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.

This bar chart displays population (people) by capital city using the aggregation sum in Angola. The data is about countries per year.

This horizontal bar chart displays male population (people) by capital city using the aggregation sum in Angola. The data is filtered where the date is 2021. The data is about countries per year.

This horizontal bar chart displays urban population (people) by capital city using the aggregation sum in Angola. The data is filtered where the date is 2021. The data is about countries per year.

Comprehensive socio-economic dataset for Angola including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

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.

This horizontal bar chart displays individuals using the Internet (% of population) by capital city using the aggregation average, weighted by population in Angola. The data is filtered where the date is 2021. The data is about countries per year.

This horizontal bar chart displays birth rate (per 1,000 people) by capital city using the aggregation average, weighted by population in Angola. The data is filtered where the date is 2021. The data is about countries per year.

This horizontal bar chart displays fertility rate (births per woman) by capital city using the aggregation average, weighted by population female in Angola. The data is about countries per year.

Provinces of Angola. name, type, Area, capital city, Country, continent, population

This horizontal bar chart displays access to electricity (% of population) by capital city using the aggregation average, weighted by population in Angola. The data is filtered where the date is 2021. The data is about countries per year.

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).

This horizontal bar chart displays death rate (per 1,000 people) by capital city using the aggregation average, weighted by population in Angola. The data is filtered where the date is 2021. The data is about countries per year.

Climate change is already affecting people’s lives and livelihoods in Angola, as well as the Angolan economy. The country is experiencing increasingly severe and frequent climate hazards, including the South’s worst prolonged droughts in decades. Climate change impacts also come with a heavy price tag: climate-related disasters (floods, storms, droughts) cost Angola nearly US$1.2 billion between 2005 and 2017, and on average droughts alone affect about a million Angolans every year. Impacts of climate variability on Angola’s water resources are expected to be particularly severe and will affect food and energy production, as well as hydropower, on which Angola relies for most of its electricity. The future does not look much brighter: climate models predict a rise in temperatures, with most of Angola becoming 1–1.5 degree Celsius warmer in 2020-2040 relative to the 1981–2010 period, with a 1.4-degree Celsius increase in the annual average temperature already recorded. The imperative to adapt and transition to a proactive model for climate risk management is urgent. Against this backdrop, and the equally urgent priority to diversify away from a highly oil-based economy, the Angola Country Climate and Development Report (CCDR) provides options for the country to adapt to a fast-warming and decarbonizing world and adopt measures for more diversified and climate-resilient development that will underpin sustainable and inclusive growth. Angola has significant renewable capital, including agricultural land, forests, water resources, and, above all, its people, who can facilitate this process. But climate change also threatens these renewable assets, and necessary investments in climate resilience will be critical to realize their potential. This report identifies five pathways to achieve a vision of a future Angolan economy that is both diversified and climate-resilient, with opportunities for all. Tailored to the national context, these approaches were identified in dialogue with the Government of Angola and build on national development priorities. Angola is rich in natural capital, not only oil, gas, and diamonds, but also abundant water resources, renewable energy potential, and fertile arable land. Therefore, to shift away from an economy driven by oil and gas extraction and toward a sustainable and diversified economy based on renewable natural capital, this CCDR recommends investing in and building the resilience of key sectors, notably 1) water resources, 2) agriculture and fisheries, and 3) renewable energy. Delivering the vision of a climate-resilient and diversified economy also entails 4) enabling green and resilient cities with economic opportunities for all Angolans; and leveraging Angola’s young population by 5) boosting human capital, through expanded, climate-resilient access to basic services and by fostering a culture of climate preparedness.

This point shapefile includes estimation on the economic value of the exposed assets in Angola as well as their physical characteristics in urban and rural agglomerations including estimation of population too. This information is key to assess the potential damages from different hazards to each of the exposed elements. The global exposure database is developed at 1km spatial resolution at coastal areas and at 5km spatial resolution everywhere else on the globe. It includes economic value, number of residents, and construction type of residential, commercial and industrial buildings, as well as hospitals and schools. Accessing national census has proved to be quite challenging. For estimating the non- residential distributions, especially for the countries for which no relevant published census data were available, several other sources such as World Housing Encyclopedia as well as expert judgment are used to make assumptions necessary to estimate the properties of the building stock. Combining all the components mentioned above, the economic value of each building class in one cell is assessed based on the disaggregation of the (national) Produced Capital at grid level. This downscaling was done by using the sub-national values of economic activity as a proxy. The result is the global distribution of the economic value of the urban and rural produced capital by construction class. Further details on the GAR Global Exposure Dataset can be found in technical background papers (De Bono, et.al, 2015), (Tolis et al., 2013) and (Pesaresi, et.al, 2015)..

