  Monitoring urban structure and development requires high-quality data at high
spatiotemporal resolution. While traditional censuses have provided
foundational insights into demographic and socioeconomic aspects of urban life,
their pace may not always align with the pace of urban development. To
complement these traditional methods, we explore the potential of analyzing
alternative big-data sources, such as human mobility data. However, these often
noisy and unstructured big data pose new challenges. Here we propose a method
to extract meaningful explanatory variables and classifications from such data.
Using movement data from Beijing, which are produced as a byproduct of mobile
communication, we show that meaningful features can be extracted, revealing,
for example, the emergence and absorption of subcentres. This method allows the
analysis of urban dynamics at a high spatial resolution (here, 500m) and near
real-time frequency, and high computational efficiency, which is especially
suitable for tracing event-driven mobility changes and their impact on urban
structures.
