SCIENTIA GEOGRAPHICA SINICA ›› 2018, Vol. 38 ›› Issue (11): 1809-1816.doi: 10.13249/j.cnki.sgs.2018.11.008

Special Issue: 地理大数据 人口与城市研究

• Orginal Article • Previous Articles     Next Articles

Space Pattern of Urban Population Distribution Based on Space Syntax and LBS Big Data

Xiaorui Zhang1,2(), Qian Hua1, Zhigang Cheng3   

  1. 1.Department of Urban Planning, Hefei University of Technology, Hefei 230601, Anhui, China
    2. Laboratory of Digital Human Habitat Studies, Hefei University of Technology, Hefei 230601, Anhui, China
    3. Anhui Institute of Urban and Rural Planning and Design, Hefei 230022, Anhui, China
  • Received:2017-11-28 Revised:2018-02-27 Online:2018-11-20 Published:2018-11-20
  • Supported by:
    National Natural Science Foundation of China (41601581)


The space pattern of urban population distribution is a classical research topic of urban science and urban planning. In terms of the current research situation of urban population distribution, the LBS big data technology which is considered as a new method and tool to observe the urban spatial and temporal characteristics is introduced into the research of urban population distribution based on the traditional space syntax model. Then, a new idea of urban population distribution research with the integration of theoretical distribution and actual distribution is established. The case study in the central urban area of Hefei City shows that: the spatial clustering areas obtained respectively by space syntax model and LBS big data analyses are different in space. According to the comprehensive comparison of space syntax model and LBS big data analysis, the central urban area of Hefei City is divided into 3 types of population distribution including high density, medium density and low density. The high density zoning consists of the old town, Shushan district and Baohe district. The medium density zoning includes Binhu district, Luyang district and High-tech area. Meanwhile, the low density zoning consists of economic developing area and Yaohai district. Finally, the suggestions of population distribution development in different density partitions are proposed. The research shows that the timely and dynamic characteristics of LBS big data can make up for the shortcomings of traditional data and greatly broaden the source and timeliness of basic data. Obviously, this will enhance the accuracy of the study. And, more importantly, it will provide more accurate and efficient tools and methods combined with the classical space syntax model for the study of urban population distribution. In addition, it is hoped that this research can make some exploration and reference for expanding the practical application field of LBS big data.

Key words: space syntax, LBS big data, urban population distribution, Hefei city

CLC Number: 

  • K901