WorldPop – Population Counts

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Product Category

• Land use
• Land cover
• Natural Disaster
• Climate Change
• Coast Management 

• Marine
• Earth's Surface Motion

Financial Domains

• Investment management 
• Risk Analysis 
• Insurance management 
• Green finance

User requirements 

UN10: Need to understand population density when making investment decisions

UN57: Automatically update changes in population density estimates based on observable land use changes

Description

Investment managers must consider population density at a localized level when formulating investment strategies. Analysing population density within smaller geographical areas provides valuable insights into consumer demand, market opportunities, and growth prospects for particular regions or sectors. This data aids in evaluating the feasibility and potential profitability of their investment choices. Conventional data sources for population density are often outdated and lack the granularity required for precise targeted interventions. Moreover, continuously tracking populations can pose difficulties, especially in low- and middle-income nations facing resource constraints, conflict, or dealing with terrain landscapes. WorldPop, a project based on the University of Southampton, and dedicated to mapping global populations. It complements conventional population data sources by incorporating dynamic data spanning from 2000 to 2020, with a high spatial resolution of 100 meters, to map the distribution of human populations. The overarching objective is to guarantee that every individual, regardless of their location, is included in the decision-making process. One important layer of these is the confidence level which gives information about the error interval associated with each grid cell.

Spatial Coverage Target

Districts within a city

Data Throughput

Rapid tasking 

Data availability

• High
• High

• Low
• Low

PRODUCT SPECIFICATIONS

Main processing steps

WorldPop generates high resolution global population density maps by using machine learning algorithms to correlate available census surveys for certain years with many other sources of data including geospatial data. The idea is to generate population grid maps which are continuous in time and space. The geospatial data used to generate these maps is categorised into raster and vector data. The raster data includes EO data such as elevation, slope, vegetation types, accessibility to major cities, land use and land cover maps, nighttime light, temperature, and precipitation data.

The population maps are generated using two different techniques called bottom-up and top-down, each of these techniques has advantages and disadvantages and the investment manager can choose any of them based on their needs. For more information about these techniques and how the maps are generated, you can open this link  https://www.worldpop.org/methods/populations/.  In addition to the EO data, the population maps are generated using geospatial data like Open Street Map (OSM) to calculate the distance to important features such as roads water bodies, hospitals, etc.

Input data sources

Optical:  land use and land cover maps, vegetation types, temperature and precipitation maps.

Radar:  Elevation, Slope

Supporting data:  Census data, settlement data, OSM

Accessibility

Optical and SAR VHR imagery: commercially available on WorldPop – population count is publicly and freely available through the University of Southampton.

Spatial resolution

100 m & 1 km

Frequency (Temporal resolution)

Annual

Latency

N.A

Geographical scale coverage

Globally

Delivery/ output format

Data type: Raster

File format: GeoTIFF

Accuracies

Thematic accuracy: varies by the region.

Spatial accuracy: 1.5-2 pixels of input data

Constraints and limitations

n WorldPop data is available at a relatively high spatial resolution (often 100 meters) and is dynamic from 2000 to 2020. However, for some applications, even higher resolution and more recent data may be required.

n The accuracy of population estimates relies on multiple factors, including the quality of input data, the assumptions made in modelling, and validation against ground truth data. Errors can occur, especially in areas with limited ground data for validation.

n There can be a lag between the actual population changes and the availability of updated WorldPop data, as it is not real-time information. However, this can be overcome by calculating the maps by an EO provider with the same methodology as WorldPop.

User's level of knowledge and skills to extract information and perform further analysis on the EO products.

Skills: Essential

Knowledge: Essential

P11: WorldPop – Population Counts

Download the gap analysis product sheet 

Maturity score

Mean: 3.2

STD: 0.90

Constraints and limitations

·  WorldPop data is available at a relatively high spatial resolution (often 100 meters) and is dynamic from 2000 to 2020. However, for some applications, even higher resolution and more recent data may be required.

Relevant user needs

UN10: Need to understand population density when making investment decisions.

UN57: Automatically update changes in population density estimates based on observable land use changes

R&D gaps

·  The accuracy of population estimates relies on multiple factors, including the quality of input data, the assumptions made in modelling, and validation against ground truth data. Errors can occur, especially in areas with limited ground data for validation.

·  There can be a lag between the actual population changes and the availability of updated WorldPop data, as it is not real-time information. However, this can be overcome by calculating the maps by an EO provider with the same methodology as WorldPop.

Potential improvements drivers

More validation is required to make the data more robust

Utilisation level review

Utilisation score

Mean: 3.00

STD: 0.89

No utilisation:

Low utilisation

·  The product is already satisfying the technical and usability requirements.

·  Unawareness of the existence of commercial EO products with better specifications.

This product is being used by the insurance sector to assess vulnerability to physical risks and potential costs.

Medium utilisation

High utilisation

Critical gaps related to relevant user needs

Guideline gap

UN57: Automatically update changes in population density estimates based on observable land use changes.