基于时空行为大数据的城市社会空间分异研究

陈梓烽

doi:10.13959/j.issn.1003-2398.2022.06.009

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人文地理 ›› 2022, Vol. 37 ›› Issue (6) : 72-80. DOI: 10.13959/j.issn.1003-2398.2022.06.009

时空间行为专栏

基于时空行为大数据的城市社会空间分异研究

陈梓烽

作者信息 +

ANALYZING URBAN SOCIO-SPATIAL SEGREGATION THROUGH SPACE-TIME BEHAVIORAL BIG DATA

CHEN Zi-feng

Author information +

文章历史 +

摘要

本文对基于时空行为大数据的社会空间分异研究进行了阶段性总结，梳理了主要的研究进展、并展望未来的研究方向。已有研究利用手机数据、社交媒体数据、交通运行数据三类时空行为大数据，在多个方面推动了社会空间分异研究的进展，包括揭示了社会空间分异的多尺度动态性、实现了时空共存下个体社会环境暴露的精细化测量、丰富了社会空间分异研究的社会网络与主观认知视角；但这些研究大多止步于现象层面的关联性分析，缺少对社会空间分异深层次机制以及理论、政策外延的剖析。未来研究需充分挖掘时空行为大数据解读社会空间的潜力，通过深入理解社会空间分异的时间性、再思社会空间分异与社会融合的关系，拓展研究的理论与政策外延。

Abstract

Studies of urban socio-spatial segregation has been increasingly benefited from the availability of space-time behavioral big data. The latter can well enrich the existing insights of activity-space segregation, which were predominantly based on conventional datasets such as activity diary data with substantially smaller sample sizes. Specifically, while studies with conventional datasets only adopted residential population as a proxy of the socioeconomic structures in the activity places, the use of space-time behavioral big data can effectively unravel the temporal variation of the socioeconomic structures by tracing the changing locations of the population over different time periods. Echoing the emerging strand of studies that utilized space-time behavioral big data to examine socio-spatial segregation, this paper presents a review of the existing studies through summarizing the types of data and analytical approaches used in those studies as well as the relevant contributions. Most of the existing studies were found being derived from three types of spacetime behavioral big datasets, namely cellphone data, social media data and transportation-derived data. Among the existing studies, three major contributions can be identified. First, the existing studies had unraveled the temporal variations and periodic patterns of socio-spatial segregation that were manifested at multiple temporal scales. Second, facilitated by space-time behavioral big data, the existing studies managed to measure individual socio-contextual exposure (i.e., co-presence) in a real-time manner. Third, the existing studies extended the analytical lens of socio-spatial segregation by including data of social network and tweet-based subjective attitudes. The present paper thus draws attention to the underrated potentials of spacetime behavioral big data of conducting critical-quantitative analyses.

导出引用

陈梓烽. 基于时空行为大数据的城市社会空间分异研究[J]. 人文地理. 2022, 37(6): 72-80 https://doi.org/10.13959/j.issn.1003-2398.2022.06.009

CHEN Zi-feng. ANALYZING URBAN SOCIO-SPATIAL SEGREGATION THROUGH SPACE-TIME BEHAVIORAL BIG DATA[J]. HUMAN GEOGRAPHY. 2022, 37(6): 72-80 https://doi.org/10.13959/j.issn.1003-2398.2022.06.009

中图分类号： K901

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