Over the last decade, socio‐spatial segregation of different population groups in activity spaces has attracted considerable attention from geographers and sociologists. A careful examination of such activity‐space segregation can provide a more comprehensive description of dynamic intergroup relations in society than conventional residential segregation, as its measurement is not limited to a particular place. However, the evaluation of activity‐space segregation is a challenging task because it requires the manipulation of large spatiotemporal data sets. The lack of software tools that can assist researchers in exploring and analyzing individuals’ travel trajectories is one of the significant impediments to empirical studies. To address this practical limitation, this article proposes a combined use of two R packages, namely, slice and seg: the former provides tools for storing and manipulating activity‐space data, and the latter implements several existing measures of segregation that can be applied to various social places. The proposed approach is applied to the measurement of activity‐space segregation between high‐ and low‐income groups in Seoul to illustrate the key features of the packages. The results demonstrate that the use of these packages would make it more convenient to conduct activity‐space segregation research and enhance our understanding of the complex socio‐spatial phenomenon.

中文翻译：

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用于测量活动空间中社会空间隔离的开源工具

在过去的十年中，活动空间中不同人群的社会空间隔离引起了地理学家和社会学家的极大关注。与常规的居住隔离相比，仔细检查这种活动-空间隔离可以比传统的住宅隔离更全面地描述社会中动态的族群关系，因为它的度量不仅限于特定的地方。但是，对活动空间隔离进行评估是一项艰巨的任务，因为它需要处理大量的时空数据集。缺乏可以帮助研究人员探索和分析个人旅行轨迹的软件工具，这是实证研究的重要障碍之一。为了解决这一实际限制，本文提出了两个R包的组合使用，即slice and seg：前者提供了用于存储和处理活动空间数据的工具，后者则实现了可用于各种社会场所的几种现有隔离措施。拟议的方法应用于首尔高收入和低收入人群之间活动空间隔离的度量，以说明这些软件包的关键特征。结果表明，使用这些软件包将使开展活动空间隔离研究更加方便，并增强我们对复杂的社会空间现象的理解。