Identifying urban areas: A new approach and comparison of national urban metrics with gridded population data

Abstract

The measurement of urbanization and other key urban indicators depends on how urban areas are defined. The Degree of Urbanization (DEGURBA) has been recently adopted to support international statistical comparability, but its rigid criteria for classify areas as urban/non-urban based upon fixed population size and density criteria is controversial. Here we present an alternative approach to urban classification, using a flexible range of population density \& count thresholds. We then compare how these thresholds affect estimation of urbanization and urban settlement counts across three of the most popular gridded population datasets (GPD). Instead of introducing further uncertainties by matching GPD to built-up area datasets, we classify urban areas in a purely spatial demographic way. By calculating national urban shares and urban area counts, we highlight the often overlooked uncertainties when using GPD. We find that the choice of GPD is generally the dominant factor in altering both of these urban indicators but the choice of urban criteria is also important. Overall, this alternative urban classification method offers a more flexible approach to human settlements classification that can be applied globally for comparative research.