A framework for mixed-use decomposition based on temporal activity signatures extracted from big geo-data

ABSTRACT

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use decomposition in terms of human activities differs from traditional land use research, and it is more reasonable to infer the actual urban function of land. The framework consists of four steps, namely temporal activity signature extraction, urban function base curve extraction, mixed-use decomposition, and result validation. First, the temporal activity signatures (TASs) of each zone are extracted as the proxy of human activity patterns. Second, the diurnal TASs of routine activities are extracted as urban function base curves (i.e. endmembers). Third, a linear decomposition model is used to decompose the mixed use and obtain multiple results (urban function composition, dynamic activity proportions, and the mixing index). Finally, result validation strategies are concluded. This framework offers method extensibility and has few requirements for the input data. It is validated by means of a case study of Beijing, based on a social media check-in dataset.

KEYWORDS:

• Mixed use
• spatial–temporal pattern
• urban function
• human activity
• big geo-data

Acknowledgments

The authors would like to thank L. Gong, L. Shi and J. Wang for their advices and the anonymous reviewers for their valuable comments.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ximeng Cheng http://orcid.org/0000-0001-9923-7240

Di Zhu http://orcid.org/0000-0002-3237-6032

Chaogui Kang http://orcid.org/0000-0002-0122-9419

Zhou Huang http://orcid.org/0000-0002-1255-1913

Yu Liu http://orcid.org/0000-0002-0016-2902

Notes

1 http://www.lx.it.pt/~bioucas/code/sisal_demo.zip

2 https://en.wikipedia.org/wiki/Box_plot

Additional information

Funding

This work was supported by the National Key R&D Program of China [grant number 2017YFB0503602], the National Natural Science Foundation of China [grant numbers 41830645, 41625003, and 41771425], Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDA19040402].