A geographic data science framework for the functional and contextual analysis of human dynamics within global cities

Abstract

This study develops a Geographic Data Science framework that transforms the Foursquare check-in locations and user origin-destination flows data into knowledge about the emerging forms and characteristics of cities' neighbourhoods. We employ a longitudinal mobility dataset describing human interactions with Foursquare venues in ten global cities: Chicago, Istanbul, Jakarta, London, Los Angeles, New York, Paris, Seoul, Singapore, Tokyo. This social media data provides spatio-temporally referenced digital traces left by human use of urban environments, giving us access to the intangible aspects of urban life, such as people behaviours and preferences. Our framework capitalizes on these new data sources, bringing about a novel Geographic Data Science and human-centered methodological approach. Combining network science – a study area with great promise for the analysis of cities and their structure – with geospatial analysis methods, we model cities as a series of global urban networks. Through a spatially weighted community detection algorithm, we uncover functional neighbourhoods for the ten global cities. Each neighbourhood is linked to hyper-local characterisations of their built environment for the Foursquare venues that compose them, and complemented with a range of measures describing their diversity, morphology and mobility. This information is used in a clustering exercise that uncovers a set of four functional neighbourhood types. Our results enable the profiling and comparison of functional neighbourhoods, based on human dynamics and their contexts, across the sample of global cities. The framework is portable to other geographic contexts where interaction data are available to bind different localities into functional agglomerations, and provide insight into their contextual and human dynamics.