The Statistical Construction of Place

Application summary

Statistical modelling shadows our every move today. Indeed, natural language processing, a subfield within artificial intelligence (AI), can now be used to represent how, when, and where we attach meaning. Modelling places, the very moments and sites in which we cohere as social beings, is increasingly at the core of research by companies such as Airbnb and Google. For my PhD, I will study and apply statistical and machine learning models to spatially and temporally encoded, linguistic data. In the first part of my thesis, I will produce a critical genealogy of how meaning-making in general, and, its temporal and spatial context in particular, have been theorised and modelled in AI and statistics research in industry and academia. In the second part, I will flip the perspective, and compare the representations of place that are produced when implementing in practice the models studied in the first part. How have cities changed over time, when viewed through historical records of Airbnb reviews and descriptions? Where does one place begin, and another end, when their boundaries are defined using the meanings attached to sites in massive archives of digitised literature? How do the answers to these questions change, as the parameters and assumptions in our models change? Through my work, I will make visible the inherent tension between the simplifications that are necessary for any mathematical model, however complex, and the singular poetics of our lived experience.