Spatial delineations of cities based on web content and built-up age - a case study on North African growth poles from 2010 to 2020

Abstract

Urban Geography is an increasingly data-rich research field where a wide range of research questions can be addressed by the joint analysis of remote-sensing and other data sources like social media data. A valuable step in data-driven explorative studies is the division of the study area into regions that are meaningful with respect to the data and the research question. Administrative regions are unlikely to fulfill this requirement. As an alternative for situations with datasets of different types, we algorithmically created clusters based on hierarchical clustering approaches with geographical constraints. Thereby, space can be divided into regions that are contiguous and accommodate requirements for within-region homogeneity. This approach uses well-established methods and is flexible and easily extendable to accommodate even heterogeneous data types. We demonstrate this in a case study from North African cities where we jointly use remotely sensed built-up age and text embeddings generated from big social media data.