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Applications of the GHSL

Why we need a global, people-based definition of cities, urban and rural areas

Several new global agendas call for the collection of harmonised indicators for cities, urban and rural areas. Because no harmonised method to delineate these areas is available, indicators rely on national definitions, which vary considerably and thus limit international comparability.

The 2030 Agenda for Sustainable Development, the New Urban Agenda and the global strategy to improve agricultural and rural statistics contain calls for the collection of harmonised indicators for cities and urban and rural areas to support consistent international comparisons across countries.

This new method, called the Degree of Urbanisation, classifies the entire territory of a country into three classes:

  1. cities
  2. towns and semi-dense areas
  3. rural areas

It has two extensions. The first extensions identifies: cities, towns, suburban or peri-urban areas, villages, dispersed rural areas and mostly uninhabited areas.
The second extension adds a commuting zone around each city to create a functional urban area or metropolitan area.

This new method has several benefits. It can be applied in a very cost effective manner. Existing data collections, such as household surveys, can often be aggregated by Degree of Urbanisation. By proposing three classes, it captures the urban-rural continuum. Because this method is based on a population grid, it reduces the distortions created by the variable size of statistical and administrative units. It improves global comparability by capturing the spatial concentration of people directly, instead of relying of proxies such as built-up areas or night lights. Last but not least, the method was explicitly designed to monitor access to services and infrastructure in areas with different population sizes and densities.

The 51st Session of the United Nations Statistical Commission has endorsed the methodology for delineation of cities and urban and rural areas for international and regional statistical comparison purposes.