Luanda is by far the largest city in Angola. As of 2022, over 2.7 million people live in the country's capital, which is also Angola's industrial, cultural and urban center. N'dalatando, formerly Vila Salazar, has the second biggest number of inhabitants, around 380 thousand. Huambo and Lobito follow closely, with a total population of over 226 thousand and 207 thousand, respectively.

Population in the largest city (% of urban population) in Angola was reported at 36.77 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Angola - Population in the largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Angola AO: Population in Largest City: as % of Urban Population data was reported at 36.769 % in 2024. This records a decrease from the previous number of 36.812 % for 2023. Angola AO: Population in Largest City: as % of Urban Population data is updated yearly, averaging 37.548 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 52.459 % in 1970 and a record low of 34.060 % in 1994. Angola AO: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Angola – Table AO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Population in largest city in Angola was reported at 9651032 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Angola - Population in largest city - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Accessibility to major cities dataset is modelled as raster-based travel time/cost analysis, computed for the 20 largest cities (>110k habitants) in the country. The following cities are included: City - Population Luanda - 6,759,313 Lubango - 600,751 Huambo - 595,304 Benguela - 555,124 Cabinda - 550,000 Malanje - 455,000 Saurimo - 393,000 Lobito - 357,950 Kuito - 355,423 Uíge - 322,531 Luena - 273,675 Moçâmedes - 255,000 Menongue - 251,178 Sumbe - 205,832 Soyo - 200,920 Dundo - 177,604 N'dalatando - 161,584 M'banza-Kongo - 148,000 Ondjiva - 121,537 Gabela - 116,903 This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (or optimal location).

Angola AO: Population in Largest City data was reported at 9,651,032.000 Person in 2024. This records an increase from the previous number of 9,292,336.000 Person for 2023. Angola AO: Population in Largest City data is updated yearly, averaging 1,698,075.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 9,651,032.000 Person in 2024 and a record low of 219,427.000 Person in 1960. Angola AO: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Angola – Table AO.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Non-deposit Taking Microfinance Institutions (MFIs) for Angola (AGOFCBMFNLNUM) from 2013 to 2015 about microfinance, Angola, and branches.

Luanda was the largest province in Angola as of 2022, with a population projection of over ************ inhabitants. The province is home for Angola's largest city, the capital Luanda, where nearly *********** people lived by the same year. Of ** Angolan provinces, ** were estimated to have more than *********** inhabitants in 2022.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Commercial Banks for Angola (AGOFCBODCLNUM) from 2005 to 2015 about Angola, branches, banks, and depository institutions.

About this dataset

Context

The dataset tabulates the Angola median household income by race. The dataset can be utilized to understand the racial distribution of Angola income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Angola, NY median household income breakdown by race betwen 2011 and 2021
• Median Household Income by Racial Categories in Angola, NY (2021, in 2022 inflation-adjusted dollars)

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Angola median household income by race. You can refer the same here

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

The raster dataset consists of a 500m score grid for slaughterhouse industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on goat and sheep production intensification potential defined using crop production, livestock production systems and goat and sheep distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, goat and sheep distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Goat and Sheep Intensification” * 0.3)

The raster dataset consists of a 500m score grid for slaughterhouse industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location.

The analysis is based on cattle production intensification potential defined using crop production, livestock production systems, and cattle distribution.

The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, cattle distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility) It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.3) + (”Cattle Intensification” * 0.3)

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Dariia Nesterenko

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2) 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap.

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Angola Slaughterhouse Location Score: Cattle (Angola- ~ 500m)

Abstract

This research is a survey of unregistered businesses conducted in Angola between June and November 2010, at the same time with Angola 2010 Enterprise Survey. Data from 119 enterprises were analyzed.

Questionnaire topics include general information about a business, infrastructure and services, sales and supplies, crime, sources and access to finance, business-government relationship, assets, AIDS and sickness (for African region), bribery, workforce composition, obstacles to get registration, reasons for not registering, and benefits that an establishment could get from registration. The mode of data collection is face-to-face interviews.

The Informal Surveys aim to accomplish the following objectives: 1) To provide information about the state of the private sector for informal businesses in client countries; 2) To generate information about the reasons of said informality; 3) To collect useful data for the research agenda on informality; 4) To provide information on the level of activity in the informal sector of selected urban centers in each country.

Geographic coverage

National

Analysis unit

The primary sampling unit of the Informal Surveys is an unregistered establishment. For Angola, informal firms were defined as those not registered as determined by a registry supplied by Dun & Bradstreet.

Universe

The whole population, or the universe, covered in the survey is the non-agricultural informal economy.

At the beginning of each survey, a screening procedure is conducted in order to identify eligible interviewees. At this point, a full description of all the activities of the business owner or manager is taken; based on its principal activity, a business is then classified in the manufacturing or services stratum using a list of activities developed from previous iterations of the survey. Certain activities are excluded such as strictly illegal activities (e.g., prostitution or drug trafficking) as well as individual activities that are forms of selling labor like domestic servants or windshield washers.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Informal Surveys are conducted in selected urban centers, which are intended to coincide with the locations for the implementation of the main Enterprise Surveys. The overall number of interviews is pre-determined.

In Angola, the urban centers identified were Luanda, Huambo and Benguela. At the outset, the target sample in Luanda was 60 interviews, in Huambo was 30 interviews, and in Benguela 30 interviews. The sample will be confined to the major cities covered in the running in parallel enterprise survey of the formal economy. The target number of interviews will reflect, as far as practical, the individuals' population distribution but with no more than 60% sample from a single city and no city with fewer than 20 interviews in total.

