A commuting spectrum analysis of the jobs–housing balance and self-containment of employment with mobile phone location big data

Abstract

Studies on the jobs–housing balance and self-containment of employment are mainly focused on observed journey-to-work trips using travel survey data. This study examines the relationship between the jobs–housing balance and the self-containment of employment through the use of mobile phone location data in Shenzhen, a megacity in southern China. Individual-level journey-to-work trips are explored based on mobile phone location big data. Self-containment of employment in the suburban districts is higher than that in the central districts. The effect of the jobs–housing balance on self-containment of employment is examined at a 2 km grid level. Jobs–housing balance policies positively affect the self-containment of employment in the suburban districts, but its effect is limited in the central districts. Two extreme commuting spectrum measures are used to analyze self-containment of employment in different journey-to-work scenarios with the same jobs–housing distribution. Workers are disaggregated into secondary and tertiary sector workers according to job types. The self-containment of employment is found to be mainly affected by the local jobs–housing balance for secondary-sector workers and the regional city level job distribution for tertiary-sector workers. The extreme scenarios of commuting behavior using the commuting spectrum method can provide benchmarks that can help to understand the observed self-containment of employment better.