2 Citations (Scopus)

Abstract

Human mobility data have become an essential means to study travel behavior and trip purpose to identify urban functional zones, which portray land use at a finer granularity and offer insights for problems such as business site selection, urban design, and planning. However, very few works have leveraged public bicycle-sharing data, which provides a useful feature in depicting people’s short-trip transportation within a city, in the studies of urban functions and structure. Because of its convenience, bicycle usage tends to be close to point-of-interest (POI) features, the combination of which will no doubt enhance the understanding of the trip purpose for characterizing different functional zones. In our study, we propose a data-driven approach that uses station-based public bicycle rental records together with POI data in Hangzhou, China to identify urban functional zones. Topic modelling, unsupervised clustering, and visual analytics are employed to delineate the function matrix, aggregate functional zones, and present mixed land uses. Our result shows that business areas, industrial areas, and residential areas can be well detected, which validates the effectiveness of data generated from this new transportation mode. The word cloud of function labels reveals the mixed land use of different types of urban functions and improves the understanding of city structures.

Original languageEnglish (US)
Article number459
JournalISPRS International Journal of Geo-Information
Volume7
Issue number12
DOIs
StatePublished - Nov 27 2018

Fingerprint

Bicycles
bicycle
Land use
land use
Site selection
transportation mode
study behavior
travel behavior
urban design
residential area
Labels
Industry
site selection
urban planning
Planning
public
China
matrix
planning
modeling

Keywords

  • Human mobility
  • K-means
  • Land use
  • Topic modelling
  • Traffic analysis zones

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

Cite this

Identifying urban functional zones using public bicycle rental records and point-of-interest data. / Zhang, Xiaoyi; Li, WenWen; Zhang, Feng; Liu, Renyi; Du, Zhenhong.

In: ISPRS International Journal of Geo-Information, Vol. 7, No. 12, 459, 27.11.2018.

Research output: Contribution to journalArticle

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