This map features the locations of the major cities of Africa, displayed at multiple scale levels. The layers are a filtered view of the World Cities layer, with just the cities intersecting with the continent of Africa.The popup for the layer includes a dynamic link to Wikipedia, using an Arcade expression.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

City population size is a crucial measure when trying to understand urban life. Many socio-economic indicators scale superlinearly with city size, whilst some infrastructure indicators scale sublinearly with city size. However, the impact of size also extends beyond the city’s limits. Here, we analyse the scaling behaviour of cities beyond their boundaries by considering the emergence and growth of nearby cities. Based on an urban network from African continental cities, we construct an algorithm to create the region of influence of cities. The number of cities and the population within a region of influence are then analysed in the context of urban scaling. Our results are compared against a random permutation of the network, showing that the observed scaling power of cities to enhance the emergence and growth of cities is not the result of randomness. By altering the radius of influence of cities, we observe three regimes. Large cities tend to be surrounded by many small towns for small distances. For medium distances (above 114 km), large cities are surrounded by many other cities containing large populations. Large cities boost urban emergence and growth (even more than 190 km away), but their scaling power decays with distance.

Lagos is Nigeria's largest city and commercial capital. Lagos is among the top ten of the world's fastest-growing cities and urban areas. The megacity has the fourth highest GDP in Africa and houses one of the largest and busiest seaports of the continent.

The goal is to determine the population growth rate from 2007 to 2024, also to build a machine learning model to predict the population in 2025

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

This dataset shows the percentage population of house holds using piped water in their houses or elsewhere in urban centres

The Urban Social Disorder (USD) dataset v2.0 contains information on urban ‘social disorder’ events occurring in capitals and other major cities of the developing world for the 1960-2014 period. Version 2 of the dataset covers 103 major cities in 89 different countries, with near complete coverage of the largest cities in Sub-Saharan Africa, Central- and East Asia, the Middle East and North-Africa, and Latin America.

The dataset contains detailed information on the individual event, coded in csv format and supplied with extracts from the Keesing’s text files. The data are further organized into aggregated annual and monthly counts of violent and non-violent events per city, facilitating cross-sectional time-series analyses. An overview table for all the cities provides useful meta-data such as geographic coordinates and ID keys for conveniently linking with other city-specific datasets.

A total of 9,018 events have been recorded for these cities, of which 3,797 involved lethal casualties. The data are compiled from electronic news reports in the Keesing’s Record of World Events and cover different forms of both violent and non-violent politically motivated disorder, including: - strikes - demonstrations - rioting - terrorism - assassinations - coup d’états - warfare/battles

Accurate delineation of the urban and rural areas has a broad range of implications on the quality and reliability of agricultural production and socio-economic statistics, design of household survey, establishment of agricultural development strategies and policies, and effective resource allocation. Two most widely-used urban/rural mapping dataset across Africa, GRUMP (Global Rural and Urban Mapping Project; http://sedac.ciesin.columbia.edu/data/collection/grump-v1) and SAGE Urban Extents (https://nelson.wisc.edu/sage/data-and-models/schneider.php), uses the underlying datasets of 2000-2002. There are various pilot studies attempting to update the dataset in major metropolitan areas or specific countries, but no African continent-wide effort has been made to date. To address this, using the GRUMP 2000 data as the baseline, we used a set of recently-published datasets to identify the newly extended urban areas across Africa. Three main data sources were the nightlights data from Defense Meteorological Satellite Program (DMSP) 2010-2013, WorldPop 2010, and the MODIS Global Land Cover 2010-2013. Country-level urban population headcounts and their share of total population were acquired from the World Bank for 2010-2013 and used to control the total size of the urban population from the analysis is consistent with the statistics data at 1 km resolution.

