Street as a big geo-data assembly and analysis unit in urban studies: A case study using Beijing taxi data

Di Zhu, Ninghua Wang, Lun Wu, Yu Liu

Research output: Contribution to journalArticlepeer-review

79 Scopus citations

Abstract

Quantitative research of urban geography has benefited greatly from the rapid development of big geo-data. Spatial assembly is an essential analytical step to summarize and perceive geographical environment from individual behaviours. Most research focuses on the methodology of how to utilize the big data, while the adopted spatial units for data aggregation remain areal in nature. This article conceptually proposes an idea of sensing cities from a street perspective, emphasizes the significance of street units in quantitative urban studies. Using a three-month taxi trajectory dataset and the major streets in Beijing, we explore the spatio-temporal patterns of urban mobility on streets, cluster streets into nine types based on their dynamic functions and capacities. Additionally, we discuss the differences and connections between the linear street unit and traditional areal units, investigate the possibility of uncovering urban communities using streets, and point out the complexity of streets. We conclude that street unit as a supplement to areal units, is able to effectively minify the modifiable areal unit problem (MAUP), sense urban dynamics, depict urban functions, and understand urban structures.

Original languageEnglish (US)
Pages (from-to)152-164
Number of pages13
JournalApplied Geography
Volume86
DOIs
StatePublished - Sep 2017
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to thank the anonymous reviewers for their constructive comments, which surely strengthened this article. This research was supported by the National Natural Science Foundation of China (Grant no. 41625003).

Publisher Copyright:
© 2017 Elsevier Ltd

Keywords

  • Big geo-data
  • Linear unit
  • Spatio-temporal pattern
  • Street
  • Urban study

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