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.

This layer features the locations of the major cities of Africa. It is 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.

This layer is provided by ESRI and presents the locations of major cities throughout the world. The original data was clipped by the WWHGD data team to only include countries within the coverage of the Combating Wildlife Trafficking (CWT) data call.

This subset of the world cities layer presents the locations of major cities in East Africa: specifically national and region capitals.To download the data for this layer as a layer package for use in ArcGIS desktop applications, please refer to World Cities.

Uganda is one of Africa’s most rapidly urbanizing countries, with a population base of 34 million, a high population growth rate of 3.4 percent and a high rate of urban growth estimated at 5.1 percent per annum and total urbanization rate of15% of the total population.

The size of the Africa Geospatial Analytics market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 6.99% during the forecast period.Geospatial analytics is a tool where the potential of location-based data, which is fast catching up in the African continent, is pooled to utilize the integration of the geographic information systems (GIS), global positioning systems (GPS), and remote sensing technologies. These empower organizations to interpret and make value out of spatial data for analytical purposes. Geospatial analytics analyzes patterns and trends as well as their relationships within geographic contexts that will give a more holistic understanding of complex phenomena.Geospatial analytics alters most aspects of life in these areas in Africa. It is helpful in optimizing crops and resources in precision farming. Farmers learn when to make agriculture decisions through analysis of data on soil quality, weather, and crop health for maximization of its produce. In urban planning, it helps in urban development, infrastructure planning, and disaster management. Mapping the pattern of growth for cities, identification of vulnerable areas, and even the optimization of resource allocation makes cities sustainable and resilient. Geospatial analytics is also important in natural resource management, environmental conservation, and climate change adaptation. Monitoring deforestation, tracking populations of wildlife, and assessing the impact of climate will help policymakers further strategize towards a more effective implementation of conservation. Africa is continuing to step into the technological fray, and the market for geospatial analytics is supposed to grow its presence multifold.From seemingly endless savannahs, countless forests, substantive natural riches, and burgeoning cities, Africa has much to harness from the insights offered by geospatial analytics: Address real challenges, unlock fresh opportunities, and drive sustainable development across the continent. Recent developments include: November 2022: A Memorandum of Understanding (MOU) was signed by SaskTel and Axiom Exploration Group to jointly explore opportunities to assist organizations throughout Saskatchewan in enhancing and modernizing their operations through the gathering and analysis of geospatial and other geophysical data., September 2022: A two-day conference on Data Analytics and visualization was held by Women in GIS Kenya in association with Pathways International, Esri Eastern Africa, Nakala Analytics, and the University of Nairobi, Department of Geospatial and Space Technology.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: High costs associated with geospatial technologies. Notable trends are: Commercialization of Spatial Data.

