Big Data and the City
Handbook of Urban Geography edited by Ronald van Kempen and Tim Schwanen. London: Edward Elgar, Forthcoming
12 Pages Posted: 12 May 2017
Date Written: May 10, 2017
As more and more aspects of contemporary urban society are tracked and quantified, the emerging cloud of so-called ‘big data’ is widely considered to represent a fundamental change in the way we interact with and understand cities. For some proponents of big data, like Anderson (2008), big data means the ‘end of theory’ and the ability to let “the numbers speak for themselves”. These emerging data-driven understandings of cities often run counter to a more theoretical and heterodox approach to urban geography and it is worth noting that these trends also pre-date the emergence of what we now call ‘big data’. In order to illustrate the potential of big data for urban geographic research, we explore how these data sources and methods might be usefully applied to the persistent question of gentrification. We first review how gentrification has been defined and measured in the existing literature, and how these definitions and metrics have shaped our understandings of the process. Next, we outline nascent attempts to use big data, especially social media data, to understand gentrification. We pay attention to more ‘naïve’ approaches that draw upon big data but in ways that do not fully engage with its messy and complicated nature, or which fail to connect with longer standing approaches within urban geography. We then contrast these perspectives with a range of more constructive possibilities for using big data to study gentrification that build from existing scholarship and recognize both the advantages and disadvantages of big data over other more conventional forms of data used in previous research. In short, we argue that big data is unlikely to be a panacea for empirical studies of gentrification, or for any particular urban issue of interest, and the “multidimensionality of gentrification” still means that “the use of a single variable to identify it is almost certain to fail” (Bostic and Martin 2003: 2431). We do argue, however, that big data can supplement existing data sources and provide a richer understanding of the multiple social and spatial processes that characterize the process of gentrification, its constituent parts, causes and effects.
Keywords: gentrification, big data, social media, urban planning
JEL Classification: R28, O21, O25, O38, O18
Suggested Citation: Suggested Citation