In 2023, the share of urban population in Senegal remained nearly unchanged at around 49.58 percent. Nevertheless, 2023 still represents a peak in the share in Senegal. A country's urbanization rate refers to the share of the total population living in an urban setting. International comparisons of urbanization rates may be inconsistent, due to discrepancies between definitions of what constitutes an urban center (based on population size, area, or space between dwellings, among others).Find more key insights for the share of urban population in countries like Niger and Gambia.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

Senegal urban population for 2023 was 8,806,799, a 3.61% increase from 2022.

• Senegal urban population for 2022 was 8,499,952, a 3.63% increase from 2021.
• Senegal urban population for 2021 was 8,202,086, a 3.7% increase from 2020.
• Senegal urban population for 2020 was 7,909,390, a 3.73% increase from 2019.

Urban population refers to people living in urban areas as defined by national statistical offices. It is calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects. Aggregation of urban and rural population may not add up to total population because of different country coverages.

For the urban and peri-urban area of St. Louis, three main flood scenarios have to be considered: a. Fluvial floods (seasonal floods from the Senegal River) after heavy tropical rains in the upper part of the catchment area (mainly from August to November) b. Floods triggered by rainfall stagnation after heavy local cloudbursts c. Floods caused by sea-level rise, tidal waves and coastal erosion Scenario a.) and b.) may occur at the same time. The flood hazard map was generated based on the occurrence of flood events during the past 10 years. The flood hazard classification in three qualitative hazard levels was done by summing up the flood occurences and reclassifying according to the following list: Number of events Flood Hazard Level 1 1 (low) 2-3 2 (medium) =4 3 (high) The estimation of the area threatened by tidal waves and coast erosion is based on available reports and press releases as well as on the extrapolation of the mapped difference (done by visual interpretation) of the coastline between 2003 and 2019

This dataset is about countries in Senegal per year, featuring 4 columns: country, date, region, and urban population. The preview is ordered by date (descending).

Access to electricity, urban (% of urban population) in Senegal was reported at 96.6 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Senegal - Access to electricity, urban - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Risk is defined as a combination of probability and consequences. A detailed and uniform land-use map is an important prerequisite to perform flood risk calculations, since it determines what is damaged in case of flooding. Two different datasets regarding the urban landuse were made available: • LULC product based on VHR data (WorldView-4 acquired on 29/11/2018) covering the core urban area (approx. 89,0 km²) • LULC product based on HR data (Sentinel 2 acquired on 09/12/2018) covering the urban area (approx. 298,8 km²) Both land-use classification results were recoded to pre-defined categories) and merged after categorization. The exposition is classified integrating economic costs, social damage, physical damage and flood duration. Four land use damage levels (A, B, C, D) are defined based on this estimation. The Flood Risk matrix is generated based on these results and on flood hazard classified into three hazard levels. The flood risk level is classified in four qualitative classes based on the combination of flood hazard and land use damage.

People practicing open defecation, urban (% of urban population) in Senegal was reported at 1.076 % in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Senegal - People practicing open defecation, urban - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

For the urban and peri-urban area of Dakar, two main flood scenarios have to be considered: a. Fluvial floods b. Floods triggered by rainfall stagnation after heavy local cloudbursts Scenario a.) and b.) may occur at the same time. The shapefile includes 6 extents of floods between 2009 and 2018 based on HR optical imagery and 7 extents of floods based on visual interpretation of VHR data as available in GoogleEarth.

This scatter chart displays urban population (people) against population (people) and is filtered where the country is Senegal. The data is about countries per year.

This dataset provides detailed information on road surfaces from OpenStreetMap (OSM) data, distinguishing between paved and unpaved surfaces across the region. This information is based on road surface prediction derived from hybrid deep learning approach. For more information on Methods, refer to the paper

Roughly 0.2436 million km of roads are mapped in OSM in this region. Based on AI-mapped estimates the share of paved and unpaved roads is approximately 0.0161 and 0.0944 (in million kms), corressponding to 6.6041% and 38.7656% respectively of the total road length in the dataset region. 0.1331 million km or 54.6302% of road surface information is missing in OSM. In order to fill this gap, Mapillary derived road surface dataset provides an additional 0.0001 million km of information (corressponding to 0.0694% of total missing information on road surface)

It is intended for use in transportation planning, infrastructure analysis, climate emissions and geographic information system (GIS) applications.

This dataset provides comprehensive information on road and urban area features, including location, surface quality, and classification metadata. This dataset includes attributes from OpenStreetMap (OSM) data, AI predictions for road surface, and urban classifications.

