Platform geographies. Digitally-mediated unevenness in urban space

Abstract - The present work aims to offer an empirically-situated study on the socio-spatial effects mediated by the digital platforms by adopting a critical and geographical data-centric approach. By focusing on Airbnb data analysis, the objective is to provide a practical understanding of platform urbanism's consequences (Barns, 2020) and its differential impacts on space and places: the uneven geographies of the platform. The hypothesis is that the growing platform pervasiveness combined with the ability to coordinate social and spatial relations generates spatially-situated fractures: on the one hand, the 'lean' platform model amplifies pre-existing spatial inequalities; on the other, it generates new algorithmic-mediated socio-spatial asymmetries. Consequently, the research questions investigate the need to "open the black box" (Bucher, 2016; Graham, 2005) to make sense of the platformization of the city. In detail, the research questions concern A) the spatial effects of the Airbnb platform in terms of urban over-touristification; B) the role of the intermediation and its consequences on the spatial (in)visibilities at the intra-urban scale; C) the socio-spatial implications of the platform's expansion strategy and its challenging local materialization. The methodology based on an emerging Geographic Data Science approach (Singleton and Arribas-Bel, 2019) ranges from data collection and mining to data analysis and visualization. The primary source of data is www.insideairbnb.com, a website that provides data (e.g. the location of apartments, the reviews, the requested price and much more) scraping the Airbnb platform. Going beyond the mere geo-tag, the work contributes to making sense of the geographical consequences of a pervasive accumulation shift and shed light on the opaque mechanisms of intermediation that end up conveying our choices, triggering unequal circular cumulative processes, which channel a selective uneven spatiality.