Profiling underprivileged residents with mid-term public transit smartcard data of Beijing

TL;DR

This study uses mid-term public transit smartcard data from Beijing to identify and profile underprivileged residents, revealing urban mobility patterns and socioeconomic dynamics through big data analysis.

Contribution

It introduces a novel approach to profile underprivileged residents using long-term smartcard data, combining multiple data sources and surveys for validation.

Findings

Identified underprivileged residents based on frequent transit use.

Classified residents into 20 groups by residence and job changes.

Provided insights into urban socioeconomic mobility over mid-term periods.

Abstract

Mobility of economically underprivileged residents in China has seldom been well profiled due to privacy issue and the characteristics of Chinese over poverty. In this paper, we identify and characterize underprivileged residents in Beijing using ubiquitous public transport smartcard transactions in 2008 and 2010, respectively. We regard these frequent bus/metro riders (FRs) in China, especially in Beijing, as economically underprivileged residents. Our argument is tested against (1) the household travel survey in 2010, (2) a small-scale survey in 2012, as well as (3) our interviews with local residents in Beijing. Cardholders' job and residence locations are identified using Smart Card Data (SCD) in 2008 and 2010. Our analysis is restricted to cardholders that use the same cards in both years. We then classify all identified FRs into 20 groups by residence changes (change, no change), workplace changes (change, no change, finding a job, losing a job, and all-time employed) during 2008-2010 and housing place in 2010 (within the fourth ring road or not). The underprivileged degree of each FR is then evaluated using the 2014 SCD. To the best of our knowledge, this is one of the first studies for understanding long- or mid-term urban dynamics using immediate "big data", and also for profiling underprivileged residents in Beijing in a fine-scale.