GEOGRAPHICAL RESEARCH ›› 2018, Vol. 37 ›› Issue (12): 2554-2566.doi: 10.11821/dlyj201812015

Special Issue: 人口与城市研究

• Articles • Previous Articles     Next Articles

Space trade-offs analysis in the urban floating population residential self-selection: A case study of Chengdu

Shaoyao ZHANG1,2(), Zhenqin SHI1,2, Xueqian SONG3, Wei DENG1,2()   

  1. 1. Institute of Mountain Hazards and Environment/Research Center for Mountain Development, CAS, Chengdu 610041, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Chengdu University of Information Technology College of Management, Chengdu 610225, China
  • Received:2018-07-10 Revised:2018-10-08 Online:2018-12-20 Published:2018-12-20
  • About author:

    Author: Shi Zhenqin (1988-), PhD, specialized in regional development and land space management in mountain areas. E-mail: kevinszq@163.com

    *Corresponding author: Deng Wei (1957-), Professor, specialized in mountain environment and regional development.

    E-mail: dengwei@imde.ac.cn

Abstract:

A large floating population has entered urban areas under the rapid urbanization in China. However, their residential space pattern is strongly affected by residential self-selection, which has reconstructed the urban population distribution pattern and social space. This study examines urban floating population residential space pattern and its formation process, by using the floating population data of 2010 and 2015 in the yearbooks of Chengdu downtown block, and choosing influence variables from the perspective of behavioral decision made by the urban floating population. Therefore, the aims of this study are to analyze the influence of urban characteristic variables for residential self-selection and the influences' spatial differentiation, and to reveal process of space trade-offs in residential self-selection and its role in the formation of urban floating population residential space pattern. The results show that from 2010 to 2015, the urban floating population of Chengdu increased rapidly in the southern part of the downtown area and in the urban centers, and a significant space agglomeration situation featured by low-west and high-east is obviously reflected. Moreover, residential space pattern of urban floating population shows that the level of residential segregation is partially related to the residential space pattern of the registered population, but a notable degree of residential segregation has reduced from 2010 to 2015. More importantly, the study proves that the proportion of urban floating population in permanent residential population, residential land area, housing rent, public transportation, enterprise, hospital, drugstore, restaurant and marketplace have influences on floating population residential self-selection. In addition, the proportion of urban floating population in permanent residential population, residential land area and housing rent are the three significant variables in the spatial error model, but it is dramatically impacted by spatial autocorrelation of floating population statistic block. A major contribution of this study is that the spatial differentiation of the variables' influence on residential self-selection is verified by using geographic weighting regression (GWR), and it reveals the process of urban floating population space trade-offs on how to realize residential self-selection by weighting spatial variability of variables’ influence among different urban regions. That is to say, the spatial difference of living cost, employment opportunities, living environment and commuting costs have shaped the floating population residential space pattern, which is a complex reflection of the urban spatial perception, spatial trade-offs and spatial self-selection of floating population. It can help us to deeply understand the formation process of urban floating population residential space pattern, and provide references to promote community integration and urban management.

Key words: residential self-selection, space trade-offs, floating population, GWR, Chengdu