  The notion of socioeconomic status (SES) of a person or family reflects the
corresponding entity's social and economic rank in society. Such information
may help applications like bank loaning decisions and provide measurable inputs
for related studies like social stratification, social welfare and business
planning. Traditionally, estimating SES for a large population is performed by
national statistical institutes through a large number of household interviews,
which is highly expensive and time-consuming. Recently researchers try to
estimate SES from data sources like mobile phone call records and online social
network platforms, which is much cheaper and faster. Instead of relying on
these data about users' cyberspace behaviors, various alternative data sources
on real-world users' behavior such as mobility may offer new insights for SES
estimation. In this paper, we leverage Smart Card Data (SCD) for public
transport systems which records the temporal and spatial mobility behavior of a
large population of users. More specifically, we develop S2S, a deep learning
based approach for estimating people's SES based on their SCD. Essentially, S2S
models two types of SES-related features, namely the temporal-sequential
feature and general statistical feature, and leverages deep learning for SES
estimation. We evaluate our approach in an actual dataset, Shanghai SCD, which
involves millions of users. The proposed model clearly outperforms several
state-of-art methods in terms of various evaluation metrics.
