Using crowd-sourced data to quantify the complex urban fabric—OpenStreetMap and the urban–rural index

Abstract

To date, hardly any classification of the urban–rural continuum exists that is based on objective and reproducible criteria. This particularly applies to regions of the world where accurate and up-to-date geodata is scarce Therefore, an Urban–Rural Index (URI) was developed as a contribution to the theoretical debate about the spatiality of urban–rural gradients as well as to make use of the increasing amount of crowd-sourced data especially in traditionally data-scarce regions of the developing world. The URI was calculated based on two subindexes representing: (1) the kernel density of existing buildings derived from high-resolution satellite imagery and (2) the travel times from the city center calculated based on OpenStreetMap data. The advantage of this index over common categorizations of urban, periurban, and rural areas lies in its ability to quantify the spatial implications of urban morphology. This paper draws on the analysis of three study sites: Bamenda in Cameroon, Moshi in Tanzania, and Bangalore in India. The URI as a reproducible representation of the spatial complexity of the urban landscape and its surrounding areas has the potential to contribute to the understanding of urban development patterns. Furthermore, it is a time- and cost-effective way for municipal town planning institutions to increase their knowledge of past, current, and future urbanization trends in their respective areas of responsibility.