The Africa Geospatial Analytics market, currently valued at $0.26 billion in 2025, is projected to experience robust growth, driven by increasing government investments in infrastructure development, rising adoption of precision agriculture techniques, and the expanding need for effective resource management across various sectors. The market's Compound Annual Growth Rate (CAGR) of 6.99% from 2025 to 2033 indicates a significant expansion over the forecast period. Key drivers include the escalating demand for accurate location-based services across industries like utilities, defense, and mining, alongside advancements in data analytics technologies, particularly in remote sensing and GIS software. The market segmentation reveals strong demand across diverse end-user verticals, with agriculture, utilities and communications, and defense and intelligence sectors likely to be significant contributors to market growth. The availability of affordable data and cloud-based solutions will further fuel market expansion. However, challenges such as limited internet penetration in certain regions and a scarcity of skilled professionals may act as restraints. Growth will be particularly strong in countries with substantial infrastructure projects and a need for efficient resource management, such as Nigeria, South Africa, and Egypt. The increasing adoption of smart city initiatives and the need for precise mapping for urban planning will further contribute to market expansion. Key players like Atkins, Autodesk, and ESRI are strategically positioning themselves to capture this market growth through partnerships, technological advancements, and tailored solutions for the African context. The market is expected to witness significant innovation in areas like 3D modeling, AI-powered analytics, and big data processing, which will further enhance the capabilities and applications of geospatial analytics in Africa. The projected increase in investment in technological infrastructure across the continent will be a key factor in accelerating market adoption and overall growth. Recent developments include: September 2024: Bayanat, a company in AI-driven geospatial solutions, has teamed up with Vay, renowned for its automotive-grade teledriving (remote driving) technology. Together, they've inked a Memorandum of Understanding (MoU) to enhance teledriving solutions by integrating geospatial data and AI. This collaboration empowers Bayanat, in tandem with Vay, to introduce and broaden the reach of teledriving technology across the Middle East, Africa, and select nations in the Asia Pacific.May 2024: AfriGIS stands out as one of the pioneering geospatial solutions firms, providing verified and validated geospatial data on administrative boundaries tied to postal codes across Africa. AfriGIS has crafted a polygon dataset for 21,600 localities (towns) and 475,000 sub-localities (suburbs) in the last three years. This dataset can be enriched via API with overlays like points of interest, administrative boundaries, cadastral data, deeds, census data, street centrelines, etc.. Key drivers for this market are: Commercialization of spatial data, Increased smart city & infrastructure projects. Potential restraints include: Commercialization of spatial data, Increased smart city & infrastructure projects. Notable trends are: Commercialization of Spatial Data.

New-ID: NBI18

The Africa Major Infrastructure and Human Settlements Dataset

Files: TOWNS2.E00 Code: 100022-002 ROADS2.E00 100021-002

Vector Members: The E00 files are in Arc/Info Export format and should be imported with the Arc/Info command Import cover In-Filename Out-Filename

The Africa major infrastructure and human settlements dataset form part of the UNEP/FAO/ESRI Database project that covers the entire world but focuses here on Africa. The maps were prepared by Environmental Systems Research Institute (ESRI), USA. Most data for the database were provided by the Soil Resources, Management and Conservation Service, Land and Water Development Division of the Food and Agriculture Organization (FAO), Italy. This dataset was developed in collaboration with the United Nations Environment Program (UNEP), Kenya. The base maps used were the UNESCO/FAO Soil Map of the world (1977) in Miller Oblated Stereographic projection, the DMA Global Navigation and Planning charts for Africa (various dates: 1976-1982) and the Rand-McNally, New International Atlas (1982). All sources were re-registered to the basemap by comparing known features on the basemap those of the source maps. The digitizing was done with a spatial resolution of 0.002 inches. The maps were then transformed from inch coordinates to latitude/longitude degrees. The transformation was done using an unpublished algorithm of the US Geological Survey and ESRI to create coverages for one-degree graticules. The Population Centers were selected based upon their inclusion in the list of major cities and populated areas in the Rand McNally New International Atlas Contact: UNEP/GRID-Nairobi, P.O. Box 30552 Nairobi, Kenya FAO, Soil Resources, Management and Conservation Service, 00100, Rome, Italy ESRI, 380 New York Street, Redlands, CA. 92373, USA The ROADS2 file shows major roads of the African continent The TOWNS2 file shows human settlements and airports for the African continent

References:

ESRI. Final Report UNEP/FAO World and Africa GIS data base (1984). Internal Publication by ESRI, FAO and UNEP

FAO. UNESCO Soil Map of the World (1977). Scale 1:5000000. UNESCO, Paris

Defence Mapping Agency. Global Navigation and Planning charts for Africa (various dates: 1976-1982). Scale 1:5000000. Washington DC.

Grosvenor. National Geographic Atlas of the World (1975). Scale 1:850000. National Geographic Society Washington DC.

DMA. Topographic Maps of Africa (various dates). Scale 1:2000000 Washington DC.