This horizontal bar chart displays life expectancy at birth (year) by capital city using the aggregation average, weighted by population in Angola. The data is about countries per year.

This statistic shows the 20 countries with the highest population growth rate in 2024. In SouthSudan, the population grew by about 4.65 percent compared to the previous year, making it the country with the highest population growth rate in 2024. The global population Today, the global population amounts to around 7 billion people, i.e. the total number of living humans on Earth. More than half of the global population is living in Asia, while one quarter of the global population resides in Africa. High fertility rates in Africa and Asia, a decline in the mortality rates and an increase in the median age of the world population all contribute to the global population growth. Statistics show that the global population is subject to increase by almost 4 billion people by 2100. The global population growth is a direct result of people living longer because of better living conditions and a healthier nutrition. Three out of five of the most populous countries in the world are located in Asia. Ultimately the highest population growth rate is also found there, the country with the highest population growth rate is Syria. This could be due to a low infant mortality rate in Syria or the ever -expanding tourism sector.

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]

This horizontal bar chart displays health expenditure per capita (current US$) by capital city using the aggregation average, weighted by population in Angola. The data is filtered where the date is 2021. The data is about countries per year.

Abstract

The National Institute of Statistics of Angola (INE), conducted the Expenses and Revenue Survey (IDR) and Expenses, Revenue and Employment Survey in Angola (IDREA). Both surveys were conducted in the same conglomerate, had the same representation and used the same questions using the recall method, that is, the head of the household was asked to report on the expenses incurred in the last 7 days. IDREA could be the benchmark for measuring poverty in the future and will be used in the production of socio-economic indicators for the collection period.

The IDR survey is intended to measure and compare poverty through consumption and uses the daily method, which consists of making alternate visits to the household, for 7 days, recording its expenses; a similar methodology used in the Integrated Survey on Population Welfare (IBEP) 2008/2009. The two surveys (IBEP 2008/2009 and IDR 2018/2019) may present different results, in relation to the levels of poverty, due to the methodology used by each of them, but they are comparable and can be used to make assessments of the trends of poverty over time.

The IDREA aimed to produce information for decision making. More specifically: - To update the Poverty Profile in Angola - To update the weights of the Consumer Price Index (CPI) - To estimate household consumption for National Accounts - To evaluate Angola's progress towards the achievement of the Sustainable Development (SDG) 2015-2030, in the implementation of the PND 2018-2022 and African Agenda 2063.

Geographic coverage

National coverage: - Capital city: Luanda - Center (urban): Urban areas of Huambo, Bié, Benguela and South Cuanza - Center (rural): Rural areas of Huambo, Bié, Benguela and South Cuanza - East (urban): Urban areas of North Lunda, South Lunda, Moxico and Cuando Cubango - East (rural): Rural areas of North Lunda, South Lunda, Moxico and Cuando Cubango - Center-north (urban): Urban areas of Bengo, Malanje and North Cuanza - Center-north (rural): Rural areas of Bengo, Malanje and North Cuanza - South (urban): Urban areas of Namibe, Cunene and Huíla - South (rural): Rural areas of Namibe, Cunene and Huíla - North (urban): Urban areas of Cabinda, Uíge and Zaire - North (rural): Rural areas of Cabinda, Uíge and Zaire

Analysis unit

• Individuals
• Households
• Communities

Kind of data

Sample survey data [ssd]

Sampling procedure

A total of 12,448 households were independently selected in each of the 18 provinces for each survey. The two surveys were representative by area of residence down to the province level. However, in order to allow the spatial and temporal disaggregation of prices, it was decided to maintain the regional breakdown that had been introduced in 2008, which grouped the conglomerates in 11 regions considered to be quite homogeneous from the point of view of price formation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

4/5 questionnaire models were designed to collect the IDR / IDREA surveys data following the recommended objectives: - Questionnaire A for capturing data on demography, health, education, housing, water and sanitation, expenses and income, production agribusiness and fisheries. - Questionnaire B and C for capturing data on expenses and consumption aggregate diary. - Questionnaire E for capturing data on prices and unit measures used in local markets. - Questionnaire F for the Community module.

More details on the questionnaire are provided as external resources.

Response rate

The response rates for the IDR /IDREA were around 86.1% and 99.7% respectively.