Big data and the city

Matthew Zook, Taylor Shelton, A. Poorthuis
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引用次数: 3

Abstract

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 ‘naive’ 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.
大数据与城市
随着当代城市社会越来越多的方面被跟踪和量化,所谓的“大数据”云的出现被广泛认为代表了我们与城市互动和理解城市的方式的根本变化。对于像Anderson(2008)这样的大数据支持者来说,大数据意味着“理论的终结”和让“数字为自己说话”的能力。这些新兴的数据驱动的城市理解往往与更理论化和非正统的城市地理学方法背道而驰,值得注意的是,这些趋势也早于我们现在所说的“大数据”的出现。为了说明大数据在城市地理研究中的潜力,我们探讨了如何将这些数据源和方法有效地应用于持续存在的高档化问题。我们首先回顾了在现有文献中如何定义和衡量中产阶级化,以及这些定义和指标如何影响我们对这一过程的理解。接下来,我们概述了利用大数据,特别是社交媒体数据来理解中产阶级化的初步尝试。我们关注的是更“幼稚”的方法,这些方法利用大数据,但没有充分利用其混乱和复杂的本质,或者未能与城市地理学中长期存在的方法联系起来。然后,我们将这些观点与利用大数据研究中产阶级化的一系列更具建设性的可能性进行对比,这些可能性建立在现有的学术基础上,并认识到大数据与之前研究中使用的其他更传统的数据形式相比的优点和缺点。简而言之,我们认为大数据不太可能成为实证研究中产阶级化或任何特定城市问题的灵丹妙药,“中产阶级化的多维度”仍然意味着“使用单一变量来识别它几乎肯定会失败”(Bostic和Martin 2003: 2431)。然而,我们确实认为,大数据可以补充现有的数据来源,并提供更丰富的理解,以表征中产阶级化过程的多种社会和空间过程,其组成部分,原因和影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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