We evaluated the ratio of jobs to workers from Smart Card Data at the transit station level in Beijing.
A year-to-year evolutionary analysis of job to worker ratios was conducted at the transit station level.
We classify general cases of steepening and flattening job-worker dynamics.
The paper finds that only temporary balance appears around a few stations in Beijing.
Job-worker ratios tend to be steepening rather than flattening from 2011 to 2015.
Abstract
As a megacity, Beijing has experienced traffic congestion, unaffordable housing issues and jobs-housing imbalance. Recent decades have seen policies and projects aiming at decentralizing urban structure and job-worker patterns, such as subway network expansion, the suburbanization of housing and firms. But it is unclear whether these changes produced a more balanced spatial configuration of jobs and workers. To answer this question, this paper evaluated the ratio of jobs to workers from Smart Card Data at the transit station level and offered a longitudinal study for regular transit commuters. The method identifies the most preferred station around each commuter’s workpalce and home location from individual smart datasets according to their travel regularity, then the amounts of jobs and workers around each station are estimated. A year-to-year evolution of job to worker ratios at the station level is conducted. We classify general cases of steepening and flattening job-worker dynamics, and they can be used in the study of other cities. The paper finds that (1) only temporary balance appears around a few stations; (2) job-worker ratios tend to be steepening rather than flattening, influencing commute patterns; (3) the polycentric configuration of Beijing can be seen from the spatial pattern of job centers identified.
Many years ago, we completed a project called Access to Destinations. The data from the project has been sitting on my hard drive for many years. I am happy that some of it is now preserved for posterity and open science by the University of Minnesota Data Conservancy. See:
Unfortunately, due to small methodological changes, these data are not directly comparable with more recent outputs, and the 1995 – 2005 data are really not directly comparable with the 2010 data either. It nevertheless might be interesting for selected applications.
Jie Huang, David Levinson, Jiaoe Wang, Jiangping Zhou, and Zi-jia Wang (2018) Tracking job and housing dynamics with smartcard data (Open Access) PNAS published ahead of print November 19, 2018 [doi]
Significance
This paper uses transit smartcards from travelers in Beijing retained over a 7-y period to track boarding and alighting stations, which are associated with home and work location. This allows us to track who moves and who remains at their homes and workplaces. Therefore, this paper provides a longitudinal study of job and housing dynamics with group conceptualization and characterization. This paper identifies four mobility groups and then infers their socioeconomic profiles. How these groups trade off housing expenditure and travel time budget is examined.
Abstract
Residential locations, the jobs–housing relationship, and commuting patterns are key elements to understand urban spatial structure and how city dwellers live. Their successive interaction is important for various fields including urban planning, transport, intraurban migration studies, and social science. However, understanding of the long-term trajectories of workplace and home location, and the resulting commuting patterns, is still limited due to lack of year-to-year data tracking individual behavior. With a 7-y transit smartcard dataset, this paper traces individual trajectories of residences and workplaces. Based on in-metro travel times before and after job and/or home moves, we find that 45 min is an inflection point where the behavioral preference changes. Commuters whose travel time exceeds the point prefer to shorten commutes via moves, while others with shorter commutes tend to increase travel time for better jobs and/or residences. Moreover, we capture four mobility groups: home mover, job hopper, job-and-residence switcher, and stayer. This paper studies how these groups trade off travel time and housing expenditure with their job and housing patterns. Stayers with high job and housing stability tend to be home (apartment unit) owners subject to middle- to high-income groups. Home movers work at places similar to stayers, while they may upgrade from tenancy to ownership. Switchers increase commute time as well as housing expenditure via job and home moves, as they pay for better residences and work farther from home. Job hoppers mainly reside in the suburbs, suffer from long commutes, change jobs frequently, and are likely to be low-income migrants.
updated August 25, 2009:
For those of you who doubt I am doing work over in London, I have completed two other papers (in addition to “Too Expensive to Meter” based on my research over here):
Network growth is a complex phenomenon. Some have suggested that it occurs in an orderly or rational way, based on the size of the places that are connected. David Levinson examines the order in which stations were added to the London surface rail and Underground rail networks in the nineteenth and twentieth centuries, testing the extent to which order correlates with population density. While population density is an important factor in explaining order, he shows that other factors were at work. The network itself helps to reshape land uses, and a network that may have been well ordered at one time may drift away from order as activities relocate.
This article examines the changes that occurred in the rail network and density of population in London during the 19th and 20th centuries. It aims to disentangle the ‘chicken and egg’ problem of which came first, network or land development, through a set of statistical analyses clearly distinguishing events by order. Using panel data representing the 33 boroughs of London over each decade from 1871 to 2001, the research finds that there is a positive feedback effect between population density and network density. Additional rail stations (either Underground or surface) are positive factors leading to subsequent increases in population in the suburbs of London, while additional population density is a factor in subsequently deploying more rail. These effects differ in central London, where the additional accessibility produced by rail led to commercial development and concomitant depopulation. There are also differences in the effects associated with surface rail stations and Underground stations, as the Underground was able to get into central London in a way that surface rail could not. However, the two networks were weak (and statistically insignificant) substitutes for each other in the suburbs, while the density of surface rail stations was a complement to the Underground in the center, though not vice versa.
Perhaps more interesting for the non-academic, we (Ahmed El-Geneidy, Feng Xie, and myself of the Nexus group) have put together three quicktime movies
1.The co-evolution of London population density and surface (National) rail
2.The co-evolution of London population density and the Underground
3.The co-evolution of London population density and surface (National) rail and the Underground
David Levinson, Transportist 
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