Sampling in the Informal Surveys is conducted within clearly delineated sampling zones, which are geographically determined divisions within each urban center. Sampling zones are defined at the beginning of fieldwork, and are delineated according to the concentration and geographical dispersion of informal business activity. After the sampling sizes are defined for each location every city is divided into several zones that may or may not correspond to the administrative districts.

In Angola, using Google maps or local city maps, the target areas within each city were identified. With input from the local agency applying local knowledge, the starting points were defined. The number of zones was determined by the target sample size for each city divided by the cluster size (4 interviews).

In Luanda, for a total of 60 interviews, 15 sampling zones were initially identified (60/4=15 zones). In Huambo, a total of 30 interviews were completed in 7 sampling zones. In Benguela, a total of 29 interviews were conducted in 8 sampling zones. As described above, the criteria used in choosing these sample sectors was a combination of territorial dispersion and the presence of informal businesses.

In order to provide information on diverse aspects of the informal economy, the sample is designed to have equal proportions of services and manufacturing (50:50). These sectors are defined by responses provided by each informal business to a question on the business's main activity included in the screener portion of the questionnaire.

As a general rule, services must constitute an ongoing business enterprise and so exclude the sale of manual labor Manufacturing activity in the informal sector includes business activity requiring inputs and/or intermediate goods. Thus, for example, the processing of coffee, sugar, oil, dried fruit, or other processed foods is considered manufacturing, while the simple selling of these goods falls under services. If an informal business conducts a mixture of these activities, the business is considered under the manufacturing stratum.

Each sampling zone was designed with the goal of obtaining two interviews in services and two interviews in manufacturing. In order to ensure a degree of geographical dispersion within each sampling zone, two starting points were identified.

Each sampling zone, including its two starting points, were marked using Google maps, with the GPS coordinates of the starting points being systematically recorded.

Additionally, when obtaining a complete interview, the exact address of the informal business (or where the interview took place) was registered by the interviewer. Once in the office, this address was searched in Google maps, and its GPS coordinates were registered in a fieldwork report.

If no address was immediately available, using local knowledge, the GPS coordinates were determined using imaging via Google maps. In order to preserve confidentiality, the exact coordinates of businesses are not published.

Due to issues of non-response, in the process of fieldwork, the implementing contractor was unable to obtain the targeted four interviews in each of the originally delineated sectors.

As a result, replacement sectors were delineated, ex post. Additionally, the implementing contractor noted that in various interviews there were notable shortfalls in response rates to certain questions. For these reasons, additional interviews were authorized. These were distributed according to the discretion of the implementing contractor in Angola, with authorization from the World Bank.

In sum, there were 30 zones in Angola; Luanda (15 zones), Huambo (7 zones), and Benguela (8 zones).

Complete information regarding the sampling methodology can be found in "Description of Angola Informal Survey Implementation" in "Technical Documents" folder.

Mode of data collection

Face-to-face [f2f]

Research instrument

The current survey instrument is available: - Informal Questionnaire.

The survey topics include general information about a business, infrastructure and services, sales and supplies, crime, sources and access to finance, business-government relationship, assets, AIDS and sickness (for African region), bribery, workforce composition, obstacles to get registration, reasons for not registering, and benefits that an establishment could get from registration.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

About this dataset

Context

The dataset tabulates the Angola household income by gender. The dataset can be utilized to understand the gender-based income distribution of Angola income.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Angola, NY annual median income by work experience and sex dataset : Aged 15+, 2010-2022 (in 2022 inflation-adjusted dollars)
• Angola, NY annual income distribution by work experience and gender dataset (Number of individuals ages 15+ with income, 2021)

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Angola income distribution by gender. You can refer the same here

The raster dataset consists of a 500m score grid for vegetables storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Vegetables. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.2) + (“Asset Wealth” * 0.1) + ("Major Ports Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

最大城市人口占城市总人口的百分比在12-01-2024达36.769%，相较于12-01-2023的36.812%有所下降。最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2024期间平均值为37.548%，共65份观测结果。该数据的历史最高值出现于12-01-1970，达52.459%，而历史最低值则出现于12-01-1994，为34.060%。CEIC提供的最大城市人口占城市总人口的百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的安哥拉 – Table AO.World Bank.WDI: Population and Urbanization Statistics。

The raster dataset consists of a 500m score grid for fruits storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Fruits. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.2) + (“Asset Wealth” * 0.1) + ("Major Ports Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

The raster dataset consists of a 500m score grid for banana storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Banana. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.2) + (“Asset Wealth” * 0.1) + ("Major Ports Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

This is a very small project that focus on yearly sales (real data that went through slightly modifications)of a company's two different stores. This stores are located in different areas of the city (Huambo, Angola) where one is in the main shopping center and the other one 13 miles from the city center. In this table you will find the mobiles brand, name, colour, battery in mAh, prices, sales per store and other features.

Although Angola is one of the most expensive countries to live in, the minimum wage is very low at about 35,000kz which is 54USD roughly.

The objective of this project is to calculate and compare how much each store sold in total, by brand and how much the company made in that year. By managing the data itself, conclusions can be drawn and assumptions can be made. However, test yourself and do Machine Learning to predict the future sales considering factores like minimum country wage, phone upgrades resulting in new phones and lower prices for the existing models, the location of the store, etc.