Abstract

Cities are essential crucibles for innovation, novelty, economic prosperity and diversity. They are not a mere reflection of individual characteristics, but instead the result of a complex interaction between people and space. Yet, little is known about this self-organized and complex relationship. Traditional approaches have either used surveys to explain in detail how a few individuals experience bits of a city, or considered cities as a whole from their outputs (e.g. total crimes). This tide has however tuned in recent years: the availability of new sources of data have allowed to observe, describe, and predict human behaviour in cities at an unprecedented scale and detail. This thesis adopts a "data mining approach" where we study urban spaces combining new sources of automatically collected data and novel statistical methods. Particularly, we focus on the relationship between the built environment, described by census information, geographical data, and images, and human behaviour proxied by extracted from mobile phone traces. The contribution of our thesis is two-fold. First, we present novel methods to describe urban vitality, by collecting and combining heterogeneous data sources. Second, we show that, by studying the built environment in conjunction with human behaviour, we can reliably estimate the effect of neighbourhood characteristics, predict housing prices and crime. Our results are relevant to researchers within a broad range of fields, from computer science to urban-planning and criminology, as well as to policymakers. The thesis is structured in two parts. In the first part, we investigate what creates urban life. We operationalize the theory of Jane Jacobs, one of the most famous authors in urban planning, who suggested that the built environment and vitality are intimately connected. Using Web and Open data to describe neighbourhoods, and mobile phone records to proxy urban vitality, we show that it is possible to predict vitality from the built environment, thus confirming Jacob's theory. Also, we investigate the effect of safety perception on urban vitality by introducing a novel Deep Learning model that relies on street-view images to extract security perception. Our model describes how perception modulates the relative presence of females, elderly and younger people in urban spaces. Altogether, we demonstrate how objective and subjective characteristics describe urban life. In the second part of this dissertation, we outline two studies that stress the importance of considering, at the same time, multiple factors to describe cities. First, we build a predictive model showing that objective and subjective neighbourhood features drive more than 20% of the housing price. Second, we describe the effect played by a neighbourhood's characteristics on the presence of crime. We present a Bayesian method to compare two alternative criminology theory, and show that the best description is achieved by considering together socio-economic characteristics, built-environment, and mobility of people. Also, we highlight the limitations of transferring what we learn from one city to another. Our findings show that new sources of data, automatically sensed from the environment, can complement the lengthy and costly survey-based collection of urban data and reliably describe neighbourhoods at an unprecedented scale and breath. We anticipate that our results will be of interest to researchers in computer science, urban planning, sociology and more broadly, computational social science.