Replication Data for Adam Storeygard; Farther on down the Road: Transport Costs, Trade and Urban Growth in Sub-Saharan Africa, The Review of Economic Studies, Volume 83, Issue 3, 1 July 2016, Pages 1263–1295, https://doi.org/10.1093/restud/rdw020. Abstract: How does isolation affect the economic activity of cities? Transport costs are widely considered an important barrier to local economic activity but their impact in developing countries is not well-studied. This paper investigates the role of inter-city transport costs in determining the income of sub-Saharan African cities. In particular, focusing on fifteen countries whose largest city is a port, I ask how important access to that city is for the income of hinterland cities. The lack of panel data on both local economic activity and transport costs has prevented rigorous empirical investigation of this question. I fill this gap with two new datasets. Satellite data on lights at night proxy for city economic activity, and new road network data allow me to calculate the shortest route between cities. Cost per unit distance is identified by plausibly exogenous world oil prices. The results show that an oil price increase of the magnitude experienced between 2002 and 2008 induces the income of cities near a major port to increase by 6.6 percent relative to otherwise identical cities one standard deviation farther away. Combined with external estimates, this implies an elasticity of city economic activity with respect to transport costs of -0.25 at that distance. Moreover, the effect differs by the surface of roads between cities. Cities connected to the port by paved roads are chiefly affected by transport costs to the port, while cities connected to the port by unpaved roads are more affected by connections to secondary centers. This dataset is part of the Global Research Program on Spatial Development of Cities funded by the Multi-Donor Trust Fund on Sustainable Urbanization of the World Bank and supported by the U.K. Department for International Development.

PM 2.5 is responsible for an estimated 4.2 million deaths around the world every year.

Data sourced from : OpenAfrica

Description of Features:

Date: Date

PM 2.5: Fine particulate matter (PM 2.5) is a general term for all small particles found in air measuring equal to or less than **2.5 μm in aerodynamic diameter. It is a complex mixture whose constituents vary in size, shape, density, surface area, and chemical composition. The 2.5 in PM 2.5 refers to the size of the pollutant, in micrometers. It is about 30 times smaller than the width of a strand of fine hair.

The Ibadan, Nigeria malaria-prevalence dataset 1996 to 2017. When using the dataset please also cite: Brown, B.J., Manescu, P., Przybylski, A.A. et al. Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa. Sci Rep 10, 15918 (2020). https://doi.org/10.1038/s41598-020-72575-6For metadata and supplementary information see:Brown, B.J., Manescu, P., Przybylski, A.A. et al. Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa. Sci Rep 10, 15918 (2020). https://doi.org/10.1038/s41598-020-72575-6

This dataset provide the definition of the 302 Large Urban Regions (LURs) in Africa.

LURs are an aggregated set of localities forming consistent regions around cities

For the definition of LUR, see Rozenblat (2020) and for the building method of African LURs see Rogromel (2024)

This dataset shows the percentage of house holds that use ordinary pit latrines or the main sewer as their main mode of Human Waste disposal.

In sub-Saharan Africa, rapid population growth, urbanization, increasing incomes, and changing dietary preferences are the main drivers of the rising demand for livestock products, especially fresh milk and derived products. To meet this demand, there is an increasing number of dairy cattle farms in the densely populated coastal zone of Benin, where the country's largest city and commercial capital Cotonou is located. To identify and characterize the peri-urban dairy production systems in this region, 190 cattle keepers were surveyed, using the snowball sampling method, in four municipalities neighboring Cotonou. Information on their socio-economic characteristics, cattle herd sizes, and herd management practices were collected through questionnaire-based face-to-face interviews. Factor analysis of mixed data followed by hierarchical clustering on principal components, implemented in R statistical software, were applied to classify the surveyed farms into homogeneous groups. Results revealed six types of peri-urban dairy cattle farms differing mainly in their cows' breeds, herd sizes, and daily amount of milk produced. Most herds (88%) were owned by urban dwellers, mainly civil servants and traders, who entrusted the management of their cattle to hired professional herders. Irrespective of farm type, cows were of local taurine (65%) or Sahelian zebu (35%) breeds and were exclusively fed on communal natural pasture. Mineral supplementation was provided to the animals on 42% of farms, with significant variation across farm types. About 45% of the farms integrated cattle production with other agricultural activities, including coconut plantations (22%), where cow manure was used as fertilizer. The herd structure was similar across farm types, with average proportions of cows and heifers ranging from 37.6 to 47.5% and from 13.1 to 19.7%, respectively. With significant differences across farm types, the produced milk was either transformed into traditional cheese (32% of farms) or sold raw (85%). Milk and cheese sales represented 84% of the total farm income for three out of the six farm types. In the current context of rapid urbanization, communal grazing lands alone cannot provide sufficient feed to support increased milk production. In addition to improved feeding strategies, herd structure should be balanced in terms of the ratio between milk-producing and non-producing animals.

Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community–based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead