Towards a new paradigm for segregation measurement in an age of big data.
Towards a new paradigm for segregation measurement in an age of big data.
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DOI：
10.1007/s44212-022-00003-3
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Barros, Joana
中科院分区：
文献类型：
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作者：
Li, Qing-Quan;Yue, Yang;Gao, Qi-Li;Zhong, Chen;Barros, Joana
最近在活动空间和大数据方面的理论和方法进步为研究社会空间隔离提供了新的机会。本综述首先提供了一个概述的文献中的测量，空间模式，根本原因，和社会后果的空间隔离。这些研究主要是以场所为中心的静态研究，忽视了由于运动的动态性而导致的各种活动空间之间的隔离体验。为了应对这一挑战，我们强调正在进行的工作，朝着一个新的模式隔离研究。具体而言，这篇评论介绍了如何和在何种程度上，活动空间的方法可以从以人为本的角度推进隔离研究。它解释了基于流动性的方法的要求，量化的动态隔离，由于在城市范围内的高流动。然后，它讨论并说明了一个动态和多维的框架，以显示大数据如何通过捕获个人的时空行为来增强对隔离的理解。审查结束时，使用大数据的隔离研究的新方向和挑战。
Recent theoretical and methodological advances in activity space and big data provide new opportunities to study socio-spatial segregation. This review first provides an overview of the literature in terms of measurements, spatial patterns, underlying causes, and social consequences of spatial segregation. These studies are mainly place-centred and static, ignoring the segregation experience across various activity spaces due to the dynamism of movements. In response to this challenge, we highlight the work in progress toward a new paradigm for segregation studies. Specifically, this review presents how and the extent to which activity space methods can advance segregation research from a people-based perspective. It explains the requirements of mobility-based methods for quantifying the dynamics of segregation due to high movement within the urban context. It then discusses and illustrates a dynamic and multi-dimensional framework to show how big data can enhance understanding segregation by capturing individuals’ spatio-temporal behaviours. The review closes with new directions and challenges for segregation research using big data.