Detecting the regional delineation from a network of social media user interactions with spatial constraint: A case study of Shenzhen, China

Tao Jia, Xuesong Yu, Wenzhong Shi, Xintao Liu, Xin Li and Yang Xu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 531, issue C

Abstract: Regions are subdivisions of the earth’s surface, and many systems of regionalization were proposed. Recently, with the availability of geotagged data, it raises the question of whether regions formed by human interactions agree with government districts. Thus, using network partitioning method with spatial constraint, we derive regional delineations at different spatial scales and examine their agreement with administrative districts. Experiments were conducted using the social media data of Shenzhen, China. Aggregately, the results show that the derived regions become inconsistent with administrative districts by increasing the spatial effect value, which can be largely attributed to the involvement of long human movements. However, the regions tend to keep stable when more long edges are included, which suggests the limitation of long movements effect. Individually, most northern administrative districts display high inconsistency with the derived regions, whereas most southern districts show high consistency. Besides, regions far from the downtown are less connected to the rest of the city, regions near the downtown are more connected, and particularly, regions in Nanshan, Futian, and Luohu are highly connected with each other, which form the backbone of total flows irrespective of spatial effect value. The results were finally validated at specific areas and compared with those using other methods, another dataset, and different spatial units, which suggest the feasibility of our regions for decision making in urban planning and management.

Keywords: Social media check-in data; Spatial constraint; Regional delineation; Network partitioning (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437119309756
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:531:y:2019:i:c:s0378437119309756

DOI: 10.1016/j.physa.2019.121719

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().