Mobility Census for monitoring rapid urban development

Abstract Monitoring urban structure and development requires high-quality data at high spatiotemporal resolution. In comparison to the accelerating and aggregating human culture in ever-larger cities and an increased pace of urban development, traditional censuses are out-of-pace. An alternative is offered by the analysis of other 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 is produced as a byproduct of mobile communication, we show that meaningful features can be extracted, revealing for example the emergence and absorption of subcenters. This method allows the analysis of urban dynamics at a high spatial resolution (here, 500m) and near real-time frequency, which is especially suitable for tracing event-driven mobility changes and their impact on urban structures.