AI features:

• pred_class: Model-predicted class for the road surface, with values "paved" or "unpaved."

• pred_label: Binary label associated with pred_class (0 = paved, 1 = unpaved).

• osm_surface_class: Classification of the surface type from OSM, categorized as "paved" or "unpaved."

• combined_surface_osm_priority: Surface classification combining pred_label and surface(OSM) while prioritizing the OSM surface tag, classified as "paved" or "unpaved."

• combined_surface_DL_priority: Surface classification combining pred_label and surface(OSM) while prioritizing DL prediction pred_label, classified as "paved" or "unpaved."

• n_of_predictions_used: Number of predictions used for the feature length estimation.

• predicted_length: Predicted length based on the DL model’s estimations, in meters.

• DL_mean_timestamp: Mean timestamp of the predictions used, for comparison.

OSM features may have these attributes(Learn what tags mean here):

• name: Name of the feature, if available in OSM.

• name:en: Name of the feature in English, if available in OSM.

• name:* (in local language): Name of the feature in the local official language, where available.

• highway: Road classification based on OSM tags (e.g., residential, motorway, footway).

• surface: Description of the surface material of the road (e.g., asphalt, gravel, dirt).

• smoothness: Assessment of surface smoothness (e.g., excellent, good, intermediate, bad).

• width: Width of the road, where available.

• lanes: Number of lanes on the road.

• oneway: Indicates if the road is one-way (yes or no).

• bridge: Specifies if the feature is a bridge (yes or no).

• layer: Indicates the layer of the feature in cases where multiple features are stacked (e.g., bridges, tunnels).

• source: Source of the data, indicating the origin or authority of specific attributes.

Urban classification features may have these attributes:

• continent: The continent where the data point is located (e.g., Europe, Asia).

• country_iso_a2: The ISO Alpha-2 code representing the country (e.g., "US" for the United States).

• urban: Binary indicator for urban areas based on the GHSU Urban Layer 2019. (0 = rural, 1 = urban)

• urban_area: Name of the urban area or city where the data point is located.

• osm_id: Unique identifier assigned by OpenStreetMap (OSM) to each feature.

• osm_type: Type of OSM element (e.g., node, way, relation).

The data originates from OpenStreetMap (OSM) and is augmented with model predictions using images downloaded from Mapillary in combination with the GHSU Global Human Settlement Urban Layer 2019 and AFRICAPOLIS2020 urban layer.

This dataset is one of many HeiGIT exports on HDX. See the HeiGIT website for more information.

We are looking forward to hearing about your use-case! Feel free to reach out to us and tell us about your research at communications@heigit.org – we would be happy to amplify your work.

Proportion of urban population served with piped water of Senegal rose by 0.21% from 87.9 % in 2021 to 88.1 % in 2022. Since the 0.22% upward trend in 2012, proportion of urban population served with piped water went up by 2.17% in 2022.

This scatter chart displays urban land area (km²) against rural population (people) and is filtered where the country is Senegal. The data is about countries per year.

Proportion of urban population with unimproved sanitation of Senegal dropped by 14.31% from 4.2 % in 2021 to 3.6 % in 2022. Since the 5.89% slump in 2012, proportion of urban population with unimproved sanitation plummeted by 62.55% in 2022.

For the urban and peri-urban area of Dakar, two main flood scenarios have to be considered: a. Fluvial floods b. Floods triggered by rainfall stagnation after heavy local cloudbursts Scenario a.) and b.) may occur at the same time. The flood hazard map was generated based on the occurrence of flood events during the past 10 years. The flood hazard classification in three qualitative hazard levels was done by summing up the flood occurences and reclassifying according to the following list: Number of events Flood Hazard Level 1 1 (low) 2-3 2 (medium) =4 3 (high) The estimation of the area threatened by tidal waves and coast erosion is based on available reports and press releases as well as on the extrapolation of the mapped difference (done by visual interpretation) of the coastline between 2003 and 2019

Proportion of urban population served with open defecation sanitation of Senegal dropped by 9.59% from 1.2 % in 2021 to 1.1 % in 2022. Since the 4.89% decline in 2012, proportion of urban population served with open defecation sanitation plummeted by 51.46% in 2022.

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

This scatter chart displays net energy imports (% of energy use) against urban population (people) and is filtered where the country is Senegal. The data is about countries per year.

This scatter chart displays urban population (people) against fossil fuel energy consumption (% of total) and is filtered where the country is Senegal. The data is about countries per year.

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