  In the era of big data, the ubiquity of location-aware portable devices
provides an unprecedented opportunity to understand inhabitants' behavior and
their interactions with the built environments. Among the widely used data
resources, mobile phone data is the one passively collected and has the largest
coverage in the population. However, mobile operators cannot pinpoint one user
within meters, leading to the difficulties in activity inference. To that end,
we propose a data analysis framework to identify user's activity via coupling
the mobile phone data with location-based social networks (LBSN) data. The two
datasets are integrated into a Bayesian inference module, considering people's
circadian rhythms in both time and space. Specifically, the framework considers
the pattern of arrival time to each type of facility and the spatial
distribution of facilities. The former can be observed from the LBSN Data and
the latter is provided by the points of interest (POIs) dataset. Taking
Shanghai as an example, we reconstruct the activity chains of 1,000,000 active
mobile phone users and analyze the temporal and spatial characteristics of each
activity type. We assess the results with some official surveys and a
real-world check-in dataset collected in Shanghai, indicating that the proposed
method can capture and analyze human activities effectively. Next, we cluster
users' inferred activity chains with a topic model to understand the behavior
of different groups of users. This data analysis framework provides an example
of reconstructing and understanding the activity of the population at an urban
scale with big data fusion.
