover the region of Kayes (11 interviewers, 2 supervisors and 1 quality controller) - Segou center to cover the region of Segou (16 interviewers, 3 supervisors and 1 quality controller) - Mopti center to cover the region of Mopti (14 interviewers, 3 supervisors and 1 quality controller) At the end of fieldwork, the total number ofeffective calls achieved was one thousand five hundred and thirty eight [1,538].

Remarks about sampling: Substitution of Households: In the case where the selected room was unoccupied, interviewers were instructed to replace with the next household. Only one substitution was allowed per dwelling structure. Call Backs/Substitution Criteria: In the case where the selected adult in the household was not available at the time of the call, interviewers were instructed to make up to two additional recalls on different times of the day including evenings when the selected respondent was said to be at home. However, where the selected adult was not available for interviewing within the days of selection, interviewers were asked to regard such a case as a non-response situation or ineffective call. No substitution of respondents within the same household/dwelling structure was allowed.

The sample size for Mali is N=1534 and includes national population aged 16 years and over for both sexes.

Mode of data collection

Face-to-face [f2f]

Research instrument

20 pilot interviews were conducted among respondents from various demographics to check that questions were understandable and amend the wording of some questions when necessary In order to facilitate the quality of field operations, the questionnaire was translated into French, the official language in Mali. During the briefing session, the questionnaire was loosely translated into Bambara (the major language spoken in Mali) for the purpose of common understanding and to facilitate communication should the interviewers encounter illiterate respondents. The following are problems encountered by or comments made by interviewers and supervisors working on this study: - The length of the questionnaire: almost all the respondents complained that the interview was too long. Some respondents even had to stop the interview half way. - There were cases of selected female respondents who refused to answer the questions of the interviewers in absence of their husbands. - The environmental concepts (such as in V111 - Global warming or the greenhouse effect-, V112 - Global warming or the greenhouse effect, V113 - Pollution of rivers, lakes and oceans) were difficult to explain to illiterate respondents. - Subjects like (V.38 & V.41) homosexuality and unmarried couples living together seemed to be embarrassing for some respondents who didnt want to talk