PROGRESS IN GEOGRAPHY ›› 2017, Vol. 36 ›› Issue (9): 1158-1166.doi: 10.18306/dlkxjz.2017.09.012

• Special Issue: Urban Cultural Sensing and Computing • Previous Articles     Next Articles

Relationship between travel behavior and income level of urban residents:A case study in Shanghai Municipality

Sihui GUO1,2,3(), Congcong WEN3,4, Yun HE1,2,3, Tao PEI1,2,*()   

  1. 1. Institute of Geographic Sciences and Nature Resources Research, CAS, Beijing 100101, China
    2. State Key Laboratory of Resources and Environmental Information System, CAS, Beijing 100101, China
    3. University of Chinese Academy of Sciences, Beijing 100049, China
    4. Institute of Remote Sensing and Digital Earth, CAS, Beijing 100094, China
  • Online:2017-09-27 Published:2017-09-27
  • Contact: Tao PEI;
  • Supported by:
    National Natural Science Foundation of China, No.41525004, No.41421001


The relationship between income and travel behavior characteristics of urban residents is of great concern in urban geography. Income level of residents is an important indicator measuring regional social development, thus understanding this relationship is of great significance for city planning. Before the Big Data Age, due to the lack of residents' travel behavior information, it was difficult to study this relationship. However, along with the innovation of information technology, the use of ubiquitous sensors, such as mobile phones, has produced a large amount of human activity information, enabling the research on the relationship between residents' travel behaviors and income levels. In this study, based on the activity trajectory data in Shanghai Municipality from 27 December 2015 to 6 January 2016, we extracted a series of residents' mobility indicator data to measure mobility characteristics and conducted principal components analyses to extract the major components. We adopted the K-Means clustering method to classify residents into mobility groups and analyzed the feature of each group. Furthermore, the distribution of workplaces is shown to verify the difference in income levels between different mobility groups. Our results show that: (1) diversity of places to travel to and range of travel are two major components measuring residents' travel behavior; (2) residents who have smaller travel range and go to fewer places have higher average salary; (3) between the mobility groups, difference in income levels relate to industrial setup. These results may be useful for city planners to make efficient economic policies.

Key words: travel behavior, mobility indicator, income level, principal component analysis, K-Means clustering, Shanghai Municipality