PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (8): 1106-1118.doi: 10.18306/dlkxjz.2018.08.010

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Urban space study based on the temporal characteristics of residents' behavior

Weijing ZHONG1(), De WANG2,*()   

  1. 1. Hangzhou City Planning and Design Academy, Hangzhou 310012, China
    2. College of Architecture and Urban Planning Tongji University, Shanghai 200092, China
  • Received:2017-11-03 Revised:2018-01-07 Online:2018-09-04 Published:2018-09-04
  • Contact: De WANG;
  • Supported by:
    National Natural Science Foundation of China, No. 41771170


As the development of economy and society enters into the "new normal" stage in China, urban planning is also gradually transformed from the traditional incremental planning to inventory planning. It is important to explore the urban spatial dynamic functional characteristics, and to optimize the use of urban activity space based on people's needs, which would enhance the quality of urban space. Advancements of information, communication, and location-aware technologies have made collections of various passively generated datasets possible. These datasets provide new opportunities to understand spatial dynamic characteristics at a low cost and large scale. This study explored the classification of urban space and spatial dynamic characteristics based on a large mobile phone location dataset from Shanghai Municipality, China. The results suggest that the geographical differences of spatial dynamic patterns in Shanghai are evident. The diurnal activity curve is consistent with the patterns of human activity. There were significant differences in intensity of day-to-day activity fluctuations and weekday activities between downtown, sub-centers, and major employment centers. Affinity propagation clustering was introduced to identify the characteristics of urban spatial structure and identify the characteristics of urban space structure of liquidity and viscosity. Several distinct patterns were extracted, and the spatial distributions of the derived clusters highlight distinct human mobility patterns in different areas of the city. We then discuss the socioeconomic and demographic characteristics of the regions covered by different cluster types to gain insights of human mobility patterns in the context of urban functional regions. The findings could offer useful information for policy and decision making.

Key words: mobile phone signaling data, temporal characteristics, residents' behavior, spatial clustering, Affinity Propagation Clustering(AP), Shanghai