Rand-McNally. The new International Atlas (1982). Scale 1:6,000,000. Rand McNally & Co.Chicago

Source: FAO Soil Map of the World. Scale 1:5000000 Publication Date: Dec 1984 Projection: Miller Type: Points Format: Arc/Info export non-compressed Related Datasets: All UNEP/FAO/ESRI Datasets ADMINLL (100012-002) administrative boundries AFURBAN (100082) urban percentage coverage Comments: There is no outline of Africa

According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite. Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction. For access to the original data, see https://search.datacite.org/works/10.15493/sarva.301020-2.

This web map references the live tiled map service from the OpenStreetMap project. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information such as free satellite imagery, and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap server: http://www.OpenStreetMap.org. See that website for additional information about OpenStreetMap. It is made available as a basemap for GIS work in Esri products under a Creative Commons Attribution-ShareAlike license.Tip: This service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Open Street Map from the Basemap control to start using this service. You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.

This map presents layers derived from Africapolis.org and NASA's Socioeconomic Data and Applications Center (SEDAC) hosted by Columbia University.Africapolis data consists of urban populations from 1950 through 2015 and percentage of the population that is urban (the urban level). SEDAC data represents population density at local scales for the continent of Africa.This map is featured in Urban Africa produced by Esri's StoryMaps team. In generating this map, the StoryMaps team downloaded the original data files from the Africapolis and SEDAC data portals, cleaned and processed the spreadsheets, and visualized the output feature layers in ArcGIS Online.AfricapolisTotal population, city and country (2015)Urban population, city and country (2015)Percent change in urban population, city and country (2000, 2015)SEDACPopulation density (2015)

Our South Africa zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

According to the United Nations, 54% of the world’s population resides in urban areas in the year 2014. It is projected that by 2050 this number will increase by 12%. The direct effect of this urban drift has had profound effects on social, economic and ecological systems, causing stresses on the environment and society. The social and economic implications include impacts from human activities such as transport, industrialization, combustion, construction etc., all of which have a direct or indirect bearing on the environment. These pollution sources have led to release of pollutants such as Nitrogen dioxide (NO2), Particulate Matter (PM) and Sulphur dioxide (SO2) into the atmosphere. It is believed that air pollution is influenced by urban dynamics.In this project, we present a method for predicting historical air quality (as measured by daily median PM25 concentration) for locations where no ground-based sensors are present, by using weather data and remote sensing data from sources like the Sentinel 5P satellite. Air quality data is obtained for 555 cities and supplemented by satellite and weather data. This is then used to build a model to predict the air quality for a given date and location. A competition hosted by Zindi was used to crowd-source the creation of the model used, with the winning code forming the basis of our modelling approach.We use the trained model to create a new dataset of historical air quality predictions for cities across Africa, available at https://github.com/johnowhitaker/air_quality_prediction. For access to the original data see https://search.datacite.org/works/10.15493/sarva.301020-2.

This dataset shows the location of community centres in the City of Cape Town.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

This web map provides a customized vector basemap for the world symbolized with a unique "newspaper" styled map, with a focus on Africa. It has a black & white appearance with select features highlighted in red. Many of the area fills have halftone patterns commonly found in traditional newspaper printing. This vector tile layer is built using the same data sources used for the World Topographic Map and other Esri basemaps. The comprehensive map data includes highways, major roads, minor roads, railways, water features, cities, parks, landmarks, building footprints, and administrative boundaries. Alignment of boundaries is a presentation of the feature provided by our data vendors and does not imply endorsement by Esri or any governing authority.Use this MapThis map is designed to be used as a basemap for overlaying other layers of information or as a stand-alone reference map. You can add layers to this web map and save as your own map. If you like, you can add this web map to a custom basemap gallery for others in your organization to use in creating web maps. If you would like to add this map as a layer in other maps you are creating, you may use the tile layer item referenced in this map.Customize this MapBecause this map includes a vector tile layer, you can customize the map to change its content and symbology. You are able to turn on and off layers, change symbols for layers, switch to alternate local language (in some areas), and refine the treatment of disputed boundaries. For details on how to customize this map, please refer to these articles on the ArcGIS Online Blog.This map was designed and created by Cindy Prostak.

The counties of Kenya are geographical units envisioned by the 2010 Constitution of Kenya as the units of devolved government. The powers are provided in Articles 191 and 192, and in the fourth schedule of the Constitution of Kenya and the County Governments Act of 2012. The counties are also single member constituencies for the election of members of parliament to the Senate of Kenya and special women members of parliament to the National Assembly of Kenya. As of 2013 general elections, there are 47 counties whose size and boundaries are based on the 47 legally recognized Districts. Following the re-organisation of Kenya's national administration, counties were integrated into a new national administration with the national government posting county commissioners to represent it at the counties.

County governments are responsible for county legislation

Buildings are the foundation of any 3D city; they create a realistic visual context for understanding the built environment. This rule can help you quickly create 3D buildings using your existing 2D building footprint polygons. Create buildings for your whole city or specific areas of interest. Use the buildings for context surrounding higher-detail buildings or proposed future developments.Already have existing 3D buildings? Check out the Textured Buildings from Mass by Building Type rule.What you getA Rule Package file named Building_FromFootprint_Textured_ByLandUse.rpk Rule works with a polygon layerGet startedIn ArcGIS Pro Use this rule to create Procedural Symbols, which are 3D symbols drawn on 2D features Create 3D objects (Multipatch layer) for sharing on the webShare on the web via a Scene LayerIn CityEngine:CityEngine File Navigator HelpParametersBuilding Type: Eave_Height: Height from the ground to the eave, units controlled by the Units parameterFloor_Height: Height of each floor, units controlled by the Units parameterLand_Use: Use on the land and type of building, this helps in assigning appropriate building texturesRoof_Form: Style of the building roof (Gable, Hip, Flat, Green)Roof_Height: Height from the eave to the top of the roof, units controlled by the Units parameterDisplay:Color_Override: Setting this to True will allow you to define a specific color using the Override_Color parameter, and will disable photo-texturing.Override_Color: Allows you to specify a building color using the color palette. Note: you must change the Color_Override parameter from False to True for this parameter to take effect.Transparency: Sets the amount of transparency of the feature Units:Units: Controls the measurement units in the rule: Meters | FeetNote: You can hook up the rule parameters to attributes in your data by clicking on the database icon to the right of each rule parameter. The database icon will change to blue when the rule parameter is mapped to an attribute field. The rule will automatically connect when field names match rule parameter names. Use layer files to preserve rule configurations unique to your data.For those who want to know moreThis rule is part of a the 3D Rule Library available in the Living Atlas. Discover more 3D rules to help you perform your work.Learn more about ArcGIS Pro in the Getting to Know ArcGIS Pro lesson

The division of the City into four planning and service delivery areas is part of the Organisational Development and Transformation Plan(ODTP). The ODTP aims to improve the way in which the City works and delivers services.Council’s current structure is the result of the creation of the Unicity in 2000, when seven former municipalities amalgamated intoone to standardise services, staff benefits and working conditions. That helped improve service delivery across the metro. This meanshaving strong central service departments combined with area-based project and performance management to ensure that servicelevels are being met in ways best suited to an area and a community’ s specific needs.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

The percentage of persons, out of the total number of persons living in an area, self-identifying as racially Black or African American (and ethnically non-Hispanic). “Black or African American” refers to a person having origins in any of the Black racial groups of Africa. This indicator includes people who identified their race as “Black”. Source: U.S. Census Bureau, American Community Survey Years Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2020, 2017-2021, 2018-2022, 2019-2023

Bridget O’Donoghue Heritage Consultant was appointed by the City of Cape Town to complete a specialist heritage survey of sculptures, memorials and commemorative monuments within the City of Cape Town Metropolitan Area. The resultant report titled "Sculptures, Memorials and Commemerative Monuments" (March 2009) was used to spatialise the data as it contained X and Y coordinates for each record. This data was captured because the Heritage Development and Promotion, Arts and Culture Unit, Social Development Department, City of Cape Town requires a survey of sculptures, memorials and commemorative monuments within the City of Cape Town Metropolitan Area. The data was captured in November 2015.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.